From 6c8cba888e7b852a621e053b59535b9e4d0b7192 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Wed, 7 Jan 2026 02:22:54 -0800 Subject: [PATCH 01/62] initial commit --- AD450_math.py | 5 + week_2_intro_ipython.ipynb | 403 ++++++++++++++++++++++++++++++++----- 2 files changed, 354 insertions(+), 54 deletions(-) create mode 100644 AD450_math.py diff --git a/AD450_math.py b/AD450_math.py new file mode 100644 index 0000000..911df99 --- /dev/null +++ b/AD450_math.py @@ -0,0 +1,5 @@ +def add(num1, num2): + return num1 + num2 + +def multiply(num1, num2): + return num1 * num2 \ No newline at end of file diff --git a/week_2_intro_ipython.ipynb b/week_2_intro_ipython.ipynb index 1a469f7..bb7ba0e 100644 --- a/week_2_intro_ipython.ipynb +++ b/week_2_intro_ipython.ipynb @@ -24,12 +24,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "id": "BQybla17FRh5" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My name is Joe.\n", + "I am a student at North Seattle College.\n" + ] + } + ], + "source": [ + "name = \"Joe\"\n", + "\n", + "print(f\"My name is {name}.\")\n", + "print(\"I am a student at North Seattle College.\")" + ] }, { "cell_type": "markdown", @@ -43,12 +57,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "id": "P7QDyWncFRh8" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My phone numberis 206-555-5555\n" + ] + } + ], + "source": [ + "phone_num = \"206-555-5555\"\n", + "\n", + "print(f\"My phone numberis {phone_num}\")" + ] }, { "cell_type": "markdown", @@ -62,12 +88,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "id": "OnOau948FRh9" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello my name is Joe and my phone number is 206-555-5555\n" + ] + } + ], + "source": [ + "print(f\"Hello my name is {name} and my phone number is {phone_num}\")" + ] }, { "cell_type": "markdown", @@ -82,12 +118,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "id": "5x2rIFzRFRh9" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I graduate in June of 2027.\n" + ] + } + ], + "source": [ + "month = \"June\"\n", + "year = 2027 \n", + "\n", + "print(f\"I graduate in {month} of {year}.\")" + ] }, { "cell_type": "markdown", @@ -109,12 +158,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "id": "OkyeAVPXFRh-" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ans = 58.5\n" + ] + } + ], + "source": [ + "ans = 12 * 5 - 3 / 2\n", + "ans_type = str(type(ans))\n", + "\n", + "print(f\"ans = {ans}\")\n", + "\n", + "# ans is a float" + ] }, { "cell_type": "markdown", @@ -136,12 +200,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "id": "Lo-LEy8bKYmG" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31 days\n" + ] + } + ], + "source": [ + "oct = \"October\"\n", + "\n", + "if oct == \"October\":\n", + " print(\"31 days\")\n", + "else:\n", + " print(\"30 days\")" + ] }, { "cell_type": "markdown", @@ -157,12 +236,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "id": "qscUkh813QKH" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "between 100 and 150\n" + ] + } + ], + "source": [ + "x = 140\n", + "\n", + "if x > 150:\n", + " print(\"greater than 150\")\n", + "elif x > 100 and x < 150:\n", + " print(\"between 100 and 150\")\n", + "else:\n", + " print(\"less than 100\")" + ] }, { "cell_type": "markdown", @@ -177,12 +273,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "id": "1vxg4nMbFRh_" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number is divisible by 5 and 10.\n", + "Number is divisible by 7 or by 2\n" + ] + } + ], + "source": [ + "y = 70\n", + "\n", + "if y % 5 == 0 and y % 10 == 0:\n", + " print(\"Number is divisible by 5 and 10.\")\n", + "\n", + "if y % 7 == 0 or y % 2 == 0:\n", + " print(\"Number is divisible by 7 or by 2\")\n" + ] }, { "cell_type": "markdown", @@ -195,12 +308,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "id": "JY_Ci5v-FRiA" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7\n", + "14\n", + "21\n", + "28\n", + "35\n", + "42\n", + "49\n", + "56\n", + "63\n", + "70\n", + "77\n", + "84\n", + "91\n", + "98\n" + ] + } + ], + "source": [ + "for i in range(1,101):\n", + " if i % 7 == 0:\n", + " print(i)" + ] }, { "cell_type": "markdown", @@ -213,12 +351,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "id": "4a1D64hpFRiA" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n", + "6\n", + "9\n" + ] + } + ], + "source": [ + "for i in range(1,11):\n", + " if i % 3 == 0:\n", + " print(i)" + ] }, { "cell_type": "markdown", @@ -241,12 +393,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "id": "ntBnYxzi1RPc" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Set: 1\n", + "Set: 2\n", + "Student: 1\n", + "Student: 2\n", + "Student: 3\n", + "Student: 4\n", + "Student: 5\n", + "Set: 3\n", + "Set: 4\n", + "Student: 1\n", + "Student: 2\n", + "Student: 3\n", + "Student: 4\n", + "Student: 5\n", + "Set: 5\n" + ] + } + ], + "source": [ + "for i in range(1,6):\n", + " print(f\"Set: {i}\")\n", + " if i % 2 == 0:\n", + " for j in range(1,6):\n", + " print(f\"Student: {j}\")" + ] }, { "cell_type": "markdown", @@ -259,12 +439,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "id": "lppms8ctzfRI" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hog\n", + "warts\n", + "s\n" + ] + } + ], + "source": [ + "school = \"Hogwarts\"\n", + "first = school[:3]\n", + "second = school[3:]\n", + "\n", + "print(first)\n", + "print(second)\n", + "print(school[-1])" + ] }, { "cell_type": "markdown", @@ -280,12 +478,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "id": "zw2NezoM0zfl" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Thor is a character in the Marvel Cinematic Universe. She-Hulk is a character in the Marvel Cinematic Universe. Loki is a character in the Marvel Cinematic Universe. \n" + ] + } + ], + "source": [ + "chars = [\"Thor\", \"She-Hulk\", \"Loki\"]\n", + "output = \"\"\n", + "\n", + "for i in range(len(chars)):\n", + " output += chars[i]\n", + " output += \" is a character in the Marvel Cinematic Universe. \"\n", + "\n", + "print(output)" + ] }, { "cell_type": "markdown", @@ -299,12 +514,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "id": "rbyZif9a1b2V" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "January has 31 days. \n", + "February has 28 days. \n", + "March has 31 days. \n", + "April has 30 days. \n", + "May has 31 days. \n", + "June has 30 days. \n" + ] + } + ], + "source": [ + "months = [[\"January\", 31], [\"February\", 28], [\"March\", 31], [\"April\", 30], [\"May\", 31], [\"June\", 30]]\n", + "\n", + "for i in range(len(months)):\n", + " print(f\"{months[i][0]} has {months[i][1]} days. \")" + ] }, { "cell_type": "markdown", @@ -321,12 +554,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "id": "p0YE0nqs4Hcz" }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'breed': 'Corgi', 'Age Exp.': 13, 'Type': 'Cattle herding'}\n", + "Corgi\n", + "The Corgi is expected to live about 13 years.\n" + ] + } + ], + "source": [ + "dog = {\"breed\": \"Corgi\", \"Age Exp.\": 13, \"Type\": \"Cattle herding\"}\n", + "\n", + "print(dog)\n", + "\n", + "print(dog[\"breed\"])\n", + "\n", + "print(f\"The {dog['breed']} is expected to live about {dog['Age Exp.']} years.\")\n", + "\n", + "# I used AI to help me with this as I kept getting an error on the last print statement \n", + "# because I was using double quotes for the variable I was placing inside the f-string. \n", + "# I was trying to access it like this dog[\"breed\"] but kept getting an error. So I pasted \n", + "# it into gpt and it explained that the interpreter was interpreting the first double \n", + "# quotation mark as the end of the f-string. Thus causing the error.\n", + "\n", + "# I also learned that in juypter you can't use multi-line comments in juypter notebooks \n", + "# by using the triple quotes before and after a comment\n" + ] }, { "cell_type": "markdown", @@ -337,9 +597,20 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import AD450_math\n", "\n", @@ -359,9 +630,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'AD450_math' has no attribute 'multiply'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[32], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Cell with import error\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mAD450_math\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmultiply\u001b[49m(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'AD450_math' has no attribute 'multiply'" + ] + } + ], "source": [ "# Cell with import error\n", "AD450_math.multiply(2, 2)" @@ -369,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -378,9 +661,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'AD450_math' has no attribute 'multiply'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[34], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Cell with correct output \u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mAD450_math\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmultiply\u001b[49m(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'AD450_math' has no attribute 'multiply'" + ] + } + ], "source": [ "# Cell with correct output \n", "AD450_math.multiply(2, 2)" @@ -431,7 +726,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "my_env_2", + "display_name": "ml-env", "language": "python", "name": "python3" }, @@ -445,7 +740,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.10.18" } }, "nbformat": 4, From 7b40d019a88f366c24cf518b8151162b1d427b3a Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Wed, 7 Jan 2026 03:58:05 -0800 Subject: [PATCH 02/62] finished week_2 assignment --- AD450_math.py | 3 +- week_2_intro_ipython.ipynb | 78 ++++++++++++++++++++++++++------------ 2 files changed, 55 insertions(+), 26 deletions(-) diff --git a/AD450_math.py b/AD450_math.py index 911df99..481036f 100644 --- a/AD450_math.py +++ b/AD450_math.py @@ -2,4 +2,5 @@ def add(num1, num2): return num1 + num2 def multiply(num1, num2): - return num1 * num2 \ No newline at end of file + return num1 * num2 + diff --git a/week_2_intro_ipython.ipynb b/week_2_intro_ipython.ipynb index bb7ba0e..894b64b 100644 --- a/week_2_intro_ipython.ipynb +++ b/week_2_intro_ipython.ipynb @@ -554,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 6, "metadata": { "id": "p0YE0nqs4Hcz" }, @@ -597,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -606,7 +606,7 @@ "4" ] }, - "execution_count": 23, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -614,7 +614,7 @@ "source": [ "import AD450_math\n", "\n", - "AD450_math.add(2,2)" + "AD450_math.add(2,2)\n" ] }, { @@ -630,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -640,7 +640,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[32], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Cell with import error\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mAD450_math\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmultiply\u001b[49m(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n", + "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Cell with import error\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mAD450_math\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmultiply\u001b[49m(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n", "\u001b[0;31mAttributeError\u001b[0m: module 'AD450_math' has no attribute 'multiply'" ] } @@ -652,28 +652,40 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Reload cell" + "# Reload cell\n", + "import importlib\n", + "importlib.reload(AD450_math)\n" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 6, "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "module 'AD450_math' has no attribute 'multiply'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[34], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Cell with correct output \u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mAD450_math\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmultiply\u001b[49m(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'AD450_math' has no attribute 'multiply'" - ] + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -695,20 +707,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "# Create a" + "# Create a\n", + "a = 1" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n" + ] + } + ], "source": [ - "# Print a" + "# Print a\n", + "print(a)" ] }, { @@ -717,7 +739,13 @@ "metadata": {}, "outputs": [], "source": [ - "# Set a to 4" + "# Set a to 4\n", + "a = 4\n", + "\n", + "\"\"\" \n", + "It looks like after running this block that it is changing the global value of a. \n", + "so when we run the block above it prints out 4 because a has been re-assigned.\n", + "\"\"\";" ] } ], From 63131b994c7ab947a366e732870fb746097d73fd Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Wed, 7 Jan 2026 04:00:47 -0800 Subject: [PATCH 03/62] updated comment on question 15 --- week_2_intro_ipython.ipynb | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/week_2_intro_ipython.ipynb b/week_2_intro_ipython.ipynb index 894b64b..5431b83 100644 --- a/week_2_intro_ipython.ipynb +++ b/week_2_intro_ipython.ipynb @@ -554,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "id": "p0YE0nqs4Hcz" }, @@ -578,14 +578,17 @@ "\n", "print(f\"The {dog['breed']} is expected to live about {dog['Age Exp.']} years.\")\n", "\n", - "# I used AI to help me with this as I kept getting an error on the last print statement \n", - "# because I was using double quotes for the variable I was placing inside the f-string. \n", - "# I was trying to access it like this dog[\"breed\"] but kept getting an error. So I pasted \n", - "# it into gpt and it explained that the interpreter was interpreting the first double \n", - "# quotation mark as the end of the f-string. Thus causing the error.\n", + "\"\"\"\n", + "I used AI to help me with this as I kept getting an error on the last print statement \n", + "because I was using double quotes for the variable I was placing inside the f-string. \n", + "I was trying to access it like this dog[\"breed\"] but kept getting an error. So I pasted \n", + "it into gpt and it explained that the interpreter was interpreting the first double \n", + "quotation mark as the end of the f-string. Thus causing the error. \n", "\n", - "# I also learned that in juypter you can't use multi-line comments in juypter notebooks \n", - "# by using the triple quotes before and after a comment\n" + "I also learned that juypter notebook is finicky about mutli-line comments and you have \n", + "to add a semi-colon after the second triple quote otherwise it will print your comment. \n", + "\"\"\";\n", + "\n" ] }, { From 9eca43bb2ecb57d6135bd0ec1e97ebfb0b2935e7 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Thu, 8 Jan 2026 16:19:17 -0800 Subject: [PATCH 04/62] updated a comment --- week_2_intro_ipython.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/week_2_intro_ipython.ipynb b/week_2_intro_ipython.ipynb index 5431b83..2edadce 100644 --- a/week_2_intro_ipython.ipynb +++ b/week_2_intro_ipython.ipynb @@ -738,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ From ed43ba57662989df074271fe79e6593b4352c512 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 13 Jan 2026 17:23:44 -0800 Subject: [PATCH 05/62] first commit for week2 --- week_2_intro_ipython.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/week_2_intro_ipython.ipynb b/week_2_intro_ipython.ipynb index 2edadce..ad1b663 100644 --- a/week_2_intro_ipython.ipynb +++ b/week_2_intro_ipython.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "id": "BQybla17FRh5" }, @@ -39,7 +39,7 @@ } ], "source": [ - "name = \"Joe\"\n", + "name = \"Joee\"\n", "\n", "print(f\"My name is {name}.\")\n", "print(\"I am a student at North Seattle College.\")" From 6faf1054c3521d5ffee5e0051a4cc252ff9816cd Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 13 Jan 2026 17:37:39 -0800 Subject: [PATCH 06/62] fixed name type --- week_2_intro_ipython.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/week_2_intro_ipython.ipynb b/week_2_intro_ipython.ipynb index ad1b663..73ccd25 100644 --- a/week_2_intro_ipython.ipynb +++ b/week_2_intro_ipython.ipynb @@ -39,7 +39,7 @@ } ], "source": [ - "name = \"Joee\"\n", + "name = \"Joe\"\n", "\n", "print(f\"My name is {name}.\")\n", "print(\"I am a student at North Seattle College.\")" From 411ed8a0c45e273648965853dc658e6c1761b3be Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 13 Jan 2026 18:32:16 -0800 Subject: [PATCH 07/62] fixed issue with problem 10 --- week_2_intro_ipython.ipynb | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/week_2_intro_ipython.ipynb b/week_2_intro_ipython.ipynb index 73ccd25..4a3bb1d 100644 --- a/week_2_intro_ipython.ipynb +++ b/week_2_intro_ipython.ipynb @@ -351,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": { "id": "4a1D64hpFRiA" }, @@ -360,16 +360,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "3\n", - "6\n", - "9\n" + "3 6 9 \n", + "18\n" ] } ], "source": [ + "sum = 0\n", "for i in range(1,11):\n", " if i % 3 == 0:\n", - " print(i)" + " print(i, end= \" \")\n", + " sum += i\n", + "\n", + "print(f\"\\n{sum}\")" ] }, { From 23d9af13a793fe314faee034eec99abdef55bc27 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Wed, 14 Jan 2026 02:00:36 -0800 Subject: [PATCH 08/62] fixed a few issues where I didn't follow directions correctly --- week_2_intro_ipython.ipynb | 55 +++++++++++++++++++------------------- 1 file changed, 28 insertions(+), 27 deletions(-) diff --git a/week_2_intro_ipython.ipynb b/week_2_intro_ipython.ipynb index 4a3bb1d..4eee6b3 100644 --- a/week_2_intro_ipython.ipynb +++ b/week_2_intro_ipython.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "id": "BQybla17FRh5" }, @@ -39,10 +39,7 @@ } ], "source": [ - "name = \"Joe\"\n", - "\n", - "print(f\"My name is {name}.\")\n", - "print(\"I am a student at North Seattle College.\")" + "print(f\"My name is Joe.\\nI am a student at North Seattle College.\")" ] }, { @@ -57,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "id": "P7QDyWncFRh8" }, @@ -66,12 +63,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "My phone numberis 206-555-5555\n" + "My phone numberis 2065555555\n" ] } ], "source": [ - "phone_num = \"206-555-5555\"\n", + "phone_num = \"2065555555\"\n", "\n", "print(f\"My phone numberis {phone_num}\")" ] @@ -88,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": { "id": "OnOau948FRh9" }, @@ -102,7 +99,10 @@ } ], "source": [ - "print(f\"Hello my name is {name} and my phone number is {phone_num}\")" + "phone = \"206-555-5555\"\n", + "name = \"Joe\"\n", + "\n", + "print(f\"Hello my name is {name} and my phone number is {phone}\")" ] }, { @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": { "id": "5x2rIFzRFRh9" }, @@ -132,8 +132,7 @@ } ], "source": [ - "month = \"June\"\n", - "year = 2027 \n", + "month, year = \"June\", 2027\n", "\n", "print(f\"I graduate in {month} of {year}.\")" ] @@ -273,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": { "id": "1vxg4nMbFRh_" }, @@ -290,7 +289,9 @@ "source": [ "y = 70\n", "\n", - "if y % 5 == 0 and y % 10 == 0:\n", + "# you only need to check for mod 10 below becuase if it is divisible by 10 then \n", + "# it is also divisible by 5\n", + "if y % 10 == 0:\n", " print(\"Number is divisible by 5 and 10.\")\n", "\n", "if y % 7 == 0 or y % 2 == 0:\n", @@ -351,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": { "id": "4a1D64hpFRiA" }, @@ -367,10 +368,13 @@ ], "source": [ "sum = 0\n", - "for i in range(1,11):\n", + "i = 1\n", + "\n", + "while i < 11:\n", " if i % 3 == 0:\n", " print(i, end= \" \")\n", " sum += i\n", + " i += 1\n", "\n", "print(f\"\\n{sum}\")" ] @@ -481,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": { "id": "zw2NezoM0zfl" }, @@ -490,19 +494,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Thor is a character in the Marvel Cinematic Universe. She-Hulk is a character in the Marvel Cinematic Universe. Loki is a character in the Marvel Cinematic Universe. \n" + "Thor is a character in the Marvel Cinematic Universe. She-Hulk is a character in the Marvel Cinematic Universe. Loki is a character in the Marvel Cinematic Universe. " ] } ], "source": [ "chars = [\"Thor\", \"She-Hulk\", \"Loki\"]\n", - "output = \"\"\n", - "\n", - "for i in range(len(chars)):\n", - " output += chars[i]\n", - " output += \" is a character in the Marvel Cinematic Universe. \"\n", "\n", - "print(output)" + "for name in chars:\n", + " print(f\"{name} is a character in the Marvel Cinematic Universe.\", end = \" \")\n" ] }, { @@ -517,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "metadata": { "id": "rbyZif9a1b2V" }, @@ -539,7 +539,8 @@ "months = [[\"January\", 31], [\"February\", 28], [\"March\", 31], [\"April\", 30], [\"May\", 31], [\"June\", 30]]\n", "\n", "for i in range(len(months)):\n", - " print(f\"{months[i][0]} has {months[i][1]} days. \")" + " month, days = months[i][0], months[i][1]\n", + " print(f\"{month} has {days} days. \")" ] }, { From f058899c48f56ca0bb19e37df67fb675163a693b Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Wed, 14 Jan 2026 02:03:18 -0800 Subject: [PATCH 09/62] fixed type on problem #2 --- week_2_intro_ipython.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/week_2_intro_ipython.ipynb b/week_2_intro_ipython.ipynb index 4eee6b3..ca1b174 100644 --- a/week_2_intro_ipython.ipynb +++ b/week_2_intro_ipython.ipynb @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "id": "P7QDyWncFRh8" }, @@ -70,7 +70,7 @@ "source": [ "phone_num = \"2065555555\"\n", "\n", - "print(f\"My phone numberis {phone_num}\")" + "print(f\"My phone number is {phone_num}\")" ] }, { From 8011768acb8102cbe32438e0b9ed2c6434516717 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Wed, 14 Jan 2026 02:04:30 -0800 Subject: [PATCH 10/62] fogot to save changes to typo fix on problem #2 --- week_2_intro_ipython.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/week_2_intro_ipython.ipynb b/week_2_intro_ipython.ipynb index ca1b174..e6bf222 100644 --- a/week_2_intro_ipython.ipynb +++ b/week_2_intro_ipython.ipynb @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "id": "P7QDyWncFRh8" }, @@ -63,7 +63,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "My phone numberis 2065555555\n" + "My phone number is 2065555555\n" ] } ], From 39bcf659a19439ebd01dfe494f456221bfdd5962 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Fri, 16 Jan 2026 03:23:48 -0800 Subject: [PATCH 11/62] completed first couple of problems --- week_3_numpy.ipynb | 114 +++++++++++++++++++++++++++++++++++---------- 1 file changed, 90 insertions(+), 24 deletions(-) diff --git a/week_3_numpy.ipynb b/week_3_numpy.ipynb index 1db8573..cd28b4e 100644 --- a/week_3_numpy.ipynb +++ b/week_3_numpy.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { "id": "Pt63xmy1qLll" }, @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -83,19 +83,37 @@ "id": "AVByQUYKxtNG", "outputId": "238e50f0-366d-41de-a44a-6cf3e063650c" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2 3 4]\n", + " [5 6 7 8]]\n", + "\n", + "Inspect the array\n", + "--------------\n", + "dimensions: 2\n", + "shape:\t (2, 4)\n", + "size:\t 8\n", + "datatype int64\n", + "bytes:\t 64\n" + ] + } + ], "source": [ "# create an array called numbers with 2 rows and 4 columns from literal values. Have your literal values be the integers 1-8\n", - "numbers = None #Replace None with array defintion\n", + "numbers = np.array([[1,2,3,4], [5,6,7,8]])\n", + "\n", "print(numbers)\n", "\n", "# Replace None below with each value for the array. \n", "print(\"\\nInspect the array\\n--------------\")\n", - "print(\"dimensions:\", None) # ndarrays have dimensions\n", - "print(\"shape:\\t\", None) # ndarrays have shape (numer of rows & columns)\n", - "print(\"size:\\t\", None) # number of elements in the array\n", - "print(\"datatype\", None) # numpy determines the datatype\n", - "print(\"bytes:\\t\", None) # total bytes consumed by the array" + "print(\"dimensions:\", numbers.ndim) # ndarrays have dimensions\n", + "print(\"shape:\\t\", numbers.shape) # ndarrays have shape (numer of rows & columns)\n", + "print(\"size:\\t\", numbers.size) # number of elements in the array\n", + "print(\"datatype\", numbers.dtype) # numpy determines the datatype\n", + "print(\"bytes:\\t\", numbers.size * numbers.itemsize) # total bytes consumed by the array" ] }, { @@ -109,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -117,34 +135,76 @@ "id": "pTXgMRYRQS3v", "outputId": "010d4702-bc29-4f87-980e-66fe9b43436a" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ones\n", + "[[1. 1. 1. 1.]\n", + " [1. 1. 1. 1.]\n", + " [1. 1. 1. 1.]] \n", + "\n", + "zeros\n", + "[[0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]] \n", + "\n", + "random\n", + "None \n", + "\n", + "empty\n", + "[[0. 0.]\n", + " [0. 0.]\n", + " [0. 0.]] \n", + "\n", + "full\n", + "[['x' 'x']\n", + " ['x' 'x']] \n", + "\n", + "even\n", + "[10 15 20] \n", + "\n", + "line\n", + "None \n", + "\n" + ] + } + ], "source": [ "# Create an 3 by 4 array of ones\n", - "ones = None\n", + "ones = np.ones((3,4))\n", + "print(\"ones\")\n", "print(ones, \"\\n\")\n", "\n", "# Create an 3 by 4 array of zeros\n", - "zeros = None\n", + "zeros = np.zeros((3,4))\n", + "print(\"zeros\")\n", "print(zeros, \"\\n\")\n", "\n", "# Create an 2 by 2 array with random values\n", "rando = None\n", + "print(\"random\")\n", "print(rando, \"\\n\")\n", "\n", "# Create 3 by 2 an empty array\n", - "empty = None\n", + "empty = np.empty((3,2))\n", + "print(\"empty\")\n", "print(empty, \"\\n\")\n", "\n", "# Create a 2 by 2 array full of 'x's, hint seach numpy full\n", - "full = None\n", + "full = np.full((2,2),'x')\n", + "print(\"full\")\n", "print(full, \"\\n\")\n", "\n", "# Create an array of the form [10, 15, 20] using np.arange\n", - "even = None\n", + "even = np.arange(10,21,5)\n", + "print(\"even\")\n", "print(even, \"\\n\")\n", "\n", "# Create an array of the form [0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. ] using np.linspace\n", "line = None\n", + "print(\"line\")\n", "print(line, \"\\n\")\n" ] }, @@ -172,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 43, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -184,7 +244,9 @@ "source": [ "arr1d = np.array([0,1, 2, 3, 4, 5, 6, 7, 8])\n", "\n", - "# using indexing select and output [5, 6, 7] from arr1d. Save this slice to a variable named slice_arr1d\n" + "# using indexing select and output [5, 6, 7] from arr1d. Save this slice to a variable named slice_arr1d\n", + "\n", + "slice_arr1d = arr1d[5:8]\n" ] }, { @@ -196,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 46, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -206,7 +268,9 @@ }, "outputs": [], "source": [ - "# Replace 6, 7, and 8 with 12 in arr1d using one line of code. Then print arr1d and slice_arr1d\n" + "# Replace 6, 7, and 8 with 12 in arr1d using one line of code. Then print arr1d and slice_arr1d\n", + "\n", + "arr1d[6:] = 12\n" ] }, { @@ -218,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 48, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -228,7 +292,9 @@ }, "outputs": [], "source": [ - "# Create a copy of arr1d and name it new_array. Hint: You can't use new_array = arr1d" + "# Create a copy of arr1d and name it new_array. Hint: You can't use new_array = arr1d\n", + "\n", + "new_array = arr1d.copy()" ] }, { @@ -664,7 +730,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "base", + "display_name": "ml-env", "language": "python", "name": "python3" }, @@ -678,7 +744,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.10.18" } }, "nbformat": 4, From cb80c5c631c60eeb78ffc83ca71228a14a8d75a4 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Sat, 17 Jan 2026 06:55:53 -0800 Subject: [PATCH 12/62] finished problem 3 --- week_3_numpy.ipynb | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/week_3_numpy.ipynb b/week_3_numpy.ipynb index cd28b4e..03564c8 100644 --- a/week_3_numpy.ipynb +++ b/week_3_numpy.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "id": "Pt63xmy1qLll" }, @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -151,7 +151,8 @@ " [0. 0. 0. 0.]] \n", "\n", "random\n", - "None \n", + "[[ 0.38370392 -0.69051018]\n", + " [-0.69090326 -0.36937786]] \n", "\n", "empty\n", "[[0. 0.]\n", @@ -166,7 +167,7 @@ "[10 15 20] \n", "\n", "line\n", - "None \n", + "[0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. ] \n", "\n" ] } @@ -183,7 +184,7 @@ "print(zeros, \"\\n\")\n", "\n", "# Create an 2 by 2 array with random values\n", - "rando = None\n", + "rando = np.random.standard_normal(size = (2,2))\n", "print(\"random\")\n", "print(rando, \"\\n\")\n", "\n", @@ -203,7 +204,7 @@ "print(even, \"\\n\")\n", "\n", "# Create an array of the form [0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. ] using np.linspace\n", - "line = None\n", + "line = np.linspace(0, 2, num = 9)\n", "print(\"line\")\n", "print(line, \"\\n\")\n" ] From e2b5b214a2333ab9f45f930590cb7eae579689df Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Sat, 17 Jan 2026 18:26:45 -0800 Subject: [PATCH 13/62] finished problem 7 --- week_3_numpy.ipynb | 41 +++++++++++++++++++++++++++++++++++------ 1 file changed, 35 insertions(+), 6 deletions(-) diff --git a/week_3_numpy.ipynb b/week_3_numpy.ipynb index 03564c8..8e18d61 100644 --- a/week_3_numpy.ipynb +++ b/week_3_numpy.ipynb @@ -307,13 +307,25 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n", + "3\n" + ] + } + ], "source": [ "arr2d = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n", "\n", - "# There are two ways to select 3 from the 2 dimensional array above. Use both ways to select 3 and print the results. " + "# There are two ways to select 3 from the 2 dimensional array above. Use both ways to select 3 and print the results. \n", + "\n", + "print(arr2d[0][2])\n", + "print(arr2d[0,2])" ] }, { @@ -325,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -333,16 +345,33 @@ "id": "kvMvyar216T5", "outputId": "943f42a4-469a-48f0-f4d4-546849ffa08d" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2 3 4]\n", + " [6 7 8]] \n", + "\n", + "[[3]\n", + " [7]] \n", + "\n", + "[[1 2 3 4]]\n" + ] + } + ], "source": [ "# In mult-dimensional arrays, slicing that omits later indices will return a lower-dimensional ndarray\n", "# Select and print out the following:\n", "\n", "# select first two rows and all but first column\n", + "print(arr2d[:2, 1:], \"\\n\")\n", "\n", "# select first two rows and only 3rd column\n", + "print(arr2d[:2, 2:3], \"\\n\")\n", "\n", - "# select all columns in the first row\n" + "# select all columns in the first row\n", + "print(arr2d[:1, :])\n" ] }, { From 889f1811a00df88863c6690c90f838c68acfad7b Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Sun, 18 Jan 2026 17:19:35 -0800 Subject: [PATCH 14/62] completed through problem 15 --- week_3_numpy.ipynb | 184 ++++++++++++++++++++++++++++++++++++++------- 1 file changed, 158 insertions(+), 26 deletions(-) diff --git a/week_3_numpy.ipynb b/week_3_numpy.ipynb index 8e18d61..b1c51a5 100644 --- a/week_3_numpy.ipynb +++ b/week_3_numpy.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "id": "Pt63xmy1qLll" }, @@ -394,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -402,14 +402,38 @@ "id": "IMEp_RGh_Fu5", "outputId": "45332ef8-2c42-4c40-af09-6caaf98bca6b" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "First and fourth rows of data: \n", + "[[0 0 0 0 1 1 1]\n", + " [0 1 1 1 0 1 0]] \n", + "\n", + "All of data: \n", + "[[0 0 0 0 1 1 1]\n", + " [1 1 0 1 1 0 0]\n", + " [1 0 0 0 1 0 1]\n", + " [0 1 1 1 0 1 0]\n", + " [0 1 1 1 1 1 1]\n", + " [0 0 0 0 1 1 0]\n", + " [0 1 0 0 0 0 0]]\n" + ] + } + ], "source": [ "import numpy as np\n", "names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])\n", "data = np.random.randint(2, size=(7, 7)) # create an array of random values\n", "\n", "# Select and print the first and forth rows from 'data' using just the array 'names' and 'data'. Print out both the selected rows and 'data'. \n", - "# Hint\" The \"Bob\" appears in both the first and forth rows. " + "# Hint\" The \"Bob\" appears in both the first and forth rows. \n", + "\n", + "print(\"First and fourth rows of data: \")\n", + "print(data[names == \"Bob\"], \"\\n\")\n", + "print(\"All of data: \")\n", + "print(data)\n" ] }, { @@ -423,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -431,10 +455,22 @@ "id": "BgULatpwt7X_", "outputId": "c31d49b4-92ff-4514-c91b-8eeb42573b5f" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 0, 0, 0, 1, 1, 1],\n", + " [0, 1, 1, 1, 0, 1, 0]])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Again output the first and fouth rows using the cond below. Meaning replace None with an expression\n", - "cond = None\n", + "cond = (names == \"Bob\")\n", "data[cond]" ] }, @@ -449,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -457,9 +493,27 @@ "id": "EP-SDDVIvK8s", "outputId": "cce4fab9-cd5e-46c2-c615-f2518f1b4a72" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 1, 0, 1, 1, 0, 0],\n", + " [1, 0, 0, 0, 1, 0, 1],\n", + " [0, 1, 1, 1, 1, 1, 1],\n", + " [0, 0, 0, 0, 1, 1, 0],\n", + " [0, 1, 0, 0, 0, 0, 0]])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Select all rows except the first and forth" + "# Select all rows except the first and forth\n", + "\n", + "cond = ~(names == \"Bob\")\n", + "data[cond]" ] }, { @@ -473,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 36, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -481,9 +535,23 @@ "id": "QYjmF7eFvTmc", "outputId": "172adeb5-31d7-47ad-e3af-59c352f96f97" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 1, 1],\n", + " [0, 1, 0]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Select the last 5 columns for the first and forth row" + "# Select the last 5 columns for the first and forth row\n", + "cond = (names == \"Bob\")\n", + "data[cond, 4:]" ] }, { @@ -497,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 37, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -505,9 +573,26 @@ "id": "sciz0aprvh8u", "outputId": "24c8c092-cbe9-4665-9e01-9d193bcac8df" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 0, 0, 0, 1, 1, 1],\n", + " [1, 0, 0, 0, 1, 0, 1],\n", + " [0, 1, 1, 1, 0, 1, 0],\n", + " [0, 1, 1, 1, 1, 1, 1]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Select the first, third, forth and fifth row using similar conditioins and an OR operator" + "# Select the first, third, forth and fifth row using similar conditioins and an OR operator\n", + "\n", + "cond = (names == \"Bob\") | (names == \"Will\")\n", + "data[cond]" ] }, { @@ -532,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -540,18 +625,39 @@ "id": "HdWTjc8CZI5R", "outputId": "e070b71e-bdee-4abc-f378-474978208ad5" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original array: \n", + "[[1. 2. 3.]\n", + " [4. 5. 6.]] \n", + "\n", + "Adding 5: \n", + "[[ 6. 7. 8.]\n", + " [ 9. 10. 11.]] \n", + "\n", + "Multiplying by 10: \n", + "[[10. 20. 30.]\n", + " [40. 50. 60.]]\n" + ] + } + ], "source": [ "array1 = np.array([[1., 2., 3.], [4., 5., 6.]])\n", "\n", - "print(array1)\n", + "print(\"Original array: \")\n", + "print(array1, \"\\n\")\n", "\n", "# Add 5 to all values in array1 and print \n", - "print()\n", + "print(\"Adding 5: \")\n", + "print(array1 + 5, \"\\n\")\n", "\n", "\n", "# Multiple all values in array1 by 10 and print \n", - "print()\n" + "print(\"Multiplying by 10: \")\n", + "print(array1 * 10)\n" ] }, { @@ -565,7 +671,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 48, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -573,9 +679,23 @@ "id": "uHr0DxrmaOEZ", "outputId": "b0f74550-427d-4333-c010-45f886677379" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[False True False]\n", + " [ True False True]]\n" + ] + } + ], "source": [ + "# array1 = np.array([[1., 2., 3.], [4., 5., 6.]])\n", + "\n", "array2 = np.array([[0., 4., 1.], [7., 2., 12.]])\n", + "result14 = np.where(array1 < array2, True, False)\n", + "\n", + "print(result14)\n", "\n", "\n", "# Test to see if pairwise elements in array1 are less than the corresponding element in array2. Output the results.\n", @@ -615,7 +735,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 49, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -623,13 +743,25 @@ "id": "k5pIynI12dmY", "outputId": "cd39dc42-7bab-4a92-c3e0-151a287d0b07" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1. 0. 1. 1. 0.]\n" + ] + } + ], "source": [ "arr1 = np.ones(5)\n", "arr2 = np.zeros(5)\n", "cond = np.array([True, False, True, True, False])\n", "\n", - "# Output the value from `arr1` whenever the corresponding value in `cond` is True, and otherwise take the value from `arr2`. \n" + "# Output the value from `arr1` whenever the corresponding value in `cond` is True, and otherwise take the value from `arr2`. \n", + "\n", + "result15 = np.where(cond, arr1, arr2)\n", + "\n", + "print(result15)\n" ] }, { From 0e59c04083acac8602280a0ac5b57a010274b401 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Mon, 19 Jan 2026 04:17:27 -0800 Subject: [PATCH 15/62] finished --- week_3_numpy.ipynb | 125 +++++++++++++++++++++++++++++++++++++++------ 1 file changed, 110 insertions(+), 15 deletions(-) diff --git a/week_3_numpy.ipynb b/week_3_numpy.ipynb index b1c51a5..d026fbd 100644 --- a/week_3_numpy.ipynb +++ b/week_3_numpy.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { "id": "Pt63xmy1qLll" }, @@ -792,7 +792,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -800,12 +800,33 @@ "id": "ImvBT7NX3aY7", "outputId": "d04b3b76-91e4-4a40-e047-1e7a55bfd4ed" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 3 3 3]\n", + " [0 3 1 0]\n", + " [1 1 1 2]\n", + " [3 3 2 0]\n", + " [1 3 2 0]] \n", + "\n", + "Mean: 1.6\n", + "Sum: 32\n" + ] + } + ], "source": [ "arr = np.random.randint(4, size=(5, 4))\n", - "print(arr)\n", + "print(arr, \"\\n\")\n", "\n", - "# Calculate and print the mean and sum for all values in the array\n" + "# Calculate and print the mean and sum for all values in the array\n", + "\n", + "mean16 = np.mean(arr)\n", + "sum16 = arr.sum()\n", + "\n", + "print(\"Mean: \", mean16)\n", + "print(\"Sum: \", sum16)\n" ] }, { @@ -817,7 +838,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -825,9 +846,32 @@ "id": "kxw7AUVm31Ea", "outputId": "b1cb6ece-5056-41b1-8c1b-f57612b9dae1" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean across rows: [2.25 1. 1.25 2. 1.5 ]\n", + "Mean across columns: [1. 2.6 1.8 1. ]\n", + "Sum across rows: [9 4 5 8 6]\n", + "Sum across columns: [ 5 13 9 5]\n" + ] + } + ], "source": [ - "# Calculate the mean and sum across the columns and the rows. Print out both\n" + "# Calculate the mean and sum across the columns and the rows. Print out both\n", + "# axis = 0 columns \n", + "# axis = 1 rows \n", + "\n", + "mean17_rows = arr.mean(axis = 1)\n", + "mean17_columns = arr.mean(axis = 0)\n", + "sum17_rows = arr.sum(axis = 1)\n", + "sum17_columns = arr.sum(axis = 0)\n", + "\n", + "print(\"Mean across rows: \", mean17_rows)\n", + "print(\"Mean across columns: \", mean17_columns)\n", + "print(\"Sum across rows: \", sum17_rows)\n", + "print(\"Sum across columns: \", sum17_columns)\n" ] }, { @@ -839,15 +883,46 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Full cummulative sum: \n", + " [ 0 3 6 9 9 12 13 13 14 15 16 18 21 24 26 26 27 30 32 32] \n", + "\n", + "Columns cumulative sum: \n", + " [[ 0 3 3 3]\n", + " [ 0 6 4 3]\n", + " [ 1 7 5 5]\n", + " [ 4 10 7 5]\n", + " [ 5 13 9 5]] \n", + "\n", + "Rows cumulative sum: \n", + " [[0 3 6 9]\n", + " [0 3 4 4]\n", + " [1 2 3 5]\n", + " [3 6 8 8]\n", + " [1 4 6 6]]\n" + ] + } + ], "source": [ "# Print out the cumulative sum for the full array, the columns and the rows. \n", "# For example the array [[1, 1, 1], [1, 1, 1]] would have the following outputs:\n", "# full array: [1, 2, 3, 4, 5, 6]\n", "# columns: [[1, 1, 1], [2, 2, 2]]\n", - "# rows: [[1, 2, 3], [1, 2, 3]]\n" + "# rows: [[1, 2, 3], [1, 2, 3]]\n", + "\n", + "cumsum18_full = arr.cumsum()\n", + "cumsum18_columns = arr.cumsum(axis = 0)\n", + "cumsum18_rows = arr.cumsum(axis = 1)\n", + "\n", + "print(\"Full cummulative sum: \\n\", cumsum18_full, \"\\n\")\n", + "print(\"Columns cumulative sum: \\n\", cumsum18_columns, \"\\n\")\n", + "print(\"Rows cumulative sum: \\n\", cumsum18_rows)\n" ] }, { @@ -859,7 +934,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -867,16 +942,36 @@ "id": "sDnJpvUR1B1W", "outputId": "c4806b83-0ba9-448e-c4b7-f9ca53a8900d" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arr: \n", + " [0 1 2 3 4 5 6 7 8 9] \n", + "\n", + "Odds: \n", + " [1 3 5 7 9] \n", + "\n", + "Arr reversed: \n", + " [9 8 7 6 5 4 3 2 1 0]\n" + ] + } + ], "source": [ "\n", "arr = np.arange(10)\n", - "print(arr)\n", + "print(\"Arr: \\n\", arr, \"\\n\")\n", "\n", "\n", "# Print out all all odd values\n", + "cond19 = arr % 2 \n", + "\n", + "odds = arr[arr % 2 == 1]\n", + "print(\"Odds: \\n\", odds, \"\\n\")\n", "\n", - "# Print out all values in reverse order\n" + "# Print out all values in reverse order\n", + "print(\"Arr reversed: \\n\", np.flip(arr))\n" ] } ], From dc217abd31aaacae21f18e606c46bb3dd27c0328 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Mon, 19 Jan 2026 04:20:32 -0800 Subject: [PATCH 16/62] initial week3 commit with new branch that is branched off of the homework branch --- week_3_numpy.ipynb | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/week_3_numpy.ipynb b/week_3_numpy.ipynb index d026fbd..879c7bf 100644 --- a/week_3_numpy.ipynb +++ b/week_3_numpy.ipynb @@ -934,7 +934,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -965,8 +965,6 @@ "\n", "\n", "# Print out all all odd values\n", - "cond19 = arr % 2 \n", - "\n", "odds = arr[arr % 2 == 1]\n", "print(\"Odds: \\n\", odds, \"\\n\")\n", "\n", From 9fb3822a0394388523a57c430957877b2fa5b3f7 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Wed, 21 Jan 2026 01:50:01 -0800 Subject: [PATCH 17/62] fixed a couple issues --- week_3_numpy.ipynb | 127 ++++++++++++++++++++++++++++----------------- 1 file changed, 79 insertions(+), 48 deletions(-) diff --git a/week_3_numpy.ipynb b/week_3_numpy.ipynb index 879c7bf..cd71e72 100644 --- a/week_3_numpy.ipynb +++ b/week_3_numpy.ipynb @@ -20,6 +20,11 @@ "* Can map data directly onto underlying disk or memory representation\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "markdown", "metadata": { @@ -75,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -113,7 +118,7 @@ "print(\"shape:\\t\", numbers.shape) # ndarrays have shape (numer of rows & columns)\n", "print(\"size:\\t\", numbers.size) # number of elements in the array\n", "print(\"datatype\", numbers.dtype) # numpy determines the datatype\n", - "print(\"bytes:\\t\", numbers.size * numbers.itemsize) # total bytes consumed by the array" + "print(\"bytes:\\t\", numbers.nbytes) # total bytes consumed by the array" ] }, { @@ -127,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -151,8 +156,8 @@ " [0. 0. 0. 0.]] \n", "\n", "random\n", - "[[ 0.38370392 -0.69051018]\n", - " [-0.69090326 -0.36937786]] \n", + "[[0.52286842 0.27818666]\n", + " [1.20170613 0.13236204]] \n", "\n", "empty\n", "[[0. 0.]\n", @@ -204,7 +209,7 @@ "print(even, \"\\n\")\n", "\n", "# Create an array of the form [0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. ] using np.linspace\n", - "line = np.linspace(0, 2, num = 9)\n", + "line = np.linspace(0, 2, 9)\n", "print(\"line\")\n", "print(line, \"\\n\")\n" ] @@ -233,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -241,13 +246,22 @@ "id": "NSMJt_4x0qan", "outputId": "81e81c55-af1d-4149-dc7c-7edc8a1a582d" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5 6 7]\n" + ] + } + ], "source": [ "arr1d = np.array([0,1, 2, 3, 4, 5, 6, 7, 8])\n", "\n", "# using indexing select and output [5, 6, 7] from arr1d. Save this slice to a variable named slice_arr1d\n", "\n", - "slice_arr1d = arr1d[5:8]\n" + "slice_arr1d = arr1d[5:8]\n", + "print(slice_arr1d)\n" ] }, { @@ -259,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -267,11 +281,20 @@ "id": "Xe-PXYhBp4bn", "outputId": "cf387d7a-d51f-4540-8d9a-571a9a24f7b5" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 12 12 12]\n" + ] + } + ], "source": [ "# Replace 6, 7, and 8 with 12 in arr1d using one line of code. Then print arr1d and slice_arr1d\n", "\n", - "arr1d[6:] = 12\n" + "arr1d[6:] = 12\n", + "print(arr1d)\n" ] }, { @@ -283,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -291,11 +314,20 @@ "id": "Eh0My7Eaqe77", "outputId": "693ef4b3-97a0-4a42-d77a-740fa2cd81db" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 12 12 12]\n" + ] + } + ], "source": [ "# Create a copy of arr1d and name it new_array. Hint: You can't use new_array = arr1d\n", "\n", - "new_array = arr1d.copy()" + "new_array = arr1d.copy()\n", + "print(new_array)" ] }, { @@ -307,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -337,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -353,10 +385,9 @@ "[[2 3 4]\n", " [6 7 8]] \n", "\n", - "[[3]\n", - " [7]] \n", + "[3 7] \n", "\n", - "[[1 2 3 4]]\n" + "[1 2 3 4]\n" ] } ], @@ -368,10 +399,10 @@ "print(arr2d[:2, 1:], \"\\n\")\n", "\n", "# select first two rows and only 3rd column\n", - "print(arr2d[:2, 2:3], \"\\n\")\n", + "print(arr2d[:2, 2], \"\\n\")\n", "\n", "# select all columns in the first row\n", - "print(arr2d[:1, :])\n" + "print(arr2d[0, :])\n" ] }, { @@ -394,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -408,17 +439,17 @@ "output_type": "stream", "text": [ "First and fourth rows of data: \n", - "[[0 0 0 0 1 1 1]\n", - " [0 1 1 1 0 1 0]] \n", + "[[1 0 0 0 0 1 1]\n", + " [0 1 1 1 0 0 0]] \n", "\n", "All of data: \n", - "[[0 0 0 0 1 1 1]\n", - " [1 1 0 1 1 0 0]\n", - " [1 0 0 0 1 0 1]\n", - " [0 1 1 1 0 1 0]\n", - " [0 1 1 1 1 1 1]\n", - " [0 0 0 0 1 1 0]\n", - " [0 1 0 0 0 0 0]]\n" + "[[1 0 0 0 0 1 1]\n", + " [1 1 0 1 1 1 1]\n", + " [0 1 0 1 0 0 1]\n", + " [0 1 1 1 0 0 0]\n", + " [1 0 1 0 1 1 1]\n", + " [1 1 1 1 1 0 0]\n", + " [1 0 0 1 0 1 0]]\n" ] } ], @@ -447,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -459,11 +490,11 @@ { "data": { "text/plain": [ - "array([[0, 0, 0, 0, 1, 1, 1],\n", - " [0, 1, 1, 1, 0, 1, 0]])" + "array([[1, 0, 0, 0, 0, 1, 1],\n", + " [0, 1, 1, 1, 0, 0, 0]])" ] }, - "execution_count": 34, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -485,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -497,14 +528,14 @@ { "data": { "text/plain": [ - "array([[1, 1, 0, 1, 1, 0, 0],\n", - " [1, 0, 0, 0, 1, 0, 1],\n", - " [0, 1, 1, 1, 1, 1, 1],\n", - " [0, 0, 0, 0, 1, 1, 0],\n", - " [0, 1, 0, 0, 0, 0, 0]])" + "array([[1, 1, 0, 1, 1, 1, 1],\n", + " [0, 1, 0, 1, 0, 0, 1],\n", + " [1, 0, 1, 0, 1, 1, 1],\n", + " [1, 1, 1, 1, 1, 0, 0],\n", + " [1, 0, 0, 1, 0, 1, 0]])" ] }, - "execution_count": 35, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -527,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -539,11 +570,11 @@ { "data": { "text/plain": [ - "array([[1, 1, 1],\n", - " [0, 1, 0]])" + "array([[0, 0, 0, 1, 1],\n", + " [1, 1, 0, 0, 0]])" ] }, - "execution_count": 36, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -551,7 +582,7 @@ "source": [ "# Select the last 5 columns for the first and forth row\n", "cond = (names == \"Bob\")\n", - "data[cond, 4:]" + "data[cond, 2:]" ] }, { From 3bd3577334413a14ec56d949b5d7e57369477e70 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Sat, 24 Jan 2026 03:25:36 -0800 Subject: [PATCH 18/62] initial commit for week4 --- week_4_panadas.ipynb | 52 +++++++++++++++++++++++++++++++++++++------- 1 file changed, 44 insertions(+), 8 deletions(-) diff --git a/week_4_panadas.ipynb b/week_4_panadas.ipynb index 9080a02..07c19f7 100644 --- a/week_4_panadas.ipynb +++ b/week_4_panadas.ipynb @@ -52,10 +52,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 6\n", + "1 7\n", + "2 8\n", + "3 9\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = pd.Series([6,7,8,9])\n", + "x" + ] }, { "cell_type": "markdown", @@ -68,8 +86,26 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "a 6\n", + "d 7\n", + "b 8\n", + "c 9\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = pd.Series([6,7,8,9], index = [\"a\",\"d\",\"b\",\"c\"])\n", + "x[[\"d\"]]" + ] }, { "cell_type": "markdown", @@ -344,7 +380,7 @@ ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": "ml-env", "language": "python", "name": "python3" }, @@ -358,7 +394,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.10.18" } }, "nbformat": 4, From 2a4e5aed3f30f0df3dce36dbaaf79addf4f9aa2e Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Sat, 24 Jan 2026 03:50:29 -0800 Subject: [PATCH 19/62] completed up to problem 5 --- week_4_panadas.ipynb | 235 ++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 219 insertions(+), 16 deletions(-) diff --git a/week_4_panadas.ipynb b/week_4_panadas.ipynb index 07c19f7..d750096 100644 --- a/week_4_panadas.ipynb +++ b/week_4_panadas.ipynb @@ -84,20 +84,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "a 6\n", "d 7\n", - "b 8\n", - "c 9\n", "dtype: int64" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -121,10 +118,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "Ohio 35000\n", + "Texas 71000\n", + "Oregon 16000\n", + "Utah 5000\n", + "dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_dict = {\"Ohio\":35000, \"Texas\":71000, \"Oregon\":16000, \"Utah\":5000}\n", + "\n", + "states_series = pd.Series(states_dict)\n", + "states_series" + ] }, { "cell_type": "markdown", @@ -135,10 +152,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "OH 35000\n", + "TX 71000\n", + "OR 16000\n", + "UT 5000\n", + "dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_series.index = [\"OH\", \"TX\", \"OR\", \"UT\"]\n", + "\n", + "states_series" + ] }, { "cell_type": "markdown", @@ -161,9 +197,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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stateyearpop
0Ohio20001.5
1Ohio20011.7
2Ohio20023.6
3Nevada20012.4
4Nevada20022.9
5Nevada20033.2
\n", + "
" + ], + "text/plain": [ + " state year pop\n", + "0 Ohio 2000 1.5\n", + "1 Ohio 2001 1.7\n", + "2 Ohio 2002 3.6\n", + "3 Nevada 2001 2.4\n", + "4 Nevada 2002 2.9\n", + "5 Nevada 2003 3.2" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = {\n", " 'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'],\n", @@ -184,10 +302,95 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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stateyearpop
zeroOhio20001.5
oneOhio20011.7
twoOhio20023.6
threeNevada20012.4
fourNevada20022.9
fiveNevada20033.2
\n", + "
" + ], + "text/plain": [ + " state year pop\n", + "zero Ohio 2000 1.5\n", + "one Ohio 2001 1.7\n", + "two Ohio 2002 3.6\n", + "three Nevada 2001 2.4\n", + "four Nevada 2002 2.9\n", + "five Nevada 2003 3.2" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.index = [\"zero\", \"one\", \"two\", \"three\", \"four\", \"five\"]\n", + "df" + ] }, { "cell_type": "markdown", From 99c9d37893fac503ed48dbdcac6d26fb95dd4ea4 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Sun, 25 Jan 2026 08:12:13 -0800 Subject: [PATCH 20/62] completed through problem 13 --- week_4_panadas.ipynb | 650 ++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 615 insertions(+), 35 deletions(-) diff --git a/week_4_panadas.ipynb b/week_4_panadas.ipynb index d750096..014c7ab 100644 --- a/week_4_panadas.ipynb +++ b/week_4_panadas.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -197,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -277,7 +277,7 @@ "5 Nevada 2003 3.2" ] }, - "execution_count": 16, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -302,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -382,7 +382,7 @@ "five Nevada 2003 3.2" ] }, - "execution_count": 23, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -401,10 +401,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zero Ohio\n", + "one Ohio\n", + "two Ohio\n", + "three Nevada\n", + "four Nevada\n", + "five Nevada\n", + "Name: state, dtype: object \n", + "\n", + "zero Ohio\n", + "one Ohio\n", + "two Ohio\n", + "three Nevada\n", + "four Nevada\n", + "five Nevada\n", + "Name: state, dtype: object\n" + ] + } + ], + "source": [ + "print(df[\"state\"], \"\\n\")\n", + "print(df.state)\n" + ] }, { "cell_type": "markdown", @@ -415,10 +440,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "state Ohio\n", + "year 2000\n", + "pop 1.5\n", + "Name: zero, dtype: object \n", + "\n", + "state Ohio\n", + "year 2000\n", + "pop 1.5\n", + "Name: zero, dtype: object\n" + ] + } + ], + "source": [ + "print(df.loc[\"zero\"], \"\\n\")\n", + "print(df.iloc[0])" + ] }, { "cell_type": "markdown", @@ -429,10 +473,102 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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stateyearpoprating
zeroOhio20001.55
oneOhio20011.74
twoOhio20023.63
threeNevada20012.42
fourNevada20022.91
fiveNevada20033.20
\n", + "
" + ], + "text/plain": [ + " state year pop rating\n", + "zero Ohio 2000 1.5 5\n", + "one Ohio 2001 1.7 4\n", + "two Ohio 2002 3.6 3\n", + "three Nevada 2001 2.4 2\n", + "four Nevada 2002 2.9 1\n", + "five Nevada 2003 3.2 0" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"rating\"] = [5,4,3,2,1,0]\n", + "df" + ] }, { "cell_type": "markdown", @@ -443,10 +579,109 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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stateyearpopratingnonsense
zeroOhio20001.557.5
oneOhio20011.746.8
twoOhio20023.6310.8
threeNevada20012.424.8
fourNevada20022.912.9
fiveNevada20033.200.0
\n", + "
" + ], + "text/plain": [ + " state year pop rating nonsense\n", + "zero Ohio 2000 1.5 5 7.5\n", + "one Ohio 2001 1.7 4 6.8\n", + "two Ohio 2002 3.6 3 10.8\n", + "three Nevada 2001 2.4 2 4.8\n", + "four Nevada 2002 2.9 1 2.9\n", + "five Nevada 2003 3.2 0 0.0" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"nonsense\"] = df[\"rating\"] * df[\"pop\"]\n", + "df" + ] }, { "cell_type": "markdown", @@ -463,10 +698,94 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 70, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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abcde
series_numerical0.0000001.0000002.0000003.0000004.000000
series_zeros0.0000000.0000000.0000000.0000000.000000
series_random0.7382310.9013290.1695780.8019320.646176
\n", + "
" + ], + "text/plain": [ + " a b c d e\n", + "series_numerical 0.000000 1.000000 2.000000 3.000000 4.000000\n", + "series_zeros 0.000000 0.000000 0.000000 0.000000 0.000000\n", + "series_random 0.738231 0.901329 0.169578 0.801932 0.646176" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = pd.Index(list(\"abcde\"))\n", + "series_numerical = pd.Series(np.arange(5), index = idx)\n", + "series_zeros = pd.Series(np.zeros(5), index = idx)\n", + "series_random = pd.Series(np.random.rand(5), index = idx)\n", + "\n", + "series_list = list([series_numerical, series_zeros, series_random])\n", + "series_index = pd.Index([\"series_numerical\", \"series_zeros\", \"series_random\"])\n", + "numeric_df = pd.DataFrame(series_list, index = series_index)\n", + "\n", + "numeric_df\n", + "\n", + "# I did consult chatgpt on this concerning the style I wrote with declaring the few extra variables (idx, series_list, series_index) and \n", + "# potential other ways to write it\n", + "\n" + ] }, { "cell_type": "markdown", @@ -477,10 +796,89 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 71, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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numericalzerosrandom
a0.00.00.738231
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c2.00.00.169578
d3.00.00.801932
e4.00.00.646176
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" + ], + "text/plain": [ + " numerical zeros random\n", + "a 0.0 0.0 0.738231\n", + "b 1.0 0.0 0.901329\n", + "c 2.0 0.0 0.169578\n", + "d 3.0 0.0 0.801932\n", + "e 4.0 0.0 0.646176" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transposed_numeric_df = numeric_df.T \n", + "transposed_numeric_df.columns = [\"numerical\", \"zeros\", \"random\"]\n", + "transposed_numeric_df" + ] }, { "cell_type": "markdown", @@ -491,10 +889,88 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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numericalzerosrandom
b1.00.00.901329
d3.00.00.801932
a0.00.00.738231
e4.00.00.646176
c2.00.00.169578
\n", + "
" + ], + "text/plain": [ + " numerical zeros random\n", + "b 1.0 0.0 0.901329\n", + "d 3.0 0.0 0.801932\n", + "a 0.0 0.0 0.738231\n", + "e 4.0 0.0 0.646176\n", + "c 2.0 0.0 0.169578" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transposed_numeric_df_random_descending = transposed_numeric_df.sort_values(\"random\", ascending = False)\n", + "transposed_numeric_df_random_descending" + ] }, { "cell_type": "markdown", @@ -505,10 +981,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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numericalzerosrandom
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e4.00.00.646176
\n", + "
" + ], + "text/plain": [ + " numerical zeros random\n", + "d 3.0 0.0 0.801932\n", + "e 4.0 0.0 0.646176" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "greater_than_2 = transposed_numeric_df.loc[transposed_numeric_df.numerical > 2]\n", + "greater_than_2" + ] }, { "cell_type": "markdown", @@ -519,10 +1052,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "a False\n", + "b False\n", + "c True\n", + "d False\n", + "e True\n", + "dtype: bool" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transposed_numeric_df[\"random5\"] = transposed_numeric_df[\"random\"] * 5\n", + "greater_than_random_5 = transposed_numeric_df[\"numerical\"] > transposed_numeric_df[\"random5\"]\n", + "greater_than_random_5" + ] }, { "cell_type": "markdown", @@ -533,10 +1086,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"Column(s) ['a', 'b', 'c', 'd', 'e'] do not exist\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[90], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m transposed_numeric_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meven\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mtransposed_numeric_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtransposed_numeric_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnumerical\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m transposed_numeric_df\n", + "File \u001b[0;32m/opt/anaconda3/envs/ml-env/lib/python3.10/site-packages/pandas/core/frame.py:10381\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10367\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10369\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10370\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10371\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10379\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10380\u001b[0m )\n\u001b[0;32m> 10381\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/opt/anaconda3/envs/ml-env/lib/python3.10/site-packages/pandas/core/apply.py:873\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 869\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumba\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 870\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 871\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumba\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m engine doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt support lists of callables yet\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 872\u001b[0m )\n\u001b[0;32m--> 873\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_list_or_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 875\u001b[0m \u001b[38;5;66;03m# all empty\u001b[39;00m\n\u001b[1;32m 876\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", + "File \u001b[0;32m/opt/anaconda3/envs/ml-env/lib/python3.10/site-packages/pandas/core/apply.py:628\u001b[0m, in \u001b[0;36mApply.apply_list_or_dict_like\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 625\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs\n\u001b[1;32m 627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dict_like(func):\n\u001b[0;32m--> 628\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_or_apply_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mapply\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 629\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 630\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magg_or_apply_list_like(op_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/opt/anaconda3/envs/ml-env/lib/python3.10/site-packages/pandas/core/apply.py:763\u001b[0m, in \u001b[0;36mNDFrameApply.agg_or_apply_dict_like\u001b[0;34m(self, op_name)\u001b[0m\n\u001b[1;32m 760\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maxis other than 0 is not supported\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 762\u001b[0m selection \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 763\u001b[0m result_index, result_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_dict_like\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 764\u001b[0m \u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 765\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 766\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrap_results_dict_like(obj, result_index, result_data)\n\u001b[1;32m 767\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m/opt/anaconda3/envs/ml-env/lib/python3.10/site-packages/pandas/core/apply.py:462\u001b[0m, in \u001b[0;36mApply.compute_dict_like\u001b[0;34m(self, op_name, selected_obj, selection, kwargs)\u001b[0m\n\u001b[1;32m 460\u001b[0m is_groupby \u001b[38;5;241m=\u001b[39m \u001b[38;5;28misinstance\u001b[39m(obj, (DataFrameGroupBy, SeriesGroupBy))\n\u001b[1;32m 461\u001b[0m func \u001b[38;5;241m=\u001b[39m cast(AggFuncTypeDict, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc)\n\u001b[0;32m--> 462\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize_dictlike_arg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselected_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 464\u001b[0m is_non_unique_col \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 465\u001b[0m selected_obj\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 466\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m selected_obj\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnunique() \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(selected_obj\u001b[38;5;241m.\u001b[39mcolumns)\n\u001b[1;32m 467\u001b[0m )\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m selected_obj\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 470\u001b[0m \u001b[38;5;66;03m# key only used for output\u001b[39;00m\n", + "File \u001b[0;32m/opt/anaconda3/envs/ml-env/lib/python3.10/site-packages/pandas/core/apply.py:663\u001b[0m, in \u001b[0;36mApply.normalize_dictlike_arg\u001b[0;34m(self, how, obj, func)\u001b[0m\n\u001b[1;32m 661\u001b[0m cols \u001b[38;5;241m=\u001b[39m Index(\u001b[38;5;28mlist\u001b[39m(func\u001b[38;5;241m.\u001b[39mkeys()))\u001b[38;5;241m.\u001b[39mdifference(obj\u001b[38;5;241m.\u001b[39mcolumns, sort\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 662\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(cols) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 663\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumn(s) \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(cols)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m do not exist\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 665\u001b[0m aggregator_types \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m, \u001b[38;5;28mdict\u001b[39m)\n\u001b[1;32m 667\u001b[0m \u001b[38;5;66;03m# if we have a dict of any non-scalars\u001b[39;00m\n\u001b[1;32m 668\u001b[0m \u001b[38;5;66;03m# eg. {'A' : ['mean']}, normalize all to\u001b[39;00m\n\u001b[1;32m 669\u001b[0m \u001b[38;5;66;03m# be list-likes\u001b[39;00m\n\u001b[1;32m 670\u001b[0m \u001b[38;5;66;03m# Cannot use func.values() because arg may be a Series\u001b[39;00m\n", + "\u001b[0;31mKeyError\u001b[0m: \"Column(s) ['a', 'b', 'c', 'd', 'e'] do not exist\"" + ] + } + ], + "source": [ + "def is_even(x):\n", + " if x[\"numerical\"] % 2 == 0:\n", + " return True\n", + " else:\n", + " False\n", + "\n", + "transposed_numeric_df[\"even\"] = transposed_numeric_df.apply(transposed_numeric_df[\"numerical\"])\n", + "transposed_numeric_df" + ] }, { "cell_type": "markdown", From 9511d22dce7b99236d734fc8a435f47ac8b2b4d3 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Mon, 26 Jan 2026 06:01:47 -0800 Subject: [PATCH 21/62] finished through problem 17 --- week_4_panadas.ipynb | 421 ++++++++++++++++++++++++++++++++++--------- 1 file changed, 336 insertions(+), 85 deletions(-) diff --git a/week_4_panadas.ipynb b/week_4_panadas.ipynb index 014c7ab..0d87d75 100644 --- a/week_4_panadas.ipynb +++ b/week_4_panadas.ipynb @@ -19,11 +19,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "#As allways we import pandas and numpy\n", + "#As always we import pandas and numpy\n", "import pandas as pd\n", "import numpy as np" ] @@ -698,7 +698,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -748,11 +748,11 @@ " \n", " \n", " series_random\n", - " 0.738231\n", - " 0.901329\n", - " 0.169578\n", - " 0.801932\n", - " 0.646176\n", + " 0.693278\n", + " 0.723256\n", + " 0.468126\n", + " 0.056753\n", + " 0.987466\n", " \n", " \n", "\n", @@ -762,10 +762,10 @@ " a b c d e\n", "series_numerical 0.000000 1.000000 2.000000 3.000000 4.000000\n", "series_zeros 0.000000 0.000000 0.000000 0.000000 0.000000\n", - "series_random 0.738231 0.901329 0.169578 0.801932 0.646176" + "series_random 0.693278 0.723256 0.468126 0.056753 0.987466" ] }, - "execution_count": 70, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -796,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -830,31 +830,31 @@ " a\n", " 0.0\n", " 0.0\n", - " 0.738231\n", + " 0.693278\n", " \n", " \n", " b\n", " 1.0\n", " 0.0\n", - " 0.901329\n", + " 0.723256\n", " \n", " \n", " c\n", " 2.0\n", " 0.0\n", - " 0.169578\n", + " 0.468126\n", " \n", " \n", " d\n", " 3.0\n", " 0.0\n", - " 0.801932\n", + " 0.056753\n", " \n", " \n", " e\n", " 4.0\n", " 0.0\n", - " 0.646176\n", + " 0.987466\n", " \n", " \n", "\n", @@ -862,14 +862,14 @@ ], "text/plain": [ " numerical zeros random\n", - "a 0.0 0.0 0.738231\n", - "b 1.0 0.0 0.901329\n", - "c 2.0 0.0 0.169578\n", - "d 3.0 0.0 0.801932\n", - "e 4.0 0.0 0.646176" + "a 0.0 0.0 0.693278\n", + "b 1.0 0.0 0.723256\n", + "c 2.0 0.0 0.468126\n", + "d 3.0 0.0 0.056753\n", + "e 4.0 0.0 0.987466" ] }, - "execution_count": 71, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -889,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -920,34 +920,34 @@ " \n", " \n", " \n", - " b\n", - " 1.0\n", + " e\n", + " 4.0\n", " 0.0\n", - " 0.901329\n", + " 0.987466\n", " \n", " \n", - " d\n", - " 3.0\n", + " b\n", + " 1.0\n", " 0.0\n", - " 0.801932\n", + " 0.723256\n", " \n", " \n", " a\n", " 0.0\n", " 0.0\n", - " 0.738231\n", + " 0.693278\n", " \n", " \n", - " e\n", - " 4.0\n", + " c\n", + " 2.0\n", " 0.0\n", - " 0.646176\n", + " 0.468126\n", " \n", " \n", - " c\n", - " 2.0\n", + " d\n", + " 3.0\n", " 0.0\n", - " 0.169578\n", + " 0.056753\n", " \n", " \n", "\n", @@ -955,14 +955,14 @@ ], "text/plain": [ " numerical zeros random\n", - "b 1.0 0.0 0.901329\n", - "d 3.0 0.0 0.801932\n", - "a 0.0 0.0 0.738231\n", - "e 4.0 0.0 0.646176\n", - "c 2.0 0.0 0.169578" + "e 4.0 0.0 0.987466\n", + "b 1.0 0.0 0.723256\n", + "a 0.0 0.0 0.693278\n", + "c 2.0 0.0 0.468126\n", + "d 3.0 0.0 0.056753" ] }, - "execution_count": 72, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -981,7 +981,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1015,13 +1015,13 @@ " d\n", " 3.0\n", " 0.0\n", - " 0.801932\n", + " 0.056753\n", " \n", " \n", " e\n", " 4.0\n", " 0.0\n", - " 0.646176\n", + " 0.987466\n", " \n", " \n", "\n", @@ -1029,11 +1029,11 @@ ], "text/plain": [ " numerical zeros random\n", - "d 3.0 0.0 0.801932\n", - "e 4.0 0.0 0.646176" + "d 3.0 0.0 0.056753\n", + "e 4.0 0.0 0.987466" ] }, - "execution_count": 80, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1052,28 +1052,78 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 15, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " numerical zeros random random5\n", + "a 0.0 0.0 0.693278 3.466389\n", + "b 1.0 0.0 0.723256 3.616281\n", + "c 2.0 0.0 0.468126 2.340629\n", + "d 3.0 0.0 0.056753 0.283767\n", + "e 4.0 0.0 0.987466 4.937328\n", + "-----------------------------------------------------------------\n", + "All rows where numerical is greater than random5:\n" + ] + }, { "data": { + "text/html": [ + "
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numericalzerosrandomrandom5
d3.00.00.0567530.283767
\n", + "
" + ], "text/plain": [ - "a False\n", - "b False\n", - "c True\n", - "d False\n", - "e True\n", - "dtype: bool" + " numerical zeros random random5\n", + "d 3.0 0.0 0.056753 0.283767" ] }, - "execution_count": 91, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transposed_numeric_df[\"random5\"] = transposed_numeric_df[\"random\"] * 5\n", - "greater_than_random_5 = transposed_numeric_df[\"numerical\"] > transposed_numeric_df[\"random5\"]\n", + "greater_than_random_5 = transposed_numeric_df[transposed_numeric_df[\"numerical\"] > transposed_numeric_df[\"random5\"]]\n", + "print(transposed_numeric_df)\n", + "print(\"-----------------------------------------------------------------\")\n", + "print(\"All rows where numerical is greater than random5:\")\n", "greater_than_random_5" ] }, @@ -1086,35 +1136,98 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 16, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "\"Column(s) ['a', 'b', 'c', 'd', 'e'] do not exist\"", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[90], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m transposed_numeric_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meven\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mtransposed_numeric_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtransposed_numeric_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnumerical\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m transposed_numeric_df\n", - "File \u001b[0;32m/opt/anaconda3/envs/ml-env/lib/python3.10/site-packages/pandas/core/frame.py:10381\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10367\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10369\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10370\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10371\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10379\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10380\u001b[0m )\n\u001b[0;32m> 10381\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m/opt/anaconda3/envs/ml-env/lib/python3.10/site-packages/pandas/core/apply.py:873\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 869\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumba\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 870\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 871\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumba\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m engine doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt support lists of callables yet\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 872\u001b[0m )\n\u001b[0;32m--> 873\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_list_or_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 875\u001b[0m \u001b[38;5;66;03m# all empty\u001b[39;00m\n\u001b[1;32m 876\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", - "File \u001b[0;32m/opt/anaconda3/envs/ml-env/lib/python3.10/site-packages/pandas/core/apply.py:628\u001b[0m, in \u001b[0;36mApply.apply_list_or_dict_like\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 625\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs\n\u001b[1;32m 627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dict_like(func):\n\u001b[0;32m--> 628\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_or_apply_dict_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mapply\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 629\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 630\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magg_or_apply_list_like(op_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m/opt/anaconda3/envs/ml-env/lib/python3.10/site-packages/pandas/core/apply.py:763\u001b[0m, in \u001b[0;36mNDFrameApply.agg_or_apply_dict_like\u001b[0;34m(self, op_name)\u001b[0m\n\u001b[1;32m 760\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maxis other than 0 is not supported\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 762\u001b[0m selection \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 763\u001b[0m result_index, result_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_dict_like\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 764\u001b[0m \u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 765\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 766\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrap_results_dict_like(obj, result_index, result_data)\n\u001b[1;32m 767\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m/opt/anaconda3/envs/ml-env/lib/python3.10/site-packages/pandas/core/apply.py:462\u001b[0m, in \u001b[0;36mApply.compute_dict_like\u001b[0;34m(self, op_name, selected_obj, selection, kwargs)\u001b[0m\n\u001b[1;32m 460\u001b[0m is_groupby \u001b[38;5;241m=\u001b[39m \u001b[38;5;28misinstance\u001b[39m(obj, (DataFrameGroupBy, SeriesGroupBy))\n\u001b[1;32m 461\u001b[0m func \u001b[38;5;241m=\u001b[39m cast(AggFuncTypeDict, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc)\n\u001b[0;32m--> 462\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalize_dictlike_arg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselected_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 464\u001b[0m is_non_unique_col \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 465\u001b[0m selected_obj\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 466\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m selected_obj\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnunique() \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(selected_obj\u001b[38;5;241m.\u001b[39mcolumns)\n\u001b[1;32m 467\u001b[0m )\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m selected_obj\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 470\u001b[0m \u001b[38;5;66;03m# key only used for output\u001b[39;00m\n", - "File \u001b[0;32m/opt/anaconda3/envs/ml-env/lib/python3.10/site-packages/pandas/core/apply.py:663\u001b[0m, in \u001b[0;36mApply.normalize_dictlike_arg\u001b[0;34m(self, how, obj, func)\u001b[0m\n\u001b[1;32m 661\u001b[0m cols \u001b[38;5;241m=\u001b[39m Index(\u001b[38;5;28mlist\u001b[39m(func\u001b[38;5;241m.\u001b[39mkeys()))\u001b[38;5;241m.\u001b[39mdifference(obj\u001b[38;5;241m.\u001b[39mcolumns, sort\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 662\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(cols) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 663\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumn(s) \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(cols)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m do not exist\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 665\u001b[0m aggregator_types \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m, \u001b[38;5;28mdict\u001b[39m)\n\u001b[1;32m 667\u001b[0m \u001b[38;5;66;03m# if we have a dict of any non-scalars\u001b[39;00m\n\u001b[1;32m 668\u001b[0m \u001b[38;5;66;03m# eg. {'A' : ['mean']}, normalize all to\u001b[39;00m\n\u001b[1;32m 669\u001b[0m \u001b[38;5;66;03m# be list-likes\u001b[39;00m\n\u001b[1;32m 670\u001b[0m \u001b[38;5;66;03m# Cannot use func.values() because arg may be a Series\u001b[39;00m\n", - 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numericalzerosrandomrandom5even
a0.00.00.6932783.466389True
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" + ], + "text/plain": [ + " numerical zeros random random5 even\n", + "a 0.0 0.0 0.693278 3.466389 True\n", + "b 1.0 0.0 0.723256 3.616281 False\n", + "c 2.0 0.0 0.468126 2.340629 True\n", + "d 3.0 0.0 0.056753 0.283767 False\n", + "e 4.0 0.0 0.987466 4.937328 True" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "def is_even(x):\n", - " if x[\"numerical\"] % 2 == 0:\n", - " return True\n", - " else:\n", - " False\n", - "\n", - "transposed_numeric_df[\"even\"] = transposed_numeric_df.apply(transposed_numeric_df[\"numerical\"])\n", + "transposed_numeric_df[\"even\"] = transposed_numeric_df[\"numerical\"] % 2 == 0\n", "transposed_numeric_df" ] }, @@ -1127,10 +1240,121 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " numerical zeros random random5 even even_odd\n", + "a 0.0 0.0 0.693278 3.466389 True even\n", + "b 1.0 0.0 0.723256 3.616281 False odd\n", + "c 2.0 0.0 0.468126 2.340629 True even\n", + "d 3.0 0.0 0.056753 0.283767 False odd\n", + "e 4.0 0.0 0.987466 4.937328 True even" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\" \n", + "I wrote this two ways. I find the one that I commented out with the lambda function \n", + "is cleaner with less code as you don't need to write the separate function. I used the \n", + "second one as that is how it was shown in the book. I found the lambda version on the \n", + "geeksforgeeks website, it's in the url below this line. \n", + "https://www.geeksforgeeks.org/python/ways-to-apply-an-if-condition-in-pandas-dataframe/\n", + "\"\"\";\n", + "\n", + "def ev_odd(x):\n", + " if x == True:\n", + " return \"even\"\n", + " else:\n", + " return \"odd\"\n", + "\n", + "# transposed_numeric_df[\"even_odd\"] = transposed_numeric_df[\"even\"].apply(lambda x: \"even\" if x == True else \"odd\")\n", + "transposed_numeric_df[\"even_odd\"] = transposed_numeric_df[\"even\"].apply(ev_odd)\n", + "transposed_numeric_df" + ] }, { "cell_type": "markdown", @@ -1143,8 +1367,35 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "numerical 10.000000\n", + "zeros 0.000000\n", + "random 2.928879\n", + "random5 14.644394\n", + "even 3.000000\n", + "dtype: float64" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "I purposely filtered out the even_odd column as calculating the sum just \n", + "creates a long string of evenoodevenoddeven. I left the even column in, \n", + "even though is just True/False as technically True == 1 and False == 0. So \n", + "you can technically add them. \n", + "\"\"\";\n", + "\n", + "columns = [\"numerical\", \"zeros\", \"random\", \"random5\", \"even\"]\n", + "\n", + "transposed_numeric_df[columns].sum(axis = 0)" + ] }, { "cell_type": "markdown", From af7655b9f88b25ea59f43b1121e067c3b1f983e8 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 27 Jan 2026 04:28:44 -0800 Subject: [PATCH 22/62] initial commit --- week_4_panadas.ipynb | 305 +++++++++++++++++++++++++++++-------------- 1 file changed, 206 insertions(+), 99 deletions(-) diff --git a/week_4_panadas.ipynb b/week_4_panadas.ipynb index 0d87d75..5c086de 100644 --- a/week_4_panadas.ipynb +++ b/week_4_panadas.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -698,7 +698,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -748,11 +748,11 @@ " \n", " \n", " series_random\n", - " 0.693278\n", - " 0.723256\n", - " 0.468126\n", - " 0.056753\n", - " 0.987466\n", + " 0.857157\n", + " 0.611313\n", + " 0.290102\n", + " 0.523485\n", + " 0.955533\n", " \n", " \n", "\n", @@ -762,10 +762,10 @@ " a b c d e\n", "series_numerical 0.000000 1.000000 2.000000 3.000000 4.000000\n", "series_zeros 0.000000 0.000000 0.000000 0.000000 0.000000\n", - "series_random 0.693278 0.723256 0.468126 0.056753 0.987466" + "series_random 0.857157 0.611313 0.290102 0.523485 0.955533" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -796,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -830,31 +830,31 @@ " a\n", " 0.0\n", " 0.0\n", - " 0.693278\n", + " 0.857157\n", " \n", " \n", " b\n", " 1.0\n", " 0.0\n", - " 0.723256\n", + " 0.611313\n", " \n", " \n", " c\n", " 2.0\n", " 0.0\n", - " 0.468126\n", + " 0.290102\n", " \n", " \n", " d\n", " 3.0\n", " 0.0\n", - " 0.056753\n", + " 0.523485\n", " \n", " \n", " e\n", " 4.0\n", " 0.0\n", - " 0.987466\n", + " 0.955533\n", " \n", " \n", "\n", @@ -862,14 +862,14 @@ ], "text/plain": [ " numerical zeros random\n", - "a 0.0 0.0 0.693278\n", - "b 1.0 0.0 0.723256\n", - "c 2.0 0.0 0.468126\n", - "d 3.0 0.0 0.056753\n", - "e 4.0 0.0 0.987466" + "a 0.0 0.0 0.857157\n", + "b 1.0 0.0 0.611313\n", + "c 2.0 0.0 0.290102\n", + "d 3.0 0.0 0.523485\n", + "e 4.0 0.0 0.955533" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -889,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -923,31 +923,31 @@ " e\n", " 4.0\n", " 0.0\n", - " 0.987466\n", - " \n", - " \n", - " b\n", - " 1.0\n", - " 0.0\n", - " 0.723256\n", + " 0.955533\n", " \n", " \n", " a\n", " 0.0\n", " 0.0\n", - " 0.693278\n", + " 0.857157\n", " \n", " \n", - " c\n", - " 2.0\n", + " b\n", + " 1.0\n", " 0.0\n", - " 0.468126\n", + " 0.611313\n", " \n", " \n", " d\n", " 3.0\n", " 0.0\n", - " 0.056753\n", + " 0.523485\n", + " \n", + " \n", + " c\n", + " 2.0\n", + " 0.0\n", + " 0.290102\n", " \n", " \n", "\n", @@ -955,14 +955,14 @@ ], "text/plain": [ " numerical zeros random\n", - "e 4.0 0.0 0.987466\n", - "b 1.0 0.0 0.723256\n", - "a 0.0 0.0 0.693278\n", - "c 2.0 0.0 0.468126\n", - "d 3.0 0.0 0.056753" + "e 4.0 0.0 0.955533\n", + "a 0.0 0.0 0.857157\n", + "b 1.0 0.0 0.611313\n", + "d 3.0 0.0 0.523485\n", + "c 2.0 0.0 0.290102" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -981,7 +981,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -1015,13 +1015,13 @@ " d\n", " 3.0\n", " 0.0\n", - " 0.056753\n", + " 0.523485\n", " \n", " \n", " e\n", " 4.0\n", " 0.0\n", - " 0.987466\n", + " 0.955533\n", " \n", " \n", "\n", @@ -1029,11 +1029,11 @@ ], "text/plain": [ " numerical zeros random\n", - "d 3.0 0.0 0.056753\n", - "e 4.0 0.0 0.987466" + "d 3.0 0.0 0.523485\n", + "e 4.0 0.0 0.955533" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1052,7 +1052,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1060,11 +1060,11 @@ "output_type": "stream", "text": [ " numerical zeros random random5\n", - "a 0.0 0.0 0.693278 3.466389\n", - "b 1.0 0.0 0.723256 3.616281\n", - "c 2.0 0.0 0.468126 2.340629\n", - "d 3.0 0.0 0.056753 0.283767\n", - "e 4.0 0.0 0.987466 4.937328\n", + "a 0.0 0.0 0.857157 4.285783\n", + "b 1.0 0.0 0.611313 3.056565\n", + "c 2.0 0.0 0.290102 1.450512\n", + "d 3.0 0.0 0.523485 2.617426\n", + "e 4.0 0.0 0.955533 4.777663\n", "-----------------------------------------------------------------\n", "All rows where numerical is greater than random5:\n" ] @@ -1098,11 +1098,18 @@ " \n", " \n", " \n", + " c\n", + " 2.0\n", + " 0.0\n", + " 0.290102\n", + " 1.450512\n", + " \n", + " \n", " d\n", " 3.0\n", " 0.0\n", - " 0.056753\n", - " 0.283767\n", + " 0.523485\n", + " 2.617426\n", " \n", " \n", "\n", @@ -1110,10 +1117,11 @@ ], "text/plain": [ " numerical zeros random random5\n", - "d 3.0 0.0 0.056753 0.283767" + "c 2.0 0.0 0.290102 1.450512\n", + "d 3.0 0.0 0.523485 2.617426" ] }, - "execution_count": 15, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1136,7 +1144,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1172,40 +1180,40 @@ " a\n", " 0.0\n", " 0.0\n", - " 0.693278\n", - " 3.466389\n", + " 0.857157\n", + " 4.285783\n", " True\n", " \n", " \n", " b\n", " 1.0\n", " 0.0\n", - " 0.723256\n", - " 3.616281\n", + " 0.611313\n", + " 3.056565\n", " False\n", " \n", " \n", " c\n", " 2.0\n", " 0.0\n", - " 0.468126\n", - " 2.340629\n", + " 0.290102\n", + " 1.450512\n", " True\n", " \n", " \n", " d\n", " 3.0\n", " 0.0\n", - " 0.056753\n", - " 0.283767\n", + " 0.523485\n", + " 2.617426\n", " False\n", " \n", " \n", " e\n", " 4.0\n", " 0.0\n", - " 0.987466\n", - " 4.937328\n", + " 0.955533\n", + " 4.777663\n", " True\n", " \n", " \n", @@ -1214,14 +1222,14 @@ ], "text/plain": [ " numerical zeros random random5 even\n", - "a 0.0 0.0 0.693278 3.466389 True\n", - "b 1.0 0.0 0.723256 3.616281 False\n", - "c 2.0 0.0 0.468126 2.340629 True\n", - "d 3.0 0.0 0.056753 0.283767 False\n", - "e 4.0 0.0 0.987466 4.937328 True" + "a 0.0 0.0 0.857157 4.285783 True\n", + "b 1.0 0.0 0.611313 3.056565 False\n", + "c 2.0 0.0 0.290102 1.450512 True\n", + "d 3.0 0.0 0.523485 2.617426 False\n", + "e 4.0 0.0 0.955533 4.777663 True" ] }, - "execution_count": 16, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1240,7 +1248,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1277,8 +1285,8 @@ " a\n", " 0.0\n", " 0.0\n", - " 0.693278\n", - " 3.466389\n", + " 0.857157\n", + " 4.285783\n", " True\n", " even\n", " \n", @@ -1286,8 +1294,8 @@ " b\n", " 1.0\n", " 0.0\n", - " 0.723256\n", - " 3.616281\n", + " 0.611313\n", + " 3.056565\n", " False\n", " odd\n", " \n", @@ -1295,8 +1303,8 @@ " c\n", " 2.0\n", " 0.0\n", - " 0.468126\n", - " 2.340629\n", + " 0.290102\n", + " 1.450512\n", " True\n", " even\n", " \n", @@ -1304,8 +1312,8 @@ " d\n", " 3.0\n", " 0.0\n", - " 0.056753\n", - " 0.283767\n", + " 0.523485\n", + " 2.617426\n", " False\n", " odd\n", " \n", @@ -1313,8 +1321,8 @@ " e\n", " 4.0\n", " 0.0\n", - " 0.987466\n", - " 4.937328\n", + " 0.955533\n", + " 4.777663\n", " True\n", " even\n", " \n", @@ -1324,14 +1332,14 @@ ], "text/plain": [ " numerical zeros random random5 even even_odd\n", - "a 0.0 0.0 0.693278 3.466389 True even\n", - "b 1.0 0.0 0.723256 3.616281 False odd\n", - "c 2.0 0.0 0.468126 2.340629 True even\n", - "d 3.0 0.0 0.056753 0.283767 False odd\n", - "e 4.0 0.0 0.987466 4.937328 True even" + "a 0.0 0.0 0.857157 4.285783 True even\n", + "b 1.0 0.0 0.611313 3.056565 False odd\n", + "c 2.0 0.0 0.290102 1.450512 True even\n", + "d 3.0 0.0 0.523485 2.617426 False odd\n", + "e 4.0 0.0 0.955533 4.777663 True even" ] }, - "execution_count": 28, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1365,21 +1373,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "numerical 10.000000\n", - "zeros 0.000000\n", - "random 2.928879\n", - "random5 14.644394\n", - "even 3.000000\n", + "numerical 10.00000\n", + "zeros 0.00000\n", + "random 3.23759\n", + "random5 16.18795\n", + "even 3.00000\n", "dtype: float64" ] }, - "execution_count": 43, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1408,8 +1416,107 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - 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numericalzerosrandomrandom5eveneven_odd
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c2.00.00.2901021.450512Trueeven
d3.00.00.5234852.617426Falseodd
e4.00.00.9555334.777663Trueeven
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" + ], + "text/plain": [ + " numerical zeros random random5 even even_odd\n", + "a 0.0 0.0 0.857157 4.285783 True even\n", + "b 1.0 0.0 0.611313 3.056565 False odd\n", + "c 2.0 0.0 0.290102 1.450512 True even\n", + "d 3.0 0.0 0.523485 2.617426 False odd\n", + "e 4.0 0.0 0.955533 4.777663 True even" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columns = [\"numerical\", \"zeros\", \"random\", \"random5\", \"even\"]\n", + "\n", + "x = 5\n", + "\n", + "transposed_numeric_df" + ] } ], "metadata": { From b24941930dade70be1eeb98b358d4bcce521b7e9 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 27 Jan 2026 04:42:57 -0800 Subject: [PATCH 23/62] completed week4 initial homework --- week_4_panadas.ipynb | 99 ++++---------------------------------------- 1 file changed, 9 insertions(+), 90 deletions(-) diff --git a/week_4_panadas.ipynb b/week_4_panadas.ipynb index 5c086de..6ba43e0 100644 --- a/week_4_panadas.ipynb +++ b/week_4_panadas.ipynb @@ -1414,108 +1414,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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" - ], "text/plain": [ - " numerical zeros random random5 even even_odd\n", - "a 0.0 0.0 0.857157 4.285783 True even\n", - "b 1.0 0.0 0.611313 3.056565 False odd\n", - "c 2.0 0.0 0.290102 1.450512 True even\n", - "d 3.0 0.0 0.523485 2.617426 False odd\n", - "e 4.0 0.0 0.955533 4.777663 True even" + "numerical e\n", + "zeros a\n", + "random e\n", + "random5 e\n", + "dtype: object" ] }, - "execution_count": 10, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "columns = [\"numerical\", \"zeros\", \"random\", \"random5\", \"even\"]\n", - "\n", - "x = 5\n", - "\n", - "transposed_numeric_df" + "transposed_numeric_df[columns].idxmax()\n", + "\n" ] } ], From ec10195ae4360853d903ac22fda6fd02b794d9bf Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Thu, 29 Jan 2026 00:32:43 -0800 Subject: [PATCH 24/62] initial commit --- week_4_panadas.ipynb | 223 ++++++++++++++++++++++--------------------- 1 file changed, 112 insertions(+), 111 deletions(-) diff --git a/week_4_panadas.ipynb b/week_4_panadas.ipynb index 6ba43e0..045cd33 100644 --- a/week_4_panadas.ipynb +++ b/week_4_panadas.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -65,14 +65,14 @@ "dtype: int64" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = pd.Series([6,7,8,9])\n", - "x" + "x\n" ] }, { @@ -100,7 +100,7 @@ } ], "source": [ - "x = pd.Series([6,7,8,9], index = [\"a\",\"d\",\"b\",\"c\"])\n", + "x = pd.Series([6,7,8,9], index = list(\"adbc\"))\n", "x[[\"d\"]]" ] }, @@ -698,7 +698,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -748,11 +748,11 @@ " \n", " \n", " series_random\n", - " 0.857157\n", - " 0.611313\n", - " 0.290102\n", - " 0.523485\n", - " 0.955533\n", + " 0.387098\n", + " 0.242602\n", + " 0.304984\n", + " 0.607978\n", + " 0.380149\n", " \n", " \n", "\n", @@ -762,10 +762,10 @@ " a b c d e\n", "series_numerical 0.000000 1.000000 2.000000 3.000000 4.000000\n", "series_zeros 0.000000 0.000000 0.000000 0.000000 0.000000\n", - "series_random 0.857157 0.611313 0.290102 0.523485 0.955533" + "series_random 0.387098 0.242602 0.304984 0.607978 0.380149" ] }, - "execution_count": 2, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -796,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -830,31 +830,31 @@ " a\n", " 0.0\n", " 0.0\n", - " 0.857157\n", + " 0.387098\n", " \n", " \n", " b\n", " 1.0\n", " 0.0\n", - " 0.611313\n", + " 0.242602\n", " \n", " \n", " c\n", " 2.0\n", " 0.0\n", - " 0.290102\n", + " 0.304984\n", " \n", " \n", " d\n", " 3.0\n", " 0.0\n", - " 0.523485\n", + " 0.607978\n", " \n", " \n", " e\n", " 4.0\n", " 0.0\n", - " 0.955533\n", + " 0.380149\n", " \n", " \n", "\n", @@ -862,14 +862,14 @@ ], "text/plain": [ " numerical zeros random\n", - "a 0.0 0.0 0.857157\n", - "b 1.0 0.0 0.611313\n", - "c 2.0 0.0 0.290102\n", - "d 3.0 0.0 0.523485\n", - "e 4.0 0.0 0.955533" + "a 0.0 0.0 0.387098\n", + "b 1.0 0.0 0.242602\n", + "c 2.0 0.0 0.304984\n", + "d 3.0 0.0 0.607978\n", + "e 4.0 0.0 0.380149" ] }, - "execution_count": 3, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -889,7 +889,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -920,34 +920,34 @@ " \n", " \n", " \n", - " e\n", - " 4.0\n", + " d\n", + " 3.0\n", " 0.0\n", - " 0.955533\n", + " 0.607978\n", " \n", " \n", " a\n", " 0.0\n", " 0.0\n", - " 0.857157\n", + " 0.387098\n", " \n", " \n", - " b\n", - " 1.0\n", + " e\n", + " 4.0\n", " 0.0\n", - " 0.611313\n", + " 0.380149\n", " \n", " \n", - " d\n", - " 3.0\n", + " c\n", + " 2.0\n", " 0.0\n", - " 0.523485\n", + " 0.304984\n", " \n", " \n", - " c\n", - " 2.0\n", + " b\n", + " 1.0\n", " 0.0\n", - " 0.290102\n", + " 0.242602\n", " \n", " \n", "\n", @@ -955,14 +955,14 @@ ], "text/plain": [ " numerical zeros random\n", - "e 4.0 0.0 0.955533\n", - "a 0.0 0.0 0.857157\n", - "b 1.0 0.0 0.611313\n", - "d 3.0 0.0 0.523485\n", - "c 2.0 0.0 0.290102" + "d 3.0 0.0 0.607978\n", + "a 0.0 0.0 0.387098\n", + "e 4.0 0.0 0.380149\n", + "c 2.0 0.0 0.304984\n", + "b 1.0 0.0 0.242602" ] }, - "execution_count": 4, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -981,7 +981,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1015,13 +1015,13 @@ " d\n", " 3.0\n", " 0.0\n", - " 0.523485\n", + " 0.607978\n", " \n", " \n", " e\n", " 4.0\n", " 0.0\n", - " 0.955533\n", + " 0.380149\n", " \n", " \n", "\n", @@ -1029,11 +1029,11 @@ ], "text/plain": [ " numerical zeros random\n", - "d 3.0 0.0 0.523485\n", - "e 4.0 0.0 0.955533" + "d 3.0 0.0 0.607978\n", + "e 4.0 0.0 0.380149" ] }, - "execution_count": 5, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1052,7 +1052,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1060,11 +1060,11 @@ "output_type": "stream", "text": [ " numerical zeros random random5\n", - "a 0.0 0.0 0.857157 4.285783\n", - "b 1.0 0.0 0.611313 3.056565\n", - "c 2.0 0.0 0.290102 1.450512\n", - "d 3.0 0.0 0.523485 2.617426\n", - "e 4.0 0.0 0.955533 4.777663\n", + "a 0.0 0.0 0.387098 1.935491\n", + "b 1.0 0.0 0.242602 1.213010\n", + "c 2.0 0.0 0.304984 1.524922\n", + "d 3.0 0.0 0.607978 3.039889\n", + "e 4.0 0.0 0.380149 1.900745\n", "-----------------------------------------------------------------\n", "All rows where numerical is greater than random5:\n" ] @@ -1101,15 +1101,15 @@ " c\n", " 2.0\n", " 0.0\n", - " 0.290102\n", - " 1.450512\n", + " 0.304984\n", + " 1.524922\n", " \n", " \n", - " d\n", - " 3.0\n", + " e\n", + " 4.0\n", " 0.0\n", - " 0.523485\n", - " 2.617426\n", + " 0.380149\n", + " 1.900745\n", " \n", " \n", "\n", @@ -1117,11 +1117,11 @@ ], "text/plain": [ " numerical zeros random random5\n", - "c 2.0 0.0 0.290102 1.450512\n", - "d 3.0 0.0 0.523485 2.617426" + "c 2.0 0.0 0.304984 1.524922\n", + "e 4.0 0.0 0.380149 1.900745" ] }, - "execution_count": 6, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1144,7 +1144,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1180,40 +1180,40 @@ " a\n", " 0.0\n", " 0.0\n", - " 0.857157\n", - " 4.285783\n", + " 0.387098\n", + " 1.935491\n", " True\n", " \n", " \n", " b\n", " 1.0\n", " 0.0\n", - " 0.611313\n", - " 3.056565\n", + " 0.242602\n", + " 1.213010\n", " False\n", " \n", " \n", " c\n", " 2.0\n", " 0.0\n", - " 0.290102\n", - " 1.450512\n", + " 0.304984\n", + " 1.524922\n", " True\n", " \n", " \n", " d\n", " 3.0\n", " 0.0\n", - " 0.523485\n", - " 2.617426\n", + " 0.607978\n", + " 3.039889\n", " False\n", " \n", " \n", " e\n", " 4.0\n", " 0.0\n", - " 0.955533\n", - " 4.777663\n", + " 0.380149\n", + " 1.900745\n", " True\n", " \n", " \n", @@ -1222,14 +1222,14 @@ ], "text/plain": [ " numerical zeros random random5 even\n", - "a 0.0 0.0 0.857157 4.285783 True\n", - "b 1.0 0.0 0.611313 3.056565 False\n", - "c 2.0 0.0 0.290102 1.450512 True\n", - "d 3.0 0.0 0.523485 2.617426 False\n", - "e 4.0 0.0 0.955533 4.777663 True" + "a 0.0 0.0 0.387098 1.935491 True\n", + "b 1.0 0.0 0.242602 1.213010 False\n", + "c 2.0 0.0 0.304984 1.524922 True\n", + "d 3.0 0.0 0.607978 3.039889 False\n", + "e 4.0 0.0 0.380149 1.900745 True" ] }, - "execution_count": 7, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1248,7 +1248,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1285,8 +1285,8 @@ " a\n", " 0.0\n", " 0.0\n", - " 0.857157\n", - " 4.285783\n", + " 0.387098\n", + " 1.935491\n", " True\n", " even\n", " \n", @@ -1294,8 +1294,8 @@ " b\n", " 1.0\n", " 0.0\n", - " 0.611313\n", - " 3.056565\n", + " 0.242602\n", + " 1.213010\n", " False\n", " odd\n", " \n", @@ -1303,8 +1303,8 @@ " c\n", " 2.0\n", " 0.0\n", - " 0.290102\n", - " 1.450512\n", + " 0.304984\n", + " 1.524922\n", " True\n", " even\n", " \n", @@ -1312,8 +1312,8 @@ " d\n", " 3.0\n", " 0.0\n", - " 0.523485\n", - " 2.617426\n", + " 0.607978\n", + " 3.039889\n", " False\n", " odd\n", " \n", @@ -1321,8 +1321,8 @@ " e\n", " 4.0\n", " 0.0\n", - " 0.955533\n", - " 4.777663\n", + " 0.380149\n", + " 1.900745\n", " True\n", " even\n", " \n", @@ -1332,14 +1332,14 @@ ], "text/plain": [ " numerical zeros random random5 even even_odd\n", - "a 0.0 0.0 0.857157 4.285783 True even\n", - "b 1.0 0.0 0.611313 3.056565 False odd\n", - "c 2.0 0.0 0.290102 1.450512 True even\n", - "d 3.0 0.0 0.523485 2.617426 False odd\n", - "e 4.0 0.0 0.955533 4.777663 True even" + "a 0.0 0.0 0.387098 1.935491 True even\n", + "b 1.0 0.0 0.242602 1.213010 False odd\n", + "c 2.0 0.0 0.304984 1.524922 True even\n", + "d 3.0 0.0 0.607978 3.039889 False odd\n", + "e 4.0 0.0 0.380149 1.900745 True even" ] }, - "execution_count": 8, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1373,21 +1373,21 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "numerical 10.00000\n", - "zeros 0.00000\n", - "random 3.23759\n", - "random5 16.18795\n", - "even 3.00000\n", + "numerical 10.000000\n", + "zeros 0.000000\n", + "random 1.922811\n", + "random5 9.614057\n", + "even 3.000000\n", "dtype: float64" ] }, - "execution_count": 9, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1414,7 +1414,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1422,12 +1422,13 @@ "text/plain": [ "numerical e\n", "zeros a\n", - "random e\n", - "random5 e\n", + "random d\n", + "random5 d\n", + "even a\n", "dtype: object" ] }, - "execution_count": 25, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } From e16b4eefe9caddb43f72c84922e5a3ffaf85e285 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Thu, 29 Jan 2026 01:01:29 -0800 Subject: [PATCH 25/62] initial commit --- week_5_vectors_applications.ipynb | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/week_5_vectors_applications.ipynb b/week_5_vectors_applications.ipynb index 11948c0..366ddd5 100644 --- a/week_5_vectors_applications.ipynb +++ b/week_5_vectors_applications.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -28,15 +28,27 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'woman' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m women \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m2\u001b[39m])\n\u001b[1;32m 3\u001b[0m man \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m5\u001b[39m])\n\u001b[0;32m----> 4\u001b[0m royalty \u001b[38;5;241m=\u001b[39m queen \u001b[38;5;241m-\u001b[39m \u001b[43mwoman\u001b[49m\n\u001b[1;32m 5\u001b[0m king \u001b[38;5;241m=\u001b[39m royalty \u001b[38;5;241m-\u001b[39m man\n", + "\u001b[0;31mNameError\u001b[0m: name 'woman' is not defined" + ] + } + ], "source": [ "queen = np.array([5, 6, 4])\n", "women = np.array([1, 3, 2])\n", "man = np.array([5, 5, 5])\n", - "royalty = None\n", - "king = None\n", + "royalty = queen - women\n", + "king = royalty - man\n", "\n" ] }, From 7338827c72e5a248dcf962de8d0fd6a94760719e Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Thu, 29 Jan 2026 17:25:05 -0800 Subject: [PATCH 26/62] started working on problem 2 --- week_5_vectors_applications.ipynb | 39 +++++++++++++++---------------- 1 file changed, 19 insertions(+), 20 deletions(-) diff --git a/week_5_vectors_applications.ipynb b/week_5_vectors_applications.ipynb index 366ddd5..26f172e 100644 --- a/week_5_vectors_applications.ipynb +++ b/week_5_vectors_applications.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,21 +28,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'woman' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[8], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m women \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m2\u001b[39m])\n\u001b[1;32m 3\u001b[0m man \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m5\u001b[39m])\n\u001b[0;32m----> 4\u001b[0m royalty \u001b[38;5;241m=\u001b[39m queen \u001b[38;5;241m-\u001b[39m \u001b[43mwoman\u001b[49m\n\u001b[1;32m 5\u001b[0m king \u001b[38;5;241m=\u001b[39m royalty \u001b[38;5;241m-\u001b[39m man\n", - "\u001b[0;31mNameError\u001b[0m: name 'woman' is not defined" - ] - } - ], + "outputs": [], "source": [ "queen = np.array([5, 6, 4])\n", "women = np.array([1, 3, 2])\n", @@ -64,13 +52,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test passed for [5]\n", + "Test failed for [3.2 0.1]\n", + "Test failed for [-2 2 2]\n", + "Test failed for [86 72 51 87 73 21 60 70 66 57]\n" + ] + } + ], "source": [ "def vector_magnitude(np_vector: npt.NDArray) -> np.float64:\n", " #Write your code here\n", - " return \n", + " return 5\n", "\n", "def test_vector_magnitude(np_vector: npt.NDArray) -> None:\n", " if vector_magnitude(np_vector) == np.linalg.norm(np_vector):\n", @@ -277,7 +276,7 @@ ], "metadata": { "kernelspec": { - "display_name": "AD_450_env", + "display_name": "ml-env", "language": "python", "name": "python3" }, @@ -291,7 +290,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.10.18" } }, "nbformat": 4, From 0c2c9ea90a33c9b41548b41d2d5407a2eeba346d Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Mon, 2 Feb 2026 22:36:58 -0800 Subject: [PATCH 27/62] completed problem 3 --- week_5_vectors_applications.ipynb | 61 ++++++++++++++++++++++++------- 1 file changed, 48 insertions(+), 13 deletions(-) diff --git a/week_5_vectors_applications.ipynb b/week_5_vectors_applications.ipynb index 26f172e..3464b2a 100644 --- a/week_5_vectors_applications.ipynb +++ b/week_5_vectors_applications.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ "women = np.array([1, 3, 2])\n", "man = np.array([5, 5, 5])\n", "royalty = queen - women\n", - "king = royalty - man\n", + "king = royalty + man\n", "\n" ] }, @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -60,16 +60,29 @@ "output_type": "stream", "text": [ "Test passed for [5]\n", - "Test failed for [3.2 0.1]\n", - "Test failed for [-2 2 2]\n", - "Test failed for [86 72 51 87 73 21 60 70 66 57]\n" + "Test passed for [3.2 0.1]\n", + "Test passed for [-2 2 2]\n", + "Test passed for [70 2 28 36 36 7 34 99 33 82]\n" ] } ], "source": [ + "\"\"\"\n", + "I wrote vector_magnitude two different ways, the commented version use \n", + "a for loop to sum the square of each element and then return the square \n", + "root of that sum. In the second version I compute everything in one line. \n", + "\"\"\"\n", + "\n", + "\n", + "# def vector_magnitude(np_vector: npt.NDArray) -> np.float64:\n", + "# sum = 0 \n", + "# for num in np_vector:\n", + "# sum += num**2 \n", + "\n", + "# return sum**0.5\n", + "\n", "def vector_magnitude(np_vector: npt.NDArray) -> np.float64:\n", - " #Write your code here\n", - " return 5\n", + " return sum((np_vector**2))**0.5\n", "\n", "def test_vector_magnitude(np_vector: npt.NDArray) -> None:\n", " if vector_magnitude(np_vector) == np.linalg.norm(np_vector):\n", @@ -86,7 +99,7 @@ "test_vector_magnitude(d1_vector)\n", "test_vector_magnitude(d2_vector)\n", "test_vector_magnitude(d3_vector)\n", - "test_vector_magnitude(d10_vector)" + "test_vector_magnitude(d10_vector)\n" ] }, { @@ -101,15 +114,37 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test passed for [1.]\n", + "Test passed for [0.99951208 0.03123475]\n", + "Test passed for [-0.57735027 0.57735027 0.57735027]\n" + ] + } + ], "source": [ "d1_unit_vector = np.array([1.])\n", "d2_unit_vector = np.array([0.99951208, 0.03123475])\n", "d3_unit_vector = np.array([-0.57735027, 0.57735027, 0.57735027])\n", "\n", - "#Impliment vector_magnitude and test_vector_magnitude here" + "#Impliment vector_magnitude and test_vector_magnitude here\n", + "def create_unit_vector(arr):\n", + " return arr * (1 / vector_magnitude(arr))\n", + "\n", + "def test_create_unit_vector(arr, test_arr):\n", + " if np.allclose(create_unit_vector(arr), test_arr):\n", + " print(f\"Test passed for {test_arr}\")\n", + " else:\n", + " print(f\"Test failed for {test_arr}\")\n", + "\n", + "test_create_unit_vector(create_unit_vector(d1_vector), d1_unit_vector)\n", + "test_create_unit_vector(create_unit_vector(d2_vector), d2_unit_vector)\n", + "test_create_unit_vector(create_unit_vector(d3_vector), d3_unit_vector)" ] }, { From cb044f84f1538d23b8e06207f2d79df61acbad45 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Mon, 2 Feb 2026 23:06:03 -0800 Subject: [PATCH 28/62] completed problem 4 --- week_5_vectors_applications.ipynb | 35 ++++++++++++++++++++++++++++--- 1 file changed, 32 insertions(+), 3 deletions(-) diff --git a/week_5_vectors_applications.ipynb b/week_5_vectors_applications.ipynb index 3464b2a..e18d293 100644 --- a/week_5_vectors_applications.ipynb +++ b/week_5_vectors_applications.ipynb @@ -161,10 +161,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test passed for [5 6 7] and [5 6 7]\n", + "Test passed for [2 3 4] and [7. 8. 9.]\n", + "Test passed for [0.0001 0.0002 0.0003] and [100000 200000 300000]\n" + ] + } + ], + "source": [ + "vector1 = np.array([5,6,7])\n", + "vector2_1 = np.array([2,3,4])\n", + "vector2_2 = np.array([7.0, 8.0, 9.0])\n", + "vector3_1 = np.array([0.0001, 0.0002, 0.0003])\n", + "vector3_2 = np.array([100_000, 200_000, 300_000])\n", + "\n", + "def my_dot(arr1, arr2):\n", + " return sum(arr1 * arr2)\n", + "\n", + "def test_my_dot(arr1, arr2):\n", + " if my_dot(arr1, arr2) == np.dot(arr1, arr2):\n", + " print(f\"Test passed for {arr1} and {arr2}\")\n", + " else:\n", + " print(f\"Test failed\")\n", + "\n", + "test_my_dot(vector1, vector1)\n", + "test_my_dot(vector2_1, vector2_2)\n", + "test_my_dot(vector3_1, vector3_2)" + ] }, { "cell_type": "markdown", From f9e154ef649682fe3818a625e90f7c16229c6561 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Mon, 2 Feb 2026 23:50:54 -0800 Subject: [PATCH 29/62] completed problem 5 --- week_5_vectors_applications.ipynb | 32 ++++++++++++++++++++++++++++--- 1 file changed, 29 insertions(+), 3 deletions(-) diff --git a/week_5_vectors_applications.ipynb b/week_5_vectors_applications.ipynb index e18d293..d184737 100644 --- a/week_5_vectors_applications.ipynb +++ b/week_5_vectors_applications.ipynb @@ -213,15 +213,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cat 1 prediction: 21.15\n", + "Cat 1 prediction error == 3.06\n", + "\n", + "\n", + "Cat 2 prediction: 15.21\n", + "Cat 2 prediction error == 1.38\n", + "\n" + ] + } + ], "source": [ "cat_1_array = np.array([28.57, 10.435])\n", "cat_1_actual_weight = 24.21\n", "\n", "cat_2_array = np.array([19.04, 6.93])\n", - "cat_2_actual_weight = 13.831" + "cat_2_actual_weight = 13.831\n", + "\n", + "def predict_weight(arr):\n", + " return 0.23 * arr[0] + 1.07 * arr[1] + 3.412\n", + "\n", + "def prediction_error(arr, actual_weight):\n", + " return abs(predict_weight(arr) - actual_weight)\n", + "\n", + "print(f\"Cat 1 prediction: {predict_weight(cat_1_array):.2f}\")\n", + "print(f\"Cat 1 prediction error == {prediction_error(cat_1_array, cat_1_actual_weight):.2f}\\n\")\n", + "print(\"\")\n", + "print(f\"Cat 2 prediction: {predict_weight(cat_2_array):.2f}\")\n", + "print(f\"Cat 2 prediction error == {prediction_error(cat_2_array, cat_2_actual_weight):.2f}\\n\")\n" ] }, { From 1e3890ca5fe906c98eee42b08c91a869d6d96579 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 3 Feb 2026 00:34:27 -0800 Subject: [PATCH 30/62] completed problem 6 --- week_5_vectors_applications.ipynb | 45 +++++++++++++++++++++++++++++-- 1 file changed, 43 insertions(+), 2 deletions(-) diff --git a/week_5_vectors_applications.ipynb b/week_5_vectors_applications.ipynb index d184737..dd0e056 100644 --- a/week_5_vectors_applications.ipynb +++ b/week_5_vectors_applications.ipynb @@ -285,8 +285,49 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loop version is faster!\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "def linear_comb_loop(scalars_arr, vectors_arr):\n", + " curr = np.array(np.zeros(3))\n", + "\n", + " for i in range(len(vectors_arr)):\n", + " curr += scalars_arr[i] * vectors_arr[i]\n", + "\n", + " return curr\n", + "\n", + "scalars_array = np.array([1, 2, -3])\n", + "vectors_array = np.array([[4,5,1], [-4,0,-4], [1,3,2]])\n", + "\n", + "def linear_comb_vectorized(scalars_arr, vectors_arr):\n", + " return np.dot(scalars_arr, vectors_arr)\n", + "\n", + "def linear_comb_speed_test(scalars_arr, vectors_arr):\n", + " start1 = time.time()\n", + " linear_comb_loop(scalars_arr, vectors_arr)\n", + " end1 = time.time() - start1\n", + "\n", + " start2 = time.time()\n", + " linear_comb_vectorized(scalars_arr, vectors_arr)\n", + " end2 = time.time() - start2\n", + "\n", + " if(end1 > end2):\n", + " print(\"Loop version is faster!\")\n", + " else:\n", + " print(\"Vectorized version is faster!\")\n", + "\n", + "linear_comb_speed_test(scalars_array, vectors_array)\n", + "\n" + ] }, { "cell_type": "markdown", From af0921dbca5a9b36f408dc2502f6291173a34f89 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 3 Feb 2026 01:15:39 -0800 Subject: [PATCH 31/62] completed problem 7 --- week_5_vectors_applications.ipynb | 38 ++++++++++++++++++++++++++----- 1 file changed, 32 insertions(+), 6 deletions(-) diff --git a/week_5_vectors_applications.ipynb b/week_5_vectors_applications.ipynb index dd0e056..2bd8e64 100644 --- a/week_5_vectors_applications.ipynb +++ b/week_5_vectors_applications.ipynb @@ -283,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -340,22 +340,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "basis_vector = np.array([1,3])\n", + "rand_dist = np.linspace(-4, 4, 100)\n", + "\n", + "\"\"\"\n", + "this is how I originally completed the problem\n", + "\n", + "sub_space = np.zeros((100, 2))\n", + "for i in range(len(rand_dist)):\n", + " sub_space[i] = rand_dist[i] * basis_vector\n", + "\"\"\"\n", + "\n", + "sub_space = rand_dist[:, None] * basis_vector" + ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 54, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "def plot_vectors(vectors):\n", " plt.scatter(*zip(*vectors))\n", - " plt.show()" + " plt.show()\n", + "\n", + "plot_vectors(sub_space)" ] }, { From e4047f79533cc12891663fa10d44946c64dae331 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 3 Feb 2026 03:37:23 -0800 Subject: [PATCH 32/62] mostly completed problem 9 --- week_5_vectors_applications.ipynb | 61 +++++++++++++++++++++++++++++-- 1 file changed, 58 insertions(+), 3 deletions(-) diff --git a/week_5_vectors_applications.ipynb b/week_5_vectors_applications.ipynb index 2bd8e64..3e82f01 100644 --- a/week_5_vectors_applications.ipynb +++ b/week_5_vectors_applications.ipynb @@ -425,10 +425,65 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pearson function passed.\n", + "Cosine test passed.\n", + "My functions are NOT equal to each other.\n" + ] + } + ], + "source": [ + "from scipy.spatial.distance import cosine\n", + "\n", + "coeff_arr1 = np.array([2,3,4])\n", + "coeff_arr2 = np.array([5,6,7])\n", + "\n", + "def pearson_corr(x_arr, y_arr):\n", + " x_mean = x_arr - np.mean(x_arr)\n", + " y_mean = y_arr - np.mean(y_arr)\n", + " numerator = np.sum((x_mean) * (y_mean)) \n", + " denominator = np.sqrt((np.sum((x_mean)**2)) * (np.sum((y_mean)**2)))\n", + " return numerator / denominator\n", + "\n", + "def cosine_simil(x_arr, y_arr):\n", + " numerator = np.dot(x_arr, y_arr)\n", + " denominator = np.linalg.norm(x_arr) * np.linalg.norm(y_arr)\n", + " return numerator / denominator\n", + "\n", + "def test_pearson(arr1, arr2):\n", + " if (pearson_corr(arr1, arr2)) == (np.corrcoef(arr1, arr2)[0,1]):\n", + " print(\"Pearson function passed.\")\n", + " else:\n", + " print(\"Pearson function failed.\")\n", + "\n", + "def test_cosine(arr1, arr2):\n", + " if (cosine_simil(arr1, arr2)) == (1 - cosine(arr1, arr2)):\n", + " print(\"Cosine test passed.\")\n", + " else:\n", + " print(\"Cosine test failed.\")\n", + "\n", + "\n", + "def test_person_cosine_equality(arr1, arr2):\n", + " if (pearson_corr(arr1, arr2)) == (cosine_simil(arr1, arr2)):\n", + " print(\"Both of my functions are equal to each other.\")\n", + " else:\n", + " print(\"My functions are NOT equal to each other.\")\n", + "\n", + "\n", + "\n", + "test_pearson(coeff_arr1, coeff_arr2)\n", + "test_cosine(coeff_arr1, coeff_arr2)\n", + "test_person_cosine_equality(coeff_arr1, coeff_arr2)\n", + "\n", + "\n", + "\n" + ] } ], "metadata": { From 592dc0e26b5b4b918624d33b0e33db45b3ebbb94 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 3 Feb 2026 18:40:27 -0800 Subject: [PATCH 33/62] initial commit --- week_5_vectors_applications.ipynb | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/week_5_vectors_applications.ipynb b/week_5_vectors_applications.ipynb index 3e82f01..b0afb48 100644 --- a/week_5_vectors_applications.ipynb +++ b/week_5_vectors_applications.ipynb @@ -425,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -481,6 +481,12 @@ "test_cosine(coeff_arr1, coeff_arr2)\n", "test_person_cosine_equality(coeff_arr1, coeff_arr2)\n", "\n", + "\"\"\"\n", + "For the test_person_cosine_equality is is printing that they are not equal, but this \n", + "is what is expected. In the book it states that these two functions can give different \n", + "results for the same data because the functions start from different assumptions.\n", + "\"\"\";\n", + "\n", "\n", "\n" ] From f92812c2e9dcc1f9181cbef872aea1755d67b50d Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Thu, 5 Feb 2026 03:13:56 -0800 Subject: [PATCH 34/62] fixed issues with problem 9 --- week_5_vectors_applications.ipynb | 44 ++++++++++++++++++++++++------- 1 file changed, 34 insertions(+), 10 deletions(-) diff --git a/week_5_vectors_applications.ipynb b/week_5_vectors_applications.ipynb index b0afb48..7314bb5 100644 --- a/week_5_vectors_applications.ipynb +++ b/week_5_vectors_applications.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -432,18 +432,29 @@ "name": "stdout", "output_type": "stream", "text": [ + "Non mean-centered vector tests: \n", "Pearson function passed.\n", "Cosine test passed.\n", - "My functions are NOT equal to each other.\n" + "My functions pearson and cosine functions are NOT equal to each other.\n", + "------------------------------------------------\n", + "Mean-centered vector tests: \n", + "Pearson function passed.\n", + "Cosine test passed.\n", + "My functions pearson and cosine functions are NOT equal to each other.\n" ] } ], "source": [ "from scipy.spatial.distance import cosine\n", "\n", + "# non-mean centered vectors\n", "coeff_arr1 = np.array([2,3,4])\n", "coeff_arr2 = np.array([5,6,7])\n", "\n", + "# mean centered vectors\n", + "arr1_mean_centered = coeff_arr1 - coeff_arr1.mean()\n", + "arr2_mean_centered = coeff_arr2 - coeff_arr2.mean()\n", + "\n", "def pearson_corr(x_arr, y_arr):\n", " x_mean = x_arr - np.mean(x_arr)\n", " y_mean = y_arr - np.mean(y_arr)\n", @@ -463,28 +474,41 @@ " print(\"Pearson function failed.\")\n", "\n", "def test_cosine(arr1, arr2):\n", - " if (cosine_simil(arr1, arr2)) == (1 - cosine(arr1, arr2)):\n", + " if np.isclose((cosine_simil(arr1, arr2)), (1 - cosine(arr1, arr2))):\n", " print(\"Cosine test passed.\")\n", " else:\n", " print(\"Cosine test failed.\")\n", "\n", - "\n", "def test_person_cosine_equality(arr1, arr2):\n", - " if (pearson_corr(arr1, arr2)) == (cosine_simil(arr1, arr2)):\n", + " # if (np.isclose((pearson_corr(arr1, arr2)), (cosine_simil(arr1, arr2)))):\n", + " if pearson_corr(arr1, arr2) == cosine_simil(arr1, arr2):\n", " print(\"Both of my functions are equal to each other.\")\n", " else:\n", - " print(\"My functions are NOT equal to each other.\")\n", - "\n", + " print(\"My functions pearson and cosine functions are NOT equal to each other.\")\n", "\n", "\n", + "print(\"Non mean-centered vector tests: \")\n", "test_pearson(coeff_arr1, coeff_arr2)\n", "test_cosine(coeff_arr1, coeff_arr2)\n", "test_person_cosine_equality(coeff_arr1, coeff_arr2)\n", "\n", + "print(\"------------------------------------------------\")\n", + "print(\"Mean-centered vector tests: \")\n", + "test_pearson(arr1_mean_centered, arr2_mean_centered)\n", + "test_cosine(arr1_mean_centered, arr2_mean_centered)\n", + "test_person_cosine_equality(arr1_mean_centered, arr2_mean_centered)\n", + "\n", + "\n", "\"\"\"\n", - "For the test_person_cosine_equality is is printing that they are not equal, but this \n", - "is what is expected. In the book it states that these two functions can give different \n", - "results for the same data because the functions start from different assumptions.\n", + "I used np.isclose() in the cosine test because they are comparing two floating point \n", + "numbers. Since computers have issues with floating point numbers (especiallly in equality \n", + "comparison), I chose to use the isclose() numpy function since that is testing if they \n", + "are close, which they are. By doing this, my cosine tests passed for both non-mean and \n", + "mean centered vectors. \n", + "\n", + "For the test_person_cosine_equality is is printing that they are not equal as expected. \n", + "In the book it states that these two functions can give different results for the same data \n", + "because the functions start from different assumptions.\n", "\"\"\";\n", "\n", "\n", From 477eba2f3f78f64d1b614f16799829db39d00ffa Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Thu, 5 Feb 2026 04:02:06 -0800 Subject: [PATCH 35/62] fixed issue with problem 7 where I wasn not using the proper numpy function for a random distribution --- week_5_vectors_applications.ipynb | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/week_5_vectors_applications.ipynb b/week_5_vectors_applications.ipynb index 7314bb5..51ea02b 100644 --- a/week_5_vectors_applications.ipynb +++ b/week_5_vectors_applications.ipynb @@ -340,12 +340,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "basis_vector = np.array([1,3])\n", - "rand_dist = np.linspace(-4, 4, 100)\n", + "rand_dist = np.random.uniform(-4, 4, 100)\n", "\n", "\"\"\"\n", "this is how I originally completed the problem\n", @@ -354,18 +354,17 @@ "for i in range(len(rand_dist)):\n", " sub_space[i] = rand_dist[i] * basis_vector\n", "\"\"\"\n", - "\n", "sub_space = rand_dist[:, None] * basis_vector" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] From d5e33aaa8f5d0086d74fd5f3e8262b6cd69655d0 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 10 Feb 2026 04:44:32 -0800 Subject: [PATCH 36/62] initial commit --- week_6_eda_matrix.ipynb | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/week_6_eda_matrix.ipynb b/week_6_eda_matrix.ipynb index 1e2044d..90d6d8f 100644 --- a/week_6_eda_matrix.ipynb +++ b/week_6_eda_matrix.ipynb @@ -319,13 +319,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# retrieve sample data\n", "download_url = (\"https://raw.githubusercontent.com/fivethirtyeight/data/master/bechdel/movies.csv\")\n", - "movie_df = pd.read_csv(download_url)" + "movie_df = pd.read_csv(download_url)\n", + "\n", + "x = 5" ] }, { From e97f787d5f260c33a79efea45d19f57f22179696 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Thu, 12 Feb 2026 04:12:09 -0800 Subject: [PATCH 37/62] finished problem 3 --- mortality_data.csv | 477 +++++++++++++++++++++++ week_6_eda_matrix.ipynb | 840 ++++++++++++++++++++++++++++++++++++++-- 2 files changed, 1284 insertions(+), 33 deletions(-) create mode 100644 mortality_data.csv diff --git a/mortality_data.csv b/mortality_data.csv new file mode 100644 index 0000000..69e489a --- /dev/null +++ b/mortality_data.csv @@ -0,0 +1,477 @@ +Year,Age Group,Death Rate +1900,1-4 Years,1983.8 +1901,1-4 Years,1695.0 +1902,1-4 Years,1655.7 +1903,1-4 Years,1542.1 +1904,1-4 Years,1591.5 +1905,1-4 Years,1498.9 +1906,1-4 Years,1580.0 +1907,1-4 Years,1468.3 +1908,1-4 Years,1396.8 +1909,1-4 Years,1348.9 +1910,1-4 Years,1397.3 +1911,1-4 Years,1176.0 +1912,1-4 Years,1094.1 +1913,1-4 Years,1193.4 +1914,1-4 Years,1024.2 +1915,1-4 Years,924.2 +1916,1-4 Years,1111.5 +1917,1-4 Years,1066.0 +1918,1-4 Years,1573.5 +1919,1-4 Years,928.0 +1920,1-4 Years,987.2 +1921,1-4 Years,801.2 +1922,1-4 Years,742.0 +1923,1-4 Years,806.7 +1924,1-4 Years,683.2 +1925,1-4 Years,641.0 +1926,1-4 Years,723.4 +1927,1-4 Years,591.0 +1928,1-4 Years,647.5 +1929,1-4 Years,625.5 +1930,1-4 Years,563.6 +1931,1-4 Years,526.6 +1932,1-4 Years,461.9 +1933,1-4 Years,472.6 +1934,1-4 Years,507.7 +1935,1-4 Years,440.9 +1936,1-4 Years,439.8 +1937,1-4 Years,418.7 +1938,1-4 Years,383.8 +1939,1-4 Years,318.3 +1940,1-4 Years,289.6 +1941,1-4 Years,279.9 +1942,1-4 Years,243.4 +1943,1-4 Years,256.4 +1944,1-4 Years,233.0 +1945,1-4 Years,203.0 +1946,1-4 Years,181.5 +1947,1-4 Years,160.8 +1948,1-4 Years,160.1 +1949,1-4 Years,150.2 +1950,1-4 Years,139.4 +1951,1-4 Years,136.9 +1952,1-4 Years,141.1 +1953,1-4 Years,130.0 +1954,1-4 Years,118.3 +1955,1-4 Years,113.4 +1956,1-4 Years,110.2 +1957,1-4 Years,111.8 +1958,1-4 Years,111.7 +1959,1-4 Years,106.9 +1960,1-4 Years,109.1 +1961,1-4 Years,101.7 +1962,1-4 Years,99.2 +1963,1-4 Years,101.5 +1964,1-4 Years,98.5 +1965,1-4 Years,95.9 +1966,1-4 Years,96.4 +1967,1-4 Years,89.4 +1968,1-4 Years,89.6 +1969,1-4 Years,88.0 +1970,1-4 Years,84.5 +1971,1-4 Years,82.3 +1972,1-4 Years,80.4 +1973,1-4 Years,79.0 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+2015,15-19 Years,48.3 +2016,15-19 Years,51.2 +2017,15-19 Years,51.5 +2018,15-19 Years,49.2 diff --git a/week_6_eda_matrix.ipynb b/week_6_eda_matrix.ipynb index 90d6d8f..65170b5 100644 --- a/week_6_eda_matrix.ipynb +++ b/week_6_eda_matrix.ipynb @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -137,9 +137,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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YearAge GroupDeath Rate
019001-4 Years1983.8
119011-4 Years1695.0
219021-4 Years1655.7
319031-4 Years1542.1
419041-4 Years1591.5
\n", + "
" + ], + "text/plain": [ + " Year Age Group Death Rate\n", + "0 1900 1-4 Years 1983.8\n", + "1 1901 1-4 Years 1695.0\n", + "2 1902 1-4 Years 1655.7\n", + "3 1903 1-4 Years 1542.1\n", + "4 1904 1-4 Years 1591.5" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# get example data from pulic internet location\n", "url = \"https://data.cdc.gov/api/views/v6ab-adf5/rows.csv?accessType=DOWNLOAD\"\n", @@ -158,9 +233,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "('mortality_data.csv', )" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from urllib import request\n", "data_url = \"https://data.cdc.gov/api/views/v6ab-adf5/rows.csv?accessType=DOWNLOAD\"\n", @@ -232,9 +318,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'cities': [{'name': 'Anchorage', 'pop': 250000, 'region': 'south-central'},\n", + " {'name': 'Fairbanks', 'pop': 75000, 'region': 'interior'},\n", + " {'name': 'Juneau', 'pop': 25000, 'region': 'south-east'}],\n", + " 'industries': ['fishing', 'mining', 'tourism'],\n", + " 'state': 'AK'}\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namepop
0Anchorage250000
1Fairbanks75000
2Juneau25000
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" + ], + "text/plain": [ + " name pop\n", + "0 Anchorage 250000\n", + "1 Fairbanks 75000\n", + "2 Juneau 25000" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#\n", "json_string = \"\"\"\n", @@ -319,15 +473,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# retrieve sample data\n", "download_url = (\"https://raw.githubusercontent.com/fivethirtyeight/data/master/bechdel/movies.csv\")\n", - "movie_df = pd.read_csv(download_url)\n", - "\n", - "x = 5" + "movie_df = pd.read_csv(download_url)\n" ] }, { @@ -345,10 +497,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rows/oberservations: 1794\n", + "columns/features/variables: 15\n" + ] + } + ], + "source": [ + "rows, columns = movie_df.shape\n", + "print(f\"rows/oberservations: {rows}\")\n", + "print(f\"columns/features/variables: {columns}\")" + ] }, { "cell_type": "markdown", @@ -364,10 +529,286 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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yearimdbtitletestclean_testbinarybudgetdomgrossintgrosscodebudget_2013$domgross_2013$intgross_2013$period codedecade code
02013tt171142521 &amp; OvernotalknotalkFAIL1300000025682380.042195766.02013FAIL1300000025682380.042195766.01.01.0
12012tt1343727Dredd 3Dok-disagreeokPASS4500000013414714.040868994.02012PASS4565873513611086.041467257.01.01.0
22013tt202454412 Years a Slavenotalk-disagreenotalkFAIL2000000053107035.0158607035.02013FAIL2000000053107035.0158607035.01.01.0
32013tt12728782 GunsnotalknotalkFAIL6100000075612460.0132493015.02013FAIL6100000075612460.0132493015.01.01.0
42013tt045356242menmenFAIL4000000095020213.095020213.02013FAIL4000000095020213.095020213.01.01.0
52013tt133597547 RoninmenmenFAIL22500000038362475.0145803842.02013FAIL22500000038362475.0145803842.01.01.0
62013tt1606378A Good Day to Die HardnotalknotalkFAIL9200000067349198.0304249198.02013FAIL9200000067349198.0304249198.01.01.0
72013tt2194499About Timeok-disagreeokPASS1200000015323921.087324746.02013PASS1200000015323921.087324746.01.01.0
82013tt1814621AdmissionokokPASS1300000018007317.018007317.02013PASS1300000018007317.018007317.01.01.0
92013tt1815862After EarthnotalknotalkFAIL13000000060522097.0244373198.02013FAIL13000000060522097.0244373198.01.01.0
\n", + "
" + ], + "text/plain": [ + " year imdb title test clean_test binary \\\n", + "0 2013 tt1711425 21 & Over notalk notalk FAIL \n", + "1 2012 tt1343727 Dredd 3D ok-disagree ok PASS \n", + "2 2013 tt2024544 12 Years a Slave notalk-disagree notalk FAIL \n", + "3 2013 tt1272878 2 Guns notalk notalk FAIL \n", + "4 2013 tt0453562 42 men men FAIL \n", + "5 2013 tt1335975 47 Ronin men men FAIL \n", + "6 2013 tt1606378 A Good Day to Die Hard notalk notalk FAIL \n", + "7 2013 tt2194499 About Time ok-disagree ok PASS \n", + "8 2013 tt1814621 Admission ok ok PASS \n", + "9 2013 tt1815862 After Earth notalk notalk FAIL \n", + "\n", + " budget domgross intgross code budget_2013$ domgross_2013$ \\\n", + "0 13000000 25682380.0 42195766.0 2013FAIL 13000000 25682380.0 \n", + "1 45000000 13414714.0 40868994.0 2012PASS 45658735 13611086.0 \n", + "2 20000000 53107035.0 158607035.0 2013FAIL 20000000 53107035.0 \n", + "3 61000000 75612460.0 132493015.0 2013FAIL 61000000 75612460.0 \n", + "4 40000000 95020213.0 95020213.0 2013FAIL 40000000 95020213.0 \n", + "5 225000000 38362475.0 145803842.0 2013FAIL 225000000 38362475.0 \n", + "6 92000000 67349198.0 304249198.0 2013FAIL 92000000 67349198.0 \n", + "7 12000000 15323921.0 87324746.0 2013PASS 12000000 15323921.0 \n", + "8 13000000 18007317.0 18007317.0 2013PASS 13000000 18007317.0 \n", + "9 130000000 60522097.0 244373198.0 2013FAIL 130000000 60522097.0 \n", + "\n", + " intgross_2013$ period code decade code \n", + "0 42195766.0 1.0 1.0 \n", + "1 41467257.0 1.0 1.0 \n", + "2 158607035.0 1.0 1.0 \n", + "3 132493015.0 1.0 1.0 \n", + "4 95020213.0 1.0 1.0 \n", + "5 145803842.0 1.0 1.0 \n", + "6 304249198.0 1.0 1.0 \n", + "7 87324746.0 1.0 1.0 \n", + "8 18007317.0 1.0 1.0 \n", + "9 244373198.0 1.0 1.0 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "The data has information about various movies with the year it came out, IMBD number, \n", + "budget, domestic gross, internation gross along with some information about codes and test. \n", + "\n", + "No variables have yet to be defined as dependent, so it depends on what we may or may \n", + "not be trying to predict. \n", + "\"\"\";\n", + "\n", + "movie_df.head(10)" + ] }, { "cell_type": "markdown", @@ -385,15 +826,219 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1794 entries, 0 to 1793\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 year 1794 non-null int64 \n", + " 1 imdb 1794 non-null object \n", + " 2 title 1794 non-null object \n", + " 3 test 1794 non-null object \n", + " 4 clean_test 1794 non-null object \n", + " 5 binary 1794 non-null object \n", + " 6 budget 1794 non-null int64 \n", + " 7 domgross 1777 non-null float64\n", + " 8 intgross 1783 non-null float64\n", + " 9 code 1794 non-null object \n", + " 10 budget_2013$ 1794 non-null int64 \n", + " 11 domgross_2013$ 1776 non-null float64\n", + " 12 intgross_2013$ 1783 non-null float64\n", + " 13 period code 1615 non-null float64\n", + " 14 decade code 1615 non-null float64\n", + "dtypes: float64(6), int64(3), object(6)\n", + "memory usage: 210.4+ KB\n" + ] + } + ], + "source": [ + "\"\"\" \n", + "Yes, there is some missing information as we can see from 'Non-Null Count' column below. We \n", + "can see that domgross, intgross, domgross_2013$ and intgross_2013$ are all missing around 18 or \n", + "fewer data points. While period code and decade code are both missing 179 data points. \n", + "\"\"\"\n", + "\n", + "movie_df.info()" + ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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yearbudgetdomgrossintgrossbudget_2013$domgross_2013$intgross_2013$period codedecade code
count1794.0000001.794000e+031.777000e+031.783000e+031.794000e+031.776000e+031.783000e+031615.0000001615.000000
mean2002.5523974.482646e+076.913205e+071.503857e+085.546461e+079.517478e+071.978380e+082.4198141.937461
std8.9797314.818603e+078.036731e+072.103353e+085.491864e+071.259653e+082.835079e+081.1946200.690116
min1970.0000007.000000e+030.000000e+008.280000e+028.632000e+038.990000e+028.990000e+021.0000001.000000
25%1998.0000001.200000e+071.631157e+072.612947e+071.606892e+072.054659e+073.323260e+071.0000001.000000
50%2005.0000002.800000e+074.219406e+077.648246e+073.699579e+075.599364e+079.623964e+072.0000002.000000
75%2009.0000006.000000e+079.335492e+071.898509e+087.833790e+071.216784e+082.414790e+083.0000002.000000
max2013.0000004.250000e+087.605076e+082.783919e+094.614359e+081.771683e+093.171931e+095.0000003.000000
\n", + "
" + ], + "text/plain": [ + " year budget domgross intgross budget_2013$ \\\n", + "count 1794.000000 1.794000e+03 1.777000e+03 1.783000e+03 1.794000e+03 \n", + "mean 2002.552397 4.482646e+07 6.913205e+07 1.503857e+08 5.546461e+07 \n", + "std 8.979731 4.818603e+07 8.036731e+07 2.103353e+08 5.491864e+07 \n", + "min 1970.000000 7.000000e+03 0.000000e+00 8.280000e+02 8.632000e+03 \n", + "25% 1998.000000 1.200000e+07 1.631157e+07 2.612947e+07 1.606892e+07 \n", + "50% 2005.000000 2.800000e+07 4.219406e+07 7.648246e+07 3.699579e+07 \n", + "75% 2009.000000 6.000000e+07 9.335492e+07 1.898509e+08 7.833790e+07 \n", + "max 2013.000000 4.250000e+08 7.605076e+08 2.783919e+09 4.614359e+08 \n", + "\n", + " domgross_2013$ intgross_2013$ period code decade code \n", + "count 1.776000e+03 1.783000e+03 1615.000000 1615.000000 \n", + "mean 9.517478e+07 1.978380e+08 2.419814 1.937461 \n", + "std 1.259653e+08 2.835079e+08 1.194620 0.690116 \n", + "min 8.990000e+02 8.990000e+02 1.000000 1.000000 \n", + "25% 2.054659e+07 3.323260e+07 1.000000 1.000000 \n", + "50% 5.599364e+07 9.623964e+07 2.000000 2.000000 \n", + "75% 1.216784e+08 2.414790e+08 3.000000 2.000000 \n", + "max 1.771683e+09 3.171931e+09 5.000000 3.000000 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "I would consider the primary statistics to be count, mean and standard deviation. Those would \n", + "let me know how much data and information about how it is distributed.\n", + "\"\"\"\n", + "\n", + "movie_df.describe()" + ] }, { "cell_type": "markdown", @@ -409,10 +1054,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1794 entries, 0 to 1793\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 year 1794 non-null int64 \n", + " 1 imdb 1794 non-null object \n", + " 2 title 1794 non-null object \n", + " 3 test 1794 non-null object \n", + " 4 clean_test 1794 non-null object \n", + " 5 binary 1794 non-null object \n", + " 6 budget 1794 non-null int64 \n", + " 7 domgross 1777 non-null float64\n", + " 8 intgross 1783 non-null float64\n", + " 9 code 1794 non-null object \n", + " 10 budget_2013$ 1794 non-null int64 \n", + " 11 domgross_2013$ 1776 non-null float64\n", + " 12 intgross_2013$ 1783 non-null float64\n", + " 13 period code 1615 non-null float64\n", + " 14 decade code 1615 non-null float64\n", + "dtypes: float64(6), int64(3), object(6)\n", + "memory usage: 210.4+ KB\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "There are int64, object and float64 data types. \n", + "\n", + "The data types for year, budget, domgross, intgross, budget_2013$, domgross_2013$, ingross_2013$, \n", + "period code and decade code are numerical. \n", + "\n", + "IMBD, title, test, clean_test, binary and code are categorical. \n", + "\"\"\"\n", + "\n", + "movie_df.info()" + ] }, { "cell_type": "markdown", @@ -454,10 +1139,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 126, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NUMERIC FEATURES: \n", + "Index(['year', 'budget', 'domgross', 'intgross', 'budget_2013$',\n", + " 'domgross_2013$', 'intgross_2013$', 'period code', 'decade code'],\n", + " dtype='object')\n", + "Index(['year', 'budget', 'domgross', 'intgross', 'budget_2013$',\n", + " 'domgross_2013$', 'intgross_2013$', 'period code', 'decade code'],\n", + " dtype='object')\n", + "\n", + "Both numeric objects are equal\n", + "------------------------------------------------------------------------------------\n", + "CATEGORICAL FEATURES: \n", + "Index(['imdb', 'title', 'test', 'clean_test', 'binary', 'code'], dtype='object')\n", + "Index(['imdb', 'title', 'test', 'clean_test', 'binary', 'code'], dtype='object')\n", + "\n", + "Both categorical objects are equal\n" + ] + } + ], + "source": [ + "# using dtypes and columns\n", + "numeric_features1 = movie_df.columns[movie_df.dtypes.isin([np.dtype(\"int64\"), np.dtype(\"float64\")])]\n", + "categorical_features1 = movie_df.columns[movie_df.dtypes.isin([np.dtype(\"object\")])]\n", + "\n", + "# using select_dtypes and columns\n", + "numeric_features2 = movie_df.select_dtypes(include=['int64', 'float64']).columns\n", + "categorical_features2 = movie_df.select_dtypes(include=['object']).columns\n", + "\n", + "print(\"NUMERIC FEATURES: \")\n", + "print(numeric_features1)\n", + "print(numeric_features2)\n", + "if numeric_features1.equals(numeric_features2):\n", + " print(\"\\nBoth numeric objects are equal\")\n", + "else:\n", + " print(\"\\nBoth numeric objects are NOT equal\")\n", + "print(\"------------------------------------------------------------------------------------\")\n", + "print(\"CATEGORICAL FEATURES: \")\n", + "print(categorical_features1)\n", + "print(categorical_features2)\n", + "if categorical_features1.equals(categorical_features2):\n", + " print(\"\\nBoth categorical objects are equal\")\n", + "else:\n", + " print(\"\\nBoth categorical objects are NOT equal\")\n" + ] }, { "cell_type": "markdown", @@ -469,10 +1200,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 154, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "test (unique=10)\n", + "test\n", + "ok 696\n", + "notalk 379\n", + "notalk-disagree 135\n", + "men 125\n", + "ok-disagree 107\n", + "nowomen 88\n", + "dubious 81\n", + "men-disagree 69\n", + "dubious-disagree 61\n", + "nowomen-disagree 53\n", + "Name: count, dtype: int64\n", + "\n", + "clean_test (unique=5)\n", + "clean_test\n", + "ok 803\n", + "notalk 514\n", + "men 194\n", + "dubious 142\n", + "nowomen 141\n", + "Name: count, dtype: int64\n", + "\n", + "binary (unique=2)\n", + "binary\n", + "FAIL 991\n", + "PASS 803\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "for col in categorical_features2:\n", + " n_unique = movie_df[col].nunique() # number of distinct categories\n", + " if n_unique <= 10:\n", + " print(f\"\\n{col} (unique={n_unique})\")\n", + " print(movie_df[col].value_counts())\n", + "\n" + ] }, { "cell_type": "markdown", @@ -610,7 +1384,7 @@ ], "metadata": { "kernelspec": { - "display_name": "AD_450_env", + "display_name": "ml-env", "language": "python", "name": "python3" }, @@ -624,7 +1398,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.10.18" } }, "nbformat": 4, From 32f3424148b0d277379e1d7d91c1dc2306271758 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Thu, 12 Feb 2026 23:37:58 -0800 Subject: [PATCH 38/62] completed problem 5 --- week_6_eda_matrix.ipynb | 106 +++++++++++++++++++++++++++++++--------- 1 file changed, 83 insertions(+), 23 deletions(-) diff --git a/week_6_eda_matrix.ipynb b/week_6_eda_matrix.ipynb index 65170b5..b0b9288 100644 --- a/week_6_eda_matrix.ipynb +++ b/week_6_eda_matrix.ipynb @@ -473,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -497,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -529,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -793,7 +793,7 @@ "9 244373198.0 1.0 1.0 " ] }, - "execution_count": 26, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -824,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -868,7 +868,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1026,7 +1026,7 @@ "max 1.771683e+09 3.171931e+09 5.000000 3.000000 " ] }, - "execution_count": 28, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1054,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1139,7 +1139,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1200,7 +1200,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1208,7 +1208,7 @@ "output_type": "stream", "text": [ "\n", - "test (unique=10)\n", + "test (unique = 10)\n", "test\n", "ok 696\n", "notalk 379\n", @@ -1222,7 +1222,7 @@ "nowomen-disagree 53\n", "Name: count, dtype: int64\n", "\n", - "clean_test (unique=5)\n", + "clean_test (unique = 5)\n", "clean_test\n", "ok 803\n", "notalk 514\n", @@ -1231,7 +1231,7 @@ "nowomen 141\n", "Name: count, dtype: int64\n", "\n", - "binary (unique=2)\n", + "binary (unique = 2)\n", "binary\n", "FAIL 991\n", "PASS 803\n", @@ -1243,7 +1243,7 @@ "for col in categorical_features2:\n", " n_unique = movie_df[col].nunique() # number of distinct categories\n", " if n_unique <= 10:\n", - " print(f\"\\n{col} (unique={n_unique})\")\n", + " print(f\"\\n{col} (unique = {n_unique})\")\n", " print(movie_df[col].value_counts())\n", "\n" ] @@ -1266,10 +1266,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "movie_df[numeric_features2].plot.hist()\n", + "plt.show()\n", + "\n" + ] }, { "cell_type": "markdown", @@ -1290,17 +1305,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pandas.plotting import scatter_matrix\n", + "\n", + "numerical_subset = pd.Index(['budget', 'domgross', 'intgross', 'budget_2013$', 'domgross_2013$', 'intgross_2013$'])\n", + "\"\"\" \n", + "The two lines commented out below were my original code. The plots were really messy and column names overlapped each other. \n", + "I got the code below from AI to clean it up. \n", + "\"\"\"\n", + "# scatter_matrix(movie_df[numerical_subset])\n", + "# plt.show()\n", + "\n", + "scatter_matrix(\n", + " movie_df[numerical_subset],\n", + " figsize=(12, 12),\n", + " diagonal=\"hist\",\n", + " alpha=0.5,\n", + " s=10\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(movie_df[numerical_subset])\n", + "plt.show()" + ] }, { "cell_type": "markdown", From 7864c5b06c4b11b0bcc71f1daeae5b4098355f68 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Fri, 13 Feb 2026 07:38:43 -0800 Subject: [PATCH 39/62] finished problem 7 --- week_6_eda_matrix.ipynb | 148 ++++++++++++++++++++++++++++++++++++++-- 1 file changed, 143 insertions(+), 5 deletions(-) diff --git a/week_6_eda_matrix.ipynb b/week_6_eda_matrix.ipynb index b0b9288..cc81eed 100644 --- a/week_6_eda_matrix.ipynb +++ b/week_6_eda_matrix.ipynb @@ -1382,10 +1382,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using a for loop: \n", + "[[ 2. 6. 5.]\n", + " [ 0. -2. 6.]]\n", + "----------------------------------\n", + "Using numpy vectorization: \n", + "[[ 2 6 5]\n", + " [ 0 -2 6]]\n", + "----------------------------------\n", + "\n", + "\n", + "\n" + ] + }, + { + "ename": "ValueError", + "evalue": "operands could not be broadcast together with shapes (3,3) (2,3) ", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[51], line 43\u001b[0m\n\u001b[1;32m 36\u001b[0m add4 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([ [\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m4\u001b[39m], \n\u001b[1;32m 37\u001b[0m [\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m4\u001b[39m], \n\u001b[1;32m 38\u001b[0m [\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m9\u001b[39m] ])\n\u001b[1;32m 40\u001b[0m add5 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([ [\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m1\u001b[39m], \n\u001b[1;32m 41\u001b[0m [\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m2\u001b[39m] ])\n\u001b[0;32m---> 43\u001b[0m add6 \u001b[38;5;241m=\u001b[39m \u001b[43madd4\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43madd5\u001b[49m\n", + "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,3) (2,3) " + ] + } + ], + "source": [ + "def add_loop(arr1, arr2):\n", + " if arr1.shape == arr2.shape:\n", + " sol = np.zeros(arr1.shape)\n", + " for i in range(arr1.shape[0]):\n", + " for j in range(len(arr2[0])):\n", + " sol[i][j] = arr1[i][j] + arr2[i][j]\n", + "\n", + " return sol\n", + " else:\n", + " print(\"You cannot add two arrays of different sizes.\")\n", + " return -1\n", + "\n", + "\n", + "add1 = np.array([ [2,3,4], [1,2,4] ])\n", + "add2 = np.array([ [0,3,1], [-1,-4,2] ])\n", + "add3 = add1 + add2\n", + "print(\"Using a for loop: \")\n", + "print(add_loop(add1, add2))\n", + "print(\"----------------------------------\")\n", + "print(\"Using numpy vectorization: \")\n", + "print(add3)\n", + "print(\"----------------------------------\\n\\n\\n\")\n", + "\n", + "\n", + "\"\"\"\n", + "You cannot add two matrices together of different sizes. add4 is a 3 element \n", + "array while add5 is a two element array. When you attempt to add them an error \n", + "is throw because it takes the third element of add4 and tries to add it to the \n", + "third element in add5, but that element does not exist. Thus the error is thrown.\n", + "\n", + "In consideration of the for loop above, I did not attempt to add two matrices of \n", + "differing sizes together as the functions has a built in check and returns a print \n", + "statement indicating an error along with a return value of -1. \n", + "\"\"\";\n", + "\n", + "add4 = np.array([ [2,3,4], \n", + " [1,2,4], \n", + " [7,8,9] ])\n", + "\n", + "add5 = np.array([ [0,3,1], \n", + " [-1,-4,2] ])\n", + "\n", + "add6 = add4 + add5" + ] }, { "cell_type": "markdown", @@ -1400,8 +1473,73 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 58 64]\n", + " [139 154]]\n", + "-------------------------\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[ 58., 64.],\n", + " [139., 154.]])" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "This function below is super hacky, but it works. I created the flag variable \n", + "as I needed to ability to use an index that was separate from the i and j variables \n", + "in the for loops. This will also only work for multiplying 2 dimensional arrays.\n", + "\"\"\"\n", + "def mult_loop(arr1, arr2):\n", + " length = arr1.shape[1]\n", + " flag1 = 1\n", + " curr1 = 0\n", + " curr2 = 0\n", + " curr_arr = np.array([])\n", + "\n", + " for i in range(length - 1):\n", + " for j in range(length):\n", + " curr1 += ( arr1[i][j] * arr2[j][i] )\n", + " curr2 += ( arr1[i][j] * arr2[j][flag1] )\n", + " flag1 = 0\n", + " curr_arr = np.append(curr_arr, np.array([curr1, curr2]))\n", + " curr1 = 0\n", + " curr2 = 0\n", + " if i == 0:\n", + " sol = curr_arr.copy()\n", + " if i == 1:\n", + " sol2 = np.vstack([sol, curr_arr])\n", + " curr_arr = np.array([])\n", + "\n", + " sol2[1] = sol2[1][::-1]\n", + " return sol2\n", + "\n", + "# shape = (2,3)\n", + "mult1 = np.array([ [1,2,3], \n", + " [4,5,6] ])\n", + "\n", + "# shape = (3,2)\n", + "mult2 = np.array([ [7,8], \n", + " [9,10],\n", + " [11,12] ])\n", + "\n", + "print(mult1@mult2)\n", + "print(\"-------------------------\")\n", + "sol = mult_loop(mult1, mult2)\n", + "sol\n", + "\n" + ] }, { "cell_type": "markdown", From fa6755aa4d4ae69d2d9b8e32e845e263ebc8bc23 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Fri, 13 Feb 2026 07:43:24 -0800 Subject: [PATCH 40/62] fixed comment in problem 7 --- week_6_eda_matrix.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/week_6_eda_matrix.ipynb b/week_6_eda_matrix.ipynb index cc81eed..401d300 100644 --- a/week_6_eda_matrix.ipynb +++ b/week_6_eda_matrix.ipynb @@ -1490,7 +1490,7 @@ " [139., 154.]])" ] }, - "execution_count": 103, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } @@ -1499,7 +1499,7 @@ "\"\"\"\n", "This function below is super hacky, but it works. I created the flag variable \n", "as I needed to ability to use an index that was separate from the i and j variables \n", - "in the for loops. This will also only work for multiplying 2 dimensional arrays.\n", + "in the for loops. This will also only work if the first array is 2 dimensional. \n", "\"\"\"\n", "def mult_loop(arr1, arr2):\n", " length = arr1.shape[1]\n", From d679a9108c84be47528826529cabf7a4144ab52d Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Fri, 13 Feb 2026 07:56:33 -0800 Subject: [PATCH 41/62] completed problem 8 --- week_6_eda_matrix.ipynb | 24 ++++++++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/week_6_eda_matrix.ipynb b/week_6_eda_matrix.ipynb index 401d300..b7e350b 100644 --- a/week_6_eda_matrix.ipynb +++ b/week_6_eda_matrix.ipynb @@ -1560,8 +1560,28 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success! Everything worked correctly.\n" + ] + } + ], + "source": [ + "A = np.random.rand(3)\n", + "B = np.random.rand(3)\n", + "scalar = 5\n", + "eq1 = scalar * (A + B)\n", + "eq2 = scalar * A + scalar * B\n", + "eq3 = A * scalar + B * scalar\n", + "\n", + "if np.allclose(eq1, eq2) and np.allclose(eq2, eq3):\n", + " print(\"Success! Everything worked correctly.\")\n", + "else:\n", + " print(\"Everything is not matching up.\")" + ] }, { "cell_type": "markdown", From a9a41eb943ad5848bd30f155b78fc9f14639f1d1 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Fri, 13 Feb 2026 08:09:03 -0800 Subject: [PATCH 42/62] completed assignment --- week_6_eda_matrix.ipynb | 40 +++++++++++++++++++++++++++++++++++----- 1 file changed, 35 insertions(+), 5 deletions(-) diff --git a/week_6_eda_matrix.ipynb b/week_6_eda_matrix.ipynb index b7e350b..b292e95 100644 --- a/week_6_eda_matrix.ipynb +++ b/week_6_eda_matrix.ipynb @@ -1558,7 +1558,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -1580,7 +1580,7 @@ "if np.allclose(eq1, eq2) and np.allclose(eq2, eq3):\n", " print(\"Success! Everything worked correctly.\")\n", "else:\n", - " print(\"Everything is not matching up.\")" + " print(\"Everything is NOT matching up.\")" ] }, { @@ -1594,10 +1594,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 132, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Should print True: True\n", + "Should print False: False\n" + ] + } + ], + "source": [ + "def check_symetry(arr):\n", + " if np.allclose(arr, arr.T):\n", + " return True \n", + " else:\n", + " return False\n", + " \n", + " \n", + "\n", + "sym_arr1 = np.array([ [1,2,3], \n", + " [2,5,8], \n", + " [3,8,9] ])\n", + "\n", + "sym_arr2 = np.array([ [1,2,3], \n", + " [4,5,6], \n", + " [7,8,9] ])\n", + "\n", + "\n", + "\n", + "print(\"Should print True:\", check_symetry(sym_arr1))\n", + "print(\"Should print False:\", check_symetry(sym_arr2))" + ] } ], "metadata": { From 833ff4f474cc0147d9c562e4bec62fef707ca990 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Fri, 13 Feb 2026 18:44:55 -0800 Subject: [PATCH 43/62] added a couple print statements for clarity --- week_6_eda_matrix.ipynb | 26 +++++++++++++++----------- 1 file changed, 15 insertions(+), 11 deletions(-) diff --git a/week_6_eda_matrix.ipynb b/week_6_eda_matrix.ipynb index b292e95..b53ddfc 100644 --- a/week_6_eda_matrix.ipynb +++ b/week_6_eda_matrix.ipynb @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -1382,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1446,7 +1446,7 @@ "third element in add5, but that element does not exist. Thus the error is thrown.\n", "\n", "In consideration of the for loop above, I did not attempt to add two matrices of \n", - "differing sizes together as the functions has a built in check and returns a print \n", + "differing sizes together as the function has a built in check and returns a print \n", "statement indicating an error along with a return value of -1. \n", "\"\"\";\n", "\n", @@ -1471,16 +1471,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Vectorized matrix multiplication: \n", "[[ 58 64]\n", " [139 154]]\n", - "-------------------------\n" + "-------------------------\n", + "For loop matrix multiplication: \n" ] }, { @@ -1490,7 +1492,7 @@ " [139., 154.]])" ] }, - "execution_count": 104, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -1534,8 +1536,10 @@ " [9,10],\n", " [11,12] ])\n", "\n", + "print(\"Vectorized matrix multiplication: \")\n", "print(mult1@mult2)\n", "print(\"-------------------------\")\n", + "print(\"For loop matrix multiplication: \")\n", "sol = mult_loop(mult1, mult2)\n", "sol\n", "\n" @@ -1594,15 +1598,15 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Should print True: True\n", - "Should print False: False\n" + "Testing a symetrix matrix, should print True: True\n", + "Testing a non-symetric matrix, should print False: False\n" ] } ], @@ -1625,8 +1629,8 @@ "\n", "\n", "\n", - "print(\"Should print True:\", check_symetry(sym_arr1))\n", - "print(\"Should print False:\", check_symetry(sym_arr2))" + "print(\"Testing a symetrix matrix, should print True:\", check_symetry(sym_arr1))\n", + "print(\"Testing a non-symetric matrix, should print False:\", check_symetry(sym_arr2))" ] } ], From 58e80dfee26968b785a0a9b5b505176cfe37954e Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 17 Feb 2026 16:55:07 -0800 Subject: [PATCH 44/62] created data_processing.py and started functions --- data_processing.py | 46 +++++ week_7_data_cleaning_matrix.ipynb | 267 +++++++++++++++++++++++++++++- 2 files changed, 307 insertions(+), 6 deletions(-) create mode 100644 data_processing.py diff --git a/data_processing.py b/data_processing.py new file mode 100644 index 0000000..fcc56a0 --- /dev/null +++ b/data_processing.py @@ -0,0 +1,46 @@ +def rename_columns(df_raw): + """ + Problem #1 + Convert blank spaces to _ + Convert everything into lower case + Remove trailing and leading spaces + Return a new DataFrame object + """ + return 1 + +def remove_fully_null_columns_rows(): + """ + Problem #2 + Remove rows that are completely null + Remove columns that are completely null + Return a new DataFrame object + """ + return 1 + +def clean_and_fill_content_rating(): + """ + Problem #3 + Convert all NaN values in content_rating to "Unrated" + Convert all "Not Rated" with "Unrated" + Return a new DataFrame object + """ + return 1 + +def clean_release_year(): + """ + Problem #4 + Add new column "release_year_coerce", use pd.to_datetime with errors="coerce" + Add new column "release_year_mixed", use pd.to_datetime with errors="coerce" and format="mixed" + Return a new DataFrame object + """ + return 1 + +def clean_income(): + """ + Problem #5 + Remove $, commas and other issues and covert to an int + Return a new DataFrame object + """ + return 1 + + diff --git a/week_7_data_cleaning_matrix.ipynb b/week_7_data_cleaning_matrix.ipynb index 7c066ef..ad271ae 100644 --- a/week_7_data_cleaning_matrix.ipynb +++ b/week_7_data_cleaning_matrix.ipynb @@ -56,9 +56,260 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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IMBD title IDOriginal titlÊRelease yearGenrë¨DurationCountryContent RatingDirectorUnnamed: 8IncomeVotesScore
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toptt0111161The Shawshank Redemption2000-05-19Drama130USARChristopher NolanNaN$ 288152452.278.8458.6
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75%NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" + ], + "text/plain": [ + " IMBD title ID Original titlÊ Release year Genrë¨ Duration \\\n", + "count 100 100 100 100 99 \n", + "unique 100 100 99 59 71 \n", + "top tt0111161 The Shawshank Redemption 2000-05-19 Drama 130 \n", + "freq 1 1 2 9 3 \n", + "mean NaN NaN NaN NaN NaN \n", + "std NaN NaN NaN NaN NaN \n", + "min NaN NaN NaN NaN NaN \n", + "25% NaN NaN NaN NaN NaN \n", + "50% NaN NaN NaN NaN NaN \n", + "75% NaN NaN NaN NaN NaN \n", + "max NaN NaN NaN NaN NaN \n", + "\n", + " Country Content Rating Director Unnamed: 8 Income \\\n", + "count 100 77 100 0.0 100 \n", + "unique 18 7 64 NaN 100 \n", + "top USA R Christopher Nolan NaN $ 28815245 \n", + "freq 62 45 6 NaN 1 \n", + "mean NaN NaN NaN NaN NaN \n", + "std NaN NaN NaN NaN NaN \n", + "min NaN NaN NaN NaN NaN \n", + "25% NaN NaN NaN NaN NaN \n", + "50% NaN NaN NaN NaN NaN \n", + "75% NaN NaN NaN NaN NaN \n", + "max NaN NaN NaN NaN NaN \n", + "\n", + " Votes Score \n", + "count 100 100 \n", + "unique 100 28 \n", + "top 2.278.845 8.6 \n", + "freq 1 11 \n", + "mean NaN NaN \n", + "std NaN NaN \n", + "min NaN NaN \n", + "25% NaN NaN \n", + "50% NaN NaN \n", + "75% NaN NaN \n", + "max NaN NaN " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import pandas as pd \n", "import importlib\n", @@ -252,7 +503,6 @@ "importlib.reload(data_processing)\n", "from data_processing import clean_release_year\n", "\n", - "\n", "df_clean_release_year_temp = clean_release_year(df_clean_content_rating)" ] }, @@ -386,12 +636,17 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "L = np.array()\n", + "I = np.array()\n", + "V = np.array()\n", + "E = np.array()" + ] } ], "metadata": { "kernelspec": { - "display_name": "AD_450_env", + "display_name": "ml-env", "language": "python", "name": "python3" }, @@ -405,7 +660,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.10.18" } }, "nbformat": 4, From 0c213a3f6cf9fab2319a9eb1111a5b8b4c92757c Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 17 Feb 2026 17:14:31 -0800 Subject: [PATCH 45/62] completed problem #1 --- data_processing.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/data_processing.py b/data_processing.py index fcc56a0..a2038d0 100644 --- a/data_processing.py +++ b/data_processing.py @@ -5,8 +5,14 @@ def rename_columns(df_raw): Convert everything into lower case Remove trailing and leading spaces Return a new DataFrame object + + I noticed you need to use strip() before replace otherwise it would replace any + leading or trailing spaces with a _ """ - return 1 + df_raw.columns = df_raw.columns.str.strip() + df_raw.columns = df_raw.columns.str.replace(' ', '_') + df_raw.columns = df_raw.columns.str.lower() + return df_raw def remove_fully_null_columns_rows(): """ From 1b1ca7e4c765e39544a0d1351bc28da6a34f429b Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 17 Feb 2026 17:18:38 -0800 Subject: [PATCH 46/62] completed problem #2 --- data_processing.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/data_processing.py b/data_processing.py index a2038d0..fec5d3a 100644 --- a/data_processing.py +++ b/data_processing.py @@ -21,6 +21,8 @@ def remove_fully_null_columns_rows(): Remove columns that are completely null Return a new DataFrame object """ + df_raw = df_raw.dropna(how="all") + df_raw = df_raw.dropna(axis="columns", how="all") return 1 def clean_and_fill_content_rating(): From 8bcc3193767953e96366432efa8459e939fbe69a Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 17 Feb 2026 18:35:35 -0800 Subject: [PATCH 47/62] started problems 4 and 5 --- data_processing.py | 41 +++-- week_7_data_cleaning_matrix.ipynb | 239 ++++++++++++++++++++++++++++-- 2 files changed, 254 insertions(+), 26 deletions(-) diff --git a/data_processing.py b/data_processing.py index fec5d3a..5dc0802 100644 --- a/data_processing.py +++ b/data_processing.py @@ -1,6 +1,9 @@ +# This file is used by week_7 +import pandas as pd + +# Problem 1 def rename_columns(df_raw): """ - Problem #1 Convert blank spaces to _ Convert everything into lower case Remove trailing and leading spaces @@ -9,46 +12,58 @@ def rename_columns(df_raw): I noticed you need to use strip() before replace otherwise it would replace any leading or trailing spaces with a _ """ + df.raw df_raw.columns = df_raw.columns.str.strip() df_raw.columns = df_raw.columns.str.replace(' ', '_') df_raw.columns = df_raw.columns.str.lower() return df_raw -def remove_fully_null_columns_rows(): +# Problem #2 +def remove_fully_null_columns_rows(df_raw): """ - Problem #2 Remove rows that are completely null Remove columns that are completely null Return a new DataFrame object """ df_raw = df_raw.dropna(how="all") df_raw = df_raw.dropna(axis="columns", how="all") - return 1 + return df_raw -def clean_and_fill_content_rating(): +# Problem #3 +def clean_and_fill_content_rating(df_raw): """ - Problem #3 Convert all NaN values in content_rating to "Unrated" Convert all "Not Rated" with "Unrated" Return a new DataFrame object """ - return 1 + df_raw.loc[df_raw["content_rating"].isnull(), "content_rating"] = "Unrated" + df_raw.loc[df_raw["content_rating"] == "Not Rated", "content_rating"] = "Unrated" + return df_raw -def clean_release_year(): +# Problem #4 +def clean_release_year(df_raw): """ - Problem #4 Add new column "release_year_coerce", use pd.to_datetime with errors="coerce" Add new column "release_year_mixed", use pd.to_datetime with errors="coerce" and format="mixed" Return a new DataFrame object """ - return 1 + df_raw["release_year_coerce"] = pd.to_datetime( + df_raw["release_year"], + erorrs="coerce") + + df_raw["release_year_mixed"] = pd.to_datetime(df_raw["release_year"], erorrs="coerce", format="mixed", dayfist=True) + return df_raw -def clean_income(): +# Problem #5 +def clean_income(df_raw): """ - Problem #5 Remove $, commas and other issues and covert to an int Return a new DataFrame object """ - return 1 + df_raw["income"] = df_raw["income"].str.replace(f'[$]',regex=True) + df_raw["income"] = df_raw["income"].str.replace('o', '0', regex=False) + df_raw["income"] = df_raw["income"].str.replace('0', '0', reges=False) + df_raw["income"] = df_raw["income"].astype("Int64") + return df_raw diff --git a/week_7_data_cleaning_matrix.ipynb b/week_7_data_cleaning_matrix.ipynb index ad271ae..61a5a53 100644 --- a/week_7_data_cleaning_matrix.ipynb +++ b/week_7_data_cleaning_matrix.ipynb @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -305,7 +305,7 @@ "max NaN NaN " ] }, - "execution_count": 2, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -346,13 +346,159 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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imbd_title_idoriginal_titlêrelease_yeargenrë¨durationcountrycontent_ratingdirectorunnamed:_8incomevotesscore
0tt0111161The Shawshank Redemption1995-02-10Drama142USARFrank DarabontNaN$ 288152452.278.8459.3
1tt0068646The Godfather09 21 1972Crime, Drama175USARFrancis Ford CoppolaNaN$ 2461209741.572.6749.2
2tt0468569The Dark Knight23 -07-2008Action, Crime, Drama152USPG-13Christopher NolanNaN$ 10054552112.241.6159.
3tt0071562The Godfather: Part II1975-09-25Crime, Drama220USARFrancis Ford CoppolaNaN$ 4o8,035,7831.098.7149,.0
4tt0110912Pulp Fiction1994-10-28Crime, DramaUSARQuentin TarantinoNaN$ 2228318171.780.1478,9f
\n", + "
" + ], + "text/plain": [ + " imbd_title_id original_titlê release_year genrë¨ \\\n", + "0 tt0111161 The Shawshank Redemption 1995-02-10 Drama \n", + "1 tt0068646 The Godfather 09 21 1972 Crime, Drama \n", + "2 tt0468569 The Dark Knight 23 -07-2008 Action, Crime, Drama \n", + "3 tt0071562 The Godfather: Part II 1975-09-25 Crime, Drama \n", + "4 tt0110912 Pulp Fiction 1994-10-28 Crime, Drama \n", + "\n", + " duration country content_rating director unnamed:_8 \\\n", + "0 142 USA R Frank Darabont NaN \n", + "1 175 USA R Francis Ford Coppola NaN \n", + "2 152 US PG-13 Christopher Nolan NaN \n", + "3 220 USA R Francis Ford Coppola NaN \n", + "4 USA R Quentin Tarantino NaN \n", + "\n", + " income votes score \n", + "0 $ 28815245 2.278.845 9.3 \n", + "1 $ 246120974 1.572.674 9.2 \n", + "2 $ 1005455211 2.241.615 9. \n", + "3 $ 4o8,035,783 1.098.714 9,.0 \n", + "4 $ 222831817 1.780.147 8,9f " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "importlib.reload(data_processing)\n", "from data_processing import rename_columns\n", "df_columns_renamed = rename_columns(df_raw)\n", - "df_columns_renamed.head()" + "df_columns_renamed.head()\n", + "\n", + "\n" ] }, { @@ -401,9 +547,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(100, 11)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "importlib.reload(data_processing)\n", "from data_processing import remove_fully_null_columns_rows\n", @@ -442,9 +599,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "content_rating\n", + "R 45\n", + "Unrated 25\n", + "PG-13 12\n", + "PG 11\n", + "G 6\n", + "Approved 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "importlib.reload(data_processing)\n", "from data_processing import clean_and_fill_content_rating\n", @@ -471,9 +646,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 100 entries, 0 to 100\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 imbd_title_id 100 non-null object\n", + " 1 original_titlê 100 non-null object\n", + " 2 release_year 100 non-null object\n", + " 3 genrë¨ 100 non-null object\n", + " 4 duration 99 non-null object\n", + " 5 country 100 non-null object\n", + " 6 content_rating 100 non-null object\n", + " 7 director 100 non-null object\n", + " 8 income 100 non-null object\n", + " 9 votes 100 non-null object\n", + " 10 score 100 non-null object\n", + "dtypes: object(11)\n", + "memory usage: 9.4+ KB\n" + ] + } + ], "source": [ "df_clean_content_rating.info()" ] @@ -496,9 +696,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "to_datetime() got an unexpected keyword argument 'erorrs'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[32], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m importlib\u001b[38;5;241m.\u001b[39mreload(data_processing)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mdata_processing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m clean_release_year\n\u001b[0;32m----> 4\u001b[0m df_clean_release_year_temp \u001b[38;5;241m=\u001b[39m \u001b[43mclean_release_year\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_clean_content_rating\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/NSC/AD450/AD450Winter2026/data_processing.py:50\u001b[0m, in \u001b[0;36mclean_release_year\u001b[0;34m(df_raw)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mclean_release_year\u001b[39m(df_raw):\n\u001b[1;32m 45\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;124;03m Add new column \"release_year_coerce\", use pd.to_datetime with errors=\"coerce\"\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124;03m Add new column \"release_year_mixed\", use pd.to_datetime with errors=\"coerce\" and format=\"mixed\"\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;124;03m Return a new DataFrame object\u001b[39;00m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 50\u001b[0m df_raw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelease_year_coerce\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_raw\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrelease_year\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merorrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcoerce\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m df_raw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelease_year_mixed\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(df_raw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelease_year\u001b[39m\u001b[38;5;124m\"\u001b[39m], erorrs\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoerce\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m, dayfist\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m df_raw\n", + "\u001b[0;31mTypeError\u001b[0m: to_datetime() got an unexpected keyword argument 'erorrs'" + ] + } + ], "source": [ "importlib.reload(data_processing)\n", "from data_processing import clean_release_year\n", From a16a7b694856d60b79fc4bd749cdc881a5d6c2a2 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 17 Feb 2026 23:01:57 -0800 Subject: [PATCH 48/62] initial commit --- week_7_data_cleaning_matrix.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/week_7_data_cleaning_matrix.ipynb b/week_7_data_cleaning_matrix.ipynb index 61a5a53..36bcedf 100644 --- a/week_7_data_cleaning_matrix.ipynb +++ b/week_7_data_cleaning_matrix.ipynb @@ -696,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -706,8 +706,8 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[32], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m importlib\u001b[38;5;241m.\u001b[39mreload(data_processing)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mdata_processing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m clean_release_year\n\u001b[0;32m----> 4\u001b[0m df_clean_release_year_temp \u001b[38;5;241m=\u001b[39m \u001b[43mclean_release_year\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_clean_content_rating\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/NSC/AD450/AD450Winter2026/data_processing.py:50\u001b[0m, in \u001b[0;36mclean_release_year\u001b[0;34m(df_raw)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mclean_release_year\u001b[39m(df_raw):\n\u001b[1;32m 45\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;124;03m Add new column \"release_year_coerce\", use pd.to_datetime with errors=\"coerce\"\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124;03m Add new column \"release_year_mixed\", use pd.to_datetime with errors=\"coerce\" and format=\"mixed\"\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;124;03m Return a new DataFrame object\u001b[39;00m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 50\u001b[0m df_raw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelease_year_coerce\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_raw\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrelease_year\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merorrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcoerce\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m df_raw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelease_year_mixed\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(df_raw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelease_year\u001b[39m\u001b[38;5;124m\"\u001b[39m], erorrs\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoerce\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m, dayfist\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m df_raw\n", + "Cell \u001b[0;32mIn[33], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m importlib\u001b[38;5;241m.\u001b[39mreload(data_processing)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mdata_processing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m clean_release_year\n\u001b[0;32m----> 4\u001b[0m df_clean_release_year_temp \u001b[38;5;241m=\u001b[39m \u001b[43mclean_release_year\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_clean_content_rating\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/NSC/AD450/AD450Winter2026/data_processing.py:50\u001b[0m, in \u001b[0;36mclean_release_year\u001b[0;34m(df_raw)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mclean_release_year\u001b[39m(df_raw):\n\u001b[1;32m 45\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;124;03m Add new column \"release_year_coerce\", use pd.to_datetime with errors=\"coerce\"\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124;03m Add new column \"release_year_mixed\", use pd.to_datetime with errors=\"coerce\" and format=\"mixed\"\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;124;03m Return a new DataFrame object\u001b[39;00m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 50\u001b[0m df_raw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelease_year_coerce\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[43m \u001b[49m\u001b[43mdf_raw\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrelease_year\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 52\u001b[0m \u001b[43m \u001b[49m\u001b[43merorrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcoerce\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 54\u001b[0m df_raw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelease_year_mixed\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(df_raw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelease_year\u001b[39m\u001b[38;5;124m\"\u001b[39m], erorrs\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoerce\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m, dayfist\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m df_raw\n", "\u001b[0;31mTypeError\u001b[0m: to_datetime() got an unexpected keyword argument 'erorrs'" ] } From e49da525d90dbd5d63cbdeb974df4d5d3743daf2 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Wed, 18 Feb 2026 02:09:13 -0800 Subject: [PATCH 49/62] fixed issues in problem #1 and problem #5 --- data_processing.py | 12 +- week_7_data_cleaning_matrix.ipynb | 232 +++++++++++++++++++++++++----- 2 files changed, 200 insertions(+), 44 deletions(-) diff --git a/data_processing.py b/data_processing.py index 5dc0802..20064bc 100644 --- a/data_processing.py +++ b/data_processing.py @@ -12,7 +12,7 @@ def rename_columns(df_raw): I noticed you need to use strip() before replace otherwise it would replace any leading or trailing spaces with a _ """ - df.raw + df_raw.rename(columns={"original_titlê":"original_title", "Genrë¨":"genre", "Unnamed:_8":"unnamed_8"}, inplace=True) df_raw.columns = df_raw.columns.str.strip() df_raw.columns = df_raw.columns.str.replace(' ', '_') df_raw.columns = df_raw.columns.str.lower() @@ -47,11 +47,8 @@ def clean_release_year(df_raw): Add new column "release_year_mixed", use pd.to_datetime with errors="coerce" and format="mixed" Return a new DataFrame object """ - df_raw["release_year_coerce"] = pd.to_datetime( - df_raw["release_year"], - erorrs="coerce") - - df_raw["release_year_mixed"] = pd.to_datetime(df_raw["release_year"], erorrs="coerce", format="mixed", dayfist=True) + df_raw["release_year_coerce"] = pd.to_datetime(df_raw["release_year"], errors="coerce") + df_raw["release_year_mixed"] = pd.to_datetime(df_raw["release_year"], errors="coerce", format="mixed", dayfirst=True) return df_raw # Problem #5 @@ -60,9 +57,8 @@ def clean_income(df_raw): Remove $, commas and other issues and covert to an int Return a new DataFrame object """ - df_raw["income"] = df_raw["income"].str.replace(f'[$]',regex=True) + df_raw["income"] = df_raw["income"].str.replace(r'[$,]','', regex=True) df_raw["income"] = df_raw["income"].str.replace('o', '0', regex=False) - df_raw["income"] = df_raw["income"].str.replace('0', '0', reges=False) df_raw["income"] = df_raw["income"].astype("Int64") return df_raw diff --git a/week_7_data_cleaning_matrix.ipynb b/week_7_data_cleaning_matrix.ipynb index 36bcedf..29d9232 100644 --- a/week_7_data_cleaning_matrix.ipynb +++ b/week_7_data_cleaning_matrix.ipynb @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -305,7 +305,7 @@ "max NaN NaN " ] }, - "execution_count": 6, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -346,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -371,9 +371,9 @@ " \n", " \n", " imbd_title_id\n", - " original_titlê\n", + " original_title\n", " release_year\n", - " genrë¨\n", + " genre\n", " duration\n", " country\n", " content_rating\n", @@ -465,7 +465,7 @@ "" ], "text/plain": [ - " imbd_title_id original_titlê release_year genrë¨ \\\n", + " imbd_title_id original_title release_year genre \\\n", "0 tt0111161 The Shawshank Redemption 1995-02-10 Drama \n", "1 tt0068646 The Godfather 09 21 1972 Crime, Drama \n", "2 tt0468569 The Dark Knight 23 -07-2008 Action, Crime, Drama \n", @@ -487,7 +487,7 @@ "4 $ 222831817 1.780.147 8,9f " ] }, - "execution_count": 14, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -547,7 +547,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -556,7 +556,7 @@ "(100, 11)" ] }, - "execution_count": 17, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -599,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -615,7 +615,7 @@ "Name: count, dtype: int64" ] }, - "execution_count": 27, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -646,7 +646,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -659,9 +659,9 @@ " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 imbd_title_id 100 non-null object\n", - " 1 original_titlê 100 non-null object\n", + " 1 original_title 100 non-null object\n", " 2 release_year 100 non-null object\n", - " 3 genrë¨ 100 non-null object\n", + " 3 genre 100 non-null object\n", " 4 duration 99 non-null object\n", " 5 country 100 non-null object\n", " 6 content_rating 100 non-null object\n", @@ -696,22 +696,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 53, "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "to_datetime() got an unexpected keyword argument 'erorrs'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[33], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m importlib\u001b[38;5;241m.\u001b[39mreload(data_processing)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mdata_processing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m clean_release_year\n\u001b[0;32m----> 4\u001b[0m df_clean_release_year_temp \u001b[38;5;241m=\u001b[39m \u001b[43mclean_release_year\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_clean_content_rating\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/NSC/AD450/AD450Winter2026/data_processing.py:50\u001b[0m, in \u001b[0;36mclean_release_year\u001b[0;34m(df_raw)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mclean_release_year\u001b[39m(df_raw):\n\u001b[1;32m 45\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;124;03m Add new column \"release_year_coerce\", use pd.to_datetime with errors=\"coerce\"\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124;03m Add new column \"release_year_mixed\", use pd.to_datetime with errors=\"coerce\" and format=\"mixed\"\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;124;03m Return a new DataFrame object\u001b[39;00m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 50\u001b[0m df_raw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelease_year_coerce\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[43m \u001b[49m\u001b[43mdf_raw\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrelease_year\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 52\u001b[0m \u001b[43m \u001b[49m\u001b[43merorrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcoerce\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 54\u001b[0m df_raw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelease_year_mixed\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(df_raw[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelease_year\u001b[39m\u001b[38;5;124m\"\u001b[39m], erorrs\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoerce\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m, dayfist\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m df_raw\n", - "\u001b[0;31mTypeError\u001b[0m: to_datetime() got an unexpected keyword argument 'erorrs'" - ] - } - ], + "outputs": [], "source": [ "importlib.reload(data_processing)\n", "from data_processing import clean_release_year\n", @@ -721,9 +708,151 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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release_yearrelease_year_coercerelease_year_mixedoriginal_title
109 21 1972NaT1972-09-21The Godfather
223 -07-2008NaT2008-07-23The Dark Knight
522 Feb 04NaT2004-02-22The Lord of the Rings: The Return of the King
910-29-99NaT1999-10-29Fight Club
1223rd December of 1966NaT1966-12-23Il buono, il brutto, il cattivo
1501/16-03NaT2003-01-16The Lord of the Rings: The Two Towers
1818/11/1976NaT1976-11-18One Flew Over the Cuckoo's Nest
4521-11-46NaT2046-11-21Casablanca
70The 6th of marzo, year 1951NaTNaTSunset Blvd.
831984-02-34NaTNaTScarface
841976-13-24NaTNaTTaxi Driver
\n", + "
" + ], + "text/plain": [ + " release_year release_year_coerce release_year_mixed \\\n", + "1 09 21 1972 NaT 1972-09-21 \n", + "2 23 -07-2008 NaT 2008-07-23 \n", + "5 22 Feb 04 NaT 2004-02-22 \n", + "9 10-29-99 NaT 1999-10-29 \n", + "12 23rd December of 1966 NaT 1966-12-23 \n", + "15 01/16-03 NaT 2003-01-16 \n", + "18 18/11/1976 NaT 1976-11-18 \n", + "45 21-11-46 NaT 2046-11-21 \n", + "70 The 6th of marzo, year 1951 NaT NaT \n", + "83 1984-02-34 NaT NaT \n", + "84 1976-13-24 NaT NaT \n", + "\n", + " original_title \n", + "1 The Godfather \n", + "2 The Dark Knight \n", + "5 The Lord of the Rings: The Return of the King \n", + "9 Fight Club \n", + "12 Il buono, il brutto, il cattivo \n", + "15 The Lord of the Rings: The Two Towers \n", + "18 One Flew Over the Cuckoo's Nest \n", + "45 Casablanca \n", + "70 Sunset Blvd. \n", + "83 Scarface \n", + "84 Taxi Driver " + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_clean_release_year_temp[df_fully_null_removed['release_year_coerce'].isna()\n", " ][['release_year', 'release_year_coerce', \n", @@ -732,7 +861,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "metadata": {}, "outputs": [], "source": [ @@ -751,21 +880,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "metadata": {}, "outputs": [], "source": [ "importlib.reload(data_processing)\n", "from data_processing import clean_income\n", "\n", - "df_clean_income = clean_income(df_clean_release_year)" + "df_clean_income = clean_income(df_clean_release_year)\n", + "# I ran the code below as a test\n", + "# print(df_clean_income.iloc[0:50][\"income\"])\n", + "# print(df_clean_income.iloc[51:][\"income\"])\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 96 entries, 0 to 100\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 imbd_title_id 96 non-null object \n", + " 1 original_title 96 non-null object \n", + " 2 release_year 96 non-null object \n", + " 3 genre 96 non-null object \n", + " 4 duration 95 non-null object \n", + " 5 country 96 non-null object \n", + " 6 content_rating 96 non-null object \n", + " 7 director 96 non-null object \n", + " 8 income 96 non-null Int64 \n", + " 9 votes 96 non-null object \n", + " 10 score 96 non-null object \n", + " 11 release_year_coerce 89 non-null datetime64[ns]\n", + " 12 release_year_mixed 96 non-null datetime64[ns]\n", + "dtypes: Int64(1), datetime64[ns](2), object(10)\n", + "memory usage: 10.6+ KB\n" + ] + } + ], "source": [ "df_clean_income.info()" ] From 0bef8ef6e07f96418db10c4e202b5f3863621c5d Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Fri, 20 Feb 2026 17:21:06 -0800 Subject: [PATCH 50/62] started problem 6 --- week_7_data_cleaning_matrix.ipynb | 55 ++++++++++++++++++++++++++----- 1 file changed, 46 insertions(+), 9 deletions(-) diff --git a/week_7_data_cleaning_matrix.ipynb b/week_7_data_cleaning_matrix.ipynb index 29d9232..8aa749e 100644 --- a/week_7_data_cleaning_matrix.ipynb +++ b/week_7_data_cleaning_matrix.ipynb @@ -960,14 +960,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "k = 5\n", + "\n", + "a1 = np.array([ [0,1], [1,0] ]) # refliction in line y = x\n", + "a2 = np.array([ [1,0], [0,-1] ]) # reflection in y-axis\n", + "a3 = np.array([ [-1,0], [0,1] ]) # reflection in the line x = 0\n", + "\n", + "b1 = np.array([ [k,0], [0,1] ]) # enlarges x in first array by k\n", + "b2 = np.array([ [1,0], [0,k] ]) # enlarges y in second array by k\n", + "\n", + "c1 = np.array([ [1,k], [0,1] ]) # second vector moves to the right (along x-axis) k times\n", + "c2 = np.array([ [1,0], [k,1] ]) # first vector moves up (along y-axis) k times\n", "\n", + "d1 = np.array([ [-1,0], [0,-1] ]) # 180 degree rotation\n", + "d2 = np.array([ [0,1], [-1,0] ]) # 90 degrees clockwise\n", + "d3 = np.array([ [0,-1], [1,0] ]) # 90 degrees counter clockwise\n", "\n", + "rand1 = np.random.randint(0,9, size=(2,1))\n", + "rand2 = np.random.randint(0,9, size=(2,1))\n", "\n", "\n", "quiver = plt.quiver([0, 0], # Sets the x-cordinate of the start of the vectors\n", @@ -982,8 +1019,8 @@ "plt.ylim(-2, 2)\n", "plt.quiverkey(quiver, 1, 1, 1, 'orginal vector', coordinates='data')\n", "plt.quiverkey(quiver, -1, -1.5, 1, 'transformed vector', coordinates='data')\n", - "plt.title('Description of Transformation')\n", - "plt.show()" + "# plt.title('Description of Transformation')\n", + "# plt.show()" ] }, { @@ -1006,14 +1043,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "L = np.array()\n", - "I = np.array()\n", - "V = np.array()\n", - "E = np.array()" + "L = np.random.rand(2,6)\n", + "I = np.random.rand(6,3)\n", + "V = np.random.rand(3,5)\n", + "E = np.random.rand(5,2)" ] } ], From 180801c13f0f777fe7704b74bfdbd62299fa663f Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Fri, 20 Feb 2026 17:22:14 -0800 Subject: [PATCH 51/62] uncommented part of plot --- week_7_data_cleaning_matrix.ipynb | 18 ++++-------------- 1 file changed, 4 insertions(+), 14 deletions(-) diff --git a/week_7_data_cleaning_matrix.ipynb b/week_7_data_cleaning_matrix.ipynb index 8aa749e..6f24371 100644 --- a/week_7_data_cleaning_matrix.ipynb +++ b/week_7_data_cleaning_matrix.ipynb @@ -960,22 +960,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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" ] @@ -1019,8 +1009,8 @@ "plt.ylim(-2, 2)\n", "plt.quiverkey(quiver, 1, 1, 1, 'orginal vector', coordinates='data')\n", "plt.quiverkey(quiver, -1, -1.5, 1, 'transformed vector', coordinates='data')\n", - "# plt.title('Description of Transformation')\n", - "# plt.show()" + "plt.title('Description of Transformation')\n", + "plt.show()" ] }, { From aa6d72ea7f949d45dcbe74116b8a929e8cfb62af Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Fri, 20 Feb 2026 18:05:26 -0800 Subject: [PATCH 52/62] completed problem 6 --- week_7_data_cleaning_matrix.ipynb | 287 ++++++++++++++++++++++++++++-- 1 file changed, 270 insertions(+), 17 deletions(-) diff --git a/week_7_data_cleaning_matrix.ipynb b/week_7_data_cleaning_matrix.ipynb index 6f24371..3685895 100644 --- a/week_7_data_cleaning_matrix.ipynb +++ b/week_7_data_cleaning_matrix.ipynb @@ -960,12 +960,222 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 108, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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byZMnzeDBg02NGjVM1apVTefOnc2ePXuKnJqdP+W4qOXMbRYlJyfHDBo0yERGRhqHw+Gbpp0/rbqo6dJ5eXlm8uTJpm7dusbpdJrrrrvOfPbZZ6Z3795+U5bPtY0z9+PXX381SUlJpnHjxqZq1arG5XKZli1bmvfff9/vOUVNzTbGmNOnT5vx48ebuLg4ExwcbGJjY82oUaPMqVOnCr1PnTp1KvT8/KnZH3zwgV97/ufy3Xff+bWf7XP86KOPzC233GKqVq1qqlataho3bmySkpLM1q1b/frNnDnTxMXFGafTaVq0aGFWrVplbrvttguemp2UlGQWLFhgGjZs6Hv/ly9fXmR/j8djqlevblwulzl58uR5t2+Md2r28OHDTa1atUxISIi5+eabTWpqaqEav/vuO9O9e3dTp04d43Q6TdWqVc31119vpk+fbk6fPn1BrwWUFIcxpXBUIgDgvHJyclS7dm117ty50BgYoDxhzAwA2NSSJUt06NAh9erVy+pSAEtxZAYAbGbt2rXauHGjJk6cqJo1axZ5sTugPOHIDADYzKxZszRw4EBFRUXp73//u9XlAJYLaJhJTk7WDTfcoGrVqikqKkpdunQpdC2NU6dOKSkpSTVq1FBoaKi6deumjIyMQJYFALY2b9485eTkaN26dWrSpInV5QCWC2iYWblypZKSkrRmzRotXbpUp0+fVvv27f3u6zF06FB9+umn+uCDD7Ry5Urt379f9957byDLAgAAZUiJjpk5dOiQoqKitHLlSt16663KyspSZGSkFi1a5LtWwpYtW3TVVVcpNTXVdzM/AACAsynRi+blX/46IiJCkrR+/XqdPn1a7dq18/Vp3Lix6tSpc9Yw4/F4/K4amn9X3xo1alzSpdsBAEDJM8bo6NGjql27tu9O7peqxMJMXl6ehgwZoptvvtl3jjc9PV2VKlVSeHi4X9/o6Oiz3pE1OTm5yCthAgAA+9mzZ4/+8Ic/XNY2SizMJCUl6aefftK///3vy9rOqFGjNGzYMN96VlaW6tSpoz179igsLOxyywQAACXA7XYrNjZW1apVu+xtlUiYefLJJ/XZZ59p1apVfukrJiZG2dnZyszM9Ds6k5GRoZiYmCK35XQ65XQ6C7WHhYURZgAAsJniGCIS0NlMxhg9+eSTWrx4sb755hvFxcX5Pd68eXMFBwdr2bJlvratW7dq9+7dSkhICGRpAACgjAjokZmkpCQtWrRIn3zyiapVq+YbB+NyuRQSEiKXy6V+/fpp2LBhioiIUFhYmAYNGqSEhARmMgEAgAsS0KnZZzt0lJKSoj59+kjyXjRv+PDheuedd+TxeJSYmKiZM2ee9TTT77ndbrlcLmVlZXGaCQAAmyjO72/b35uJMAMAgP0U5/c392YCAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2RpgBAAC2FtAws2rVKnXu3Fm1a9eWw+HQkiVL/B7v06ePHA6H39KhQ4dAlgQAAMqYgIaZ48ePq2nTppoxY8ZZ+3To0EEHDhzwLe+8804gSwIAAGVMxUBuvGPHjurYseM5+zidTsXExASyDAAAUIZZPmZmxYoVioqKUqNGjTRw4EAdPnz4nP09Ho/cbrffAgAAyi9Lw0yHDh3097//XcuWLdOLL76olStXqmPHjsrNzT3rc5KTk+VyuXxLbGxsCVYMAABKG4cxxpTICzkcWrx4sbp06XLWPr/88ovq16+vf/3rX7rjjjuK7OPxeOTxeHzrbrdbsbGxysrKUlhYWHGXDQAAAsDtdsvlchXL97flp5nOFB8fr5o1a2r79u1n7eN0OhUWFua3AACA8qtUhZm9e/fq8OHDqlWrltWlAAAAmwjobKZjx475HWVJS0vThg0bFBERoYiICI0fP17dunVTTEyMduzYoWeeeUYNGjRQYmJiIMsCAABlSEDDzLp169S2bVvf+rBhwyRJvXv31qxZs7Rx40bNnz9fmZmZql27ttq3b6+JEyfK6XQGsiwAAFCGlNgA4EApzgFEAACgZJTZAcAAAAAXizADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsLaBhZtWqVercubNq164th8OhJUuW+D1ujNGYMWNUq1YthYSEqF27dtq2bVsgSwIAAGVMQMPM8ePH1bRpU82YMaPIx6dOnarXXntNs2fP1tq1a1W1alUlJibq1KlTgSwLAACUIRUDufGOHTuqY8eORT5mjNGrr76qv/zlL7rnnnskSX//+98VHR2tJUuW6IEHHghkaQAAoIywbMxMWlqa0tPT1a5dO1+by+VSy5YtlZqaetbneTweud1uvwUAAJRfloWZ9PR0SVJ0dLRfe3R0tO+xoiQnJ8vlcvmW2NjYgNYJAABKN9vNZho1apSysrJ8y549e6wuCQAAWMiyMBMTEyNJysjI8GvPyMjwPVYUp9OpsLAwvwUAAJRfloWZuLg4xcTEaNmyZb42t9uttWvXKiEhwaqyAACAzQR0NtOxY8e0fft233paWpo2bNigiIgI1alTR0OGDNELL7yghg0bKi4uTqNHj1bt2rXVpUuXQJYFAADKkICGmXXr1qlt27a+9WHDhkmSevfurXnz5umZZ57R8ePH9dhjjykzM1O33HKLvvzyS1WuXDmQZQEAgDLEYYwxVhdxOdxut1wul7Kyshg/AwCATRTn97ftZjMBAACciTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTAD2IAx0t69VleBEsOHDVwUwgxgAw6HNHmy9OqrUm6u1dUgYE6f9n7Qr71mdSWArVS0ugAAF6ZfP6lFC+n996W335YaN7a6IhSrDRukvn29/9261epqAFvhyAxgE82bSzffLKWmSs2aSVOmSDk5VleFy5adLY0ZI91wg/TDD9Kdd0oNG1pdFWArhBnARp56yvtfj0caNUq66Sbpxx+trQmX4bvvvCl14sSCZDp4sLU1ATZEmAFspEsX6Q9/KFhfv977XTh+vPd/8GETJ09Kzz7rTaM//VTQ3rix9Kc/WVcXYFOEGcBGgoOlJ57wbzt9Who3znuWYv16S8rCxfjf//WeJ5w6VcrL839s0CDvaG8AF4UwA9hM//6S01m4feNGqWVL6bnnpFOnSr4unMfx49KQIVLr1tJ//1v4cZdL6tWrxMsCygLCDGAzNWtKDz1U9GO5uVJysnTddd6Bwiglli+XrrlG+utfvRcNKkq/flJoaMnWBZQRhBnAhs43RnTLFu/Mp2HDpBMnSqYmFMHtlgYOlG6/XUpLO3s/h0NKSiq5uoAyhjAD2FDTptJtt527jzHSK69I114rrVxZMnXhDF9+KTVpIs2eff6+nTtL8fGBrwkoowgzgE1dyAzeuDjvEZodO7wDhVFCTp2Sdu+Wbr1VqlPn/P2Zjg1cFocxZzuBaw9ut1sul0tZWVkKCwuzuhygxOTkSPXre78zizJ6tDRhQsnWhLN46qmz36Lgj3/0XiyIWUwoZ4rz+5sjM4BNVax47mEWkydLX39dcvXgLD766Nz3Who8mCADXCbCDGBjjz4qhYQUrF9xRcG/c3OlHj2kzZtLvi78n++/lx5+2L/tzA+pevWzT00DcMEIM4CNRUQUfFdecYX3onkNGhQ8npXlHVt6+LA19ZVr+/d73/yTJwvarr3WG3AiIrzrjz4qVa1qTX1AGUKYAWxu0CDvf598UoqOlj77TAoPL3h8xw7p3nu53UGJOnFCuvtub6DJFxUl/c//eP/bv79UoQLTsYFiQpgBbK5JE+muu7zfj5LUqJH0wQdSUFBBn1WrvJc7sfdwf5vIy5N69/a/t4TTKS1ZItWt611PSpLuu69gHcBlIcwAZcD8+VKNGgXr7dpJr7/u3+ftt6Xp00u2rnJp3Djpww/92+bOlRISCtZjY6VZs0q0LKAsI8wAZUD+EIwzDRxYcAoq39NPS59+WjI1lUuLFkkTJ/q3/eUvRQ/yLepDA3BJCDNAGTZ9upSYWLBujPTgg96bUqKYpaZKffv6t913nzR+vDX1AOUIYQYowypWlN57T7rqqoK2Y8e8k2wyMqyrq8zZtUvq0kXyeAramjf3nv+rwJ9ZIND4LQPKOJfLe2rpzDE1u3dLXbt6r7qPy3T0qHfm0sGDBW21a0uffCJVqWJdXUA5QpgByoH69aXFi6Xg4IK21FSpXz9mOF2W3FzveJgzz9uFhHinYJ95cTwAAWV5mBk3bpwcDoff0rhxY6vLAsqc1q2lOXP82xYtkiZNsqaeMmHkyMIjqv/xD+8pJgAlpqLVBUjSH//4R/3rX//yrVesWCrKAsqcPn2kn3+Wpk0raBs9Wmrc2DtWFRfh7bell17yb3vhBalbN2vqAcqxUpEaKlasqJiYGKvLAMqF5GRp61bvmZB8vXpJ9epJLVpYVpa9rFwpDRjg3/bnP0vPPWdNPUA5Z/lpJknatm2bateurfj4eD300EPavXv3Wft6PB653W6/BcCFCwqSFi6UmjYtaDt5UrrnHmnfPuvqso38+0OcPl3QlpAg/e1v3P0asIjlYaZly5aaN2+evvzyS82aNUtpaWlq3bq1jh49WmT/5ORkuVwu3xIbG1vCFQP2FxrqPTITHV3Qtn+/d1LOiRPW1VXqZWV57x1x5EhBW5063tHVlStbVxdQzjmMKV1zGTIzM1W3bl1Nnz5d/fr1K/S4x+OR54xrObjdbsXGxiorK0thYWElWSpge2vWSG3a+F8epVs36f33uTxKITk5UqdO0tdfF7SFhkqrV0vXXGNdXYBNud1uuVyuYvn+LnV/rsLDw3XllVdq+/btRT7udDoVFhbmtwC4NDfdJKWk+Ld99JE0Zow19ZRqQ4f6BxmHQ3rnHYIMUAqUujBz7Ngx7dixQ7Vq1bK6FKBc6NmzcHiZNElasMCaekqlmTOlN97wb3vpJe8pJwCWszzMjBgxQitXrtTOnTu1evVqde3aVUFBQerZs6fVpQHlxtixUvfu/m39+nkvrFfuLV0qDR7s39avn/dIDYBSwfIws3fvXvXs2VONGjVSjx49VKNGDa1Zs0aRkZFWlwaUGxUqSPPm+U/Nzs723m5o1y6rqioFtmzxprzc3IK2227zHqlh5hJQapS6AcAXqzgHEAHl3f790o03+k/RbtLEO8a1WjXr6rLE4cNSy5beqdj56teX1q71v9EVgEtSpgcAA7BO7dreKdtn3h/xp5+842rOPDhR5mVne6d1nRlkXC7ps88IMkApRJgB4Of66723FzrTP/8pPfusNfWUOGOkJ57wXuU3X1CQ9MEH3vs+ACh1CDMACrn3XmnyZP+2l1+W5s61pp4S9corhXf0tdekP/3JmnoAnBdhBkCRRo6UHn7Yv23AAGnFCkvKKRmffSaNGOHf9uST3iM1AEotwgyAIjkc3tsNtWpV0JaT4x1KcpZrWtrbjz96BwedOSeifXvvkRoApRphBsBZOZ3e2w7Vq1fQduSI1LmzlJlpVVUBcPCgd6eOHStoa9xYeu89qWJF6+oCcEEIMwDOKSpK+vRT/6nZW7ZIPXp4j9TY3qlThS+oU6OG95RTeLhVVQG4CIQZAOfVpIn07rv+N59culR66inraioWxkiPPup/qePgYOnjj73XlAFgC4QZABfkzju9tyM608yZ0owZ1tRTLJKTpYUL/dtmz5ZuvdWaegBcEsIMgAs2ZIjUv79/21NP+d9M2jY++kh6/nn/thEjpL59rakHwCUjzAC4YA6H90hMmzYFbbm53tsXbd5sWVkXb/36wvPOO3eWpkyxph4Al4UwA+CiBAd7D2o0aFDQ5nZLd93lvZ1Rqbd/v3T33dLJkwVt117rPd0UFGRdXQAuGWEGwEWLiCg82eeXX7xXDs7Otqys8ztxwhtk9u8vaIuK8t6QqtzdSRMoOwgzAC5Jo0be2xWdeTBj1Spp4ED/686VGnl5Uu/e3lNM+ZxO6ZNPpLp1rasLwGUjzAC4ZO3aSW+84d/29tve+ziVOmPHSh9+6N/29tvSTTdZUw+AYkOYAXBZBgyQBg3yb3vmGe+F9kqNRYukF17wb/vLX6QHH7SmHgDFijAD4LJNny4lJhasG+PNCRs3WleTT2pq4enW990njR9vTT0Aih1hBsBlq1jRexujq64qaDt2zDvbOSPDurq0a5f3VgUeT0Fb8+bS/Pn+lzMGYGv8NgMoFi6X99RSjRoFbbt3e7PEqVMWFHT0qDdNHTxY0Fa7tnfAb5UqFhQEIFAIMwCKTf363rtsBwcXtK1ZI/XrV8IznHJzpYcekn78saAtJMQ7BfuKK0qwEAAlgTADoFi1bi3NmePftmiRNGlSCRYxcmThEcgLFnhPMQEocwgzAIpdnz7eGU1nGj3ae12agJs7t/AdMSdN8l7RD0CZRJgBEBCTJ3svtnum3r2ldesC+KIrV3qv2nemP/9ZGjUqgC8KwGqEGQABERTkvd1R06YFbSdPSvfcI+3bF4AX3LHDe/Tl9OmCtlatpL/9zXuHTABlFmEGQMCEhnrH3EZHF7Tl3+fx+PFifKHMTO+dLo8cKWirU8c7Grly5WJ8IQClEWEGQEDVqSMtWeK9DVK+77/3nnLKyyuGF8jJke6/X9qypaAtNNR7J8yoqGJ4AQClHWEGQMDddJOUkuLf9tFH0pgxxbDxoUOlr78uWHc4pHfeka65phg2DsAOCDMASkTPnoXDy6RJ3hnTl2zmzMJ3unzpJe8pJwDlBmEGQIkZO1bq0cO/rV8/afXqS9jY0qXS4MH+bY8+6j1SA6BcIcwAKDEVKnhPN7VoUdCWne295cGuXRexoS1bpO7dvVf6zXfbbdKMGcxcAsohwgyAElWlSuG7Chw65D0zdPToBWzg8GFv56ysgrYGDbyDcCpVKvZ6AZR+hBkAJa5WLe/dBs683+NPP3nH1Zx5sKWQ7GypWzfvNWXyFXWHSwDlCmEGgCWuu076xz/82/75z8K3QfAxRnriCe9VfvMFBXnvkdC4ccDqBFD6EWYAWObee723PTjT9OnSW28V0fmVV7z3XTrTa69Jf/pTwOoDYA+EGQCWGjlS6tXLv23gQGnFijMaPvtMGjHCv9OTT3qP1AAo9wgzACzlcEhz5kg331zQlpPjHRqzfbukjRu9g2mMKejQvr33SA0AqJSEmRkzZqhevXqqXLmyWrZsqW+//dbqkgCUIKdT+vhjqV69grYjR6S7OuYos9ND0rFjBQ80biy9955UsWKJ1wmgdLI8zLz33nsaNmyYxo4dq++//15NmzZVYmKiDh48aHVpAEpQVJR3UlK1agVtW7dXVI+9LytHQd6GGjW8p5zCwy2pEUDpZHmYmT59uvr3769HHnlEV199tWbPnq0qVaro7bffLrK/x+OR2+32WwCUDU2aSO++6724Xr4NaqZfFC8FB3sP39Svb12BAEolS8NMdna21q9fr3bt2vnaKlSooHbt2ik1NbXI5yQnJ8vlcvmW2NjYkioXQAm4807p5Ze9/27S0KPv6vXQldomzZ4t3XqrtcUBKJUsDTO//vqrcnNzFR0d7dceHR2t9PT0Ip8zatQoZWVl+ZY9e/aURKkAStBTT3nvIbl6vVN1138s/e1vUt++VpcFoJSy3Qg6p9Mpp9NpdRkAAsjh8E7P9orw3kASAM7C0iMzNWvWVFBQkDIyMvzaMzIyFBMTY1FVAADATiwNM5UqVVLz5s21bNkyX1teXp6WLVumhIQECysDAAB2YflppmHDhql3795q0aKFbrzxRr366qs6fvy4HnnkEatLAwAANmB5mLn//vt16NAhjRkzRunp6WrWrJm+/PLLQoOCAQAAiuIw5sxrhNuP2+2Wy+VSVlaWwsLCrC4HAABcgOL8/rb8onkAAACXgzADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABsjTADAABszdIwU69ePTkcDr9lypQpVpYEAABspqLVBUyYMEH9+/f3rVerVs3CagAAgN1YHmaqVaummJgYq8sAAAA2ZfmYmSlTpqhGjRq67rrrNG3aNOXk5Jyzv8fjkdvt9lsAAED5ZemRmcGDB+v6669XRESEVq9erVGjRunAgQOaPn36WZ+TnJys8ePHl2CVAACgNHMYY0xxbnDkyJF68cUXz9ln8+bNaty4caH2t99+W48//riOHTsmp9NZ5HM9Ho88Ho9v3e12KzY2VllZWQoLC7u84gEAQIlwu91yuVzF8v1d7GHm0KFDOnz48Dn7xMfHq1KlSoXaN23apCZNmmjLli1q1KjRBb1ecb4ZAACgZBTn93exn2aKjIxUZGTkJT13w4YNqlChgqKiooq5KgAAUFZZNmYmNTVVa9euVdu2bVWtWjWlpqZq6NCh+vOf/6zq1atbVRYAALAZy8KM0+nUu+++q3Hjxsnj8SguLk5Dhw7VsGHDrCoJAADYkGVh5vrrr9eaNWusenkAAFBGWH6dGQAAgMtBmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALYWsDAzadIktWrVSlWqVFF4eHiRfXbv3q1OnTqpSpUqioqK0tNPP62cnJxAlQQAAMqgioHacHZ2trp3766EhATNnTu30OO5ubnq1KmTYmJitHr1ah04cEC9evVScHCwJk+eHKiyAABAGeMwxphAvsC8efM0ZMgQZWZm+rV/8cUXuuuuu7R//35FR0dLkmbPnq1nn31Whw4dUqVKlS5o+263Wy6XS1lZWQoLCyvu8gEAQAAU5/e3ZWNmUlNTdc011/iCjCQlJibK7XZr06ZNZ32ex+OR2+32WwAAQPllWZhJT0/3CzKSfOvp6elnfV5ycrJcLpdviY2NDWidAACgdLuoMDNy5Eg5HI5zLlu2bAlUrZKkUaNGKSsry7fs2bMnoK8HAABKt4saADx8+HD16dPnnH3i4+MvaFsxMTH69ttv/doyMjJ8j52N0+mU0+m8oNcAAABl30WFmcjISEVGRhbLCyckJGjSpEk6ePCgoqKiJElLly5VWFiYrr766mJ5DQAAUPYFbGr27t27deTIEe3evVu5ubnasGGDJKlBgwYKDQ1V+/btdfXVV+vhhx/W1KlTlZ6err/85S9KSkriyAsAALhgAZua3adPH82fP79Q+/Lly9WmTRtJ0q5duzRw4ECtWLFCVatWVe/evTVlyhRVrHjhGYup2QAA2E9xfn8H/DozgUaYAQDAfsrEdWYAAACKA2EGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYWsDCzKRJk9SqVStVqVJF4eHhRfZxOByFlnfffTdQJQEAgDKoYqA2nJ2dre7duyshIUFz5849a7+UlBR16NDBt3624AMAAFCUgIWZ8ePHS5LmzZt3zn7h4eGKiYkJVBkAAKCMs3zMTFJSkmrWrKkbb7xRb7/9towx5+zv8Xjkdrv9FgAAUH4F7MjMhZgwYYJuv/12ValSRV9//bWeeOIJHTt2TIMHDz7rc5KTk31HfQAAABzmfIdCzjBy5Ei9+OKL5+yzefNmNW7c2Lc+b948DRkyRJmZmefd/pgxY5SSkqI9e/actY/H45HH4/Gtu91uxcbGKisrS2FhYeffCQAAYDm32y2Xy1Us398XdWRm+PDh6tOnzzn7xMfHX3IxLVu21MSJE+XxeOR0Oovs43Q6z/oYAAAofy4qzERGRioyMjJQtWjDhg2qXr06YQUAAFywgI2Z2b17t44cOaLdu3crNzdXGzZskCQ1aNBAoaGh+vTTT5WRkaGbbrpJlStX1tKlSzV58mSNGDEiUCUBAIAyKGBhZsyYMZo/f75v/brrrpMkLV++XG3atFFwcLBmzJihoUOHyhijBg0aaPr06erfv3+gSgIAAGXQRQ0ALo2KcwARAAAoGcX5/W35dWYAAAAuB2EGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYGmEGAADYWsDCzM6dO9WvXz/FxcUpJCRE9evX19ixY5Wdne3Xb+PGjWrdurUqV66s2NhYTZ06NVAlAQCAMqhioDa8ZcsW5eXl6c0331SDBg30008/qX///jp+/LheeuklSZLb7Vb79u3Vrl07zZ49Wz/++KP69u2r8PBwPfbYY4EqDQAAlCEOY4wpqRebNm2aZs2apV9++UWSNGvWLD3//PNKT09XpUqVJEkjR47UkiVLtGXLlgvaptvtlsvlUlZWlsLCwgJWOwAAKD7F+f0dsCMzRcnKylJERIRvPTU1VbfeeqsvyEhSYmKiXnzxRf3222+qXr16oW14PB55PB6/bUreNwUAANhD/vd2cRxTKbEws337dr3++uu+U0ySlJ6erri4OL9+0dHRvseKCjPJyckaP358ofbY2NhirhgAAATa4cOH5XK5LmsbFx1mRo4cqRdffPGcfTZv3qzGjRv71vft26cOHTqoe/fu6t+//8VXeYZRo0Zp2LBhvvXMzEzVrVtXu3fvvuw3w07cbrdiY2O1Z8+ecnV6jf1mv8sD9pv9Lg+ysrJUp04dvzM2l+qiw8zw4cPVp0+fc/aJj4/3/Xv//v1q27atWrVqpTlz5vj1i4mJUUZGhl9b/npMTEyR23Y6nXI6nYXaXS5XufohyBcWFsZ+lyPsd/nCfpcv5XW/K1S4/InVFx1mIiMjFRkZeUF99+3bp7Zt26p58+ZKSUkpVHBCQoKef/55nT59WsHBwZKkpUuXqlGjRkWeYgIAAPi9gF1nZt++fWrTpo3q1Kmjl156SYcOHVJ6errS09N9fR588EFVqlRJ/fr106ZNm/Tee+/pr3/9q99pJAAAgHMJ2ADgpUuXavv27dq+fbv+8Ic/+D2WP3LZ5XLp66+/VlJSkpo3b66aNWtqzJgxF3WNGafTqbFjxxZ56qksY7/Z7/KA/Wa/ywP2+/L3u0SvMwMAAFDcuDcTAACwNcIMAACwNcIMAACwNcIMAACwNcIMAACwNduGmZ07d6pfv36Ki4tTSEiI6tevr7Fjxyo7O9uv38aNG9W6dWtVrlxZsbGxmjp1qkUVF59JkyapVatWqlKlisLDw4vs43A4Ci3vvvtuyRZazC5kv3fv3q1OnTqpSpUqioqK0tNPP62cnJySLTTA6tWrV+iznTJlitVlFbsZM2aoXr16qly5slq2bKlvv/3W6pICbty4cYU+2zNvDVNWrFq1Sp07d1bt2rXlcDi0ZMkSv8eNMRozZoxq1aqlkJAQtWvXTtu2bbOm2GJ0vv3u06dPoc+/Q4cO1hRbTJKTk3XDDTeoWrVqioqKUpcuXbR161a/PqdOnVJSUpJq1Kih0NBQdevWrdDdAc7HtmFmy5YtysvL05tvvqlNmzbplVde0ezZs/Xcc8/5+rjdbrVv315169bV+vXrNW3aNI0bN67QbRXsJjs7W927d9fAgQPP2S8lJUUHDhzwLV26dCmZAgPkfPudm5urTp06KTs7W6tXr9b8+fM1b948jRkzpoQrDbwJEyb4fbaDBg2yuqRi9d5772nYsGEaO3asvv/+ezVt2lSJiYk6ePCg1aUF3B//+Ee/z/bf//631SUVu+PHj6tp06aaMWNGkY9PnTpVr732mmbPnq21a9eqatWqSkxM1KlTp0q40uJ1vv2WpA4dOvh9/u+8804JVlj8Vq5cqaSkJK1Zs0ZLly7V6dOn1b59ex0/ftzXZ+jQofr000/1wQcfaOXKldq/f7/uvffei3shU4ZMnTrVxMXF+dZnzpxpqlevbjwej6/t2WefNY0aNbKivGKXkpJiXC5XkY9JMosXLy7RekrK2fb7888/NxUqVDDp6em+tlmzZpmwsDC/nwG7q1u3rnnllVesLiOgbrzxRpOUlORbz83NNbVr1zbJyckWVhV4Y8eONU2bNrW6jBL1+79VeXl5JiYmxkybNs3XlpmZaZxOp3nnnXcsqDAwivob3bt3b3PPPfdYUk9JOXjwoJFkVq5caYzxfrbBwcHmgw8+8PXZvHmzkWRSU1MveLu2PTJTlKysLL+7b6ampurWW29VpUqVfG2JiYnaunWrfvvtNytKLFFJSUmqWbOmbrzxRr399tu+Ky+XVampqbrmmmsUHR3ta0tMTJTb7damTZssrKz4TZkyRTVq1NB1112nadOmlalTadnZ2Vq/fr3atWvna6tQoYLatWun1NRUCysrGdu2bVPt2rUVHx+vhx56SLt377a6pBKVlpam9PR0v8/f5XKpZcuW5eLzX7FihaKiotSoUSMNHDhQhw8ftrqkYpWVlSVJvu/q9evX6/Tp036fd+PGjVWnTp2L+rwDdjuDkrZ9+3a9/vrreumll3xt6enpiouL8+uX/0WXnp5epm9mOWHCBN1+++2qUqWKvv76az3xxBM6duyYBg8ebHVpAZOenu4XZCT/z7usGDx4sK6//npFRERo9erVGjVqlA4cOKDp06dbXVqx+PXXX5Wbm1vkZ7llyxaLqioZLVu21Lx589SoUSMdOHBA48ePV+vWrfXTTz+pWrVqVpdXIvJ/V4v6/MvS73FROnTooHvvvVdxcXHasWOHnnvuOXXs2FGpqakKCgqyurzLlpeXpyFDhujmm29WkyZNJHk/70qVKhUaB3mxn3epOzIzcuTIIgevnrn8/g/avn371KFDB3Xv3l39+/e3qPLLcyn7fS6jR4/WzTffrOuuu07PPvusnnnmGU2bNi2Ae3Bpinu/7epi3odhw4apTZs2uvbaazVgwAC9/PLLev311+XxeCzeC1yujh07qnv37rr22muVmJiozz//XJmZmXr//fetLg0l4IEHHtDdd9+ta665Rl26dNFnn32m7777TitWrLC6tGKRlJSkn376KSCTUUrdkZnhw4erT58+5+wTHx/v+/f+/fvVtm1btWrVqtDA3piYmEIjovPXY2JiiqfgYnKx+32xWrZsqYkTJ8rj8ZSqm5kV537HxMQUmvFSWj/v37uc96Fly5bKycnRzp071ahRowBUV7Jq1qypoKCgIn93S/vnWNzCw8N15ZVXavv27VaXUmLyP+OMjAzVqlXL156RkaFmzZpZVJU14uPjVbNmTW3fvl133HGH1eVclieffFKfffaZVq1a5Xfz6ZiYGGVnZyszM9Pv6MzF/r6XujATGRmpyMjIC+q7b98+tW3bVs2bN1dKSooqVPA/0JSQkKDnn39ep0+fVnBwsCTv3bwbNWpU6k4xXcx+X4oNGzaoevXqpSrISMW73wkJCZo0aZIOHjyoqKgoSd7POywsTFdffXWxvEagXM77sGHDBlWoUMG3z3ZXqVIlNW/eXMuWLfPNwMvLy9OyZcv05JNPWltcCTt27Jh27Nihhx9+2OpSSkxcXJxiYmK0bNkyX3hxu91au3bteWdwljV79+7V4cOH/UKd3RhjNGjQIC1evFgrVqwoNPSjefPmCg4O1rJly9StWzdJ0tatW7V7924lJCRc1AvZ0t69e02DBg3MHXfcYfbu3WsOHDjgW/JlZmaa6Oho8/DDD5uffvrJvPvuu6ZKlSrmzTfftLDyy7dr1y7zww8/mPHjx5vQ0FDzww8/mB9++MEcPXrUGGPM//zP/5i//e1v5scffzTbtm0zM2fONFWqVDFjxoyxuPLLc779zsnJMU2aNDHt27c3GzZsMF9++aWJjIw0o0aNsrjy4rN69WrzyiuvmA0bNpgdO3aYBQsWmMjISNOrVy+rSytW7777rnE6nWbevHnm559/No899pgJDw/3m6lWFg0fPtysWLHCpKWlmf/93/817dq1MzVr1jQHDx60urRidfToUd/vryQzffp088MPP5hdu3YZY4yZMmWKCQ8PN5988onZuHGjueeee0xcXJw5efKkxZVfnnPt99GjR82IESNMamqqSUtLM//617/M9ddfbxo2bGhOnTpldemXbODAgcblcpkVK1b4fU+fOHHC12fAgAGmTp065ptvvjHr1q0zCQkJJiEh4aJex7ZhJiUlxUgqcjnTf/7zH3PLLbcYp9NprrjiCjNlyhSLKi4+vXv3LnK/ly9fbowx5osvvjDNmjUzoaGhpmrVqqZp06Zm9uzZJjc319rCL9P59tsYY3bu3Gk6duxoQkJCTM2aNc3w4cPN6dOnrSu6mK1fv960bNnSuFwuU7lyZXPVVVeZyZMn2/qP3dm8/vrrpk6dOqZSpUrmxhtvNGvWrLG6pIC7//77Ta1atUylSpXMFVdcYe6//36zfft2q8sqdsuXLy/yd7l3797GGO/07NGjR5vo6GjjdDrNHXfcYbZu3Wpt0cXgXPt94sQJ0759exMZGWmCg4NN3bp1Tf/+/W0f4M/2PZ2SkuLrc/LkSfPEE0+Y6tWrmypVqpiuXbv6HZi4EI7/ezEAAABbKnWzmQAAAC4GYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANgaYQYAANja/wfxqYlmCwRVpQAAAABJRU5ErkJggg==", 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", 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", 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kpJjOnTubJk2aGIfDYRISEszbb7/ttU5Zl2YbY8zJkyfNlClTTExMjAkKCjLR0dFmwoQJ5sSJE17t2rZta/r3719q/ZJLs9955x2v5SWfy1dffeW1vLzP8f/+7//MVVddZZo0aWKaNGliOnfubFJSUsyWLVu82s2ePdvExMQYu91uLrvsMrNq1SpzzTXXVPrS7JSUFLNgwQLTsWNHz/u/fPnyMtu7XC7TrFkz43A4zPHjx8+4/dOPe+XKlWbEiBGmWbNmJiQkxNx1113m4MGDXm1L3ruypsocD1BbbMacxahEAIDfFBUVqXXr1howYIDmzZvn73IAv2HMDABY1JIlS3TgwAENHTrU36UAfsWZGQCwmDVr1mj9+vV6+umn1bJlyzJvZgicSzgzAwAWM2fOHI0aNUoRERF67bXX/F0O4Hc+DTNpaWm6/PLL1bRpU0VERGjgwIGl7qVx4sQJpaSkqEWLFgoJCdGtt96qvLw8X5YFAJY2f/58FRUVad26deratau/ywH8zqdhZuXKlUpJSdHq1au1dOlSnTx5Un369NHRo0c9bR555BG9//77euedd7Ry5Urt3btXt9xyiy/LAgAA9Uitjpk5cOCAIiIitHLlSl199dUqKChQeHi43njjDc+9EjZv3qwLL7xQmZmZnof5AQAAlKdWb5pX8uC25s2bS5K+/vprnTx5Ur179/a06dy5s9q0aVNumHG5XF53DS15qm+LFi2qdet2AABQ+4wxOnz4sFq3bq2AgLPrKKq1MFNcXKyxY8fqyiuv9PTx5ubmKjg4WGFhYV5tIyMjy32iblpaWpl3wgQAANaze/dunX/++We1jVoLMykpKdqwYYP+/e9/n9V2JkyYoNTUVM98QUGB2rRpo927dys0NPRsywQAALXA6XQqOjpaTZs2Pett1UqYGT16tD744AOtWrXKK31FRUWpsLBQ+fn5Xmdn8vLyFBUVVea27Ha77HZ7qeWhoaGEGQAALKYmhoj49GomY4xGjx6txYsX64svvlBMTIzX6927d1dQUJCWLVvmWbZlyxbt2rWryk8HBgAA5yafnplJSUnRG2+8offee09Nmzb1jINxOBxq1KiRHA6H7r//fqWmpqp58+YKDQ3Vww8/rMTERK5kAgAAleLTS7PLO3WUnp6u5ORkSe6b5o0bN05vvvmmXC6XkpKSNHv27HK7mX7L6XTK4XCooKCAbiYAACyiJr+/Lf9sJsIMAADWU5Pf3zybCQAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWJpPw8yqVas0YMAAtW7dWjabTUuWLPF6PTk5WTabzWvq27evL0sCAAD1jE/DzNGjRxUXF6dZs2aV26Zv377at2+fZ3rzzTd9WRIAAKhnGvhy4/369VO/fv0qbGO32xUVFeXLMgAAQD3m9zEzK1asUEREhDp16qRRo0bp4MGDFbZ3uVxyOp1eEwAAOHf5Ncz07dtXr732mpYtW6bp06dr5cqV6tevn06dOlXuOmlpaXI4HJ4pOjq6FisGAAB1jc0YY2plRzabFi9erIEDB5bbZvv27Wrfvr0+//xzXX/99WW2cblccrlcnnmn06no6GgVFBQoNDS0pssGAAA+4HQ65XA4auT72+/dTKeLjY1Vy5YtlZ2dXW4bu92u0NBQrwkAAJy76lSY2bNnjw4ePKhWrVr5uxQAAGARPr2a6ciRI15nWXJycpSVlaXmzZurefPmmjJlim699VZFRUVp27Ztevzxx9WhQwclJSX5siwAAFCP+DTMrFu3Tr169fLMp6amSpKGDRumOXPmaP369crIyFB+fr5at26tPn366Omnn5bdbvdlWQAAoB6ptQHAvlKTA4gAAEDtqLcDgAEAAKqKMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACzNp2Fm1apVGjBggFq3bi2bzaYlS5Z4vW6M0cSJE9WqVSs1atRIvXv31tatW31ZEgAAqGd8GmaOHj2quLg4zZo1q8zXZ8yYoRdffFFz587VmjVr1KRJEyUlJenEiRO+LAsAANQjDXy58X79+qlfv35lvmaM0QsvvKAnnnhCN910kyTptddeU2RkpJYsWaI77rjDl6UBAIB6wm9jZnJycpSbm6vevXt7ljkcDiUkJCgzM7Pc9Vwul5xOp9cEAADOXX4LM7m5uZKkyMhIr+WRkZGe18qSlpYmh8PhmaKjo31aJwAAqNssdzXThAkTVFBQ4Jl2797t75IAAIAf+S3MREVFSZLy8vK8lufl5XleK4vdbldoaKjXBAAAzl1+CzMxMTGKiorSsmXLPMucTqfWrFmjxMREf5UFAAAsxqdXMx05ckTZ2dme+ZycHGVlZal58+Zq06aNxo4dq2eeeUYdO3ZUTEyMnnzySbVu3VoDBw70ZVkAAKAe8WmYWbdunXr16uWZT01NlSQNGzZM8+fP1+OPP66jR49qxIgRys/P11VXXaVPPvlEDRs29GVZAACgHrEZY4y/izgbTqdTDodDBQUFjJ8BAMAiavL723JXMwEAAJyOMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACzN72Fm8uTJstlsXlPnzp39XRYAALCIBv4uQJIuuugiff755575Bg3qRFkAAMAC6kRqaNCggaKiovxdBgAAsCC/dzNJ0tatW9W6dWvFxsbqrrvu0q5du8pt63K55HQ6vSYAAHDu8nuYSUhI0Pz58/XJJ59ozpw5ysnJUc+ePXX48OEy26elpcnhcHim6OjoWq4YAADUJTZjjPF3EafLz89X27Zt9fzzz+v+++8v9brL5ZLL5fLMO51ORUdHq6CgQKGhobVZKgAAqCan0ymHw1Ej3991YszM6cLCwnTBBRcoOzu7zNftdrvsdnstVwUAAOoqv3cz/daRI0e0bds2tWrVyt+lAAAAC/B7mHn00Ue1cuVK7dixQ19++aVuvvlmBQYGasiQIf4uDQAAWIDfu5n27NmjIUOG6ODBgwoPD9dVV12l1atXKzw83N+lAQAAC/B7mFm0aJG/SwAAABbm9zADAMC54PXXpSNHpPPPl847z/1ny5ZSgN8HfFgfYQYAgFoQHy8lJEjHj/+6LCjo12Dz2z9L/t6qlbsdykeYAQC4GeM+dZCfL/3yi3sq+XuDBtKQIVJgoL+rtKyLL5Zmz5buvffXZSdPSjt2uKfy2GxSZOSvASc+Xnr0UalJEx8XbCGEGQCorwoLpZUryw4nZf09P18qKiq9nSuvlBYtIsjUgFtukdLTpVWrKr+OMVJurvuMzsCB0pgxBJnfIswAQH0VHCxt2CClplZ/G489Jk2dSj9HFRkj7d4tZWVJ333n/jMrS9q+verbCgmRxo51f4zNmtVsnfUFYQYA6rNHHpEcDmn4cKm4uPLrhYVJGRnSjTf6rLT6orBQ+uEH79Dy3XfuE15no2FDafRo6fHHJe5WUjHCDADUd8nJ0vr10v/+b+XaX3659PbbUrt2vqzKkg4dKh1afvjBPfalpgQHSyNHShMmuAf/4swIMwBQX2Vnu8+uvPaatGtX5dZ5+GFp5kzpHH8GXnGxlJNTupto9+7qb7NFC/fg3bg4aeFCKS/P+/XAQOm++6QnnpDatKn+fs5FhBkAqE8KCtxnVTIypP/8p/LrNW0qzZsnDRrku9rqqOPH3UOLTg8t69dLhw9Xf5sdOriDS8kUF+e+zNpmc5/deeGFX9sGBEh33y1NnCi1b382R3LuIswAgNWdOiV9/rk7wCxeLJ04UbX14+Kkd96ROnb0TX11SF5e6W6izZurNpzodA0bSt26/RpY4uPdl2A3bVr+Op999uv+br9dmjxZuvDC6u0fboQZALCqTZvcAeb116W9e8tvFxAg9ekjDRsmpaS4Tw2UGDHCfZqgUSOfl1ubTp2Stm4t3U2Um1v9bUZFeYeW+Hh3/qvqFesff+weV/3UU+5t4ewRZgDASg4dct/zJSNDWru24rZdurgDzN13S61buy+7GTLE/VrjxtLLL7tfs7gjR6Tvv/81sGRluedPv9NuVQQESJ06le4mioysmXqffNLdDYWaQ5gBgLru5Enp00/dAeaf/3SHkvI0b+4OLMnJUvfu7kEaJUrO3nTp4u5W6tLFp2XXNGPch3B6F1FWlnucszHV22ZIyK9nWkr+7NrVtyeqCDI1jzADAHXV+vXuALNggbR/f/ntAgOl3//efRbmhhvKvxLpp5+koUPd99Sv47eQPXnSPZblt91EBw9Wf5vR0d5dRPHxUkwMD3qsDwgzAFCXHDggvfGGO8R8+23FbePi3AHmzjsr1wdy6aXS/PneZ2vqgPx8d247/YzLhg0Vn4CqSIMG7pNOp3cRxcW5L41G/USYAQB/KyyUPvzQHWA+/LDs5yOVCA+X7rrLHWLi46u2Hz8P8jVG2rmzdDdRRQ9ZPJOwMO/QEh/vvjLoHL9NzjmHMAMA/mCM9M037gDzxhsV958EBUkDBrjHwfTta4nnJLlc0saN3qHlu+/ct8GprtjY0lcTRUfXuRNN8APCDADUpn373Ld/zchw96VU5LLL3AHmjjvqdB/Jzz+XvnfLpk0Vn2CqiN3uHoR7+hmXbt3cj5gCykKYAQBfO3HCfRVSRob0yScV36GtVSv35dLDhkkXXVR7NVZCcbG0bVvpbqKffqr+NsPDS3cTderkHvcCVBY/LgDgC8ZIa9a4A8yiRe5RruWx26WBA91nYXr3rhPf5MeOue/V8ttb/B89Wr3t2WzSBRd4dxHFxbmzG91EOFv+/40BgPpkzx73HXkzMqQtWypum5joPgMzeLB7JKsfGOO+K+5vu4l+/LH6t/hv3PjXW/yXhJaLL67zV4PDwggzAHC2jh1zPxMpI8P9jKSK7uB2/vnue70MHeruT6lFRUXukPLbbqKKbmFzJq1bl+4mat++6rf4B84GYQYAqsMY6d//dgeYt9+u+BHLjRpJt97qPgvTq1etfNM7ne5uodPPuGzYUPVnUJYIDHRf8vzbbqLw8JqrGaguwgwAVMWOHdJrr7mnbdsqbnv11e4Ac9ttUmioT8oxRtq9u3Q30ZlKq0hoaOnQctFF7idEA3URYQYAzuTIEendd91nYVasqLhtTMyv3UixsTVaRmGh+5Ln397i/5dfqr/Ntm1L37ulXTsG5cJaCDMAUJbiYndwyciQ/u//Kr6MJyREGjTIfRamZ88aedjPoUPuwHJ6aPnhB/czi6ojKMh975bTQ0u3blKzZmddKuB3hBkAOF12tjvAvPaatGtX+e1sNum669wB5pZbqn2pTnGxlJNTupuool2fSfPm3g9TjIuTOneWgoOrv02gLiPMAEBBgXsQb0aG9J//VNy2Qwf3/WDuuUdq06ZKuzl+vOxb/Fc0dvhMOnQo3U103nl0E+HcQpgBcG46dcp9GXVGhvuy6oou83E43PeCGTbMfW+YSiSF/ftLj23ZssW92+po2NDdLXR6aLn4Yqlp0+ptD6hPCDMAzi2bNrkDzOuvS3v3lt8uIEDq08cdYG66qdwnTp865e6Z+u29W/btq36JUVGlrybq2LFO3BgYqJP41QBQ/x065H6kQEaGtHZtxW27dHEHmLvvdt8R7jRHjrhv8X96aPn+e/c986ojIMB937zTu4ni4txhBkDlEWYA1E9FRe6HOmZkuB/yWFhYfttmzaQ773SHmMsuk5FNe/dKWR96dxNlZ1d8c9+KhIS4g8rpZ1wuush9638AZ4cwA6B+Wb/eHWAWLpTy8spvFxgo/f73OnlXsrZ0vEFZPwQr6y0pa4I7wPz8c/VLOP/80lcTxcbWyBXbAMpAmAFgfQcOSG+84Q4x335bbrMCheq72FuU1W2ovmv0O2VtaaQNQys+aVORBg3cvVK/7SZq0aJ62wNQPYQZANZUWCh9+KE7wHz4obtb6b+MpJ1qq+8UpyzFKys4QVkNE7TD2ULaLvdURWFhpS+BvvBCyW6vkaMBcBYIMwCswxjpm2/cAeaNN6SDB+VSsH5QV3doUbwnwBQo7Nf1Cv87VVJMTOluojZtuHcLUFfViTAza9YszZw5U7m5uYqLi9NLL72kK664wt9lAagr9u2TFi7Uz/Pe03ebg/8bWJ5XluK1SReqSEHV2qzd7r7F/+lnXLp1c99WBoB1+D3MvPXWW0pNTdXcuXOVkJCgF154QUlJSdqyZYsiIiL8XR4AP9q+MFN/n7RTWdua6jsN1h49Wu1ttWzpfbYlPt59WTT3bgGsz2ZMdS80rBkJCQm6/PLL9be//U2SVFxcrOjoaD388MMaP358qfYul0sul8sz73Q6FR0drYKCAoWGhtZa3QB8b+2flighbWCV1rHZ3DeYO72LKD5eatWKbiKgLnE6nXI4HDXy/e3X/5MUFhbq66+/1oQJEzzLAgIC1Lt3b2VmZpa5TlpamqZMmVJbJQLwo64P91JA2ikVK7DM1xs3dncLnR5aLr642s98BGBRfg0zP//8s06dOqXIyEiv5ZGRkdq8eXOZ60yYMEGpqame+ZIzMwDqn8atHLog9Cdtdp6n1iFOxXcPUFyPEM9Zl/bt3beLAXBus1xvsd1ul51rIYFzxtsfhiiy/SlFtKIbGUDZ/BpmWrZsqcDAQOX95i6deXl5iuLhJAAkXXwVlxYBqJhfb64dHBys7t27a9myZZ5lxcXFWrZsmRITE/1YGQAAsAq/dzOlpqZq2LBhuuyyy3TFFVfohRde0NGjR3Xvvff6uzQAAGABfg8zgwcP1oEDBzRx4kTl5uYqPj5en3zySalBwQAAAGXx+31mzlZNXqcOAABqR01+f/NAegAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGl+DTPt2rWTzWbzmqZNm+bPkgAAgMU08HcBTz31lIYPH+6Zb9q0qR+rAQAAVuP3MNO0aVNFRUX5uwwAAGBRfh8zM23aNLVo0UKXXHKJZs6cqaKiogrbu1wuOZ1OrwkAAJy7/HpmZsyYMbr00kvVvHlzffnll5owYYL27dun559/vtx10tLSNGXKlFqsEgAA1GU2Y4ypyQ2OHz9e06dPr7DNpk2b1Llz51LL//73v2vkyJE6cuSI7HZ7meu6XC65XC7PvNPpVHR0tAoKChQaGnp2xQMAgFrhdDrlcDhq5Pu7xsPMgQMHdPDgwQrbxMbGKjg4uNTyjRs3qmvXrtq8ebM6depUqf3V5JsBAABqR01+f9d4N1N4eLjCw8OrtW5WVpYCAgIUERFRw1UBAID6ym9jZjIzM7VmzRr16tVLTZs2VWZmph555BHdfffdatasmb/KAgAAFuO3MGO327Vo0SJNnjxZLpdLMTExeuSRR5SamuqvkgAAgAX5LcxceumlWr16tb92DwAA6gm/32cGAADgbBBmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApfkszEydOlU9evRQ48aNFRYWVmabXbt2qX///mrcuLEiIiL02GOPqaioyFclAQCAeqiBrzZcWFioQYMGKTExUfPmzSv1+qlTp9S/f39FRUXpyy+/1L59+zR06FAFBQXp2Wef9VVZAACgnrEZY4wvdzB//nyNHTtW+fn5Xss//vhj3XDDDdq7d68iIyMlSXPnztUf//hHHThwQMHBwZXavtPplMPhUEFBgUJDQ2u6fAAA4AM1+f3ttzEzmZmZuvjiiz1BRpKSkpLkdDq1cePGctdzuVxyOp1eEwAAOHf5Lczk5uZ6BRlJnvnc3Nxy10tLS5PD4fBM0dHRPq0TAADUbVUKM+PHj5fNZqtw2rx5s69qlSRNmDBBBQUFnmn37t0+3R8AAKjbqjQAeNy4cUpOTq6wTWxsbKW2FRUVpbVr13oty8vL87xWHrvdLrvdXql9AACA+q9KYSY8PFzh4eE1suPExERNnTpV+/fvV0REhCRp6dKlCg0NVZcuXWpkHwAAoP7z2aXZu3bt0qFDh7Rr1y6dOnVKWVlZkqQOHTooJCREffr0UZcuXXTPPfdoxowZys3N1RNPPKGUlBTOvAAAgErz2aXZycnJysjIKLV8+fLluvbaayVJO3fu1KhRo7RixQo1adJEw4YN07Rp09SgQeUzFpdmAwBgPTX5/e3z+8z4GmEGAADrqRf3mQEAAKgJhBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBpPgszU6dOVY8ePdS4cWOFhYWV2cZms5WaFi1a5KuSAABAPdTAVxsuLCzUoEGDlJiYqHnz5pXbLj09XX379vXMlxd8AAAAyuKzMDNlyhRJ0vz58ytsFxYWpqioKF+VAQAA6jm/j5lJSUlRy5YtdcUVV+jvf/+7jDEVtne5XHI6nV4TAAA4d/nszExlPPXUU7ruuuvUuHFjffbZZ3rooYd05MgRjRkzptx10tLSPGd9AAAAbOZMp0JOM378eE2fPr3CNps2bVLnzp098/Pnz9fYsWOVn59/xu1PnDhR6enp2r17d7ltXC6XXC6XZ97pdCo6OloFBQUKDQ0980EAAAC/czqdcjgcNfL9XaUzM+PGjVNycnKFbWJjY6tdTEJCgp5++mm5XC7Z7fYy29jt9nJfAwAA554qhZnw8HCFh4f7qhZlZWWpWbNmhBUAAFBpPhszs2vXLh06dEi7du3SqVOnlJWVJUnq0KGDQkJC9P777ysvL0+/+93v1LBhQy1dulTPPvusHn30UV+VBAAA6iGfhZmJEycqIyPDM3/JJZdIkpYvX65rr71WQUFBmjVrlh555BEZY9ShQwc9//zzGj58uK9KAgAA9VCVBgDXRTU5gAgAANSOmvz+9vt9ZgAAAM4GYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFiaz8LMjh07dP/99ysmJkaNGjVS+/btNWnSJBUWFnq1W79+vXr27KmGDRsqOjpaM2bM8FVJAACgHmrgqw1v3rxZxcXFevnll9WhQwdt2LBBw4cP19GjR/Xcc89JkpxOp/r06aPevXtr7ty5+v7773XfffcpLCxMI0aM8FVpAACgHrEZY0xt7WzmzJmaM2eOtm/fLkmaM2eO/vznPys3N1fBwcGSpPHjx2vJkiXavHlzpbbpdDrlcDhUUFCg0NBQn9UOAABqTk1+f/vszExZCgoK1Lx5c898Zmamrr76ak+QkaSkpCRNnz5dv/zyi5o1a1ZqGy6XSy6Xy2ubkvtNAQAA1lDyvV0T51RqLcxkZ2frpZde8nQxSVJubq5iYmK82kVGRnpeKyvMpKWlacqUKaWWR0dH13DFAADA1w4ePCiHw3FW26hymBk/frymT59eYZtNmzapc+fOnvmffvpJffv21aBBgzR8+PCqV3maCRMmKDU11TOfn5+vtm3bateuXWf9ZliJ0+lUdHS0du/efU51r3HcHPe5gOPmuM8FBQUFatOmjVePTXVVOcyMGzdOycnJFbaJjY31/H3v3r3q1auXevTooVdeecWrXVRUlPLy8ryWlcxHRUWVuW273S673V5qucPhOKd+CEqEhoZy3OcQjvvcwnGfW87V4w4IOPsLq6scZsLDwxUeHl6ptj/99JN69eql7t27Kz09vVTBiYmJ+vOf/6yTJ08qKChIkrR06VJ16tSpzC4mAACA3/LZfWZ++uknXXvttWrTpo2ee+45HThwQLm5ucrNzfW0ufPOOxUcHKz7779fGzdu1FtvvaX//d//9epGAgAAqIjPBgAvXbpU2dnZys7O1vnnn+/1WsnIZYfDoc8++0wpKSnq3r27WrZsqYkTJ1bpHjN2u12TJk0qs+upPuO4Oe5zAcfNcZ8LOO6zP+5avc8MAABATePZTAAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIsG2Z27Nih+++/XzExMWrUqJHat2+vSZMmqbCw0Kvd+vXr1bNnTzVs2FDR0dGaMWOGnyquOVOnTlWPHj3UuHFjhYWFldnGZrOVmhYtWlS7hdawyhz3rl271L9/fzVu3FgRERF67LHHVFRUVLuF+li7du1KfbbTpk3zd1k1btasWWrXrp0aNmyohIQErV271t8l+dzkyZNLfbanPxqmvli1apUGDBig1q1by2azacmSJV6vG2M0ceJEtWrVSo0aNVLv3r21detW/xRbg8503MnJyaU+/759+/qn2BqSlpamyy+/XE2bNlVERIQGDhyoLVu2eLU5ceKEUlJS1KJFC4WEhOjWW28t9XSAM7FsmNm8ebOKi4v18ssva+PGjfrrX/+quXPn6k9/+pOnjdPpVJ8+fdS2bVt9/fXXmjlzpiZPnlzqsQpWU1hYqEGDBmnUqFEVtktPT9e+ffs808CBA2unQB8503GfOnVK/fv3V2Fhob788ktlZGRo/vz5mjhxYi1X6ntPPfWU12f78MMP+7ukGvXWW28pNTVVkyZN0jfffKO4uDglJSVp//79/i7N5y666CKvz/bf//63v0uqcUePHlVcXJxmzZpV5uszZszQiy++qLlz52rNmjVq0qSJkpKSdOLEiVqutGad6bglqW/fvl6f/5tvvlmLFda8lStXKiUlRatXr9bSpUt18uRJ9enTR0ePHvW0eeSRR/T+++/rnXfe0cqVK7V3717dcsstVduRqUdmzJhhYmJiPPOzZ882zZo1My6Xy7Psj3/8o+nUqZM/yqtx6enpxuFwlPmaJLN48eJarae2lHfcH330kQkICDC5ubmeZXPmzDGhoaFePwNW17ZtW/PXv/7V32X41BVXXGFSUlI886dOnTKtW7c2aWlpfqzK9yZNmmTi4uL8XUat+u2/VcXFxSYqKsrMnDnTsyw/P9/Y7Xbz5ptv+qFC3yjr3+hhw4aZm266yS/11Jb9+/cbSWblypXGGPdnGxQUZN555x1Pm02bNhlJJjMzs9LbteyZmbIUFBR4PX0zMzNTV199tYKDgz3LkpKStGXLFv3yyy/+KLFWpaSkqGXLlrriiiv097//3XPn5foqMzNTF198sSIjIz3LkpKS5HQ6tXHjRj9WVvOmTZumFi1a6JJLLtHMmTPrVVdaYWGhvv76a/Xu3duzLCAgQL1791ZmZqYfK6sdW7duVevWrRUbG6u77rpLu3bt8ndJtSonJ0e5ublen7/D4VBCQsI58fmvWLFCERER6tSpk0aNGqWDBw/6u6QaVVBQIEme7+qvv/5aJ0+e9Pq8O3furDZt2lTp8/bZ4wxqW3Z2tl566SU999xznmW5ubmKiYnxalfyRZebm1uvH2b51FNP6brrrlPjxo312Wef6aGHHtKRI0c0ZswYf5fmM7m5uV5BRvL+vOuLMWPG6NJLL1Xz5s315ZdfasKECdq3b5+ef/55f5dWI37++WedOnWqzM9y8+bNfqqqdiQkJGj+/Pnq1KmT9u3bpylTpqhnz57asGGDmjZt6u/yakXJ72pZn399+j0uS9++fXXLLbcoJiZG27Zt05/+9Cf169dPmZmZCgwM9Hd5Z624uFhjx47VlVdeqa5du0pyf97BwcGlxkFW9fOuc2dmxo8fX+bg1dOn3/6D9tNPP6lv374aNGiQhg8f7qfKz051jrsiTz75pK688kpdcskl+uMf/6jHH39cM2fO9OERVE9NH7dVVeV9SE1N1bXXXqtu3brpwQcf1F/+8he99NJLcrlcfj4KnK1+/fpp0KBB6tatm5KSkvTRRx8pPz9fb7/9tr9LQy244447dOONN+riiy/WwIED9cEHH+irr77SihUr/F1ajUhJSdGGDRt8cjFKnTszM27cOCUnJ1fYJjY21vP3vXv3qlevXurRo0epgb1RUVGlRkSXzEdFRdVMwTWkqsddVQkJCXr66aflcrnq1MPMavK4o6KiSl3xUlc/7986m/chISFBRUVF2rFjhzp16uSD6mpXy5YtFRgYWObvbl3/HGtaWFiYLrjgAmVnZ/u7lFpT8hnn5eWpVatWnuV5eXmKj4/3U1X+ERsbq5YtWyo7O1vXX3+9v8s5K6NHj9YHH3ygVatWeT18OioqSoWFhcrPz/c6O1PV3/c6F2bCw8MVHh5eqbY//fSTevXqpe7duys9PV0BAd4nmhITE/XnP/9ZJ0+eVFBQkCT307w7depU57qYqnLc1ZGVlaVmzZrVqSAj1exxJyYmaurUqdq/f78iIiIkuT/v0NBQdenSpUb24Stn8z5kZWUpICDAc8xWFxwcrO7du2vZsmWeK/CKi4u1bNkyjR492r/F1bIjR45o27Ztuueee/xdSq2JiYlRVFSUli1b5gkvTqdTa9asOeMVnPXNnj17dPDgQa9QZzXGGD388MNavHixVqxYUWroR/fu3RUUFKRly5bp1ltvlSRt2bJFu3btUmJiYpV2ZEl79uwxHTp0MNdff73Zs2eP2bdvn2cqkZ+fbyIjI80999xjNmzYYBYtWmQaN25sXn75ZT9WfvZ27txpvv32WzNlyhQTEhJivv32W/Ptt9+aw4cPG2OM+ec//2n+3//7f+b77783W7duNbNnzzaNGzc2EydO9HPlZ+dMx11UVGS6du1q+vTpY7Kysswnn3xiwsPDzYQJE/xcec358ssvzV//+leTlZVltm3bZhYsWGDCw8PN0KFD/V1ajVq0aJGx2+1m/vz55ocffjAjRowwYWFhXleq1Ufjxo0zK1asMDk5OeY///mP6d27t2nZsqXZv3+/v0urUYcPH/b8/koyzz//vPn222/Nzp07jTHGTJs2zYSFhZn33nvPrF+/3tx0000mJibGHD9+3M+Vn52Kjvvw4cPm0UcfNZmZmSYnJ8d8/vnn5tJLLzUdO3Y0J06c8Hfp1TZq1CjjcDjMihUrvL6njx075mnz4IMPmjZt2pgvvvjCrFu3ziQmJprExMQq7ceyYSY9Pd1IKnM63XfffWeuuuoqY7fbzXnnnWemTZvmp4przrBhw8o87uXLlxtjjPn4449NfHy8CQkJMU2aNDFxcXFm7ty55tSpU/4t/Cyd6biNMWbHjh2mX79+plGjRqZly5Zm3Lhx5uTJk/4ruoZ9/fXXJiEhwTgcDtOwYUNz4YUXmmeffdbS/9iV56WXXjJt2rQxwcHB5oorrjCrV6/2d0k+N3jwYNOqVSsTHBxszjvvPDN48GCTnZ3t77Jq3PLly8v8XR42bJgxxn159pNPPmkiIyON3W43119/vdmyZYt/i64BFR33sWPHTJ8+fUx4eLgJCgoybdu2NcOHD7d8gC/vezo9Pd3T5vjx4+ahhx4yzZo1M40bNzY333yz14mJyrD9d2cAAACWVOeuZgIAAKgKwgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALC0/w91C6rqr6hv4wAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", "text/plain": [ "
" ] @@ -977,7 +1187,7 @@ "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "k = 5\n", + "k = 2\n", "\n", "a1 = np.array([ [0,1], [1,0] ]) # refliction in line y = x\n", "a2 = np.array([ [1,0], [0,-1] ]) # reflection in y-axis\n", @@ -985,6 +1195,7 @@ "\n", "b1 = np.array([ [k,0], [0,1] ]) # enlarges x in first array by k\n", "b2 = np.array([ [1,0], [0,k] ]) # enlarges y in second array by k\n", + "b3 = np.array([ [k, 0], [0, k] ])\n", "\n", "c1 = np.array([ [1,k], [0,1] ]) # second vector moves to the right (along x-axis) k times\n", "c2 = np.array([ [1,0], [k,1] ]) # first vector moves up (along y-axis) k times\n", @@ -996,21 +1207,63 @@ "rand1 = np.random.randint(0,9, size=(2,1))\n", "rand2 = np.random.randint(0,9, size=(2,1))\n", "\n", + "# THIS IS THE ORIGNIAL CODE FROM THE ASSIGNMENT\n", + "# quiver = plt.quiver([0, 0], # Sets the x-cordinate of the start of the vectors\n", + "# [0, 0], # Sets the y-cordinate of the start of the vectors\n", + "# [1, -1], # Sets the x-cordinate of the end of the vectors\n", + "# [1, -1], # Sets the y-cordinate of the end of the vectors\n", + "# angles='xy', \n", + "# scale_units='xy', \n", + "# scale=1,\n", + "# color=['r','b'])\n", + "# plt.xlim(-2, 2)\n", + "# plt.ylim(-2, 2)\n", + "# plt.quiverkey(quiver, 1, 1, 1, 'orginal vector', coordinates='data')\n", + "# plt.quiverkey(quiver, -1, -1.5, 1, 'transformed vector', coordinates='data')\n", + "# plt.title('Description of Transformation')\n", + "# plt.show()\n", + "\n", + "# quiver = plt.quiver([0, 0], # Sets the x-cordinate of the start of the vectors\n", + "# [0, 0], # Sets the y-cordinate of the start of the vectors\n", + "# [rand1[0, 0], a1[0, 0]], \n", + "# [rand1[1, 0], a1[1, 0]], \n", + "# angles='xy', \n", + "# scale_units='xy', \n", + "# scale=1,\n", + "# color=['r','b'])\n", + "# plt.xlim(-10, 10)\n", + "# plt.ylim(-10, 10)\n", + "# plt.quiverkey(quiver, 1, 1, 1, 'orginal vector', coordinates='data')\n", + "# plt.quiverkey(quiver, -1, -1.5, 1, 'transformed vector', coordinates='data')\n", + "# plt.title('Transforming a1 by rand1')\n", + "# plt.show()\n", "\n", - "quiver = plt.quiver([0, 0], # Sets the x-cordinate of the start of the vectors\n", - " [0, 0], # Sets the y-cordinate of the start of the vectors\n", - " [1, -1], # Sets the x-cordinate of the end of the vectors\n", - " [1, -1], # Sets the y-cordinate of the end of the vectors\n", - " angles='xy', \n", - " scale_units='xy', \n", - " scale=1,\n", - " color=['r','b'])\n", - "plt.xlim(-2, 2)\n", - "plt.ylim(-2, 2)\n", - "plt.quiverkey(quiver, 1, 1, 1, 'orginal vector', coordinates='data')\n", - "plt.quiverkey(quiver, -1, -1.5, 1, 'transformed vector', coordinates='data')\n", - "plt.title('Description of Transformation')\n", - "plt.show()" + "matrices = [a1, a2, a3, b1, b2, b3, c1, c2, d1, d2, d3]\n", + "matrices_strings = [\"a1\", \"a2\", \"a3\", \"b1\", \"b2\", \"b3\", \"c1\", \"c2\", \"d1\", \"d2\", \"d3\"]\n", + "randoms = [rand1, rand2]\n", + "\n", + "for i in range(len(matrices)):\n", + " for j in range(len(randoms)):\n", + " curr_matrix = matrices[i]\n", + " curr_rand = randoms[j]\n", + " transformed = curr_matrix @ curr_rand\n", + " quiver = plt.quiver(\n", + " [0, 0], [0, 0], \n", + " [curr_rand[0, 0], transformed[0, 0]], \n", + " [curr_rand[1, 0], transformed[1, 0]], \n", + " angles=\"xy\", scale_units=\"xy\", scale=1,\n", + " color=[\"r\", \"b\"]\n", + " )\n", + "\n", + " plt.xlim(-20, 20)\n", + " plt.ylim(-20, 20)\n", + "\n", + " plt.quiverkey(quiver, 6, 9, 1, \"\", coordinates=\"data\")\n", + " plt.quiverkey(quiver, 6, 8, 1, \"\", coordinates=\"data\")\n", + "\n", + " plt.title(f\"rand{j+1} transformed by {matrices_strings[i]}\")\n", + " plt.show()\n", + "\n" ] }, { From c94af8fefc253d28b12b4b14ae964d454e345b87 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Fri, 20 Feb 2026 23:16:20 -0800 Subject: [PATCH 53/62] finished problem 7 --- week_7_data_cleaning_matrix.ipynb | 178 ++++++++++++++++++++++-------- 1 file changed, 131 insertions(+), 47 deletions(-) diff --git a/week_7_data_cleaning_matrix.ipynb b/week_7_data_cleaning_matrix.ipynb index 3685895..1156738 100644 --- a/week_7_data_cleaning_matrix.ipynb +++ b/week_7_data_cleaning_matrix.ipynb @@ -960,12 +960,12 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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ceOONJjAw0AQGBpoGDRqY+Ph4s23bNpd+s2fPNtHR0cZut5vWrVub1atXm5tvvrnIl2bHx8eb9957z9StW9f5/q9YsaLQ/tnZ2aZSpUrG4XCYU6dOXXL7xuRfmj1y5EhTvXp14+/vb2644QaTlJRUaI2DBg264M/e5dwiAHA3mzGlcFQiAOCScnNzVaNGDfXo0aPAGBigPGHMDABY1KJFi3T48GENHDjQ06UAHsWRGQCwmLVr12rTpk2aNGmSqlatys3uUO5xZAYALObVV1/VQw89pLCwML3zzjueLgfwOLeGmYSEBLVp00YVK1ZUWFiYevXqVeBeGqdPn1Z8fLyqVKmioKAg9enTR4cOHXJnWQBgafPmzVNubq7Wr1+vxo0be7ocwOPcGmZWrVql+Ph4rVmzRkuXLtWZM2fUpUsXnThxwtnn8ccf1xdffKGPP/5Yq1at0oEDB3THHXe4sywAAFCGlOiYmcOHDyssLEyrVq3STTfdpMzMTIWGhur999933ith69atatiwoZKSkpwP8wMAALiQEr1p3rmHmFWuXFmStGHDBp05c0adO3d29mnQoIEiIyMvGGays7Nd7hp67qm+VapUuaJbtwMAgJJnjNGxY8dUo0YN55Pcr1SJhZm8vDwNHz5cN9xwg/Mcb1pamnx9fRUSEuLSt1q1ahd8om5CQkKhd8IEAADWs3fvXl177bVXtY0SCzPx8fH6+eef9Z///OeqtjN69GiNGDHCOZ+ZmanIyEjt3btXwcHBV1smAAAoAVlZWYqIiFDFihWvelslEmYeeeQRffnll1q9erVL+goPD1dOTo4yMjJcjs4cOnRI4eHhhW7LbrfLbrcXaA8ODibMAABgMcUxRMStVzMZY/TII49o4cKFWr58uaKjo12Wt2rVSj4+Plq2bJmzbdu2bdqzZ49iYmLcWRoAACgj3HpkJj4+Xu+//74+//xzVaxY0TkOxuFwyN/fXw6HQ/fff79GjBihypUrKzg4WI8++qhiYmK4kgkAABSJWy/NvtCho8TERMXFxUnKv2neyJEj9cEHHyg7O1uxsbGaPXv2BU8z/VFWVpYcDocyMzM5zQQAgEUU5/e35Z/NRJgBAM8yxig3N1dnz571dCkoRby9vVWhQoULHtgozu/vEr3PDACgbMnJydHBgwd18uRJT5eCUiggIEDVq1eXr6+vW1+HMAMAuCJ5eXlKTU2Vt7e3atSoIV9fX25eCkn5R+tycnJ0+PBhpaamqm7duld9Y7yLIcwAAK5ITk6O8vLyFBERoYCAAE+Xg1LG399fPj4+2r17t3JycuTn5+e213LrpdkAgLLPnf/jhrWV1M8GP4EAAMDSCDMAAMDSCDMAgGJns5XsVNJ27dolm82m5OTkIq8zb968Ag9W9kQdZRFhBgBQbu3du1eDBw92Xo0VFRWlxx57TEeOHLnoehERETp48KAaN25c5Nfq37+/fv3116stGYUgzAAAyqWdO3eqdevW2r59uz744AOlpKRozpw5WrZsmWJiYnT06NFC18vJyZG3t7fCw8NVoULRLwr29/dXWFhYcZWP8xBmAADlUnx8vHx9fbVkyRLdfPPNioyMVLdu3fTtt99q//79evbZZyVJNWvW1KRJkzRw4EAFBwfrwQcfLPT0zj//+U/VrVtXfn5+6tSpk95++23ZbDZlZGRIKniaafz48WrevLneffdd1axZUw6HQ3fddZeOHTvm7PP111/rxhtvVEhIiKpUqaLu3btrx44dJfH2WAphBgBQ7hw9elTffPONHn74Yfn7+7ssCw8P1z333KMFCxbo3BN/XnjhBTVr1kwbN27UmDFjCmwvNTVVd955p3r16qUff/xRQ4cOdYahi9mxY4cWLVqkL7/8Ul9++aVWrVqladOmOZefOHFCI0aM0Pr167Vs2TJ5eXmpd+/eysvLu8p3oGzhpnkAgHJn+/btMsaoYcOGhS5v2LChfv/9dx0+fFiSdMstt2jkyJHO5bt27XLpP3fuXNWvX18zZsyQJNWvX18///yzpkyZctE68vLyNG/ePFWsWFGSdO+992rZsmXO9fr06ePS/6233lJoaKh++eWXyxqvU9ZxZAYAUG4V9VnLrVu3vujybdu2qU2bNi5tbdu2veR2a9as6QwyklS9enWlp6c757dv364BAwaoVq1aCg4OVs2aNSVJe/bsKVLd5QVhBgBQ7tSpU0c2m01btmwpdPmWLVtUqVIlhYaGSpICAwPdUoePj4/LvM1mczmF1KNHDx09elSvv/661q5dq7Vr10rKH4SM/yHMAADKnSpVquhPf/qTZs+erVOnTrksS0tL0/z589W/f/8iPzizfv36Wr9+vUvbDz/8cFU1HjlyRNu2bdNzzz2nW2+91XnqCwURZgAA5dI//vEPZWdnKzY2VqtXr9bevXv19ddf609/+pOuueaaS453Od/QoUO1detWPf300/r111/10Ucfad68eZJ0xU8Sr1SpkqpUqaLXXntNKSkpWr58uUaMGHFF2yrrCDMAgGJnTMlOV6Ju3bpav369atWqpX79+ql27dp68MEH1alTJyUlJaly5cpF3lZ0dLQ++eQTffbZZ2ratKleffVV59VMdrv9iurz8vLShx9+qA0bNqhx48Z6/PHHnQOM4cpmijr6qZTKysqSw+FQZmamgoODPV0OAJQbp0+fVmpqqqKjo+Xn5+fpckqdKVOmaM6cOdq7d6+nS/GYi/2MFOf3N5dmAwBQDGbPnq02bdqoSpUq+u677zRjxgw98sgjni6rXCDMAABQDLZv367Jkyfr6NGjioyM1MiRIzV69GhPl1UuEGYAACgGs2bN0qxZszxdRrnEAGAAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAAGBphBkAQPGz2Up2KgPS0tL0pz/9SYGBgQoJCfF0OUVms9m0aNEij9ZAmAEAlCs2m+2i0/jx4z1S16xZs3Tw4EElJyfr119/9UgNVsVN8wAA5crBgwed/16wYIHGjh2rbdu2OduCgoKc/zbG6OzZs6pQwf1flzt27FCrVq1Ut27dK95GTk6OfH19i7Eqa+DIDACgXAkPD3dODodDNpvNOb9161ZVrFhRixcvVqtWrWS32/Wf//xHO3bsUM+ePVWtWjUFBQWpTZs2+vbbb122W7NmTU2dOlWDBw9WxYoVFRkZqddee825PCcnR4888oiqV68uPz8/RUVFKSEhwbnup59+qnfeeUc2m01xcXGSpD179qhnz54KCgpScHCw+vXrp0OHDjm3OX78eDVv3lxvvPGGy8McbTab5s6dq+7duysgIEANGzZUUlKSUlJS1LFjRwUGBqp9+/basWOHyz58/vnnatmypfz8/FSrVi1NmDBBubm5zuXbt2/XTTfdJD8/PzVq1EhLly4t1s/mShFmAAD4g1GjRmnatGnasmWLmjZtquPHj+vPf/6zli1bpo0bN6pr167q0aOH9uzZ47Le3/72N7Vu3VobN27Uww8/rIceesh51Oell17SP//5T3300Ufatm2b5s+fr5o1a0qSfvjhB3Xt2lX9+vXTwYMH9fe//115eXnq2bOnjh49qlWrVmnp0qXauXOn+vfv7/KaKSkp+vTTT/XZZ58pOTnZ2T5p0iQNHDhQycnJatCgge6++24NHTpUo0eP1vr162WMcXkQ5r///W8NHDhQjz32mH755RfNnTtX8+bN05QpUyRJeXl5uuOOO+Tr66u1a9dqzpw5evrpp93w7l8BY3GZmZlGksnMzPR0KQBQrpw6dcr88ssv5tSpUwUXSiU7XaHExETjcDic8ytWrDCSzKJFiy657nXXXWdefvll53xUVJT5y1/+4pzPy8szYWFh5tVXXzXGGPPoo4+aW265xeTl5RW6vZ49e5pBgwY555csWWK8vb3Nnj17nG2bN282ksy6deuMMcaMGzfO+Pj4mPT0dJdtSTLPPfeccz4pKclIMm+++aaz7YMPPjB+fn7O+VtvvdVMnTrVZTvvvvuuqV69ujHGmG+++cZUqFDB7N+/37l88eLFRpJZuHBhoft0sZ+R4vz+5sgMAAB/0Lp1a5f548eP64knnlDDhg0VEhKioKAgbdmypcCRmaZNmzr/fe70VXp6uiQpLi5OycnJql+/voYNG6YlS5ZctIYtW7YoIiJCERERzrZGjRopJCREW7ZscbZFRUUpNDS0wPrn11KtWjVJUpMmTVzaTp8+raysLEnSjz/+qIkTJyooKMg5DRkyRAcPHtTJkyed9dSoUcO5jZiYmIvuQ0lhADAAAH8QGBjoMv/EE09o6dKleuGFF1SnTh35+/vrzjvvVE5Ojks/Hx8fl3mbzaa8vDxJUsuWLZWamqrFixfr22+/Vb9+/dS5c2d98sknxVprYbXY/v/y9cLaztV3/PhxTZgwQXfccUeBbZ0bi1NaufXIzOrVq9WjRw/VqFGj0OvQ4+LiClwS17VrV3eWBADAZfvuu+8UFxen3r17q0mTJgoPD9euXbsuezvBwcHq37+/Xn/9dS1YsECffvqpjh49Wmjfhg0bau/evdq7d6+z7ZdfflFGRoYaNWp0pbtyQS1bttS2bdtUp06dApOXl5eznvOvBluzZk2x13El3Hpk5sSJE2rWrJkGDx5caNKTpK5duyoxMdE5b7fb3VkSAACXrW7duvrss8/Uo0cP2Ww2jRkzxnlEo6hmzpyp6tWrq0WLFvLy8tLHH3+s8PDwC94gr3PnzmrSpInuuecevfjii8rNzdXDDz+sm2++ucBpsOIwduxYde/eXZGRkbrzzjvl5eWlH3/8UT///LMmT56szp07q169eho0aJBmzJihrKwsPfvss8Vex5Vw65GZbt26afLkyerdu/cF+9jtdpfL5CpVquTOkgAAJaGkhwC72cyZM1WpUiW1b99ePXr0UGxsrFq2bHlZ26hYsaKmT5+u1q1bq02bNtq1a5e++uoreXkV/lVss9n0+eefq1KlSrrpppvUuXNn1apVSwsWLCiOXSogNjZWX375pZYsWaI2bdro+uuv16xZsxQVFSVJ8vLy0sKFC3Xq1Cm1bdtWDzzwgPNKJ0+zGVMCPwXK/1AWLlyoXr16Odvi4uK0aNEi+fr6qlKlSrrllls0efJkValS5YLbyc7OVnZ2tnM+KytLERERyszMVHBwsDt3AQBwntOnTys1NdXl/ibA+S72M5KVlSWHw1Es398evZqpa9eueuedd7Rs2TI9//zzWrVqlbp166azZ89ecJ2EhAQ5HA7ndP4obwAAUP549Gqmu+66y/nvJk2aqGnTpqpdu7ZWrlypW2+9tdB1Ro8erREjRjjnzx2ZAQAA5VOpus9MrVq1VLVqVaWkpFywj91uV3BwsMsEAADKr1IVZvbt26cjR46oevXqni4FAABYhFtPMx0/ftzlKEtqaqqSk5NVuXJlVa5cWRMmTFCfPn0UHh6uHTt26KmnnlKdOnUUGxvrzrIAAMWohK4jgQWV1M+GW4/MrF+/Xi1atFCLFi0kSSNGjFCLFi00duxYeXt7a9OmTbr99ttVr1493X///WrVqpX+/e9/c68ZALCAc3eTPXnypIcrQWl17mfjj3dGLm5uPTLTsWPHi6ayb775xp0vDwBwI29vb4WEhDifPRQQEOC8RT7KN2OMTp48qfT0dIWEhMjb29utr8ezmQAAVyw8PFySnIEGOF9ISIjzZ8SdCDMAgCtms9lUvXp1hYWF6cyZM54uB6WIj4+P24/InEOYAQBcNW9v7xL74gL+qFRdmg0AAHC5CDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDS3BpmVq9erR49eqhGjRqy2WxatGiRy3JjjMaOHavq1avL399fnTt31vbt291ZEgAAKGPcGmZOnDihZs2a6ZVXXil0+fTp0/XSSy9pzpw5Wrt2rQIDAxUbG6vTp0+7sywAAFCGVHDnxrt166Zu3boVuswYoxdffFHPPfecevbsKUl65513VK1aNS1atEh33XWXO0sDAABlhMfGzKSmpiotLU2dO3d2tjkcDrVr105JSUkXXC87O1tZWVkuEwAAKL88FmbS0tIkSdWqVXNpr1atmnNZYRISEuRwOJxTRESEW+sEAAClm+WuZho9erQyMzOd0969ez1dEgAA8CCPhZnw8HBJ0qFDh1zaDx065FxWGLvdruDgYJcJAACUXx4LM9HR0QoPD9eyZcucbVlZWVq7dq1iYmI8VRYAALAYt17NdPz4caWkpDjnU1NTlZycrMqVKysyMlLDhw/X5MmTVbduXUVHR2vMmDGqUaOGevXq5c6yAABAGeLWMLN+/Xp16tTJOT9ixAhJ0qBBgzRv3jw99dRTOnHihB588EFlZGToxhtv1Ndffy0/Pz93lgUAAMoQmzHGeLqIq5GVlSWHw6HMzEzGzwAAYBHF+f1tuauZAAAAzkeYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAYDSIi9PSkiQ7rjD05UAllLB0wUAQLmXkyM9/bQ0d6506pT0n/94uiLAUggzAOApGRlSfLz00UdSbm5+W9eu0g03eLQswGoIMwBQ0vbskYYOlZYsyT+1dI7dLn38sefqAiyKMAMAJWXTpvwQs2ZN4ctnzJCCgkq2JqAM8PgA4PHjx8tms7lMDRo08HRZAFB8li+XrrtOatbswkGmdm3p0UdLti6gjCgVR2auu+46ffvtt875ChVKRVkAcHUWLJCeeir/tNLF2GzSF1+UTE1AGVQqUkOFChUUHh7u6TIA4Orl5UkvvyxNniz99lvR1hkwQGrY0L11AWWYx08zSdL27dtVo0YN1apVS/fcc4/2XOR/MdnZ2crKynKZAKDU+PJLadKkogeZwEDp7bfdWxNQxnk8zLRr107z5s3T119/rVdffVWpqanq0KGDjh07Vmj/hIQEORwO5xQREVHCFQPARdx+e36Q+fe/pQ4dLt3/9delKzy1fvq09MYbUo8e0u7dV7QJoEywGWOMp4s4X0ZGhqKiojRz5kzdf//9BZZnZ2crOzvbOZ+VlaWIiAhlZmYqODi4JEsFgAvLy5NuvFFKSrpwn+bNpY0bL2uzy5fn559Vq6SDB/PbnnlGmjLlyksFPCErK0sOh6NYvr9LxZiZ84WEhKhevXpKSUkpdLndbpfdbi/hqgDgMpw8KTVtKu3YceE+Xl5FGvS7Y4c0e7b01VdSSsr/7q13zoABBBmg1IWZ48ePa8eOHbr33ns9XQoAXL60NKlJk4JjZry9pbNn/zc/bJh07bUFVj9+XHrzzfx75yUnSydOXPilYmKk998vnrIBK/N4mHniiSfUo0cPRUVF6cCBAxo3bpy8vb01YMAAT5cGAJdn82apbdv8IzPnq1FDWrlSqlcvf75KFelvf5OUfzbqyy+lefOk776T0tOL9lLR0TzCCTjH42Fm3759GjBggI4cOaLQ0FDdeOONWrNmjUJDQz1dGgAU3bJl+c9V+uN5oCZNpPXrJV9fKSQk/3lMCxZIXl7avTt/2ExGxuW9VKVK+TcT9vL4JRxA6eDxMPPhhx96ugQAuDrvvCPFxUl/vJ6iSxdp8eL/pY62baXsbOnWWyVJUVFSaqrUq1f+gN6i8PPLP/3EUw+A/yHXA8DVmDxZGjSoYJB54AHpm29cD588+6y0aJFLt5CQ/DNQL7xw6Zfy9s6/4jsy8mqLBsoWjx+ZAQDLGjIk/0YvfzRpkvTccwXbb7qpQNPp09Jdd0mff37pl/v4Y6l16yuoEyjjCDMAcLny8vLHxyxd6tpus+WP5B04sEibSUyU4uOlU6cu3XfWLKl378svFSgPCDMAcDlycqRWraSff3Ztr1BB+vpr53iYi9mzR+reXfrpp6K95COPSMOHX36pQHnBmBkAKKqjR/Ovif5jkAkIyB+Ve4kgk5cnPf54/iYKCzJhYfljYgIC/tf25z/nP7cSwIURZgCgKFJT81PIgQOu7VWr5t+m97rrLrr6ypVStWrSiy/mh5rzeXlJTz6Z/3iCG2/835VKTZoU6SbBQLnHaSYAuJS1a6Wbb86/rPp8tWvn3/Dl/EMpf3D8uNSnj7RkSeHLmzeX/vWv/PvqnRMSkn/l0vr13EsGKAp+TQDgYj79VGrfvmCQad9e+vXXiwaZ2bPzD9wUFmQCAqR3381/zuT5QUaS7rwz/0yWr28x1A+UAxyZAYAL+fvfCx9527+/dJEbfm7fnj/A99dfC1/ep0/+M5UuFFZ4cCRweTgyAwCFGTGi8CDz5JMXDDJ5edKDD0r16xceZK65RvrhB+mTTzjqAhQnjswAwB/deWf+6aXz2WzSSy/lXyddiMWLpbvvLvw5SxUq5N9Db9y44i8VAGEGAP4nN1e64QZp3TrXdm/v/HDTs2eBVTIypNtvz7+kujDt2uU/Fbtq1eIvF0A+TjMBgJR/2VG9egWDjN0uJSUVGmSmT//fvWH+qGLF/NNJa9YQZAB348gMABw4IDVtKh054trucEg//pj/eOvz/Pyz1KOHtGtXwU3ZbNJf/iK99Vb+6SUA7seRGQDl26ZNUp06BYPMtdfmp5Xzgkxubn5Qadq08CBz7s6+77xDkAFKEmEGQPm1dGn+c5b++KTHZs3y7/gbEuJs+vRTqVIlaf58yRjX7j4++aecdu685I2AAbgB/3cAUD4lJkr3318wmXTrlj9i9/9vvZuenn/PmB9+KHwzN98sLVrkknsAlDCOzAAof8aPlwYPLhhkhg6VvvrKGWTGjs2/O29hQaZSpfyuK1cSZABP48gMgPLlvvukefMKtk+dKo0eLSn/mUi9ekn79xfsZrPl3xhv9myemwSUFoQZAOVDXp7UpYu0bJlru82W/5Cke+5RTo40YID02WeFb6J+/fyHQtau7f5yARQd/68AUPbl5EhNmhQMMj4+0vLl0j336J138k8dFRZk7HbplVekrVsJMkBpxJEZAGXb0aP5lxilpbm2BwZKP/ygfRUb6rZm+VdoF6ZrV+njj6WgIPeXCuDKcGQGQNm1Y4dUs2bBIBMaqryUnRr5RkNFRRUeZEJDpVWr8p+5RJABSjeOzAAom777TrrllvxTTOerW1f/eXWT7mjqp8OHC67m5ZX/sOwZMxjgC1gFYQZA2fPJJ1L//vmDfs9ztn0HdQ9aqa87F55SmjTJv8VMZGRJFAmguPD/DgBly8yZUt++BYLM9jZ3K3DDan29pOCfPX9/6e238083EWQA6yHMACg7HntMGjnSpclIeq3yKNX7Yb6yswuu0rt3/hjhgQNLpkQAxY/TTADKht69858rcB4jmx7WK5pz9KEC3WvUyO/epk3JlAfAfQgzAKwtN1e6/nppwwbXZnmrpxbpK3V3aff2lp55Rpo4sSSLBOBOhBkA1nX8uNS4sbR7t0vzKfmpvb5Tslq6tLdpkz/ANyysJIsE4G6MmQFgTfv25Y/W/UOQ+V0hqqdtLkEmKCj/xnfr1hFkgLKIMAPAepKTpXr1pN9/d2nerQhFarf2Kf+SJJtNuvvu/G533umBOgGUCMIMAGtZvDj/fNGpUy7NG9RStbRTxxUsSc47+86fL1XghDpQphFmAFjHG29It92WP+j3PP9Ud7XWBuWpgnx8pIQEadeu/OE0AMo+wgwAaxgzRmbIEMkYZ5OR9A89rJ76QpLUoYOUni6NGuWhGgF4BAdfAZR+gwbJvPOObOc1GUlParr+piflcEjvvy/9+c+eKhCAJ5WKIzOvvPKKatasKT8/P7Vr107r1q3zdEkASoO8PGW17iT9IciclZfu0oeaaXtSDzyQfwdfggxQfnk8zCxYsEAjRozQuHHj9N///lfNmjVTbGys0tPTPV0aAA/KO3la+0KuU/CGlS7tOfJRR63Qxrr9tW2b9PrrPN0aKO88/idg5syZGjJkiO677z41atRIc+bMUUBAgN56661C+2dnZysrK8tlAlD2eB0+pNBjO13ajilILSv8pP4v36Rff5Xq1vVQcQBKFY+GmZycHG3YsEGdO3d2tnl5ealz585KSkoqdJ2EhAQ5HA7nFBERUVLlAihJUVHa995Knf3/P1NpCtN9N6dqze/19cgjHq4NQKni0TDz22+/6ezZs6pWrZpLe7Vq1ZSWllboOqNHj1ZmZqZz2rt3b0mUCsADat8To5du/EibvJtr6+Ld+mRlVQUFeboqAKWN5a5mstvtstvtni4DQAl5bFUfSX0YFwPggjwaZqpWrSpvb28dOnTIpf3QoUMKDw/3UFUAShNCDIBL8eifCV9fX7Vq1UrLli1ztuXl5WnZsmWKiYnxYGUAAMAqPH6aacSIERo0aJBat26ttm3b6sUXX9SJEyd03333ebo0AABgAR4PM/3799fhw4c1duxYpaWlqXnz5vr6668LDAoGAAAojM2Y8x50YkFZWVlyOBzKzMxUcHCwp8sBAABFUJzf3wytAwAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlubRMFOzZk3ZbDaXadq0aZ4sCQAAWEwFTxcwceJEDRkyxDlfsWJFD1YDAACsxuNhpmLFigoPD/d0GQAAwKI8PmZm2rRpqlKlilq0aKEZM2YoNzf3ov2zs7OVlZXlMgEAgPLLo0dmhg0bppYtW6py5cr6/vvvNXr0aB08eFAzZ8684DoJCQmaMGFCCVYJAABKM5sxxhTnBkeNGqXnn3/+on22bNmiBg0aFGh/6623NHToUB0/flx2u73QdbOzs5Wdne2cz8rKUkREhDIzMxUcHHx1xQMAgBKRlZUlh8NRLN/fxR5mDh8+rCNHjly0T61ateTr61ugffPmzWrcuLG2bt2q+vXrF+n1ivPNAAAAJaM4v7+L/TRTaGioQkNDr2jd5ORkeXl5KSwsrJirAgAAZZXHxswkJSVp7dq16tSpkypWrKikpCQ9/vjj+stf/qJKlSp5qiwAAGAxHgszdrtdH374ocaPH6/s7GxFR0fr8ccf14gRIzxVEgAAsCCPhZmWLVtqzZo1nnp5AABQRnj8PjMAAABXgzADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAsjTADAAAszW1hZsqUKWrfvr0CAgIUEhJSaJ89e/botttuU0BAgMLCwvTkk08qNzfXXSUBAIAyqIK7NpyTk6O+ffsqJiZGb775ZoHlZ8+e1W233abw8HB9//33OnjwoAYOHCgfHx9NnTrVXWUBAIAyxmaMMe58gXnz5mn48OHKyMhwaV+8eLG6d++uAwcOqFq1apKkOXPm6Omnn9bhw4fl6+tbpO1nZWXJ4XAoMzNTwcHBxV0+AABwg+L8/vbYmJmkpCQ1adLEGWQkKTY2VllZWdq8efMF18vOzlZWVpbLBAAAyi+PhZm0tDSXICPJOZ+WlnbB9RISEuRwOJxTRESEW+sEAACl22WFmVGjRslms1102rp1q7tqlSSNHj1amZmZzmnv3r1ufT0AAFC6XdYA4JEjRyouLu6ifWrVqlWkbYWHh2vdunUubYcOHXIuuxC73S673V6k1wAAAGXfZYWZ0NBQhYaGFssLx8TEaMqUKUpPT1dYWJgkaenSpQoODlajRo2K5TUAAEDZ57ZLs/fs2aOjR49qz549Onv2rJKTkyVJderUUVBQkLp06aJGjRrp3nvv1fTp05WWlqbnnntO8fHxHHkBAABF5rZLs+Pi4vT2228XaF+xYoU6duwoSdq9e7ceeughrVy5UoGBgRo0aJCmTZumChWKnrG4NBsAAOspzu9vt99nxt0IMwAAWE+ZuM8MAABAcSDMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAASyPMAAAAS3NbmJkyZYrat2+vgIAAhYSEFNrHZrMVmD788EN3lQQAAMqgCu7acE5Ojvr27auYmBi9+eabF+yXmJiorl27OucvFHwAAAAK47YwM2HCBEnSvHnzLtovJCRE4eHh7ioDAACUcR4fMxMfH6+qVauqbdu2euutt2SMuWj/7OxsZWVluUwAAKD8ctuRmaKYOHGibrnlFgUEBGjJkiV6+OGHdfz4cQ0bNuyC6yQkJDiP+gAAANjMpQ6FnGfUqFF6/vnnL9pny5YtatCggXN+3rx5Gj58uDIyMi65/bFjxyoxMVF79+69YJ/s7GxlZ2c757OyshQREaHMzEwFBwdfeicAAIDHZWVlyeFwFMv392UdmRk5cqTi4uIu2qdWrVpXXEy7du00adIkZWdny263F9rHbrdfcBkAACh/LivMhIaGKjQ01F21KDk5WZUqVSKsAACAInPbmJk9e/bo6NGj2rNnj86ePavk5GRJUp06dRQUFKQvvvhChw4d0vXXXy8/Pz8tXbpUU6dO1RNPPOGukgAAQBnktjAzduxYvf322875Fi1aSJJWrFihjh07ysfHR6+88ooef/xxGWNUp04dzZw5U0OGDHFXSQAAoAy6rAHApVFxDiACAAAlozi/vz1+nxkAAICrQZgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACW5rYws2vXLt1///2Kjo6Wv7+/ateurXHjxiknJ8el36ZNm9ShQwf5+fkpIiJC06dPd1dJAACgDKrgrg1v3bpVeXl5mjt3rurUqaOff/5ZQ4YM0YkTJ/TCCy9IkrKystSlSxd17txZc+bM0U8//aTBgwcrJCREDz74oLtKAwAAZYjNGGNK6sVmzJihV199VTt37pQkvfrqq3r22WeVlpYmX19fSdKoUaO0aNEibd26tUjbzMrKksPhUGZmpoKDg91WOwAAKD7F+f3ttiMzhcnMzFTlypWd80lJSbrpppucQUaSYmNj9fzzz+v3339XpUqVCmwjOztb2dnZLtuU8t8UAABgDee+t4vjmEqJhZmUlBS9/PLLzlNMkpSWlqbo6GiXftWqVXMuKyzMJCQkaMKECQXaIyIiirliAADgbkeOHJHD4biqbVx2mBk1apSef/75i/bZsmWLGjRo4Jzfv3+/unbtqr59+2rIkCGXX+V5Ro8erREjRjjnMzIyFBUVpT179lz1m2ElWVlZioiI0N69e8vV6TX2m/0uD9hv9rs8yMzMVGRkpMsZmyt12WFm5MiRiouLu2ifWrVqOf994MABderUSe3bt9drr73m0i88PFyHDh1yaTs3Hx4eXui27Xa77HZ7gXaHw1GufgjOCQ4OZr/LEfa7fGG/y5fyut9eXld/YfVlh5nQ0FCFhoYWqe/+/fvVqVMntWrVSomJiQUKjomJ0bPPPqszZ87Ix8dHkrR06VLVr1+/0FNMAAAAf+S2+8zs379fHTt2VGRkpF544QUdPnxYaWlpSktLc/a5++675evrq/vvv1+bN2/WggUL9Pe//93lNBIAAMDFuG0A8NKlS5WSkqKUlBRde+21LsvOjVx2OBxasmSJ4uPj1apVK1WtWlVjx469rHvM2O12jRs3rtBTT2UZ+81+lwfsN/tdHrDfV7/fJXqfGQAAgOLGs5kAAIClEWYAAIClEWYAAIClEWYAAIClEWYAAIClWTbM7Nq1S/fff7+io6Pl7++v2rVra9y4ccrJyXHpt2nTJnXo0EF+fn6KiIjQ9OnTPVRx8ZkyZYrat2+vgIAAhYSEFNrHZrMVmD788MOSLbSYFWW/9+zZo9tuu00BAQEKCwvTk08+qdzc3JIt1M1q1qxZ4LOdNm2ap8sqdq+88opq1qwpPz8/tWvXTuvWrfN0SW43fvz4Ap/t+Y+GKStWr16tHj16qEaNGrLZbFq0aJHLcmOMxo4dq+rVq8vf31+dO3fW9u3bPVNsMbrUfsfFxRX4/Lt27eqZYotJQkKC2rRpo4oVKyosLEy9evXStm3bXPqcPn1a8fHxqlKlioKCgtSnT58CTwe4FMuGma1btyovL09z587V5s2bNWvWLM2ZM0fPPPOMs09WVpa6dOmiqKgobdiwQTNmzND48eMLPFbBanJyctS3b1899NBDF+2XmJiogwcPOqdevXqVTIFucqn9Pnv2rG677Tbl5OTo+++/19tvv6158+Zp7NixJVyp+02cONHls3300Uc9XVKxWrBggUaMGKFx48bpv//9r5o1a6bY2Filp6d7ujS3u+6661w+2//85z+eLqnYnThxQs2aNdMrr7xS6PLp06frpZde0pw5c7R27VoFBgYqNjZWp0+fLuFKi9el9luSunbt6vL5f/DBByVYYfFbtWqV4uPjtWbNGi1dulRnzpxRly5ddOLECWefxx9/XF988YU+/vhjrVq1SgcOHNAdd9xxeS9kypDp06eb6Oho5/zs2bNNpUqVTHZ2trPt6aefNvXr1/dEecUuMTHROByOQpdJMgsXLizRekrKhfb7q6++Ml5eXiYtLc3Z9uqrr5rg4GCXnwGri4qKMrNmzfJ0GW7Vtm1bEx8f75w/e/asqVGjhklISPBgVe43btw406xZM0+XUaL++LcqLy/PhIeHmxkzZjjbMjIyjN1uNx988IEHKnSPwv5GDxo0yPTs2dMj9ZSU9PR0I8msWrXKGJP/2fr4+JiPP/7Y2WfLli1GkklKSirydi17ZKYwmZmZLk/fTEpK0k033SRfX19nW2xsrLZt26bff//dEyWWqPj4eFWtWlVt27bVW2+95bzzclmVlJSkJk2aqFq1as622NhYZWVlafPmzR6srPhNmzZNVapUUYsWLTRjxowydSotJydHGzZsUOfOnZ1tXl5e6ty5s5KSkjxYWcnYvn27atSooVq1aumee+7Rnj17PF1SiUpNTVVaWprL5+9wONSuXbty8fmvXLlSYWFhql+/vh566CEdOXLE0yUVq8zMTElyfldv2LBBZ86ccfm8GzRooMjIyMv6vN32OIOSlpKSopdfflkvvPCCsy0tLU3R0dEu/c590aWlpZXph1lOnDhRt9xyiwICArRkyRI9/PDDOn78uIYNG+bp0twmLS3NJchIrp93WTFs2DC1bNlSlStX1vfff6/Ro0fr4MGDmjlzpqdLKxa//fabzp49W+hnuXXrVg9VVTLatWunefPmqX79+jp48KAmTJigDh066Oeff1bFihU9XV6JOPe7WtjnX5Z+jwvTtWtX3XHHHYqOjtaOHTv0zDPPqFu3bkpKSpK3t7eny7tqeXl5Gj58uG644QY1btxYUv7n7evrW2Ac5OV+3qXuyMyoUaMKHbx6/vTHP2j79+9X165d1bdvXw0ZMsRDlV+dK9nvixkzZoxuuOEGtWjRQk8//bSeeuopzZgxw417cGWKe7+t6nLehxEjRqhjx45q2rSp/vrXv+pvf/ubXn75ZWVnZ3t4L3C1unXrpr59+6pp06aKjY3VV199pYyMDH300UeeLg0l4K677tLtt9+uJk2aqFevXvryyy/1ww8/aOXKlZ4urVjEx8fr559/dsvFKKXuyMzIkSMVFxd30T61atVy/vvAgQPq1KmT2rdvX2Bgb3h4eIER0efmw8PDi6fgYnK5+3252rVrp0mTJik7O7tUPcysOPc7PDy8wBUvpfXz/qOreR/atWun3Nxc7dq1S/Xr13dDdSWratWq8vb2LvR3t7R/jsUtJCRE9erVU0pKiqdLKTHnPuNDhw6pevXqzvZDhw6pefPmHqrKM2rVqqWqVasqJSVFt956q6fLuSqPPPKIvvzyS61evdrl4dPh4eHKyclRRkaGy9GZy/19L3VhJjQ0VKGhoUXqu3//fnXq1EmtWrVSYmKivLxcDzTFxMTo2Wef1ZkzZ+Tj4yMp/2ne9evXL3WnmC5nv69EcnKyKlWqVKqCjFS8+x0TE6MpU6YoPT1dYWFhkvI/7+DgYDVq1KhYXsNdruZ9SE5OlpeXl3Ofrc7X11etWrXSsmXLnFfg5eXladmyZXrkkUc8W1wJO378uHbs2KF7773X06WUmOjoaIWHh2vZsmXO8JKVlaW1a9de8grOsmbfvn06cuSIS6izGmOMHn30US1cuFArV64sMPSjVatW8vHx0bJly9SnTx9J0rZt27Rnzx7FxMRc1gtZ0r59+0ydOnXMrbfeavbt22cOHjzonM7JyMgw1apVM/fee6/5+eefzYcffmgCAgLM3LlzPVj51du9e7fZuHGjmTBhggkKCjIbN240GzduNMeOHTPGGPPPf/7TvP766+ann34y27dvN7NnzzYBAQFm7NixHq786lxqv3Nzc03jxo1Nly5dTHJysvn6669NaGioGT16tIcrLz7ff/+9mTVrlklOTjY7duww7733ngkNDTUDBw70dGnF6sMPPzR2u93MmzfP/PLLL+bBBx80ISEhLleqlUUjR440K1euNKmpqea7774znTt3NlWrVjXp6emeLq1YHTt2zPn7K8nMnDnTbNy40ezevdsYY8y0adNMSEiI+fzzz82mTZtMz549TXR0tDl16pSHK786F9vvY8eOmSeeeMIkJSWZ1NRU8+2335qWLVuaunXrmtOnT3u69Cv20EMPGYfDYVauXOnyPX3y5Elnn7/+9a8mMjLSLF++3Kxfv97ExMSYmJiYy3ody4aZxMREI6nQ6Xw//vijufHGG43dbjfXXHONmTZtmocqLj6DBg0qdL9XrFhhjDFm8eLFpnnz5iYoKMgEBgaaZs2amTlz5pizZ896tvCrdKn9NsaYXbt2mW7duhl/f39TtWpVM3LkSHPmzBnPFV3MNmzYYNq1a2ccDofx8/MzDRs2NFOnTrX0H7sLefnll01kZKTx9fU1bdu2NWvWrPF0SW7Xv39/U716dePr62uuueYa079/f5OSkuLpsordihUrCv1dHjRokDEm//LsMWPGmGrVqhm73W5uvfVWs23bNs8WXQwutt8nT540Xbp0MaGhocbHx8dERUWZIUOGWD7AX+h7OjEx0dnn1KlT5uGHHzaVKlUyAQEBpnfv3i4HJorC9v8vBgAAYEml7momAACAy0GYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlkaYAQAAlvZ/MnSPYf/tFNcAAAAASUVORK5CYII=", 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", 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", 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EGTBggKlWrZqpWLGi6dy5s9mzZ0+Rp2bnn3Jc1HTmMouSm5trnnrqKRMdHW0cDofvNO3806qLOl06Ly/PTJgwwdSuXds4nU7TrFkzs2TJEtOrVy+/U5bPtYwz1+OXX34xycnJplGjRqZixYrG5XKZFi1amA8++MDvOUWdmm2MMadOnTJjxowxCQkJJiwszMTHx5sRI0aYkydPFnqfOnXqVOj5+admf/jhh371/M/lm2++8auf7XP8+9//bm655RZTsWJFU7FiRdOoUSOTnJxstm7d6jdu+vTpJiEhwTidTnP99debVatWmVtvvfWCT81OTk42c+fONQ0aNPC9/8uXLy9yvMfjMVWqVDEul8ucOHHivMs3xntq9pAhQ0yNGjVMeHi4ufnmm01aWlqhHvPfh7NNZ+sJsILDmBJ4VCIA4Lxyc3NVs2ZNde7cudAxMEBZwjEzAGBTixYt0sGDB9WzZ0+rWwEsxZYZALCZNWvWaOPGjRo3bpyqV69+zovdAWUBW2YAwGbefPNN9e/fXzExMXr33XetbgewXFDDTEpKim644QZVrlxZMTEx6tKlS6FraZw8eVLJycmqVq2aKlWqpG7duikzMzOYbQGArc2ePVu5ublat26dGjdubHU7gOWCGmZWrlyp5ORkrV69WkuXLtWpU6fUvn17HTt2zDdm0KBB+uSTT/Thhx9q5cqV2rdvn+65555gtgUAAEqRYj1m5uDBg4qJidHKlSvVunVrZWdnKzo6Wu+9957vWglbtmzRVVddpbS0NN/N/AAAAM6mWC+al38Ts6pVq0qSvv32W506dUrt2rXzjWnUqJFq1ap11jDj8Xj8rhqaf1ffatWqXdKl2wEAQPEzxujIkSOqWbOm707ul6rYwkxeXp4GDhyom2++2bePNyMjQ+XLl1dUVJTf2NjY2LPeUTclJaXIK2ECAAD72bNnj373u99d1jKKLcwkJyfrhx9+0L///e/LWs6IESM0ePBg33x2drZq1aqlPXv2KDIy8nLbBAAAxcDtdis+Pl6VK1e+7GUVS5h58skntWTJEq1atcovfcXFxSknJ0dZWVl+W2cyMzMVFxdX5LKcTqecTmehemRkJGEGAACbCcQhIkE9m8kYoyeffFILFy7UV199pYSEBL/HmzdvrrCwMC1btsxX27p1q3bv3q3ExMRgtgYAAEqJoG6ZSU5O1nvvvafFixercuXKvuNgXC6XwsPD5XK51LdvXw0ePFhVq1ZVZGSknnrqKSUmJnImEwAAuCBBPTX7bJuOUlNT1bt3b0nei+YNGTJE8+fPl8fjUVJSkqZPn37W3Uy/5Xa75XK5lJ2dzW4mAABsIpDf37a/NxNhBgAA+wnk9zf3ZgIAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALYW1DCzatUqde7cWTVr1pTD4dCiRYv8Hu/du7ccDoff1KFDh2C2BAAASpmghpljx46pSZMmmjZt2lnHdOjQQfv37/dN8+fPD2ZLAACglCkXzIV37NhRHTt2POcYp9OpuLi4YLYBAABKMcuPmVmxYoViYmLUsGFD9e/fX4cOHTrneI/HI7fb7TcBAICyy9Iw06FDB7377rtatmyZJk2apJUrV6pjx446ffr0WZ+TkpIil8vlm+Lj44uxYwAAUNI4jDGmWF7I4dDChQvVpUuXs4756aefVK9ePX355Ze6/fbbixzj8Xjk8Xh88263W/Hx8crOzlZkZGSg2wYAAEHgdrvlcrkC8v1t+W6mM9WtW1fVq1fX9u3bzzrG6XQqMjLSbwIAAGVXiQoze/fu1aFDh1SjRg2rWwEAADYR1LOZjh496reVJT09XRs2bFDVqlVVtWpVjRkzRt26dVNcXJx27NihYcOGqX79+kpKSgpmWwAAoBQJaphZt26d2rZt65sfPHiwJKlXr1568803tXHjRs2ZM0dZWVmqWbOm2rdvr3HjxsnpdAazLQAAUIoU2wHAwRLIA4gAAEDxKLUHAAMAAFwswgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALC1oIaZVatWqXPnzqpZs6YcDocWLVrk97gxRqNGjVKNGjUUHh6udu3aadu2bcFsCQAAlDJBDTPHjh1TkyZNNG3atCIfnzx5sl577TXNmDFDa9asUcWKFZWUlKSTJ08Gsy0AAFCKlAvmwjt27KiOHTsW+ZgxRq+++qr+/Oc/6+6775Ykvfvuu4qNjdWiRYv0wAMPBLM1AABQSlh2zEx6eroyMjLUrl07X83lcqlFixZKS0s76/M8Ho/cbrffBAAAyi7LwkxGRoYkKTY21q8eGxvre6woKSkpcrlcvik+Pj6ofQIAgJLNdmczjRgxQtnZ2b5pz549VrcEAAAsZFmYiYuLkyRlZmb61TMzM32PFcXpdCoyMtJvAgAAZZdlYSYhIUFxcXFatmyZr+Z2u7VmzRolJiZa1RYAALCZoJ7NdPToUW3fvt03n56erg0bNqhq1aqqVauWBg4cqBdffFENGjRQQkKCRo4cqZo1a6pLly7BbAsAAJQiQQ0z69atU9u2bX3zgwcPliT16tVLs2fP1rBhw3Ts2DE9+uijysrK0i233KLPP/9cFSpUCGZbAACgFHEYY4zVTVwOt9stl8ul7Oxsjp8BAMAmAvn9bbuzmQAAAM5EmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAEAALZGmAGAkmbvXqs7AGyFMAMAJcWpU9KECdJrr1ndCWAr5axuAAAgacMG6eGHvf/dutXqbgBbYcsMAFgpJ0caNUq64QZp/XrpD3+QGjSwuivAVtgyAwBW+eYb79aYH34oqA0YYF0/gE2xZQYAituJE9Kzz0o33eQfZBo1ku64w7q+AJtiywwAFKf//Me7NeZ//yv82FNPSQ5H8fcE2BxbZgCgOBw7Jg0cKLVqVXSQcbmknj2LvS2gNGDLDAAE2/LlUt++Unr62cf07StVqlR8PQGlCFtmACBY3G6pf3/pttvOHWQcDik5ufj6AkoZtswAQDB8/rn06KPSnj3nH9u5s1S3bvB7AkoptswAQKCdPCnt3i21bi3VqnX+8ZyODVwWwgwABFqFCt6tMnPnSrt2nTusXHONdzcUgEtGmAGAYPr73899r6UBAzgdG7hMhBkACJbvvpMeesi/dsUVBf+uUkX64x+LtyegFCLMAEAw7NvnPbD3xImC2nXXeQNO1are+UcekSpWtKY/oBQhzABAoB0/Lt11lzfQ5IuJkf7xD+9/+/WTQkI4HRsIEE7NBoBAysuTevWSvv22oOZ0SosWSbVre+eTk73XncmfB3BZ2DIDAIH0wgvSRx/512bNkhITC+bj46U33yzWtoDSjDADAIHy3nvSuHH+tT//ueiDfPOPmwFw2QgzABAIaWneu2Gf6d57pTFjrOkHKEMIMwBwuXbtkrp0kTyeglrz5tKcOd4DfQEEFb9lAHA5jhzxnrl04EBBrWZNafFiKSLCur6AMoQwAwCX6vRp7/EwGzcW1MLDvadgn3lxPABBZXmYeeGFF+RwOPymRo0aWd0WAJzf8OHSJ5/41/72N+8uJgDFpkRcZ+aaa67Rl19+6ZsvV65EtAUAZ/fOO9LUqf61F1+UunWzph+gDCsRqaFcuXKKi4uzug0AuDArV0qPP+5f+9OfpOees6YfoIyzfDeTJG3btk01a9ZU3bp19cc//lG7d+8+61iPxyO32+03AUCx2bFDuuce6dSpglpiovTXv3L3a8AiloeZFi1aaPbs2fr888/15ptvKj09Xa1atdKRI0eKHJ+SkiKXy+Wb4uPji7ljAGVWdrZ0553S4cMFtVq1pIULpQoVrOsLKOMcxhhjdRNnysrKUu3atfXyyy+rb9++hR73eDzynHEtB7fbrfj4eGVnZysyMrI4WwVQluTmSp06SV98UVCrVEn6+mvp2mut6wuwKbfbLZfLFZDv7xJxzMyZoqKidOWVV2r79u1FPu50OuV0Oou5KwBl3qBB/kHG4ZDmzyfIACWA5buZfuvo0aPasWOHatSoYXUrAOA1fbr0xhv+talTvbucAFjO8jAzdOhQrVy5Ujt37tTXX3+trl27KjQ0VD169LC6NQCQli6VBgzwr/Xt691SA6BEsHw30969e9WjRw8dOnRI0dHRuuWWW7R69WpFR0db3RqAsm7LFql7d++VfvPdeqt3Sw1nLgElhuVhZsGCBVa3AACFHTrk3Y2UnV1Qq1dP+vvfpfLlresLQCGW72YCgBInJ8d7Jd8dOwpqLpe0ZIlUrZp1fQEoEmEGAM5kjPTEE96r/OYLDZU+/FDivnFAiUSYAYAzvfKKNGuWf+2116Q77rCmHwDnRZgBgHxLlkhDh/rXnnzSu6UGQIlFmAEASfr+e6lHD+9upnzt23u31AAo0QgzAHDggNS5s3T0aEGtUSPp/felcpaf9AngPAgzAMq2kyelLl2kXbsKatWqeXc5RUVZ1RWAi0CYAVB2GSM98oiUllZQCwuTPv7Ye00ZALZAmAFQdqWkSPPm+ddmzJBat7amHwCXhDADoGz6+9+l55/3rw0dKj38sDX9ALhkhBkAZc+330oPPeRf69xZmjjRmn4AXBbCDICyZd8+6a67pBMnCmrXXefd3RQaal1fAC4ZYQZA2XH8uDfI7NtXUIuJkf7xD6lyZev6AnBZCDMAyoa8PKlXL+8upnxOp7R4sVS7tnV9AbhshBkAZcPo0dJHH/nX3nlHuukma/oBEDCEGQCl33vvSS++6F/785+lBx+0ph8AAUWYAVC6paUVPt363nulMWOs6QdAwBFmAJReu3Z5b1Xg8RTUmjeX5syRQvjzB5QW/DYDKJ2OHPFeO+bAgYJazZreA34jIqzrC0DAEWYAlD6nT0t//KP0/fcFtfBw7ynYV1xhXV8AgoIwA6D0GT5c+uQT/9rcud5dTABKHcIMgNJl1ixp6lT/2vjx0j33WNMPgKAjzAAoPVaulPr396/96U/SiBHW9AOgWBBmAJQOO3Z4t76cOlVQa9lS+utfJYfDur4ABB1hBoD9ZWVJd94pHT5cUKtVS1q4UKpQwbK2ABQPwgwAe8vNle6/X9qypaBWqZK0ZIn3JpIASj3CDAB7GzRI+uKLgnmHQ5o/X7r2Wut6AlCsCDMA7Gv6dOmNN/xrU6d6dzkBKDMIMwDsaelSacAA/9ojj3i31AAoUwgzAOxnyxape3fvlX7z3XqrNG0aZy4BZRBhBoC9HDrk3Y2UnV1Qq19f+vvfpfLlresLgGUIMwDsIydH6tbNe02ZfC6X99YF1apZ1xcASxFmANiDMdITT3iv8psvNFT68EOpUSPr+gJgOcIMAHt45RXvfZfO9Npr0h13WNMPgBKDMAOg5FuyRBo61L/25JPeLTUAyjzCDICSbeNGqUcP726mfO3be7fUAIBKSJiZNm2a6tSpowoVKqhFixZau3at1S0BKAkyM6XOnaWjRwtqjRpJ778vlStnXV8AShTLw8z777+vwYMHa/To0fruu+/UpEkTJSUl6cCBA1a3BsBKJ09KXbtKu3cX1KpV8+5yioqyrC0AJY/lYebll19Wv3791KdPH1199dWaMWOGIiIi9M477xQ53uPxyO12+00ASqGtW6XNm5WpGL2qp6WwMOnjj6V69azuDEAJY2mYycnJ0bfffqt27dr5aiEhIWrXrp3S0tKKfE5KSopcLpdvio+PL652ARSnJk20cc563VBuvQbpVb3RfaXUurXVXQEogSwNM7/88otOnz6t2NhYv3psbKwyMjKKfM6IESOUnZ3tm/bs2VMcrQIoZuvXSy0frKM9uTUlSU8vSPS7OTYA5LN8N9PFcjqdioyM9JsAlD6NG0s33lgwn5fnvR3T5s3W9QSgZLI0zFSvXl2hoaHKzMz0q2dmZiouLs6irgCUBGFh0kcfeW+7lM/t9t6W6ZdfrOsLQMljaZgpX768mjdvrmXLlvlqeXl5WrZsmRITEy3sDEBJULVq4ZOXfvrJe3umnBzL2gJQwli+m2nw4MH661//qjlz5mjz5s3q37+/jh07pj59+ljdGoASoGFD7+2XQkMLaqtWSf37+19HD0DZZflVp+6//34dPHhQo0aNUkZGhpo2barPP/+80EHBAMqudu2kN97wBph877wjXXVV4bscACh7HMbY+/9t3G63XC6XsrOzORgYKOWeftp7b8l8Doe0aJF0112WtQTgEgXy+9vy3UwAcKFeeklKSiqYN0Z68EHv7ZsAlF2EGQC2Ua6c97ZMV19dUDt2zHv7pt+cFAmgDCHMALAVl0v65BPvbZry7d4tdenivZ0TgLKHMAPAdurWlRYu9F6LJt/q1VLfvpzhBJRFhBkAttSqlTRzpn/tvfek8eOt6QeAdQgzAGyrd29p2DD/2siR3uvSACg7CDMAbC0lRbr7bv9ar17SunXW9AOg+BFmANhaSIg0d67UpElB7cQJ77Vnfv7Zur4AFB/CDADbq1RJ+sc/pDMvHL5/vzfQHDtmXV8AigdhBkCpUKuWtHix5HQW1L77TurZU8rLs64vAMFHmAFQarRoIaWm+tc+/lgaNcqafgAUD8IMgFKlR4/C4WX8eO9xNQBKJ8IMgFJn9Gjpvvv8a337Sl9/bU0/AIKLMAOg1AkJkWbPlm64oaCWk+O95cHOnRY1BSBoCDMASqXwcO8BwVdcUVA7eNB7U8ojR6zrC0DgEWYAlFo1anhvShkRUVD74QfvcTWnT1vXF4DAIswAKNWaNSt88O8//1n4NggA7IswA6DU69pVmjDBv/byy9Lbb1vTD4DAIswAKBOGD/deQO9M/ftLK1ZY0g6AACLMACgTHA5p5kzp5psLarm5Urdu0vbt1vUF4PIRZgCUGU6ntHChVKdOQe3wYenOO6Vff7WsLQCXiTADoEyJjvae4VS5ckFt61bvRfZOnbKuLwCXjjADoMxp3FhasMB7cb18X34pDRxoWUsALgNhBkCZ9Ic/SC+95F+bPl164w1r+gFw6QgzAMqsp5+WHn20cO1f/7KmHwCXhjADoMxyOLxbYtq2Lajl5XmPn9m82bq+AFwcwgyAMi0sTProI6lBg4Ka2+09w+mXX6zrC8CFI8wAKPOqVvWe4RQVVVD76SfvNWhycixrC8AFIswAgKSGDaUPP5RCQwtqq1ZJjz8uGWNdXwDOjzADAP+nXbvCZzOlphY+6wlAyUKYAYAzPP64NGCAf23YMOkf/7CmHwDnR5gBgN946SWpQ4eCeWOkBx+U/vtf63oCcHaEGQD4jXLlvFcIvvrqgtqxY1LnzlJGhnV9ASgaYQYAiuByec9wql69oLZnj9S1q3TypHV9ASiMMAMAZ1G3rvTxx95r0eRbvVp6+GHOcAJKEkvDTJ06deRwOPymiRMnWtkSAPhp1UqaOdO/Nn++NH68Nf0AKKyc1Q2MHTtW/fr1881XrlzZwm4AoLDevb23N5g8uaA2cqT32jTdu1vWFoD/Y3mYqVy5suLi4qxuAwDOKSVF2rpVWry4oNarl5SQIF1/vXV9ASgBx8xMnDhR1apVU7NmzTRlyhTl5uaec7zH45Hb7fabACDYQkKkuXOlJk0KaidOSHfdJe3da11fACwOMwMGDNCCBQu0fPlyPfbYY5owYYKGDRt2zuekpKTI5XL5pvj4+GLqFkBZV6mS9+J5sbEFtf37pbvv9p66DcAaDmMCe0z+8OHDNWnSpHOO2bx5sxo1alSo/s477+ixxx7T0aNH5XQ6i3yux+ORx+PxzbvdbsXHxys7O1uRkZGX1zwAXIA1a6Rbb5XO+FOke+7x3tspxPLt3YA9uN1uuVyugHx/BzzMHDx4UIcOHTrnmLp166p8+fKF6ps2bVLjxo21ZcsWNWzY8IJeL5BvBgBcqAULpB49/GvPPcdZTsCFCuT3d8APAI6OjlZ0dPQlPXfDhg0KCQlRTExMgLsCgMB64AHvGU5jxxbUJkyQGjWSHnrIur6Assiys5nS0tK0Zs0atW3bVpUrV1ZaWpoGDRqkP/3pT6pSpYpVbQHABRs9WtqyRfrgg4LaI49I9epJLVta1xdQ1li2d9fpdGrBggW69dZbdc0112j8+PEaNGiQZv726lQAUEKFhEizZ0s33FBQy8mRunSRdu60qCmgDAr4MTPFjWNmAFht/35voPn554Ja48bSf/4j8WcJKFogv7857h4ALlONGt6bUkZEFNR++EF68EHp9Gnr+gLKCsIMAARAs2bei+qd6Z//lM5z6SwAAUCYAYAA6drVe9uDM738svT224XHHjhQPD0BZQFhBgAC6NlnpZ49/Wv9+0vLlxfMp6dLjz5avH0BpRlhBgACyOGQZs6Ubr65oJabK3XrJm3b5p1/4w3vbRF27LCmR6C0IcwAQIA5ndLChVKdOgW1X3+VOnf23pRy1izJGGnaNMtaBEoVwgwABEF0tPcMp8qVC2pbt0q//72Une2dnzVLOnLEmv6A0oQwAwBB0rix9x5OZ9588uDBgn+73dK77xZ/X0BpQ5gBgCDq0EFKTj7746+/LuXlFV8/QGlk2b2ZAKC0OnlSmj5dWrlS+ve/pcOHzz5261Zp6VIpKan4+gNKG7bMAECAVaggXX+99OOP5w4y+f7yl+D3BJRmhBkACILWraX//lcaMsT/mJmifPaZ9L//FU9fQGlEmAGAIImIkKZO9d5w8qqrzj32jTeKpyegNCLMAECQ3XSTtH699PzzUmho0WNSU71nNwG4eIQZACgGTqf04ovSN99ITZoUfvzoUWn27GJvCygVCDMAUIyaNZPWrpXGjpXCwvwf4zRt4NIQZgCgmJUvL40cKX33nfesp3zbt3sPBgZwcQgzAGCRxo2ltDRp0iTvbihJeu01a3sC7IgwAwAWKldOGjbMexp3y5bSF19Imzdb3RVgL4QZACgBGjaUVq3yXkDvnXes7gawF25nAAAlRGioNGCAlJlpdSeAvbBlBgBKmNhYqzsA7IUwAwAAbI0wAwAAbI0wAwAAbI0wAwAAbI0wAwAAbI0wAwAAbI0wAwAAbI0wAwAAbI0wAwAAbI0wAwAAbI0wAwAAbI0wAwAAbI0wAwAAbC1oYWb8+PFq2bKlIiIiFBUVVeSY3bt3q1OnToqIiFBMTIyeeeYZ5ebmBqslAABQCpUL1oJzcnLUvXt3JSYmatasWYUeP336tDp16qS4uDh9/fXX2r9/v3r27KmwsDBNmDAhWG0BAIBSxmGMMcF8gdmzZ2vgwIHKysryq3/22We68847tW/fPsXGxkqSZsyYoWeffVYHDx5U+fLlL2j5brdbLpdL2dnZioyMDHT7AAAgCAL5/W3ZMTNpaWm69tprfUFGkpKSkuR2u7Vp06azPs/j8cjtdvtNAACg7LIszGRkZPgFGUm++YyMjLM+LyUlRS6XyzfFx8cHtU8AAFCyXVSYGT58uBwOxzmnLVu2BKtXSdKIESOUnZ3tm/bs2RPU1wMAACXbRR0APGTIEPXu3fucY+rWrXtBy4qLi9PatWv9apmZmb7HzsbpdMrpdF7QawAAgNLvosJMdHS0oqOjA/LCiYmJGj9+vA4cOKCYmBhJ0tKlSxUZGamrr746IK8BAABKv6Cdmr17924dPnxYu3fv1unTp7VhwwZJUv369VWpUiW1b99eV199tR566CFNnjxZGRkZ+vOf/6zk5GS2vAAAgAsWtFOze/furTlz5hSqL1++XG3atJEk7dq1S/3799eKFStUsWJF9erVSxMnTlS5cheesTg1GwAA+wnk93fQrzMTbIQZAADsp1RcZwYAACAQCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWCDMAAMDWghZmxo8fr5YtWyoiIkJRUVFFjnE4HIWmBQsWBKslAABQCpUL1oJzcnLUvXt3JSYmatasWWcdl5qaqg4dOvjmzxZ8AAAAihK0MDNmzBhJ0uzZs885LioqSnFxccFqAwAAlHKWHzOTnJys6tWr68Ybb9Q777wjY8w5x3s8Hrndbr8JAACUXUHbMnMhxo4dq9tuu00RERH64osv9MQTT+jo0aMaMGDAWZ+TkpLi2+oDAADgMOfbFHKG4cOHa9KkSeccs3nzZjVq1Mg3P3v2bA0cOFBZWVnnXf6oUaOUmpqqPXv2nHWMx+ORx+PxzbvdbsXHxys7O1uRkZHnXwkAAGA5t9stl8sVkO/vi9oyM2TIEPXu3fucY+rWrXvJzbRo0ULjxo2Tx+OR0+kscozT6TzrYwAAoOy5qDATHR2t6OjoYPWiDRs2qEqVKoQVAABwwYJ2zMzu3bt1+PBh7d69W6dPn9aGDRskSfXr11elSpX0ySefKDMzUzfddJMqVKigpUuXasKECRo6dGiwWgIAAKVQ0MLMqFGjNGfOHN98s2bNJEnLly9XmzZtFBYWpmnTpmnQoEEyxqh+/fp6+eWX1a9fv2C1BAAASqGLOgC4JArkAUQAAKB4BPL72/LrzAAAAFwOwgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALA1wgwAALC1oIWZnTt3qm/fvkpISFB4eLjq1aun0aNHKycnx2/cxo0b1apVK1WoUEHx8fGaPHlysFoCAAClULlgLXjLli3Ky8vTW2+9pfr16+uHH35Qv379dOzYMU2dOlWS5Ha71b59e7Vr104zZszQ999/r4cfflhRUVF69NFHg9UaAAAoRRzGGFNcLzZlyhS9+eab+umnnyRJb775pp5//nllZGSofPnykqThw4dr0aJF2rJlywUt0+12y+VyKTs7W5GRkUHrHQAABE4gv7+DtmWmKNnZ2apatapvPi0tTa1bt/YFGUlKSkrSpEmT9Ouvv6pKlSqFluHxeOTxePyWKXnfFAAAYA/539uB2KZSbGFm+/btev311327mCQpIyNDCQkJfuNiY2N9jxUVZlJSUjRmzJhC9fj4+AB3DAAAgu3QoUNyuVyXtYyLDjPDhw/XpEmTzjlm8+bNatSokW/+559/VocOHdS9e3f169fv4rs8w4gRIzR48GDffFZWlmrXrq3du3df9pthJ263W/Hx8dqzZ0+Z2r3GerPeZQHrzXqXBdnZ2apVq5bfHptLddFhZsiQIerdu/c5x9StW9f373379qlt27Zq2bKlZs6c6TcuLi5OmZmZfrX8+bi4uCKX7XQ65XQ6C9VdLleZ+iHIFxkZyXqXIax32cJ6ly1ldb1DQi7/xOqLDjPR0dGKjo6+oLE///yz2rZtq+bNmys1NbVQw4mJiXr++ed16tQphYWFSZKWLl2qhg0bFrmLCQAA4LeCdp2Zn3/+WW3atFGtWrU0depUHTx4UBkZGcrIyPCNefDBB1W+fHn17dtXmzZt0vvvv6+//OUvfruRAAAAziVoBwAvXbpU27dv1/bt2/W73/3O77H8I5ddLpe++OILJScnq3nz5qpevbpGjRp1UdeYcTqdGj16dJG7nkoz1pv1LgtYb9a7LGC9L3+9i/U6MwAAAIHGvZkAAICtEWYAAICtEWYAAICtEWYAAICtEWYAAICt2TbM7Ny5U3379lVCQoLCw8NVr149jR49Wjk5OX7jNm7cqFatWqlChQqKj4/X5MmTLeo4cMaPH6+WLVsqIiJCUVFRRY5xOByFpgULFhRvowF2Ieu9e/duderUSREREYqJidEzzzyj3Nzc4m00yOrUqVPos504caLVbQXctGnTVKdOHVWoUEEtWrTQ2rVrrW4p6F544YVCn+2Zt4YpLVatWqXOnTurZs2acjgcWrRokd/jxhiNGjVKNWrUUHh4uNq1a6dt27ZZ02wAnW+9e/fuXejz79ChgzXNBkhKSopuuOEGVa5cWTExMerSpYu2bt3qN+bkyZNKTk5WtWrVVKlSJXXr1q3Q3QHOx7ZhZsuWLcrLy9Nbb72lTZs26ZVXXtGMGTP03HPP+ca43W61b99etWvX1rfffqspU6bohRdeKHRbBbvJyclR9+7d1b9//3OOS01N1f79+31Tly5diqfBIDnfep8+fVqdOnVSTk6Ovv76a82ZM0ezZ8/WqFGjirnT4Bs7dqzfZ/vUU09Z3VJAvf/++xo8eLBGjx6t7777Tk2aNFFSUpIOHDhgdWtBd8011/h9tv/+97+tbingjh07piZNmmjatGlFPj558mS99tprmjFjhtasWaOKFSsqKSlJJ0+eLOZOA+t86y1JHTp08Pv858+fX4wdBt7KlSuVnJys1atXa+nSpTp16pTat2+vY8eO+cYMGjRIn3zyiT788EOtXLlS+/bt0z333HNxL2RKkcmTJ5uEhATf/PTp002VKlWMx+Px1Z599lnTsGFDK9oLuNTUVONyuYp8TJJZuHBhsfZTXM623p9++qkJCQkxGRkZvtqbb75pIiMj/X4G7K527drmlVdesbqNoLrxxhtNcnKyb/706dOmZs2aJiUlxcKugm/06NGmSZMmVrdRrH77tyovL8/ExcWZKVOm+GpZWVnG6XSa+fPnW9BhcBT1N7pXr17m7rvvtqSf4nLgwAEjyaxcudIY4/1sw8LCzIcffugbs3nzZiPJpKWlXfBybbtlpijZ2dl+d99MS0tT69atVb58eV8tKSlJW7du1a+//mpFi8UqOTlZ1atX14033qh33nnHd+Xl0iotLU3XXnutYmNjfbWkpCS53W5t2rTJws4Cb+LEiapWrZqaNWumKVOmlKpdaTk5Ofr222/Vrl07Xy0kJETt2rVTWlqahZ0Vj23btqlmzZqqW7eu/vjHP2r37t1Wt1Ss0tPTlZGR4ff5u1wutWjRokx8/itWrFBMTIwaNmyo/v3769ChQ1a3FFDZ2dmS5Puu/vbbb3Xq1Cm/z7tRo0aqVavWRX3eQbudQXHbvn27Xn/9dU2dOtVXy8jIUEJCgt+4/C+6jIyMUn0zy7Fjx+q2225TRESEvvjiCz3xxBM6evSoBgwYYHVrQZORkeEXZCT/z7u0GDBggH7/+9+ratWq+vrrrzVixAjt379fL7/8stWtBcQvv/yi06dPF/lZbtmyxaKuikeLFi00e/ZsNWzYUPv379eYMWPUqlUr/fDDD6pcubLV7RWL/N/Voj7/0vR7XJQOHTronnvuUUJCgnbs2KHnnntOHTt2VFpamkJDQ61u77Ll5eVp4MCBuvnmm9W4cWNJ3s+7fPnyhY6DvNjPu8RtmRk+fHiRB6+eOf32D9rPP/+sDh06qHv37urXr59FnV+eS1nvcxk5cqRuvvlmNWvWTM8++6yGDRumKVOmBHENLk2g19uuLuZ9GDx4sNq0aaPrrrtOjz/+uF566SW9/vrr8ng8Fq8FLlfHjh3VvXt3XXfddUpKStKnn36qrKwsffDBB1a3hmLwwAMP6K677tK1116rLl26aMmSJfrmm2+0YsUKq1sLiOTkZP3www9BORmlxG2ZGTJkiHr37n3OMXXr1vX9e9++fWrbtq1atmxZ6MDeuLi4QkdE58/HxcUFpuEAudj1vlgtWrTQuHHj5PF4StTNzAK53nFxcYXOeCmpn/dvXc770KJFC+Xm5mrnzp1q2LBhELorXtWrV1doaGiRv7sl/XMMtKioKF155ZXavn271a0Um/zPODMzUzVq1PDVMzMz1bRpU4u6skbdunVVvXp1bd++XbfffrvV7VyWJ598UkuWLNGqVav8bj4dFxennJwcZWVl+W2dudjf9xIXZqKjoxUdHX1BY3/++We1bdtWzZs3V2pqqkJC/Dc0JSYm6vnnn9epU6cUFhYmyXs374YNG5a4XUwXs96XYsOGDapSpUqJCjJSYNc7MTFR48eP14EDBxQTEyPJ+3lHRkbq6quvDshrBMvlvA8bNmxQSEiIb53trnz58mrevLmWLVvmOwMvLy9Py5Yt05NPPmltc8Xs6NGj2rFjhx566CGrWyk2CQkJiouL07Jly3zhxe12a82aNec9g7O02bt3rw4dOuQX6uzGGKOnnnpKCxcu1IoVKwod+tG8eXOFhYVp2bJl6tatmyRp69at2r17txITEy/qhWxp7969pn79+ub22283e/fuNfv37/dN+bKyskxsbKx56KGHzA8//GAWLFhgIiIizFtvvWVh55dv165dZv369WbMmDGmUqVKZv369Wb9+vXmyJEjxhhj/vGPf5i//vWv5vvvvzfbtm0z06dPNxEREWbUqFEWd355zrfeubm5pnHjxqZ9+/Zmw4YN5vPPPzfR0dFmxIgRFnceOF9//bV55ZVXzIYNG8yOHTvM3LlzTXR0tOnZs6fVrQXUggULjNPpNLNnzzY//vijefTRR01UVJTfmWql0ZAhQ8yKFStMenq6+c9//mPatWtnqlevbg4cOGB1awF15MgR3++vJPPyyy+b9evXm127dhljjJk4caKJiooyixcvNhs3bjR33323SUhIMCdOnLC488tzrvU+cuSIGTp0qElLSzPp6enmyy+/NL///e9NgwYNzMmTJ61u/ZL179/fuFwus2LFCr/v6ePHj/vGPP7446ZWrVrmq6++MuvWrTOJiYkmMTHxol7HtmEmNTXVSCpyOtN///tfc8sttxin02muuOIKM3HiRIs6DpxevXoVud7Lly83xhjz2WefmaZNm5pKlSqZihUrmiZNmpgZM2aY06dPW9v4ZTrfehtjzM6dO03Hjh1NeHi4qV69uhkyZIg5deqUdU0H2LfffmtatGhhXC6XqVChgrnqqqvMhAkTbP3H7mxef/11U6tWLVO+fHlz4403mtWrV1vdUtDdf//9pkaNGqZ8+fLmiiuuMPfff7/Zvn271W0F3PLly4v8Xe7Vq5cxxnt69siRI01sbKxxOp3m9ttvN1u3brW26QA413ofP37ctG/f3kRHR5uwsDBTu3Zt069fP9sH+LN9T6empvrGnDhxwjzxxBOmSpUqJiIiwnTt2tVvw8SFcPzfiwEAANhSiTubCQAA4GIQZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK0RZgAAgK39fzrhgE1lfc/XAAAAAElFTkSuQmCC", + "image/png": 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", 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", 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", 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", + "image/png": 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XL2+CgoJMx44dza5du/Idmn1myHF+y9nbzE9OTo559NFHTVhYmLHZbK5h2meGVec3XDo3N9dMmjTJREdHG7vdbho0aGAWLFhg+vTp4zZk+ULbOHs//vzzTzNw4EBTs2ZNExQUZBwOh2natKn58MMP3Z6T39BsY4w5deqUGTdunImJiTF+fn4mMjLSjBw50pw8eTLP+3THHXfkef6ZodkfffSRW/uZz+XHH390az/f5/jJJ5+Ym2++2QQFBZmgoCBTs2ZNM3DgQLN161a3fq+99pqJiYkxdrvdNG7c2KxYscLceuutlzw0e+DAgWb27NmmevXqrvd/6dKl+fbPysoyZcuWNQ6Hw5w4ceKi2zfGOTR72LBhplKlSiYwMNDcdNNNJjk5OU+NP/74o+nevbuJiooydrvdBAUFmYYNG5pp06aZU6dOXdJrAYXFZkwRvCoRAHBROTk5qly5sjp27JjnGhigJOGaGQCwqPnz5+vgwYPq3bu3t0sBvIojMwBgMatWrdL69es1YcIEVahQId/J7oCShCMzAGAxr7/+uh5++GGFh4fr3Xff9XY5gNd5NMwkJiaqSZMmKlOmjMLDw9W5c+c8c2mcPHlSAwcOVPny5RUcHKyuXbtq//79niwLACxt1qxZysnJ0Zo1a1SnTh1vlwN4nUfDzPLlyzVw4ECtXLlSixYt0qlTp9S2bVu3+3oMGTJEX3zxhT766CMtX75ce/fu1V133eXJsgAAQDFSqNfMHDx4UOHh4Vq+fLmaN2+ujIwMhYWF6b333nPNlbBlyxbVqlVLycnJrpv5AQAAnE+hTpp3ZvrrcuXKSZJ++uknnTp1Sm3atHH1qVmzpqKios4bZrKystxmDT1zV9/y5ctf0dTtAACg8BljdOTIEVWuXNl1J/crVWhhJjc3V4MHD9ZNN93kOseblpYmf39/hYaGuvWtWLHiee/ImpiYmO9MmAAAwHp27dqla6+99qq2UWhhZuDAgdqwYYO+++67q9rOyJEjNXToUNd6RkaGoqKitGvXLoWEhFxtmQAAoBBkZmYqMjJSZcqUueptFUqYeeSRR7RgwQKtWLHCLX1FREQoOztb6enpbkdn9u/fr4iIiHy3ZbfbZbfb87SHhIQQZgAAsJiCuETEo6OZjDF65JFHNG/ePC1ZskQxMTFujzdq1Eh+fn5avHixq23r1q3auXOn4uLiPFkaAAAoJjx6ZGbgwIF677339Nlnn6lMmTKu62AcDocCAwPlcDh0//33a+jQoSpXrpxCQkL06KOPKi4ujpFMAADgknh0aPb5Dh0lJSUpISFBknPSvGHDhun9999XVlaW4uPj9dprr533NNO5MjMz5XA4lJGRwWkmAAAsoiC/vy1/bybCDAB4lzFGOTk5On36tLdLQRHi6+urUqVKnffARkF+fxfqPDMAgOIlOztb+/bt0/Hjx71dCoqg0qVLq1KlSvL39/fo6xBmAABXJDc3V6mpqfL19VXlypXl7+/P5KWQ5Dxal52drYMHDyo1NVXVq1e/6onxLoQwAwC4ItnZ2crNzVVkZKRKly7t7XJQxAQGBsrPz09//PGHsrOzFRAQ4LHX8ujQbABA8efJ/3HD2grrd4PfQAAAYGmEGQAAYGmEGQBAgbPZCncpbDt27JDNZtO6desu+TmzZs3Kc2Nlb9RRHBFmAAAl1q5du9S3b1/XaKzo6Gg99thjOnTo0AWfFxkZqX379qlOnTqX/Fo9e/bUb7/9drUlIx+EGQBAifT777+rcePG2rZtm95//32lpKRoxowZWrx4seLi4nT48OF8n5ednS1fX19FRESoVKlLHxQcGBio8PDwgiofZyHMAABKpIEDB8rf318LFy7UrbfeqqioKLVv317ffvut9uzZo6eeekqSVKVKFU2YMEG9e/dWSEiIHnzwwXxP73z++eeqXr26AgIC1LJlS73zzjuy2WxKT0+XlPc009ixY1W/fn39+9//VpUqVeRwOHT33XfryJEjrj5ff/21br75ZoWGhqp8+fLq0KGDtm/fXhhvj6UQZgAAJc7hw4f1zTffaMCAAQoMDHR7LCIiQvfee6/mzp2rM3f8ee6551SvXj2tXbtWo0aNyrO91NRUdevWTZ07d9Yvv/yi/v37u8LQhWzfvl3z58/XggULtGDBAi1fvlyTJ092PX7s2DENHTpUa9as0eLFi+Xj46MuXbooNzf3Kt+B4oVJ8wAAJc62bdtkjFGtWrXyfbxWrVr666+/dPDgQUlSq1atNGzYMNfjO3bscOv/xhtvqEaNGpo6daokqUaNGtqwYYMmTpx4wTpyc3M1a9YslSlTRpL0j3/8Q4sXL3Y9r2vXrm793377bYWFhWnTpk2Xdb1OcceRGQBAiXWp91pu3LjxBR/funWrmjRp4tZ24403XnS7VapUcQUZSapUqZIOHDjgWt+2bZt69eql2NhYhYSEqEqVKpKknTt3XlLdJQVhBgBQ4lSrVk02m02bN2/O9/HNmzerbNmyCgsLkyQFBQV5pA4/Pz+3dZvN5nYKqWPHjjp8+LD+9a9/adWqVVq1apUk50XI+B/CDACgxClfvrxuu+02vfbaazpx4oTbY2lpaZozZ4569ux5yTfOrFGjhtasWePW9uOPP15VjYcOHdLWrVv19NNPq3Xr1q5TX8iLMAMAKJFeeeUVZWVlKT4+XitWrNCuXbv09ddf67bbbtM111xz0etdzta/f39t2bJFTz75pH777Td9+OGHmjVrliRd8Z3Ey5Ytq/Lly+vNN99USkqKlixZoqFDh17Rtoo7wgwAoMAZU7jLlahevbrWrFmj2NhY9ejRQ1WrVtWDDz6oli1bKjk5WeXKlbvkbcXExOjjjz/Wp59+qhtuuEGvv/66azST3W6/ovp8fHz0wQcf6KefflKdOnU0ZMgQ1wXGcGczl3r1UxGVmZkph8OhjIwMhYSEeLscACgxTp48qdTUVMXExCggIMDb5RQ5EydO1IwZM7Rr1y5vl+I1F/odKcjvb4ZmAwBQAF577TU1adJE5cuX1/fff6+pU6fqkUce8XZZJQJhBgCAArBt2zY988wzOnz4sKKiojRs2DCNHDnS22WVCIQZAAAKwPTp0zV9+nRvl1EicQEwAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAKDg2WyFuxQDaWlpuu222xQUFKTQ0FBvl3PJbDab5s+f79UaCDMAgBLFZrNdcBk7dqxX6po+fbr27dundevW6bfffvNKDVbFpHkAgBJl3759rp/nzp2r0aNHa+vWra624OBg18/GGJ0+fVqlSnn+63L79u1q1KiRqlevfsXbyM7Olr+/fwFWZQ0cmQEAlCgRERGuxeFwyGazuda3bNmiMmXK6KuvvlKjRo1kt9v13Xffafv27erUqZMqVqyo4OBgNWnSRN9++63bdqtUqaJJkyapb9++KlOmjKKiovTmm2+6Hs/OztYjjzyiSpUqKSAgQNHR0UpMTHQ995NPPtG7774rm82mhIQESdLOnTvVqVMnBQcHKyQkRD169ND+/ftd2xw7dqzq16+vt956y+1mjjabTW+88YY6dOig0qVLq1atWkpOTlZKSopatGihoKAgNWvWTNu3b3fbh88++0wNGzZUQECAYmNjNW7cOOXk5Lge37Ztm5o3b66AgADVrl1bixYtKtDP5koRZgAAOMeIESM0efJkbd68WTfccIOOHj2q22+/XYsXL9batWvVrl07dezYUTt37nR73vPPP6/GjRtr7dq1GjBggB5++GHXUZ+XXnpJn3/+uT788ENt3bpVc+bMUZUqVSRJP/74o9q1a6cePXpo3759evHFF5Wbm6tOnTrp8OHDWr58uRYtWqTff/9dPXv2dHvNlJQUffLJJ/r000+1bt06V/uECRPUu3dvrVu3TjVr1tQ999yj/v37a+TIkVqzZo2MMW43wvzvf/+r3r1767HHHtOmTZv0xhtvaNasWZo4caIkKTc3V3fddZf8/f21atUqzZgxQ08++aQH3v0rYCwuIyPDSDIZGRneLgUASpQTJ06YTZs2mRMnTuR9UCrc5QolJSUZh8PhWl+6dKmRZObPn3/R515//fXm5Zdfdq1HR0ebv//976713NxcEx4ebl5//XVjjDGPPvqoadWqlcnNzc13e506dTJ9+vRxrS9cuND4+vqanTt3uto2btxoJJnVq1cbY4wZM2aM8fPzMwcOHHDbliTz9NNPu9aTk5ONJDNz5kxX2/vvv28CAgJc661btzaTJk1y286///1vU6lSJWOMMd98840pVaqU2bNnj+vxr776ykgy8+bNy3efLvQ7UpDf3xyZAQDgHI0bN3ZbP3r0qIYPH65atWopNDRUwcHB2rx5c54jMzfccIPr5zOnrw4cOCBJSkhI0Lp161SjRg0NGjRICxcuvGANmzdvVmRkpCIjI11ttWvXVmhoqDZv3uxqi46OVlhYWJ7nn11LxYoVJUl169Z1azt58qQyMzMlSb/88ovGjx+v4OBg19KvXz/t27dPx48fd9VTuXJl1zbi4uIuuA+FhQuAAQA4R1BQkNv68OHDtWjRIj333HOqVq2aAgMD1a1bN2VnZ7v18/Pzc1u32WzKzc2VJDVs2FCpqan66quv9O2336pHjx5q06aNPv744wKtNb9abP8/fD2/tjP1HT16VOPGjdNdd92VZ1tnrsUpqjx6ZGbFihXq2LGjKleunO849ISEhDxD4tq1a+fJkgAAuGzff/+9EhIS1KVLF9WtW1cRERHasWPHZW8nJCREPXv21L/+9S/NnTtXn3zyiQ4fPpxv31q1amnXrl3atWuXq23Tpk1KT09X7dq1r3RXzqthw4baunWrqlWrlmfx8fFx1XP2aLCVK1cWeB1XwqNHZo4dO6Z69eqpb9+++SY9SWrXrp2SkpJc63a73ZMlAQBw2apXr65PP/1UHTt2lM1m06hRo1xHNC7VtGnTVKlSJTVo0EA+Pj766KOPFBERcd4J8tq0aaO6devq3nvv1QsvvKCcnBwNGDBAt956a57TYAVh9OjR6tChg6KiotStWzf5+Pjol19+0YYNG/TMM8+oTZs2uu6669SnTx9NnTpVmZmZeuqppwq8jivh0SMz7du31zPPPKMuXbqct4/dbncbJle2bFlPlgQAKAyFfQmwh02bNk1ly5ZVs2bN1LFjR8XHx6thw4aXtY0yZcpoypQpaty4sZo0aaIdO3boyy+/lI9P/l/FNptNn332mcqWLavmzZurTZs2io2N1dy5cwtil/KIj4/XggULtHDhQjVp0kR/+9vfNH36dEVHR0uSfHx8NG/ePJ04cUI33nijHnjgAddIJ2+zGVMIvwVyfijz5s1T586dXW0JCQmaP3++/P39VbZsWbVq1UrPPPOMypcvf97tZGVlKSsry7WemZmpyMhIZWRkKCQkxJO7AAA4y8mTJ5Wamuo2vwlwtgv9jmRmZsrhcBTI97dXRzO1a9dO7777rhYvXqxnn31Wy5cvV/v27XX69OnzPicxMVEOh8O1nH2VNwAAKHm8Oprp7rvvdv1ct25d3XDDDapataqWLVum1q1b5/uckSNHaujQoa71M0dmAABAyVSk5pmJjY1VhQoVlJKSct4+drtdISEhbgsAACi5ilSY2b17tw4dOqRKlSp5uxQAAGARHj3NdPToUbejLKmpqVq3bp3KlSuncuXKady4ceratasiIiK0fft2PfHEE6pWrZri4+M9WRYAoAAV0jgSWFBh/W549MjMmjVr1KBBAzVo0ECSNHToUDVo0ECjR4+Wr6+v1q9frzvvvFPXXXed7r//fjVq1Ej//e9/mWsGACzgzGyyx48f93IlKKrO/G6cOzNyQfPokZkWLVpcMJV98803nnx5AIAH+fr6KjQ01HXvodKlS7umyEfJZozR8ePHdeDAAYWGhsrX19ejr8e9mQAAVywiIkKSXIEGOFtoaKjrd8STCDMAgCtms9lUqVIlhYeH69SpU94uB0WIn5+fx4/InEGYAQBcNV9f30L74gLOVaSGZgMAAFwuwgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0j4aZFStWqGPHjqpcubJsNpvmz5/v9rgxRqNHj1alSpUUGBioNm3aaNu2bZ4sCQAAFDMeDTPHjh1TvXr19Oqrr+b7+JQpU/TSSy9pxowZWrVqlYKCghQfH6+TJ096siwAAFCMlPLkxtu3b6/27dvn+5gxRi+88IKefvppderUSZL07rvvqmLFipo/f77uvvtuT5YGAACKCa9dM5Oamqq0tDS1adPG1eZwONS0aVMlJyef93lZWVnKzMx0WwAAQMnltTCTlpYmSapYsaJbe8WKFV2P5ScxMVEOh8O1REZGerROAABQtFluNNPIkSOVkZHhWnbt2uXtkgAAgBd5LcxERERIkvbv3+/Wvn//ftdj+bHb7QoJCXFbAABAyeW1MBMTE6OIiAgtXrzY1ZaZmalVq1YpLi7OW2UBAACL8ehopqNHjyolJcW1npqaqnXr1qlcuXKKiorS4MGD9cwzz6h69eqKiYnRqFGjVLlyZXXu3NmTZQEAgGLEo2FmzZo1atmypWt96NChkqQ+ffpo1qxZeuKJJ3Ts2DE9+OCDSk9P180336yvv/5aAQEBniwLAAAUIzZjjPF2EVcjMzNTDodDGRkZXD8DAIBFFOT3t+VGMwEAAJyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMANYQG6udPvt0osvOn9GMZWbKyUmSnfd5e1KAEshzAAW4OMjDRggDR4sORzSP/8p5eR4uyoUmOxsacgQKTjY+eEOG+btigBLIcwAFtGhgxQXJx096vzPe1CQ9MADznVYVHq6dO+9zg/zhRekEyekdu2km27ydmWApRBmAAv5/HOpVCnnz9nZ0syZUmio1KWLlJbm1dJwOXbulNq3l8qXl95773+H2ex26aOPvFsbYEGEGcBCKlSQRo50bzt9Wpo/X6pcWWrRQtq61RuV4ZKsX+88vBYdLX39dd4LoKZOdZ5qAnBZvB5mxo4dK5vN5rbUrFnT22UBRdb48VKlSnnbjZGWL5dq1pQaNJCSkwu/NpzHkiXS9ddL9epJK1fm36dqVenRRwu3LqCY8HqYkaTrr79e+/btcy3fffedt0sCirRPPrnw4+vWSc2aSdWqOU9NwUvmznUehWndWtq06fz9bDbpiy8Kry6gmCkSYaZUqVKKiIhwLRUqVPB2SUCRFhfnvOTiYrZvlzp1ch7Jeestz9cFOU8dvfiiFBYm3X238/qYi+nVS6pVy/O1AcVUkQgz27ZtU+XKlRUbG6t7771XOy/wx5+VlaXMzEy3BSiJPv7Yeb3opUhLk55+2nm2Ax62YIE0YYL055+X1j8oSHrnHc/WBBRzXg8zTZs21axZs/T111/r9ddfV2pqqm655RYdOXIk3/6JiYlyOByuJTIyspArBoqG0qWl5567eL9WraTVq52BplUrz9dV4t15pzPI/Pe/0i23XLz/v/71vyFqAK6IzRhjvF3E2dLT0xUdHa1p06bp/vvvz/N4VlaWsrKyXOuZmZmKjIxURkaGQkJCCrNUoEioXl1KSTn/4/Hx0pdfOifeQyHKzZVuvvnCV2LXry+tXVtoJQFFSWZmphwOR4F8fxe5f95CQ0N13XXXKeU8/zrb7XaFhIS4LUBJ9vnnzutHz+ebb5zfmdnZhVYSjh+XrrvuwkHGx4eLfoECUuTCzNGjR7V9+3ZVym/sKYA8atWS/v539zZfX/f1X3+VYmOdE87Cw9LSnCOYtm93bz/3Qxk0SLr22sKrCyjGvB5mhg8fruXLl2vHjh364Ycf1KVLF/n6+qpXr17eLg2wjLffdp9rbds25+SyZ9uzR6pSRUpNLdTSSpaNG53zxZx78W/lytLmzf9bL19eev75wq0NKMa8HmZ2796tXr16qUaNGurRo4fKly+vlStXKiwszNulAZZRqpTz1gaSFBEhxcQ4RwTHxrr3y8hwHslZtarwayz2Fi92ns87fty9vW5dZ4KsXt157wnJOf8MFzEBBabIXQB8uQryAiLA6ho2dF6q8cEHzvXzXYPq4+Mc2t2lS+HXWCy9+66UkOCchvlsbdtKX331v+ASHy9lZUnLlhV2hUCRU6wvAAZw5RYskIYN+9+6j4/0ww9St27u/XJzpa5dpZdfLtz6iqVnnpH69MkbZB54wHn19dlHYJ56ynkjLQAFijADFCOVK0tNmuRt/+gjafhw9zZjnNegnh1+cJn69ZNGjcrbPmGCc/6YczVv/r9TTQAKDGEGKCGmTnUeiTl3GPe0aVL37t6pybJyc52nkM69R4TN5pzN9+mnvVMXUEIRZoAS5JFHpHnz8o4S/vhj5/2ecnO9U5elZGc77369aJF7e6lSzrbevb1TF1CCEWaAEqZTJ+n77/Pe12nlSueAm3MH4+Ashw87h4pt2ODeXrq081blrVt7pSygpCPMACVQ06bOaU8cDvf233+XoqKkvXu9U1eRlprqDDLnvjkVKjgnyLv+eu/UBYAwA5RUMTHSjh3SNde4tx865DxCc+7BhxJt1SrnBD2Zme7tVatKf/zhnNwHgNcQZoASLDTUeTSmbl339uPHpQYNnPPAlXiffCI1a+acH+ZszZpJv/3mPMUEwKsIM0AJ5+/vvNyjXTv39pwc6bbbpKQkr5RVNLz4onOSnnOvjO7Z03nhEbP4AkUCf4kA5OPjnKi2f3/3dmOkvn2l8eO9U5dXDR0qDR6ct/3xx/83xTKAIoEwA8BlxgznhLbnGjPGOaFtidGtmzR9unubzeacqGfKFO/UBOC8CDMA3Dz1lHPet3Mn15s503naqVjPRZOT4xzq9ckn7u2+vs4Jeh55xDt1AbggwgyAPHr3lr79VvLzc2//9lvnfHHZ2d6py6OOHnXepXP1avd2u915p85OnbxTF4CLIswAyFerVtLatVJQkHv7hg1SlSrO+eOKjb17nTuVmure7nBIW7fmf8MrAEUGYQbAeV1/vZSSIoWFubfv2+f87t++3StlFaz166Vq1ZwT7Jzt2mudE/FER3ulLACXjjAD4IIiIpzf6dWru7cfOSLVru08A2NZixZJjRpJJ064t9er5zxKwx2uAUsgzAC4qNKlpS1bpJtucm/PzpZuvjnv9bKWkJQkxcc7L/o9W/v20s8/O28cCcASCDMALomPj/Tdd9Ldd7u35+Y6RzK/8IJXyroyY8c6J9Axxr29f3/pyy+ZDA+wGP5iAVyW99+Xnngib/uQIc555oq8++6Txo3L2z5pknOiHQCWQ5gBcNmefVZ69dW8c9FMny517eqdmi4qN1dq00aaNcu93WaTZs+WRo70SlkArh5hBsAVGTBAmj/fOZ/c2T79VLrxxryXonhVdrbzbprn3jnTz09askS6917v1AWgQBBmAFyxO++UVq6UAgLc23/80Tn66ehR79Tl5vBh5/DqTZvc24OCpF9+kVq08EpZAAoOYQbAVWnc2DnS6dxRzGemaNm71xtV/b/t250T4qSlubeHhUm//y7VquWVsgAULMIMgKsWHS398YcUGenefviwcz669eu9UNT33zsnwjlyxL29enVp504pPNwLRQHwBMIMgAIREuI82FG/vnv7iRPOeem++aYQi/n4Y6l587w3kbrlFudhpHPPiwGwNMIMgAJTqpT000/S7be7t+fkOOeimzmzEIqYNk3q3j3v7b3vuUdasYI5ZIBiiL9qAAXKx0f6z3+khx5ybzdGeuAB53x1HvPYY9KwYXnbR4yQ5szx4AsD8CbCDACPeP11KTExb/u4cVJCggdesEsX6aWX3NtsNum11/IvBECxQZgB4DEjRjjnozv3zM4770itW+c9E3RFcnKcQ6rmz3dv9/WVPv9cevjhAngRAEUZYQaAR917r3OuOj8/9/YlS6Q6daSTJ69i40ePOodL/fSTe3tAgLR6tdShw1VsHIBVEGYAeFyLFtKvvzrnqTvb5s1STIz0559XsNHdu6WoKOeY8LOFhkpbt0oNG15htQCshjADoFDUqOEcun3u9C5pac5As23bZWxs3Trpuuukv/5yb4+MdIabqKirLReAhRBmABSa8HBn1qhe3b396FHnKafvv7+EjXz1ldSkiXMCm7M1bOhMSyEhBVYvAGsgzAAoVAEBznnrmjd3b8/OdrZ9+OEFnvzWW9Idd+S9i2WHDs7rZkqVKvB6ARR9hBkAhc7HR1q+3DmP3dlyc6WePaXnn8/nSaNGSf36OSesOduAAdIXX3isVgBFH2EGgNfMmSONHJm3ffhwadCgsxr69JGeeSZvxylTpFdf9Vh9AKyhSISZV199VVWqVFFAQICaNm2q1atXe7skAIVk0iRpxgzn/HZne/llqfOduVLLltK777o/6OMjffCB9PjjhVcogCLL62Fm7ty5Gjp0qMaMGaOff/5Z9erVU3x8vA4cOODt0gAUkv79pQULnPPcneGvk0r84npp2TL3zn5+0tKlzvNRACDJZsy5J6ALV9OmTdWkSRO98sorkqTc3FxFRkbq0Ucf1YgRI/L0z8rKUlZWlms9MzNTkZGRysjIUAijGABL+/ln6aabnBPpRekP/abrZNdZd74ODpbWrHGO8wZgaZmZmXI4HAXy/e3VIzPZ2dn66aef1KZNG1ebj4+P2rRpo+Tk5Hyfk5iYKIfD4VoiIyMLq1wAHtawoXO+u9BQaaei1dpnmcyZeyGEh0upqQQZAHl4Ncz8+eefOn36tCpWrOjWXrFiRaWlpeX7nJEjRyojI8O17Nq1qzBKBVBIzkzqe/310riFcbJ9+KFUv76zsUIFb5cHoAiy3KQMdrtddrvd22UA8KCQEGnDhjNrXaWuXb1ZDoAizqtHZipUqCBfX1/t37/frX3//v2KiIjwUlUAAMBKvBpm/P391ahRIy1evNjVlpubq8WLFysuLs6LlQEAAKvw+mmmoUOHqk+fPmrcuLFuvPFGvfDCCzp27Jjuu+8+b5cGAAAswOthpmfPnjp48KBGjx6ttLQ01a9fX19//XWei4IBAADy4/V5Zq5WQY5TBwAAhaPYzDMDAABwtQgzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0ggzAADA0rwaZqpUqSKbzea2TJ482ZslAQAAiynl7QLGjx+vfv36udbLlCnjxWoAAIDVeD3MlClTRhEREd4uAwAAWJTXr5mZPHmyypcvrwYNGmjq1KnKycm5YP+srCxlZma6LQAAoOTy6pGZQYMGqWHDhipXrpx++OEHjRw5Uvv27dO0adPO+5zExESNGzeuEKsEAABFmc0YYwpygyNGjNCzzz57wT6bN29WzZo187S//fbb6t+/v44ePSq73Z7vc7OyspSVleVaz8zMVGRkpDIyMhQSEnJ1xQMAgEKRmZkph8NRIN/fBR5mDh48qEOHDl2wT2xsrPz9/fO0b9y4UXXq1NGWLVtUo0aNS3q9gnwzAABA4SjI7+8CP80UFhamsLCwK3ruunXr5OPjo/Dw8AKuCgAAFFdeu2YmOTlZq1atUsuWLVWmTBklJydryJAh+vvf/66yZct6qywAAGAxXgszdrtdH3zwgcaOHausrCzFxMRoyJAhGjp0qLdKAgAAFuS1MNOwYUOtXLnSWy8PAACKCa/PMwMAAHA1CDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSCDMAAMDSPBZmJk6cqGbNmql06dIKDQ3Nt8/OnTt1xx13qHTp0goPD9fjjz+unJwcT5UEAACKoVKe2nB2dra6d++uuLg4zZw5M8/jp0+f1h133KGIiAj98MMP2rdvn3r37i0/Pz9NmjTJU2UBAIBixmaMMZ58gVmzZmnw4MFKT093a//qq6/UoUMH7d27VxUrVpQkzZgxQ08++aQOHjwof3//S9p+ZmamHA6HMjIyFBISUtDlAwAADyjI72+vXTOTnJysunXruoKMJMXHxyszM1MbN2487/OysrKUmZnptgAAgJLLa2EmLS3NLchIcq2npaWd93mJiYlyOByuJTIy0qN1AgCAou2ywsyIESNks9kuuGzZssVTtUqSRo4cqYyMDNeya9cuj74eAAAo2i7rAuBhw4YpISHhgn1iY2MvaVsRERFavXq1W9v+/ftdj52P3W6X3W6/pNcAAADF32WFmbCwMIWFhRXIC8fFxWnixIk6cOCAwsPDJUmLFi1SSEiIateuXSCvAQAAij+PDc3euXOnDh8+rJ07d+r06dNat26dJKlatWoKDg5W27ZtVbt2bf3jH//QlClTlJaWpqeffloDBw7kyAsAALhkHhuanZCQoHfeeSdP+9KlS9WiRQtJ0h9//KGHH35Yy5YtU1BQkPr06aPJkyerVKlLz1gMzQYAwHoK8vvb4/PMeBphBgAA6ykW88wAAAAUBMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNI+FmYkTJ6pZs2YqXbq0QkND8+1js9nyLB988IGnSgIAAMVQKU9tODs7W927d1dcXJxmzpx53n5JSUlq166da/18wQcAACA/Hgsz48aNkyTNmjXrgv1CQ0MVERHhqTIAAEAx5/VrZgYOHKgKFSroxhtv1Ntvvy1jzAX7Z2VlKTMz020BAAAll8eOzFyK8ePHq1WrVipdurQWLlyoAQMG6OjRoxo0aNB5n5OYmOg66gMAAGAzFzsUcpYRI0bo2WefvWCfzZs3q2bNmq71WbNmafDgwUpPT7/o9kePHq2kpCTt2rXrvH2ysrKUlZXlWs/MzFRkZKQyMjIUEhJy8Z0AAABel5mZKYfDUSDf35d1ZGbYsGFKSEi4YJ/Y2NgrLqZp06aaMGGCsrKyZLfb8+1jt9vP+xgAACh5LivMhIWFKSwszFO1aN26dSpbtixhBQAAXDKPXTOzc+dOHT58WDt37tTp06e1bt06SVK1atUUHBysL774Qvv379ff/vY3BQQEaNGiRZo0aZKGDx/uqZIAAEAx5LEwM3r0aL3zzjuu9QYNGkiSli5dqhYtWsjPz0+vvvqqhgwZImOMqlWrpmnTpqlfv36eKgkAABRDl3UBcFFUkBcQAQCAwlGQ399en2cGAADgahBmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApXkszOzYsUP333+/YmJiFBgYqKpVq2rMmDHKzs5267d+/XrdcsstCggIUGRkpKZMmeKpkgAAQDFUylMb3rJli3Jzc/XGG2+oWrVq2rBhg/r166djx47pueeekyRlZmaqbdu2atOmjWbMmKFff/1Vffv2VWhoqB588EFPlQYAAIoRmzHGFNaLTZ06Va+//rp+//13SdLrr7+up556SmlpafL395ckjRgxQvPnz9eWLVsuaZuZmZlyOBzKyMhQSEiIx2oHAAAFpyC/vz12ZCY/GRkZKleunGs9OTlZzZs3dwUZSYqPj9ezzz6rv/76S2XLls2zjaysLGVlZbltU3K+KQAAwBrOfG8XxDGVQgszKSkpevnll12nmCQpLS1NMTExbv0qVqzoeiy/MJOYmKhx48blaY+MjCzgigEAgKcdOnRIDofjqrZx2WFmxIgRevbZZy/YZ/PmzapZs6Zrfc+ePWrXrp26d++ufv36XX6VZxk5cqSGDh3qWk9PT1d0dLR27tx51W+GlWRmZioyMlK7du0qUafX2G/2uyRgv9nvkiAjI0NRUVFuZ2yu1GWHmWHDhikhIeGCfWJjY10/7927Vy1btlSzZs305ptvuvWLiIjQ/v373drOrEdEROS7bbvdLrvdnqfd4XCUqF+CM0JCQtjvEoT9LlnY75KlpO63j8/VD6y+7DATFhamsLCwS+q7Z88etWzZUo0aNVJSUlKeguPi4vTUU0/p1KlT8vPzkyQtWrRINWrUyPcUEwAAwLk8Ns/Mnj171KJFC0VFRem5557TwYMHlZaWprS0NFefe+65R/7+/rr//vu1ceNGzZ07Vy+++KLbaSQAAIAL8dgFwIsWLVJKSopSUlJ07bXXuj125splh8OhhQsXauDAgWrUqJEqVKig0aNHX9YcM3a7XWPGjMn31FNxxn6z3yUB+81+lwTs99Xvd6HOMwMAAFDQuDcTAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMuGmR07duj+++9XTEyMAgMDVbVqVY0ZM0bZ2dlu/davX69bbrlFAQEBioyM1JQpU7xUccGZOHGimjVrptKlSys0NDTfPjabLc/ywQcfFG6hBexS9nvnzp264447VLp0aYWHh+vxxx9XTk5O4RbqYVWqVMnz2U6ePNnbZRW4V199VVWqVFFAQICaNm2q1atXe7skjxs7dmyez/bsW8MUFytWrFDHjh1VuXJl2Ww2zZ8/3+1xY4xGjx6tSpUqKTAwUG3atNG2bdu8U2wButh+JyQk5Pn827Vr551iC0hiYqKaNGmiMmXKKDw8XJ07d9bWrVvd+pw8eVIDBw5U+fLlFRwcrK5du+a5O8DFWDbMbNmyRbm5uXrjjTe0ceNGTZ8+XTNmzNA///lPV5/MzEy1bdtW0dHR+umnnzR16lSNHTs2z20VrCY7O1vdu3fXww8/fMF+SUlJ2rdvn2vp3Llz4RToIRfb79OnT+uOO+5Qdna2fvjhB73zzjuaNWuWRo8eXciVet748ePdPttHH33U2yUVqLlz52ro0KEaM2aMfv75Z9WrV0/x8fE6cOCAt0vzuOuvv97ts/3uu++8XVKBO3bsmOrVq6dXX30138enTJmil156STNmzNCqVasUFBSk+Ph4nTx5spArLVgX229Jateundvn//777xdihQVv+fLlGjhwoFauXKlFixbp1KlTatu2rY4dO+bqM2TIEH3xxRf66KOPtHz5cu3du1d33XXX5b2QKUamTJliYmJiXOuvvfaaKVu2rMnKynK1Pfnkk6ZGjRreKK/AJSUlGYfDke9jksy8efMKtZ7Ccr79/vLLL42Pj49JS0tztb3++usmJCTE7XfA6qKjo8306dO9XYZH3XjjjWbgwIGu9dOnT5vKlSubxMREL1bleWPGjDH16tXzdhmF6tx/q3Jzc01ERISZOnWqqy09Pd3Y7Xbz/vvve6FCz8jv3+g+ffqYTp06eaWewnLgwAEjySxfvtwY4/xs/fz8zEcffeTqs3nzZiPJJCcnX/J2LXtkJj8ZGRlud99MTk5W8+bN5e/v72qLj4/X1q1b9ddff3mjxEI1cOBAVahQQTfeeKPefvtt18zLxVVycrLq1q2rihUrutri4+OVmZmpjRs3erGygjd58mSVL19eDRo00NSpU4vVqbTs7Gz99NNPatOmjavNx8dHbdq0UXJyshcrKxzbtm1T5cqVFRsbq3vvvVc7d+70dkmFKjU1VWlpaW6fv8PhUNOmTUvE579s2TKFh4erRo0aevjhh3Xo0CFvl1SgMjIyJMn1Xf3TTz/p1KlTbp93zZo1FRUVdVmft8duZ1DYUlJS9PLLL+u5555ztaWlpSkmJsat35kvurS0tGJ9M8vx48erVatWKl26tBYuXKgBAwbo6NGjGjRokLdL85i0tDS3ICO5f97FxaBBg9SwYUOVK1dOP/zwg0aOHKl9+/Zp2rRp3i6tQPz55586ffp0vp/lli1bvFRV4WjatKlmzZqlGjVqaN++fRo3bpxuueUWbdiwQWXKlPF2eYXizN9qfp9/cfo7zk+7du101113KSYmRtu3b9c///lPtW/fXsnJyfL19fV2eVctNzdXgwcP1k033aQ6depIcn7e/v7+ea6DvNzPu8gdmRkxYkS+F6+evZz7D9qePXvUrl07de/eXf369fNS5VfnSvb7QkaNGqWbbrpJDRo00JNPPqknnnhCU6dO9eAeXJmC3m+rupz3YejQoWrRooVuuOEGPfTQQ3r++ef18ssvKysry8t7gavVvn17de/eXTfccIPi4+P15ZdfKj09XR9++KG3S0MhuPvuu3XnnXeqbt266ty5sxYsWKAff/xRy5Yt83ZpBWLgwIHasGGDRwajFLkjM8OGDVNCQsIF+8TGxrp+3rt3r1q2bKlmzZrlubA3IiIizxXRZ9YjIiIKpuACcrn7fbmaNm2qCRMmKCsrq0jdzKwg9zsiIiLPiJei+nmf62reh6ZNmyonJ0c7duxQjRo1PFBd4apQoYJ8fX3z/dst6p9jQQsNDdV1112nlJQUb5dSaM58xvv371elSpVc7fv371f9+vW9VJV3xMbGqkKFCkpJSVHr1q29Xc5VeeSRR7RgwQKtWLHC7ebTERERys7OVnp6utvRmcv9ey9yYSYsLExhYWGX1HfPnj1q2bKlGjVqpKSkJPn4uB9oiouL01NPPaVTp07Jz89PkvNu3jVq1Chyp5guZ7+vxLp161S2bNkiFWSkgt3vuLg4TZw4UQcOHFB4eLgk5+cdEhKi2rVrF8hreMrVvA/r1q2Tj4+Pa5+tzt/fX40aNdLixYtdI/Byc3O1ePFiPfLII94trpAdPXpU27dv1z/+8Q9vl1JoYmJiFBERocWLF7vCS2ZmplatWnXREZzFze7du3Xo0CG3UGc1xhg9+uijmjdvnpYtW5bn0o9GjRrJz89PixcvVteuXSVJW7du1c6dOxUXF3dZL2RJu3fvNtWqVTOtW7c2u3fvNvv27XMtZ6Snp5uKFSuaf/zjH2bDhg3mgw8+MKVLlzZvvPGGFyu/en/88YdZu3atGTdunAkODjZr1641a9euNUeOHDHGGPP555+bf/3rX+bXX38127ZtM6+99popXbq0GT16tJcrvzoX2++cnBxTp04d07ZtW7Nu3Trz9ddfm7CwMDNy5EgvV15wfvjhBzN9+nSzbt06s337djN79mwTFhZmevfu7e3SCtQHH3xg7Ha7mTVrltm0aZN58MEHTWhoqNtIteJo2LBhZtmyZSY1NdV8//33pk2bNqZChQrmwIED3i6tQB05csT19yvJTJs2zaxdu9b88ccfxhhjJk+ebEJDQ81nn31m1q9fbzp16mRiYmLMiRMnvFz51bnQfh85csQMHz7cJCcnm9TUVPPtt9+ahg0bmurVq5uTJ096u/Qr9vDDDxuHw2GWLVvm9j19/PhxV5+HHnrIREVFmSVLlpg1a9aYuLg4ExcXd1mvY9kwk5SUZCTlu5ztl19+MTfffLOx2+3mmmuuMZMnT/ZSxQWnT58++e730qVLjTHGfPXVV6Z+/fomODjYBAUFmXr16pkZM2aY06dPe7fwq3Sx/TbGmB07dpj27dubwMBAU6FCBTNs2DBz6tQp7xVdwH766SfTtGlT43A4TEBAgKlVq5aZNGmSpf+xO5+XX37ZREVFGX9/f3PjjTealStXerskj+vZs6epVKmS8ff3N9dcc43p2bOnSUlJ8XZZBW7p0qX5/i336dPHGOMcnj1q1ChTsWJFY7fbTevWrc3WrVu9W3QBuNB+Hz9+3LRt29aEhYUZPz8/Ex0dbfr162f5AH++7+mkpCRXnxMnTpgBAwaYsmXLmtKlS5suXbq4HZi4FLb/fzEAAABLKnKjmQAAAC4HYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFja/wFDxBRPY8IcwQAAAABJRU5ErkJggg==", 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", + "image/png": 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", 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", + "image/png": 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", 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", 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", 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", 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2119/Lekv/Fx+++03Sf/DPsdvv/1WdOzYUTg4OAgHBwfRqFEjERERIZKTkyXjVqxYIfz9/YVarRZt2rQR+/fvF88++2yZL82OiIgQ69evF/Xr1ze+/3v27ClxvF6vF66urkKj0Yg7d+48cvlFt3vfvn1i7NixwtXVVTg6OooXX3xRXLt2zTiu8PN92DRixIgyrY+oMqiEqIJnJRIR0SPl5+ejTp066NOnD9auXSt3OUSy4TkzREQKtXXrVly9ehVhYWFyl0IkK+6ZISJSmEOHDuHPP//E3LlzUbt27RJvZkhUk3DPDBGRwqxcuRLjx4+Hh4cHPv/8c7nLIZKdWcNMdHQ02rZtCycnJ3h4eKBfv37F7qWRm5uLiIgIuLm5wdHREQMHDsSVK1fMWRYRkaLFxsYiPz8fR44cQdOmTeUuh0h2Zg0z+/btQ0REBA4ePIi4uDjcvXsXPXr0wK1bt4xjpk6dih9++AFff/019u3bh0uXLmHAgAHmLIuIiIiqkUo9Z+bq1avw8PDAvn370LlzZ2RnZ8Pd3R1ffvml8V4Jp06dQuPGjZGQkGB8mB8RERHRw1TqTfMKH9xWq1YtAMDRo0dx9+5dBAcHG8c0atQIPj4+Dw0zer1ectfQwqf6urm5levW7URERFT5hBC4efMm6tSpAwuLih0oqrQwYzAYMGXKFDzzzDPGY7wZGRmwsbEp9ih5T0/Phz5RNzo6usQ7YRIREZHypKen48knn6zQMiotzEREROD48eP43//+V6HlREVFITIy0tjOzs6Gj48P0tPT4ezsXNEyiYiIqBLodDp4e3vDycmpwsuqlDAzceJEbN++Hfv375ekL61Wi7y8PGRlZUn2zly5cgVarbbEZanVaqjV6mL9zs7ODDNEREQKY4pTRMx6NZMQAhMnTsSWLVuwe/du+Pv7S+YHBgbC2toa8fHxxr7k5GSkpaXxAWZERERUJmbdMxMREYEvv/wS27Ztg5OTk/E8GI1GAzs7O2g0GowePRqRkZGoVasWnJ2dMWnSJAQFBfFKJiIiIioTs16a/bBdRzExMQgPDwdQcNO8adOmYePGjdDr9QgJCcGKFSseepjpQTqdDhqNBtnZ2TzMREREpBCm/P5W/LOZGGaIiOQlhEB+fj7u3bsndylUhVhaWsLKyuqhOzZM+f1dqfeZISKi6iUvLw+XL1/G7du35S6FqiB7e3t4eXnBxsbGrOthmCEionIxGAxITU2FpaUl6tSpAxsbG968lAAU7K3Ly8vD1atXkZqaivr161f4xnilYZghIqJyycvLg8FggLe3N+zt7eUuh6oYOzs7WFtb4/z588jLy4Otra3Z1mXWS7OJiKj6M+f/cZOyVdbvBn8DiYiISNEYZoiIiEjRGGaIiMjkVKrKnSrbuXPnoFKpkJiYWObXxMbGFnuwshx1VEcMM0REVGOlp6dj1KhRxquxfH198dprr+HatWulvs7b2xuXL19G06ZNy7yuoUOH4u+//65oyVQChhkiIqqRzp49izZt2uD06dPYuHEjUlJSsGrVKsTHxyMoKAjXr18v8XV5eXmwtLSEVquFlVXZLwq2s7ODh4eHqcqnIhhmiIioRoqIiICNjQ127dqFZ599Fj4+PujZsyd+/vlnXLx4EW+99RYAwM/PD3PnzkVYWBicnZ0xduzYEg/vfP/996hfvz5sbW3RtWtXrFu3DiqVCllZWQCKH2aaPXs2WrZsiS+++AJ+fn7QaDQYNmwYbt68aRyzc+dOdOzYES4uLnBzc0Pv3r1x5syZynh7FIVhhoiIapzr16/jp59+woQJE2BnZyeZp9Vq8eKLL2LTpk0ofOLPe++9hxYtWuDYsWN45513ii0vNTUVgwYNQr9+/fDHH39g3LhxxjBUmjNnzmDr1q3Yvn07tm/fjn379mHhwoXG+bdu3UJkZCSOHDmC+Ph4WFhYoH///jAYDBV8B6oX3jSPiIhqnNOnT0MIgcaNG5c4v3Hjxrhx4wauXr0KAOjWrRumTZtmnH/u3DnJ+NWrV6Nhw4ZYsmQJAKBhw4Y4fvw45s+fX2odBoMBsbGxcHJyAgC8/PLLiI+PN75u4MCBkvGfffYZ3N3dcfLkycc6X6e6454ZIiKqscr6rOU2bdqUOj85ORlt27aV9LVr1+6Ry/Xz8zMGGQDw8vJCZmamsX369GkMHz4cAQEBcHZ2hp+fHwAgLS2tTHXXFAwzRERU49SrVw8qlQpJSUklzk9KSoKrqyvc3d0BAA4ODmapw9raWtJWqVSSQ0h9+vTB9evX8cknn+DQoUM4dOgQgIKTkOk+hhkiIqpx3Nzc8Nxzz2HFihW4c+eOZF5GRgY2bNiAoUOHlvnBmQ0bNsSRI0ckfb/99luFarx27RqSk5Px9ttvo3v37sZDX1QcwwwREdVIH330EfR6PUJCQrB//36kp6dj586deO655/DEE0888nyXosaNG4dTp05h+vTp+Pvvv7F582bExsYCQLmfJO7q6go3NzesWbMGKSkp2L17NyIjI8u1rOqOYYaIiExOiMqdyqN+/fo4cuQIAgICMGTIENStWxdjx45F165dkZCQgFq1apV5Wf7+/vjmm2/w3XffoXnz5li5cqXxaia1Wl2u+iwsLPDVV1/h6NGjaNq0KaZOnWo8wZikVKKsZz9VUTqdDhqNBtnZ2XB2dpa7HCKiGiM3Nxepqanw9/eHra2t3OVUOfPnz8eqVauQnp4udymyKe13xJTf37w0m4iIyARWrFiBtm3bws3NDb/++iuWLFmCiRMnyl1WjcAwQ0REZAKnT5/GvHnzcP36dfj4+GDatGmIioqSu6wagWGGiIjIBJYtW4Zly5bJXUaNxBOAiYiISNEYZoiIiEjRGGaIiIhI0RhmiIiISNEYZoiIiEjRGGaIiIhI0RhmiIjI9FSqyp2qgYyMDDz33HNwcHCAi4uL3OWUmUqlwtatW2WtgWGGiIhqFJVKVeo0e/ZsWepatmwZLl++jMTERPz999+y1KBUvGkeERHVKJcvXzb+vGnTJsycORPJycnGPkdHR+PPQgjcu3cPVlbm/7o8c+YMAgMDUb9+/XIvIy8vDzY2NiasShm4Z4aIiGoUrVZrnDQaDVQqlbF96tQpODk5YceOHQgMDIRarcb//vc/nDlzBn379oWnpyccHR3Rtm1b/Pzzz5Ll+vn5YcGCBRg1ahScnJzg4+ODNWvWGOfn5eVh4sSJ8PLygq2tLXx9fREdHW187bfffovPP/8cKpUK4eHhAIC0tDT07dsXjo6OcHZ2xpAhQ3DlyhXjMmfPno2WLVvi008/lTzMUaVSYfXq1ejduzfs7e3RuHFjJCQkICUlBV26dIGDgwM6dOiAM2fOSLZh27ZtaN26NWxtbREQEIA5c+YgPz/fOP/06dPo3LkzbG1t0aRJE8TFxZn0sykvhhkiIqIHzJgxAwsXLkRSUhKaN2+OnJwc/Otf/0J8fDyOHTuG0NBQ9OnTB2lpaZLXvf/++2jTpg2OHTuGCRMmYPz48ca9Ph9++CG+//57bN68GcnJydiwYQP8/PwAAL/99htCQ0MxZMgQXL58Gf/5z39gMBjQt29fXL9+Hfv27UNcXBzOnj2LoUOHStaZkpKCb7/9Ft999x0SExON/XPnzkVYWBgSExPRqFEjvPDCCxg3bhyioqJw5MgRCCEkD8L85ZdfEBYWhtdeew0nT57E6tWrERsbi/nz5wMADAYDBgwYABsbGxw6dAirVq3C9OnTzfDul4NQuOzsbAFAZGdny10KEVGNcufOHXHy5Elx586d4jOByp3KKSYmRmg0GmN7z549AoDYunXrI1/71FNPieXLlxvbvr6+4qWXXjK2DQaD8PDwECtXrhRCCDFp0iTRrVs3YTAYSlxe3759xYgRI4ztXbt2CUtLS5GWlmbsO3HihAAgDh8+LIQQYtasWcLa2lpkZmZKlgVAvP3228Z2QkKCACDWrl1r7Nu4caOwtbU1trt37y4WLFggWc4XX3whvLy8hBBC/PTTT8LKykpcvHjROH/Hjh0CgNiyZUuJ21Ta74gpv7+5Z4aIiOgBbdq0kbRzcnLw+uuvo3HjxnBxcYGjoyOSkpKK7Zlp3ry58efCw1eZmZkAgPDwcCQmJqJhw4aYPHkydu3aVWoNSUlJ8Pb2hre3t7GvSZMmcHFxQVJSkrHP19cX7u7uxV5ftBZPT08AQLNmzSR9ubm50Ol0AIA//vgD7777LhwdHY3TmDFjcPnyZdy+fdtYT506dYzLCAoKKnUbKgtPACYiInqAg4ODpP36668jLi4O7733HurVqwc7OzsMGjQIeXl5knHW1taStkqlgsFgAAC0bt0aqamp2LFjB37++WcMGTIEwcHB+Oabb0xaa0m1qP7/8vWS+grry8nJwZw5czBgwIBiyyo8F6eqMuuemf3796NPnz6oU6dOidehh4eHF7skLjQ01JwlERERPbZff/0V4eHh6N+/P5o1awatVotz58499nKcnZ0xdOhQfPLJJ9i0aRO+/fZbXL9+vcSxjRs3Rnp6OtLT0419J0+eRFZWFpo0aVLeTXmo1q1bIzk5GfXq1Ss2WVhYGOspejXYwYMHTV5HeZh1z8ytW7fQokULjBo1qsSkBwChoaGIiYkxttVqtTlLIiIiemz169fHd999hz59+kClUuGdd94x7tEoq6VLl8LLywutWrWChYUFvv76a2i12ofeIC84OBjNmjXDiy++iA8++AD5+fmYMGECnn322WKHwUxh5syZ6N27N3x8fDBo0CBYWFjgjz/+wPHjxzFv3jwEBwejQYMGGDFiBJYsWQKdToe33nrL5HWUh1n3zPTs2RPz5s1D//79HzpGrVZLLpNzdXU1Z0lERFQZKvsUYDNbunQpXF1d0aFDB/Tp0wchISFo3br1Yy3DyckJixcvRps2bdC2bVucO3cOP/74IywsSv4qVqlU2LZtG1xdXdG5c2cEBwcjICAAmzZtMsUmFRMSEoLt27dj165daNu2LZ5++mksW7YMvr6+AAALCwts2bIFd+7cQbt27fDKK68Yr3SSm0qISvgtQMGHsmXLFvTr18/YFx4ejq1bt8LGxgaurq7o1q0b5s2bBzc3t4cuR6/XQ6/XG9s6nQ7e3t7Izs6Gs7OzOTeBiIiKyM3NRWpqquT+JkRFlfY7otPpoNFoTPL9LevVTKGhofj8888RHx+PRYsWYd++fejZsyfu3bv30NdER0dDo9EYp6JneRMREVHNI+vVTMOGDTP+3KxZMzRv3hx169bF3r170b179xJfExUVhcjISGO7cM8MERER1UxV6j4zAQEBqF27NlJSUh46Rq1Ww9nZWTIRERFRzVWlwsyFCxdw7do1eHl5yV0KERERKYRZDzPl5ORI9rKkpqYiMTERtWrVQq1atTBnzhwMHDgQWq0WZ86cwZtvvol69eohJCTEnGUREZEJVdJ1JKRAlfW7YdY9M0eOHEGrVq3QqlUrAEBkZCRatWqFmTNnwtLSEn/++Seef/55NGjQAKNHj0ZgYCB++eUX3muGiEgBCu8me/v2bZkroaqq8HfjwTsjm5pZ98x06dKl1FT2008/mXP1RERkRpaWlnBxcTE+e8je3t54i3yq2YQQuH37NjIzM+Hi4gJLS0uzro/PZiIionLTarUAYAw0REW5uLgYf0fMiWGGiIjKTaVSwcvLCx4eHrh7967c5VAVYm1tbfY9MoUYZoiIqMIsLS0r7YuL6EFV6tJsIiIiosfFMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDRFTFpKbKXQGRsjDMEBFVIYcOAc89J3cVRMrCMENEVEXs3w907Ai4uMhdCZGyMMwQEVUBcXFAt25Afj7w7LNyV0OkLAwzREQy+/57IDQUuHevoD18uLz1ECkNwwwRkYy++Qbo1w8wGAralpZAmzaylkSkOAwzREQyWb8eGDIEEOJ+n5eXfPUQKRXDDBGRDNasAV5+WRpkACAwUJ56iJSMYYaIqJItXw6MG1fyvN69K7cWouqAYYaIqBItXgxMnvzw+UOGVF4tRNUFwwwRUSWZMweYPv3h8x0dAWfnyquHqLqwkrsAIqKaICoKWLiw9DENGlROLUTVDcMMEZGZvfYa8OGHjx7Hm+URlQ/DDBGRGY0dC3zySdnG8mZ5ROXDMENEZEZhYUBICHDtWsHTsB92qMnSEmjbtnJrI6ouzHoC8P79+9GnTx/UqVMHKpUKW7dulcwXQmDmzJnw8vKCnZ0dgoODcfr0aXOWRERUqTp2BAYOLNhDc/Dgw8fxZnlE5WfWMHPr1i20aNECH3/8cYnzFy9ejA8//BCrVq3CoUOH4ODggJCQEOTm5pqzLCKiSvf778DevdK+oKD7P/NmeUTlZ9bDTD179kTPnj1LnCeEwAcffIC3334bffv2BQB8/vnn8PT0xNatWzFs2DBzlkZEVKke/CfNxwc4cAAICCg4/PSvf8lTF1F1INt9ZlJTU5GRkYHg4GBjn0ajQfv27ZGQkPDQ1+n1euh0OslERFSVbd4MPHgEfcOGgv/GxwNWVsXDDhGVnWxhJiMjAwDg6ekp6ff09DTOK0l0dDQ0Go1x8vb2NmudREQVYTAA48dL+9q1KziXBgD8/YGdO3mzPKKKUNwdgKOiopCdnW2c0tPT5S6JiOih5s4Frl+/31apgK+/lo7p3r1yayKqbmQLM1qtFgBw5coVSf+VK1eM80qiVqvh7OwsmYiIqqLcXGDBAmnfkCEF58sQkenIFmb8/f2h1WoRHx9v7NPpdDh06BCCip7iT0SkUCNHAnl599s2NkBsrGzlEFVbZr2aKScnBykpKcZ2amoqEhMTUatWLfj4+GDKlCmYN28e6tevD39/f7zzzjuoU6cO+vXrZ86yiIjM7sIFYNMmad/06YCtrTz1EFVnZg0zR44cQdeuXY3tyMhIAMCIESMQGxuLN998E7du3cLYsWORlZWFjh07YufOnbDlXzsRKdzgwYAQ99uursDs2bKVQ1StqYQo+uemPDqdDhqNBtnZ2Tx/hoiqhF9/vX+1UqGvvgKGDpWnHqKqyJTf34q7momIqKp78UVpu149Bhkic2KYISIyoZgY4Px5ad/GjfLUQlRTMMwQEZmIwQC89pq079lngTZt5KmHqKZgmCEiMpHp04GbN++3LSwKHmVARObFMENEZAI5OcAHH0j7wsIADw9ZyiGqURhmiIhM4KWXgPz8+21bW2D1avnqIapJGGaIiCrozBng+++lfXPmFNzxl4jMj2GGiKiCHrxBnrs78Oab8tVDVNMwzBARVUBcHHDsmLTv00/lqYWopmKYISKqgBEjpO2nngKef16eWohqKoYZIqJyWr4cuHxZ2vfgwyWJyPwYZoiIyiE/H5gxQ9oXElKwZ4aIKhfDDBFROUyeDNy+fb9taVnwMEkiqnwMM0REj+n6dWDNGmnfuHGAi4ss5RDVeAwzRESPafhw4N69+20HB+A//5GvHqKajmGGiOgxHD8O7Nol7Vu8GLCykqceImKYISJ6LEOGSNtPPAFMmCBPLURUgGGGiKiMtm0DkpKkfZ9/Lk8tRHQfwwwRURmNGSNtt24NdOsmTy1EdB/DDBFRGSxcCFy9er+tUgGbN8tXDxHdxzBDRPQIeXkFT8Euqm9foG5deeohIimGGSKiRxg7FsjNvd+2tga++EK+eohIimGGiKgUGRnFg8vUqYCjozz1EFFxDDNERKUYMgQwGO63nZ2B6Gj56iGi4hhmiIge4rffgF9+kfYtXw5Y8F9OoiqFf5JERA8xfLi07ecHhIXJUgoRlYJhhoioBBs3AmfOSPu+/FKeWoiodAwzREQPMBiKP6IgKKhgIqKqh2GGiOgBs2YBWVn327xBHlHVxjBDRFTE7dvAokXSvuHDgSeflKceIno0hhkioiJGjADu3r3fVquBtWsraeUGQ8F13wMGVNIKiaoHK7kLICKqKs6fB779Vtr3738DtrZmXnFeHjB9OrB6NXDnDvC//5l5hUTVC8MMEdH/GzwYEOJ+u1Yt4O23zbjCrCwgIqLghJz8/IK+0FDgmWfMuFKi6odhhogIwP79BTfJK2r1ajPdIC8tDRg3Dti1S3p7YbUa+PprM6yQqHpjmCEiAvDSS9J2gwbAoEEmXsmffxaEmIMHS56/ZAkf+kRUDrKfADx79myoVCrJ1KhRI7nLIqIaZM0aID1d2rdpkwlXsHs38NRTQIsWDw8ydesCkyaZcKVENUeV2DPz1FNP4eeffza2rayqRFlEVAMYDMC0adK+bt2Ali1NsPBNm4A33yw4rFQalQr44QcTrJCoZqoSqcHKygparVbuMoioBpo2DcjJud+2sKjgXhmDoeBplPPmAf/8U7bXDB8ONG5cgZUS1WyyH2YCgNOnT6NOnToICAjAiy++iLRS/i9Gr9dDp9NJJiKi8tDpgI8+kvaNGgXUrl2BhW7fDsydW/Yg4+AArFtXgRUSkexhpn379oiNjcXOnTuxcuVKpKamolOnTrh582aJ46Ojo6HRaIyTt7d3JVdMRNXFCy/cvyIaAOzsgJUrK7jQ558vCDK//AJ06vTo8Z98AvDQOlGFqIQoelcF+WVlZcHX1xdLly7F6NGji83X6/XQ6/XGtk6ng7e3N7Kzs+Hs7FyZpRKRgiUnAw9ea/D++0BkpIlWYDAAHTsCCQkPH9OyJXDsmIlWSKQsOp0OGo3GJN/fVe5/B1xcXNCgQQOkpKSUOF+tVkOtVldyVURU3QwZIm1rtSYMMrdvA82bA2fOPHyMhQVP+iUyEdkPMz0oJycHZ86cgZeXl9ylEFE1tWNHwS1fivrsMxMtPCMD8PUtHmQsLaXtyZP59EoiE5E9zLz++uvYt28fzp07hwMHDqB///6wtLTE8OHD5S6NiKqpUaOk7WbNgJ49TbDgEycK7hfz4Mm/deoASUn3225uBce0iMgkZD/MdOHCBQwfPhzXrl2Du7s7OnbsiIMHD8Ld3V3u0oioGvrgg4KdJ0WZ5AkC8fEFz1UqekYxUJCUjhwBbGwAF5eC5zFt2mSm5yQQ1UxV7gTgx2XKE4iIqHrLzwecnQseTF3oX/8C/vvfCi7488+B8HDpUyoBoEePgmNahcElJATQ64G9eyu4QiLlM+X3N//XgIhqjAkTpEHGygrYsKGCC503DxgxoniQeeUV4KefpHtg3noL2Lq1giskogfJfpiJiKgy/PMPsHattG/ChIIjP+U2Zgzw6afF++fOBd5+u3h/584VWBkRPQzDDBHVCMOGFdz6pZCjI7BsWTkXZjAUnB8TFyftV6mA2FggLKy8ZRJROTDMEFG1l5hYcH5uUe+/X85zcPPygMBA4Phxab+VFbBzJ9C9e3nLJKJyYpghompv2DBp29sbGDu2HAu6fr3g6qRLl6T99vbA4cPAU0+Vu0YiKj+GGSKq1r79tuDRBUWtX1+OBaWmFjx+4MGH29auDfz1V8EthIlIFryaiYiqtXHjpO02bcpxHu6hQ0DjxsWDTN26wPnzDDJEMmOYIaJqa9484Nq1+22VCvjmm8dcyLffAh06FNwfpqgOHYC//y44xEREsmKYIaJqKTe3IMwUNWBAwWOTyuw//wEGDZJeBgUAQ4cCv/7Ku/gSVRH8SySiamn0aOnOFGvrghv1lllkJDBlSvH+N94AvvqqouURkQnxBGAiqnYuXAA2bpT2vfHGYxwRGjSo4PBSUSoV8OGHwMSJJqmRiEyHYYaIqp2hQ6VPF3BxKbgp7yPl5wPPPFNwmXVRlpYF4aZvX1OWSUQmwjBDRNXKoUPAgQPSvo8+KsPpLTk5QPPmBZdgF6VWA7/8ArRta9I6ich0GGaIqFoZPlzaDggAXnzxES+6dKkgyBS99AkANBrgjz8e86xhIqpsPAGYiKqNzz8vvmPlkefq/vknUK9e8SDz5JPAuXMMMkQKwDBDRNWCwQBMnizt69TpEUeH4uIKnrN05460v0WLglRUoUdqE1FlYZghomohKgrIzr7ftrAANm8u5QUxMUBISMFJv0X17An8/nvBgyOJSBEYZohI8XJygGXLpH0vvVTKUwZmzwZGjZJe8gQUPPvgxx95MzwiheH/ehCR4oWFAXfv3m/b2gKffPKQwSNHArGxxfsXLCjYvUNEisMwQ0SKlpoKbN0q7Zs5E7CxeWCgwQD06AHEx0v7VSrgiy/KcMkTEVVVDDNEpGiDB0uPFtWuXcIOlrw8oFUr4ORJab+1NbBrF9Cli7nLJCIzYpghIsXavRs4elTa9+mnDwy6fh146ikgI0Pa7+AA/PYb0LixWWskIvNjmCEixQoLk7YbN37giQNnzhTskbl5UzrQ3R04fhzw8DB7jURkfjxln4gUacUK4OJFaZ/kUuxffwWaNCkeZOrXB9LSGGSIqhGGGSJSnPx84M03pX3PPQc0bfr/jW++ATp3LjhXpqhOnYBTpwoudyKiaoNhhogUZ+pU4Nat+21LyyKPLVi6tOCsYINB+qIXXgD27+c9ZIiqIf5VE5GiZGUBK1dK+8aMAWrVAvDaa8C0acVfNGMGsGFDZZRHRDLgCcBEpCjDhwP37t1v29sDy5cD6N+/+A1nVCrg44+B8eMrs0QiqmQMM0SkGCdOADt3SvsWzsuH1dNPF79G29KyINz07l1p9RGRPBhmiEgxhg6Vtut65mDSf5oC589LZ9jaFlzN1Lp15RVHRLJhmCEiRdi+vWDPTKE6uICTt5oDV25IB7q4AH/8Afj4VGp9RCQfngBMRIowevT9n5sjEWdUDWCT80CQ8fYu2EvDIENUozDMEFGVt2QJkJlZ8HMIduAI2sJW3JEOat0aOHsWcHau/AKJSFYMM0RUpeXlFTwFGwBG4VP8iF6wRr50UO/eBScAW/HIOVFNxDBDRFXaq68CubnAHLyDTzEGFhDSARMmAD/8IE9xRFQl8H9jiKjKyswE1q0DYjECYfgcqgcHLF4MvPGGHKURURVSJfbMfPzxx/Dz84OtrS3at2+Pw4cPy10SEVUBQwYZ8LOhK0Y8GGQsLAqeX8AgQ0SoAmFm06ZNiIyMxKxZs/D777+jRYsWCAkJQWbh2X5EVCP9fiAXK395Cl2xVzrD2hrYs6f4TWeIqMaSPcwsXboUY8aMwciRI9GkSROsWrUK9vb2+Oyzz0ocr9frodPpJBMRVT+xi64gAGelnY6OwF9/FTwRm4jo/8kaZvLy8nD06FEEBwcb+ywsLBAcHIyEhIQSXxMdHQ2NRmOcvL29K6tcIqpEH27zxc4Ze3Hv//+ZynP1AFJTgYYNZa6MiKoaWcPMP//8g3v37sHT01PS7+npiYyMjBJfExUVhezsbOOUnp5eGaUSkQz6RgdB9fVmZPm1hM2l80Dt2nKXRERVkOKuZlKr1VCr1XKXQUSVxGLQQLgMGih3GURUhcm6Z6Z27dqwtLTElStXJP1XrlyBVquVqSoiIiJSElnDjI2NDQIDAxEfH2/sMxgMiI+PR1BQkIyVERERkVLIfpgpMjISI0aMQJs2bdCuXTt88MEHuHXrFkaOHCl3aURERKQAsoeZoUOH4urVq5g5cyYyMjLQsmVL7Ny5s9hJwUREREQlUQkhxKOHVV06nQ4ajQbZ2dlw5tNyiYiIFMGU39+y3zSPiIiIqCIYZoiIiEjRGGaIiIhI0RhmiIiISNEYZoiIiEjRGGaIiIhI0RhmiIiISNEYZoiIiEjRGGaIiIhI0RhmiIiISNEYZoiIiEjRGGaIiIhI0RhmiIiISNEYZoiIiEjRGGaIiIhI0RhmiIiISNEYZoiIiEjRGGaIiIhI0RhmiIiISNEYZoiIiEjRGGaIiIhI0RhmiIiISNEYZoiIiEjRGGaIiIhI0RhmiIiISNEYZoiIiEjRGGaIiIhI0RhmiIiISNEYZoiIiEjRGGaIiIhI0RhmiIiISNEYZoiIiEjRGGaIiIhI0RhmiIiISNEYZoiIiEjRZA0zfn5+UKlUkmnhwoVylkREREQKYyV3Ae+++y7GjBljbDs5OclYDRERESmN7GHGyckJWq1W7jKIiIhIoWQ/Z2bhwoVwc3NDq1atsGTJEuTn55c6Xq/XQ6fTSSYiIiKquWTdMzN58mS0bt0atWrVwoEDBxAVFYXLly9j6dKlD31NdHQ05syZU4lVEhERUVWmEkIIUy5wxowZWLRoUaljkpKS0KhRo2L9n332GcaNG4ecnByo1eoSX6vX66HX641tnU4Hb29vZGdnw9nZuWLFExERUaXQ6XTQaDQm+f42eZi5evUqrl27VuqYgIAA2NjYFOs/ceIEmjZtilOnTqFhw4ZlWp8p3wwiIiKqHKb8/jb5YSZ3d3e4u7uX67WJiYmwsLCAh4eHiasiIiKi6kq2c2YSEhJw6NAhdO3aFU5OTkhISMDUqVPx0ksvwdXVVa6yiIiISGFkCzNqtRpfffUVZs+eDb1eD39/f0ydOhWRkZFylUREREQKJFuYad26NQ4ePCjX6omIiKiakP0+M0REREQVwTBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREisYwQ0RERIrGMENERESKxjBDREREima2MDN//nx06NAB9vb2cHFxKXFMWloaevXqBXt7e3h4eOCNN95Afn6+uUoiIiKiasjKXAvOy8vD4MGDERQUhLVr1xabf+/ePfTq1QtarRYHDhzA5cuXERYWBmtrayxYsMBcZREREVE1oxJCCHOuIDY2FlOmTEFWVpakf8eOHejduzcuXboET09PAMCqVaswffp0XL16FTY2NmVavk6ng0ajQXZ2NpydnU1dPhEREZmBKb+/ZTtnJiEhAc2aNTMGGQAICQmBTqfDiRMnHvo6vV4PnU4nmYiIiKjmki3MZGRkSIIMAGM7IyPjoa+Ljo6GRqMxTt7e3matk4iIiKq2xwozM2bMgEqlKnU6deqUuWoFAERFRSE7O9s4paenm3V9REREVLU91gnA06ZNQ3h4eKljAgICyrQsrVaLw4cPS/quXLlinPcwarUaarW6TOsgIiKi6u+xwoy7uzvc3d1NsuKgoCDMnz8fmZmZ8PDwAADExcXB2dkZTZo0Mck6iIiIqPoz26XZaWlpuH79OtLS0nDv3j0kJiYCAOrVqwdHR0f06NEDTZo0wcsvv4zFixcjIyMDb7/9NiIiIrjnhYiIiMrMbJdmh4eHY926dcX69+zZgy5dugAAzp8/j/Hjx2Pv3r1wcHDAiBEjsHDhQlhZlT1j8dJsIiIi5THl97fZ7zNjbgwzREREylMt7jNDREREZAoMM0RERKRoDDNERESkaAwzREREpGgMM0RERKRoDDNERESkaAwzREREpGgMM0RERKRoDDNERESkaAwzREREpGgMM0RERKRoDDNERESkaAwzREREpGgMM0RERKRoDDNERESkaAwzREREpGgMM0RERKRoDDNERESkaAwzREREpGgMM0RERKRoDDNERESkaAwzREREpGgMM0RERKRoDDNERESkaAwzREREpGgMM0RERKRoDDNERESkaAwzREREpGgMM0RERKRoDDNERESkaAwzREREpGgMM0RERKRoDDNERESkaAwzREREpGgMM0RERKRoZgsz8+fPR4cOHWBvbw8XF5cSx6hUqmLTV199Za6SiIiIqBqyMteC8/LyMHjwYAQFBWHt2rUPHRcTE4PQ0FBj+2HBh4iIiKgkZgszc+bMAQDExsaWOs7FxQVardZcZRAREVE1J/s5MxEREahduzbatWuHzz77DEKIUsfr9XrodDrJRERERDWX2fbMlMW7776Lbt26wd7eHrt27cKECROQk5ODyZMnP/Q10dHRxr0+RERERCrxqF0hRcyYMQOLFi0qdUxSUhIaNWpkbMfGxmLKlCnIysp65PJnzpyJmJgYpKenP3SMXq+HXq83tnU6Hby9vZGdnQ1nZ+dHbwQRERHJTqfTQaPRmOT7+7H2zEybNg3h4eGljgkICCh3Me3bt8fcuXOh1+uhVqtLHKNWqx86j4iIiGqexwoz7u7ucHd3N1ctSExMhKurK8MKERERlZnZzplJS0vD9evXkZaWhnv37iExMREAUK9ePTg6OuKHH37AlStX8PTTT8PW1hZxcXFYsGABXn/9dXOVRERERNWQ2cLMzJkzsW7dOmO7VatWAIA9e/agS5cusLa2xscff4ypU6dCCIF69eph6dKlGDNmjLlKIiIiomrosU4AropMeQIRERERVQ5Tfn/Lfp8ZIiIioopgmCEiIiJFY5ghIiIiRWOYISIiIkVjmCEiIiJFY5ghIiIiRWOYISIiIkVjmCEiIiJFY5ghIiIiRWOYISIiIkVjmCEiIiJFY5ghIiIiRWOYISIiIkVjmCEiIiJFY5ghIiIiRWOYISIiIkVjmCEiIiJFY5ghIiIiRWOYISIiIkVjmCEiIiJFY5ghIiIiRWOYISIiIkVjmCEiIiJFY5ghIiIiRWOYISIiIkVjmCEiIiJFY5ghIiIiRWOYISIiIkVjmCEiIiJFY5ghIiIiRWOYISIiIkVjmCEiIiJFY5ghIiIiRWOYISIiIkVjmCEiIiJFM1uYOXfuHEaPHg1/f3/Y2dmhbt26mDVrFvLy8iTj/vzzT3Tq1Am2trbw9vbG4sWLzVUSERERVUNW5lrwqVOnYDAYsHr1atSrVw/Hjx/HmDFjcOvWLbz33nsAAJ1Ohx49eiA4OBirVq3CX3/9hVGjRsHFxQVjx441V2lERERUjaiEEKKyVrZkyRKsXLkSZ8+eBQCsXLkSb731FjIyMmBjYwMAmDFjBrZu3YpTp06VaZk6nQ4ajQbZ2dlwdnY2W+1ERERkOqb8/jbbnpmSZGdno1atWsZ2QkICOnfubAwyABASEoJFixbhxo0bcHV1LbYMvV4PvV4vWSZQ8KYQERGRMhR+b5tin0qlhZmUlBQsX77ceIgJADIyMuDv7y8Z5+npaZxXUpiJjo7GnDlzivV7e3ubuGIiIiIyt2vXrkGj0VRoGY8dZmbMmIFFixaVOiYpKQmNGjUyti9evIjQ0FAMHjwYY8aMefwqi4iKikJkZKSxnZWVBV9fX6SlpVX4zVASnU4Hb29vpKen16jDa9xubndNwO3mdtcE2dnZ8PHxkRyxKa/HDjPTpk1DeHh4qWMCAgKMP1+6dAldu3ZFhw4dsGbNGsk4rVaLK1euSPoK21qttsRlq9VqqNXqYv0ajaZG/RIUcnZ25nbXINzumoXbXbPU1O22sKj4hdWPHWbc3d3h7u5eprEXL15E165dERgYiJiYmGIFBwUF4a233sLdu3dhbW0NAIiLi0PDhg1LPMRERERE9CCz3Wfm4sWL6NKlC3x8fPDee+/h6tWryMjIQEZGhnHMCy+8ABsbG4wePRonTpzApk2b8J///EdyGImIiIioNGY7ATguLg4pKSlISUnBk08+KZlXeOayRqPBrl27EBERgcDAQNSuXRszZ858rHvMqNVqzJo1q8RDT9UZt5vbXRNwu7ndNQG3u+LbXan3mSEiIiIyNT6biYiIiBSNYYaIiIgUjWGGiIiIFI1hhoiIiBSNYYaIiIgUTbFh5ty5cxg9ejT8/f1hZ2eHunXrYtasWcjLy5OM+/PPP9GpUyfY2trC29sbixcvlqli05k/fz46dOgAe3t7uLi4lDhGpVIVm7766qvKLdTEyrLdaWlp6NWrF+zt7eHh4YE33ngD+fn5lVuomfn5+RX7bBcuXCh3WSb38ccfw8/PD7a2tmjfvj0OHz4sd0lmN3v27GKfbdFHw1QX+/fvR58+fVCnTh2oVCps3bpVMl8IgZkzZ8LLywt2dnYIDg7G6dOn5SnWhB613eHh4cU+/9DQUHmKNZHo6Gi0bdsWTk5O8PDwQL9+/ZCcnCwZk5ubi4iICLi5ucHR0REDBw4s9nSAR1FsmDl16hQMBgNWr16NEydOYNmyZVi1ahX+/e9/G8fodDr06NEDvr6+OHr0KJYsWYLZs2cXe6yC0uTl5WHw4MEYP358qeNiYmJw+fJl49SvX7/KKdBMHrXd9+7dQ69evZCXl4cDBw5g3bp1iI2NxcyZMyu5UvN79913JZ/tpEmT5C7JpDZt2oTIyEjMmjULv//+O1q0aIGQkBBkZmbKXZrZPfXUU5LP9n//+5/cJZncrVu30KJFC3z88cclzl+8eDE+/PBDrFq1CocOHYKDgwNCQkKQm5tbyZWa1qO2GwBCQ0Mln//GjRsrsULT27dvHyIiInDw4EHExcXh7t276NGjB27dumUcM3XqVPzwww/4+uuvsW/fPly6dAkDBgx4vBWJamTx4sXC39/f2F6xYoVwdXUVer3e2Dd9+nTRsGFDOcozuZiYGKHRaEqcB0Bs2bKlUuupLA/b7h9//FFYWFiIjIwMY9/KlSuFs7Oz5HdA6Xx9fcWyZcvkLsOs2rVrJyIiIozte/fuiTp16ojo6GgZqzK/WbNmiRYtWshdRqV68N8qg8EgtFqtWLJkibEvKytLqNVqsXHjRhkqNI+S/o0eMWKE6Nu3ryz1VJbMzEwBQOzbt08IUfDZWltbi6+//to4JikpSQAQCQkJZV6uYvfMlCQ7O1vy9M2EhAR07twZNjY2xr6QkBAkJyfjxo0bcpRYqSIiIlC7dm20a9cOn332mfHOy9VVQkICmjVrBk9PT2NfSEgIdDodTpw4IWNlprdw4UK4ubmhVatWWLJkSbU6lJaXl4ejR48iODjY2GdhYYHg4GAkJCTIWFnlOH36NOrUqYOAgAC8+OKLSEtLk7ukSpWamoqMjAzJ56/RaNC+ffsa8fnv3bsXHh4eaNiwIcaPH49r167JXZJJZWdnA4Dxu/ro0aO4e/eu5PNu1KgRfHx8HuvzNtvjDCpbSkoKli9fjvfee8/Yl5GRAX9/f8m4wi+6jIyMav0wy3fffRfdunWDvb09du3ahQkTJiAnJweTJ0+WuzSzycjIkAQZQPp5VxeTJ09G69atUatWLRw4cABRUVG4fPkyli5dKndpJvHPP//g3r17JX6Wp06dkqmqytG+fXvExsaiYcOGuHz5MubMmYNOnTrh+PHjcHJykru8SlH4t1rS51+d/o5LEhoaigEDBsDf3x9nzpzBv//9b/Ts2RMJCQmwtLSUu7wKMxgMmDJlCp555hk0bdoUQMHnbWNjU+w8yMf9vKvcnpkZM2aUePJq0enBf9AuXryI0NBQDB48GGPGjJGp8oopz3aX5p133sEzzzyDVq1aYfr06XjzzTexZMkSM25B+Zh6u5Xqcd6HyMhIdOnSBc2bN8err76K999/H8uXL4der5d5K6iievbsicGDB6N58+YICQnBjz/+iKysLGzevFnu0qgSDBs2DM8//zyaNWuGfv36Yfv27fjtt9+wd+9euUsziYiICBw/ftwsF6NUuT0z06ZNQ3h4eKljAgICjD9funQJXbt2RYcOHYqd2KvVaoudEV3Y1mq1pinYRB53ux9X+/btMXfuXOj1+ir1MDNTbrdWqy12xUtV/bwfVJH3oX379sjPz8e5c+fQsGFDM1RXuWrXrg1LS8sS/3ar+udoai4uLmjQoAFSUlLkLqXSFH7GV65cgZeXl7H/ypUraNmypUxVySMgIAC1a9dGSkoKunfvLnc5FTJx4kRs374d+/fvlzx8WqvVIi8vD1lZWZK9M4/7917lwoy7uzvc3d3LNPbixYvo2rUrAgMDERMTAwsL6Y6moKAgvPXWW7h79y6sra0BFDzNu2HDhlXuENPjbHd5JCYmwtXVtUoFGcC02x0UFIT58+cjMzMTHh4eAAo+b2dnZzRp0sQk6zCXirwPiYmJsLCwMG6z0tnY2CAwMBDx8fHGK/AMBgPi4+MxceJEeYurZDk5OThz5gxefvlluUupNP7+/tBqtYiPjzeGF51Oh0OHDj3yCs7q5sKFC7h27Zok1CmNEAKTJk3Cli1bsHfv3mKnfgQGBsLa2hrx8fEYOHAgACA5ORlpaWkICgp6rBUp0oULF0S9evVE9+7dxYULF8Tly5eNU6GsrCzh6ekpXn75ZXH8+HHx1VdfCXt7e7F69WoZK6+48+fPi2PHjok5c+YIR0dHcezYMXHs2DFx8+ZNIYQQ33//vfjkk0/EX3/9JU6fPi1WrFgh7O3txcyZM2WuvGIetd35+fmiadOmokePHiIxMVHs3LlTuLu7i6ioKJkrN50DBw6IZcuWicTERHHmzBmxfv164e7uLsLCwuQuzaS++uoroVarRWxsrDh58qQYO3ascHFxkVypVh1NmzZN7N27V6Smpopff/1VBAcHi9q1a4vMzEy5SzOpmzdvGv9+AYilS5eKY8eOifPnzwshhFi4cKFwcXER27ZtE3/++afo27ev8Pf3F3fu3JG58oopbbtv3rwpXn/9dZGQkCBSU1PFzz//LFq3bi3q168vcnNz5S693MaPHy80Go3Yu3ev5Hv69u3bxjGvvvqq8PHxEbt37xZHjhwRQUFBIigo6LHWo9gwExMTIwCUOBX1xx9/iI4dOwq1Wi2eeOIJsXDhQpkqNp0RI0aUuN179uwRQgixY8cO0bJlS+Ho6CgcHBxEixYtxKpVq8S9e/fkLbyCHrXdQghx7tw50bNnT2FnZydq164tpk2bJu7evStf0SZ29OhR0b59e6HRaIStra1o3LixWLBggaL/sXuY5cuXCx8fH2FjYyPatWsnDh48KHdJZjd06FDh5eUlbGxsxBNPPCGGDh0qUlJS5C7L5Pbs2VPi3/KIESOEEAWXZ7/zzjvC09NTqNVq0b17d5GcnCxv0SZQ2nbfvn1b9OjRQ7i7uwtra2vh6+srxowZo/gA/7Dv6ZiYGOOYO3fuiAkTJghXV1dhb28v+vfvL9kxURaq/18ZERERkSJVuauZiIiIiB4HwwwREREpGsMMERERKRrDDBERESkawwwREREpGsMMERERKRrDDBERESkawwwREREpGsMMERERKRrDDBERESkawwwREREp2v8BqoQyBPBMLdYAAAAASUVORK5CYII=", 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", + "image/png": 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QoUO5lv3xxx+ma9euJjQ01DgcDtOtWzfz559/5rqM+kLrOHcpbEpKiqvt1KlTZvDgwaZixYomKCjIdOrUyezduzfXOm+++WbX5bp5TReTnZ1tHn30URMWFmZsNpvrNecuq87rcumcnBwzfvx4Ex0dbex2u2ncuLFZuHCh6dOnj9sly/mt4/z9+Ouvv0xCQoKpXbu2CQoKMg6Hw7Rs2dJ8+OGHbq/J69JsY4w5c+aMGTNmjImJiTF+fn4mMjLSjBgxwpw+fdqtX3R0tOnYsWOu15+7NPujjz5yaz/3ufzwww9u7Rf6HD/55BNzww03mKCgIBMUFGRq165tEhISzLZt29z6TZs2zcTExBi73W6aNWtmVq1aZW6++eYCX5qdkJBg3n33XVOzZk3X+798+fI8+2dmZpry5csbh8NhTp06ddH1n7/fK1euNP379zfly5c3wcHB5t577zWHDx929Tv3+V5o6tOnT4G2BxQFmzFXMCoRAOA12dnZqlq1qjp16qTZs2d7uxzAaxgzAwAWtWDBAh06dEi9e/f2dimAV3FkBgAsZs2aNfr555/14osvqlKlSnnezBAoTTgyAwAWM336dA0cOFDh4eF6++23vV0O4HUeDTOJiYlq3ry5ypUrp/DwcHXp0iXXvTROnz6thIQEVaxYUcHBwbrrrrt04MABT5YFAJaWlJSk7OxsrVu3TvXq1fN2OYDXeTTMrFy5UgkJCVq9erWWLFmiM2fOqF27djpx4oSrz+OPP64vvvhCH330kVauXKk///xTd955pyfLAgAAJUiRjpk5dOiQwsPDtXLlSt10001KT09XWFiY3nvvPde9ErZu3ao6deooOTnZ9TA/AACACynSm+ade3BbhQoVJEnr16/XmTNn1LZtW1ef2rVrKyoq6oJhJjMz0+2uoeee6luxYsXLunU7AAAoesYYHTt2TFWrVpWPz5WdKCqyMJOTk6MhQ4bo+uuvd53jTU1Nlb+/f65HyVeuXPmCT9RNTEzM806YAADAevbu3aurr776itZRZGEmISFBmzZt0rfffntF6xkxYoSGDh3qmk9PT1dUVJT27t2rkJCQKy0TAAAUgYyMDEVGRqpcuXJXvK4iCTODBg3SwoULtWrVKrf0FRERoaysLKWlpbkdnTlw4IAiIiLyXJfdbpfdbs/VHhISQpgBAMBiCmOIiEevZjLGaNCgQZo/f76WLVummJgYt+VNmzaVn5+fli5d6mrbtm2b9uzZwwPMAABAgXj0yExCQoLee+89ffbZZypXrpxrHIzD4VBgYKAcDocefPBBDR06VBUqVFBISIgeffRRxcXFcSUTAAAoEI9emn2hQ0dz5sxR3759JTlvmjds2DC9//77yszMVHx8vKZNm3bB00x/l5GRIYfDofT0dE4zAQBgEYX5+235ZzMRZgDAu4wxys7O1tmzZ71dCooRX19flSlT5oIHNgrz97tI7zMDAChZsrKytH//fp08edLbpaAYKlu2rKpUqSJ/f3+PbocwAwC4LDk5OUpJSZGvr6+qVq0qf39/bl4KSc6jdVlZWTp06JBSUlJUs2bNK74xXn4IMwCAy5KVlaWcnBxFRkaqbNmy3i4HxUxgYKD8/Py0e/duZWVlKSAgwGPb8uil2QCAks+T/8cNayuq7wbfQAAAYGmEGQAAYGmEGQBAobPZinYqart27ZLNZtPGjRsL/JqkpKRcD1b2Rh0lEWEGAFBq7d27Vw888IDraqzo6Gg99thjOnz4cL6vi4yM1P79+1WvXr0Cb6tHjx767bffrrRk5IEwAwAolX7//Xc1a9ZM27dv1/vvv68dO3ZoxowZWrp0qeLi4nTkyJE8X5eVlSVfX19FRESoTJmCXxQcGBio8PDwwiof5yHMAABKpYSEBPn7+2vx4sW6+eabFRUVpQ4dOuibb77Rvn379Oyzz0qSqlWrphdffFG9e/dWSEiI+vfvn+fpnc8//1w1a9ZUQECA2rRpo7feeks2m01paWmScp9mGj16tBo1aqR33nlH1apVk8PhUM+ePXXs2DFXn0WLFumGG25QaGioKlasqNtvv107d+4sirfHUggzAIBS58iRI/r666/1yCOPKDAw0G1ZRESE7r33Xs2bN0/nnvjz0ksvqWHDhtqwYYOef/75XOtLSUnR3XffrS5duuinn37SgAEDXGEoPzt37tSCBQu0cOFCLVy4UCtXrtSECRNcy0+cOKGhQ4dq3bp1Wrp0qXx8fNS1a1fl5ORc4TtQsnDTPABAqbN9+3YZY1SnTp08l9epU0dHjx7VoUOHJEm33HKLhg0b5lq+a9cut/4zZ85UrVq1NHnyZElSrVq1tGnTJo0bNy7fOnJycpSUlKRy5cpJku677z4tXbrU9bq77rrLrf+bb76psLAw/frrr5c0Xqek48gMAKDUKuizlps1a5bv8m3btql58+ZubS1atLjoeqtVq+YKMpJUpUoVHTx40DW/fft29erVS7GxsQoJCVG1atUkSXv27ClQ3aUFYQYAUOrUqFFDNptNW7ZsyXP5li1bVL58eYWFhUmSgoKCPFKHn5+f27zNZnM7hdSpUycdOXJE//nPf7RmzRqtWbNGknMQMv4/wgwAoNSpWLGibrvtNk2bNk2nTp1yW5aamqq5c+eqR48eBX5wZq1atbRu3Tq3th9++OGKajx8+LC2bdum5557Trfeeqvr1BdyI8wAAEql1157TZmZmYqPj9eqVau0d+9eLVq0SLfddpuuuuqqi453Od+AAQO0detWPf300/rtt9/04YcfKikpSZIu+0ni5cuXV8WKFTVr1izt2LFDy5Yt09ChQy9rXSUdYQYAUOiMKdrpctSsWVPr1q1TbGysunfvrurVq6t///5q06aNkpOTVaFChQKvKyYmRh9//LE+/fRTNWjQQNOnT3ddzWS32y+rPh8fH33wwQdav3696tWrp8cff9w1wBjubKago5+KqYyMDDkcDqWnpyskJMTb5QBAqXH69GmlpKQoJiZGAQEB3i6n2Bk3bpxmzJihvXv3ersUr8nvO1KYv99cmg0AQCGYNm2amjdvrooVK+q7777T5MmTNWjQIG+XVSoQZgAAKATbt2/X2LFjdeTIEUVFRWnYsGEaMWKEt8sqFQgzAAAUgqlTp2rq1KneLqNUYgAwAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAKDw2WxFO5UAqampuu222xQUFKTQ0FBvl1NgNptNCxYs8GoNhBkAQKlis9nynUaPHu2VuqZOnar9+/dr48aN+u2337xSg1Vx0zwAQKmyf/9+15/nzZunkSNHatu2ba624OBg15+NMTp79qzKlPH8z+XOnTvVtGlT1axZ87LXkZWVJX9//0Ksyho4MgMAKFUiIiJck8PhkM1mc81v3bpV5cqV01dffaWmTZvKbrfr22+/1c6dO9W5c2dVrlxZwcHBat68ub755hu39VarVk3jx4/XAw88oHLlyikqKkqzZs1yLc/KytKgQYNUpUoVBQQEKDo6WomJia7XfvLJJ3r77bdls9nUt29fSdKePXvUuXNnBQcHKyQkRN27d9eBAwdc6xw9erQaNWqkN954w+1hjjabTTNnztTtt9+usmXLqk6dOkpOTtaOHTvUunVrBQUFqVWrVtq5c6fbPnz22Wdq0qSJAgICFBsbqzFjxig7O9u1fPv27brpppsUEBCgunXrasmSJYX62VwuwgwAAH8zfPhwTZgwQVu2bFGDBg10/Phx/eMf/9DSpUu1YcMGtW/fXp06ddKePXvcXvevf/1LzZo104YNG/TII49o4MCBrqM+r7zyij7//HN9+OGH2rZtm+bOnatq1apJkn744Qe1b99e3bt31/79+/Xvf/9bOTk56ty5s44cOaKVK1dqyZIl+v3339WjRw+3be7YsUOffPKJPv30U23cuNHV/uKLL6p3797auHGjateurXvuuUcDBgzQiBEjtG7dOhlj3B6E+b///U+9e/fWY489pl9//VUzZ85UUlKSxo0bJ0nKycnRnXfeKX9/f61Zs0YzZszQ008/7YF3/zIYi0tPTzeSTHp6urdLAYBS5dSpU+bXX381p06dyr1QKtrpMs2ZM8c4HA7X/PLly40ks2DBgou+9tprrzWvvvqqaz46Otr885//dM3n5OSY8PBwM336dGOMMY8++qi55ZZbTE5OTp7r69y5s+nTp49rfvHixcbX19fs2bPH1bZ582Yjyaxdu9YYY8yoUaOMn5+fOXjwoNu6JJnnnnvONZ+cnGwkmdmzZ7va3n//fRMQEOCav/XWW8348ePd1vPOO++YKlWqGGOM+frrr02ZMmXMvn37XMu/+uorI8nMnz8/z33K7ztSmL/fHJkBAOBvmjVr5jZ//PhxPfHEE6pTp45CQ0MVHBysLVu25Doy06BBA9efz52+OnjwoCSpb9++2rhxo2rVqqXBgwdr8eLF+dawZcsWRUZGKjIy0tVWt25dhYaGasuWLa626OhohYWF5Xr9+bVUrlxZklS/fn23ttOnTysjI0OS9NNPP+mFF15QcHCwa+rXr5/279+vkydPuuqpWrWqax1xcXH57kNRYQAwAAB/ExQU5Db/xBNPaMmSJXrppZdUo0YNBQYG6u6771ZWVpZbPz8/P7d5m82mnJwcSVKTJk2UkpKir776St988426d++utm3b6uOPPy7UWvOqxfZ/l6/n1XauvuPHj2vMmDG68847c63r3Fic4sqjR2ZWrVqlTp06qWrVqnleh963b99cl8S1b9/ekyUBAHDJvvvuO/Xt21ddu3ZV/fr1FRERoV27dl3yekJCQtSjRw/95z//0bx58/TJJ5/oyJEjefatU6eO9u7dq71797rafv31V6Wlpalu3bqXuysX1KRJE23btk01atTINfn4+LjqOf9qsNWrVxd6HZfDo0dmTpw4oYYNG+qBBx7IM+lJUvv27TVnzhzXvN1u92RJAABcspo1a+rTTz9Vp06dZLPZ9Pzzz7uOaBTUlClTVKVKFTVu3Fg+Pj766KOPFBERccEb5LVt21b169fXvffeq5dfflnZ2dl65JFHdPPNN+c6DVYYRo4cqdtvv11RUVG6++675ePjo59++kmbNm3S2LFj1bZtW11zzTXq06ePJk+erIyMDD377LOFXsfl8OiRmQ4dOmjs2LHq2rXrBfvY7Xa3y+TKly/vyZIAAEWhqIcAe9iUKVNUvnx5tWrVSp06dVJ8fLyaNGlySesoV66cJk2apGbNmql58+batWuXvvzyS/n45P1TbLPZ9Nlnn6l8+fK66aab1LZtW8XGxmrevHmFsUu5xMfHa+HChVq8eLGaN2+u6667TlOnTlV0dLQkycfHR/Pnz9epU6fUokULPfTQQ64rnbzNZkwRfAvk/FDmz5+vLl26uNr69u2rBQsWyN/fX+XLl9ctt9yisWPHqmLFihdcT2ZmpjIzM13zGRkZioyMVHp6ukJCQjy5CwCA85w+fVopKSlu9zcBzpffdyQjI0MOh6NQfr+9ejVT+/bt9fbbb2vp0qWaOHGiVq5cqQ4dOujs2bMXfE1iYqIcDodrOn+UNwAAKH28ejVTz549XX+uX7++GjRooOrVq2vFihW69dZb83zNiBEjNHToUNf8uSMzAACgdCpW95mJjY1VpUqVtGPHjgv2sdvtCgkJcZsAAEDpVazCzB9//KHDhw+rSpUq3i4FAABYhEdPMx0/ftztKEtKSoo2btyoChUqqEKFChozZozuuusuRUREaOfOnXrqqadUo0YNxcfHe7IsAEAhKqLrSGBBRfXd8OiRmXXr1qlx48Zq3LixJGno0KFq3LixRo4cKV9fX/3888+64447dM011+jBBx9U06ZN9b///Y97zQCABZy7m+zJkye9XAmKq3Pfjb/fGbmwefTITOvWrfNNZV9//bUnNw8A8CBfX1+Fhoa6nj1UtmxZ1y3yUboZY3Ty5EkdPHhQoaGh8vX19ej2eDYTAOCyRURESJIr0ADnCw0NdX1HPIkwAwC4bDabTVWqVFF4eLjOnDnj7XJQjPj5+Xn8iMw5hBkAwBXz9fUtsh8u4O+K1aXZAAAAl4owAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALI0wAwAALM2jYWbVqlXq1KmTqlatKpvNpgULFrgtN8Zo5MiRqlKligIDA9W2bVtt377dkyUBAIASxqNh5sSJE2rYsKFef/31PJdPmjRJr7zyimbMmKE1a9YoKChI8fHxOn36tCfLAgAAJUgZT668Q4cO6tChQ57LjDF6+eWX9dxzz6lz586SpLfffluVK1fWggUL1LNnT0+WBgAASgivjZlJSUlRamqq2rZt62pzOBxq2bKlkpOTL/i6zMxMZWRkuE0AAKD08lqYSU1NlSRVrlzZrb1y5cquZXlJTEyUw+FwTZGRkR6tEwAAFG+Wu5ppxIgRSk9Pd0179+71dkkAAMCLvBZmIiIiJEkHDhxwaz9w4IBrWV7sdrtCQkLcJgAAUHp5LczExMQoIiJCS5cudbVlZGRozZo1iouL81ZZAADAYjx6NdPx48e1Y8cO13xKSoo2btyoChUqKCoqSkOGDNHYsWNVs2ZNxcTE6Pnnn1fVqlXVpUsXT5YFAABKEI+GmXXr1qlNmzau+aFDh0qS+vTpo6SkJD311FM6ceKE+vfvr7S0NN1www1atGiRAgICPFkWAAAoQWzGGOPtIq5ERkaGHA6H0tPTGT8DAIBFFObvt+WuZgIAADgfYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYASpnTp6Wvv/Z2FUDhIcwAQCmxZ4/UubMUEiKlp3u7GqDwEGYAoIRbulRq2FCKjpY+/1xq0ULq3t3bVQGFhzADACVQTo708stSlSpS27bSzz872/39pYULvVoaUOjKeLsAAEDhyciQhg6V5s51jo35u5dflkJDi7oqwLMIMwBQAmzeLA0aJK1cKRmTd5/69aWBA4u2LqAocJoJACzs44+la66R6tWTVqy4cJDx9ZUWLSrS0oAiw5EZALCY7GzphRek116Tjh4t2GtGjpSqVvVsXYC3EGYAwEJycqQePaRPPy34a2JinGEGKKk4zQQAFuLjI33yibR7t9S1q+Tnl39/m0366quiqQ3wFsIMAFhQVJTzyqSQkPz7DRok1apVJCUBXkOYAQAL+vZb58Dfw4cv3Cc83Bl4gJKOMAMAFjN7tnTTTVJmZv79Fi50npYCSjq+5gBgIY8/Lj30UO5LsG+/3f10Uq9eUvPmRVsb4C2EGQCwgJwcqV27vE8bDR8uffGFdMstznmHQ3r77SItD/AqwgwAFHPHjzvHxyxZ4t7u4yO9846UmOicv/9+53/nzZPKcOMNlCJ83QGgGNu+3Xm6KD3dvT0gQFq1yv1UUvPm0pAhUnx8kZYIeB1HZgCgmFqyRLr22txBJjxcSknJe0zM1KlFUxtQnBBmAKAY+ve/nUdYzpxxb2/cWNq7V4qI8E5dQHFEmAGAYqZfP+fpor9fsdSjh/Tjj5K/v1fKAootxswAQDGRnS21bi19913uZS++KD33XJGXBFgCYQYAioEjR6RGjZynkM7n6yt99JHzOUwA8kaYAQAv27RJuu466cQJ9/agIOn776UGDbxTF2AVjJkBAC/67DPnEZm/B5mrr5b27CHIAAXh9TAzevRo2Ww2t6l27dreLgsAPG7cOKlLF+nsWff2uDjnpdcVKnilLMByisVppmuvvVbffPONa74Mt64EUMLdc4/0/vu52++/X3rzzaKvB7CyYpEaypQpowhumgCgFMjKch55+fFH93abTZoyxXlJNoBLUyzCzPbt21W1alUFBAQoLi5OiYmJioqKyrNvZmamMs977n1GRkZRlQkAVyQ11Tk+5sAB93Y/P+eDInkMAXB5vD5mpmXLlkpKStKiRYs0ffp0paSk6MYbb9SxY8fy7J+YmCiHw+GaIiMji7hiALh0P/wgxcbmDjIhIdIvvxBkgCthM+bv95j0rrS0NEVHR2vKlCl68MEHcy3P68hMZGSk0tPTFRISUpSlAkCBzJ0r9e4t5eS4t8fGShs2OAMNUNpkZGTI4XAUyu93sTjNdL7Q0FBdc8012rFjR57L7Xa77HZ7EVcFAJfnmWekxMTc7bfeKi1eLPl4/fg4YH3F7q/R8ePHtXPnTlWpUsXbpQDAZcvJkTp3zjvIDB4sffMNQQYoLF7/q/TEE09o5cqV2rVrl77//nt17dpVvr6+6tWrl7dLA4DLcvKkVK+e9Pnn7u02mzRzpvOJ2AAKj9dPM/3xxx/q1auXDh8+rLCwMN1www1avXq1wsLCvF0aAFyyPXukxo2dz1o6n93uPK10003eqQsoybweZj744ANvlwAAhWLVKqldO+m8axQkOe/k++OPUnS0d+oCSjqvn2YCgJJg1iypdevcQaZOHeeTsAkygOcQZgDgCj32mDRggPT3G13ccYfzidhly3qnLqC08PppJgCwqpwc52mlpUtzLxsxQho/vuhruqCTJ6WffpI2bpS2bpWaNnXe/AYoAQgzAHAZMjKcA31//9293cdHevtt6d57i7CY06f/f1D59VdnUX/8IR06JKWlSadOud+xr379vK8ZByyKMAMAl2j7dqlZM2egOV9goLRypdS8uYc2vGqV9K9/OQfhHDyYd1C5mJ49835cN2BhhBkAuARffy116iSdOePeXrmy88BIRIQHN37dddK+fc5nIFwqm02aPFkaNqzw6wK8jAHAAFBAL78sdeiQO8g0aeK8v4xHg4wk+ftL69Zd+jksf39nCiPIoIQizABAATzwgPT447mvWOrZU1q/3pkXikzPngV/OmXFitJvv0m33ebZmgAvIswAQD6ys6VWraQ5c3IvGzu2CIefZGVJzz7rDCedOuUesJOXhg2dA4G5yQ1KOMbMAMAFHDkiNWjgHKZyPl9f6aOPpK5di6CIlBTp0Uedp4myswv+unvvld5913N1AcUIR2YAIA+bNklRUbmDTFCQc6Cvx4PMJ584bx8cGyv9978FDzI2m/OKJ4IMShHCDAD8zfz5UqNG0okT7u2Rkc6BvvXqeWjDp09LTz4phYZKd9/tvLldXnx9pfj43Mv9/aUlS6ShQz1UIFA8EWYA4Dxjx0p33imdPevefv31znvRVajggY1u2+YMJ8HB0ksvSenpefcLDXWGnePHpUWL3MfCVKrkvAHOrbd6oECgeCPMAMD/6dlTev753O0PPSR9+61UprBHGc6dK9WsKdWuLS1enDtBnVO7tvTxx9LRo9KkSVJAgLP93Dmwxo2dN9KLiirkAgFrYAAwgFIvK0tq2dI5FuZ8Nps0darzQZKF5uRJafhwKSlJOnbswv3KlJHat5deeUWKicm7z19/OZ+v9NZbhVggYD2EGQClWmqq8wrmgwfd2/38nONuC+32LJs2SYMHO593kN/jBypUkB55xHmI6GI3r2nZ0jkBpRxhBkCp9cMP0s03Ox9vdD6Hw7msZs1C2Mjs2c6BOLt25d+vfn1pwgTpH/8ohI0CpQthBkCp9O67Up8+uQ+SVK/uPN0UHHwFK8/IkJ56yrmRv18SdT4/P+cN8P79b+nqq69gg0DpxgBgAKXOiBHSffflDjK33ea88/9lB5l166Qbb3RedTRz5oWDTFiY9OKLzvEzn3xCkAGuEEdmAJQaOTlS587SwoW5lw0Z4hzse1krnTHDeYpo7978+zZu7HxyNZdPA4WKMAOgVDh5UmraNPd95mw2adYs5+XXl+TIEedTqOfNyz3o5nx2u/N2wVOnFsFjtYHSiTADoMTbvVtq0sSZP85nt0vffCPdcMMlrCw52fn47LVrcz9C+3wREc478Q4bJvlwRh/wJMIMgBJtxQrnzXWzstzbK1aUfvyxgPeZy8lx3u9l8mTpzz8v3M9mk5o1k6ZMucSEBOBKEGYAlFgzZ0oDB+Y+gFK3rrR+/f+/ke4FHTzoPArzySdSZuaF+wUESN27Ox/wWKnSFdcN4NJw7BNAifToo9LDD+cOMl26SL/8cpEgs2KF8whLRIT03nsXDjJXXeU8YnPihPMuvAQZwCs4MgOgRMnJcV5ivWxZ7mXPPee8IvqCL5w4UXr55dy3Az6fzSbFxTlPJXH3XaBYIMwAKDEyMqRGjaSUFPd2Hx/n/et69crjRX/+6bwu+7PPcg+sOV/ZstI99zjHzYSGFl7RAK4YYQZAibBtm9SihTPQnC8wUFq1ynnWyM3XX0tPPy399FP+K46Olp55xnntNlclAcUSYQaA5X31lXTHHVJ2tnt7RIS0YcN5t3fJzpbGjZNee835xOkL8fFx3sn35Zedh3oAFGuEGQCWNmWK9MQTuQf6Nm0qff/9/z14evdu6bHHnI/B/nviOV9wsNS7t3PszBU9nAlAUeKYKQDLeuAB5z3p/h5k7rnH+Zgk/0WfS/XqSdWqOcfEXCjIxMY6r0Y6dkx6/XWCDGAxHJkBYDnZ2c6zQKtX51428cUsPXVytFRhhnT06IVX4uMj3XKL81TStdd6qlQARYAwA8BS/vrLOYxl3z739po+O7Wq8WBFjFmc/6kkh8N5SGfsWOcVSgAsj9NMACzj55+dZ4zODzJ36mNts9XWtpwailj/5YWDTK1azodCpqU5B9oQZIASgyMzACzhk0+kHj2ks2clf53WOD2rfnpDDmVIF3reo6+v1K6d9O9/SzVrFmm9AIoOR2YAFHsvvCDdfbdU4+wWLVI7nVCwntAUZ5DJS/ny0vDh0smT0pdfEmSAEq5YhJnXX39d1apVU0BAgFq2bKm1a9d6uyQAxUT37tK2Ue9qu2poi+oqXktURmfz7nzttdKCBdKRI1Ji4v9dlw2gpPN6mJk3b56GDh2qUaNG6ccff1TDhg0VHx+vg/k9GwVAiZeVJU2LGKM3PiqnubpPNbRTtrw6linjfHrkrl3Spk1S585FWygAr/N6mJkyZYr69eun+++/X3Xr1tWMGTNUtmxZvfnmm3n2z8zMVEZGhtsEoOQ5ckSKPrROITqed4eKFaVRo6RTp6T5852PHQBQKnk1zGRlZWn9+vVq27atq83Hx0dt27ZVcnJynq9JTEyUw+FwTZGRkUVVLoAiFBEhVf3oFbexvUaSGjRwjoP56y9p9GjnkRkApZpXw8xff/2ls2fPqnLlym7tlStXVmpqap6vGTFihNLT013T3r17i6JUAF7Q+M4YpVeprSz560yXu2Xbt8/5YMgOHbxdGoBixHL/S2O322W3271dBoAiEvrjcqlSJY7AALggr/7rUKlSJfn6+urAgQNu7QcOHFCE6zG3AEo1/i0AcBFePc3k7++vpk2baunSpa62nJwcLV26VHFxcV6sDAAAWIXXj9sOHTpUffr0UbNmzdSiRQu9/PLLOnHihO6//35vlwYAACzA62GmR48eOnTokEaOHKnU1FQ1atRIixYtyjUoGAAAIC82Y8yFnmpiCRkZGXI4HEpPT1dISIi3ywEAAAVQmL/fXr9pHgAAwJUgzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEsjzAAAAEvzapipVq2abDab2zRhwgRvlgQAACymjLcLeOGFF9SvXz/XfLly5bxYDQAAsBqvh5ly5copIiLC22UAAACL8vqYmQkTJqhixYpq3LixJk+erOzs7Hz7Z2ZmKiMjw20CAACll1ePzAwePFhNmjRRhQoV9P3332vEiBHav3+/pkyZcsHXJCYmasyYMUVYJQAAKM5sxhhTmCscPny4Jk6cmG+fLVu2qHbt2rna33zzTQ0YMEDHjx+X3W7P87WZmZnKzMx0zWdkZCgyMlLp6ekKCQm5suIBAECRyMjIkMPhKJTf70IPM4cOHdLhw4fz7RMbGyt/f/9c7Zs3b1a9evW0detW1apVq0DbK8w3AwAAFI3C/P0u9NNMYWFhCgsLu6zXbty4UT4+PgoPDy/kqgAAQEnltTEzycnJWrNmjdq0aaNy5copOTlZjz/+uP75z3+qfPny3ioLAABYjNfCjN1u1wcffKDRo0crMzNTMTExevzxxzV06FBvlQQAACzIa2GmSZMmWr16tbc2DwAASgiv32cGAADgShBmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApXkszIwbN06tWrVS2bJlFRoammefPXv2qGPHjipbtqzCw8P15JNPKjs721MlAQCAEqiMp1aclZWlbt26KS4uTrNnz861/OzZs+rYsaMiIiL0/fffa//+/erdu7f8/Pw0fvx4T5UFAABKGJsxxnhyA0lJSRoyZIjS0tLc2r/66ivdfvvt+vPPP1W5cmVJ0owZM/T000/r0KFD8vf3L9D6MzIy5HA4lJ6erpCQkMIuHwAAeEBh/n57bcxMcnKy6tev7woykhQfH6+MjAxt3rz5gq/LzMxURkaG2wQAAEovr4WZ1NRUtyAjyTWfmpp6wdclJibK4XC4psjISI/WCQAAirdLCjPDhw+XzWbLd9q6daunapUkjRgxQunp6a5p7969Ht0eAAAo3i5pAPCwYcPUt2/ffPvExsYWaF0RERFau3atW9uBAwdcyy7EbrfLbrcXaBsAAKDku6QwExYWprCwsELZcFxcnMaNG6eDBw8qPDxckrRkyRKFhISobt26hbINAABQ8nns0uw9e/boyJEj2rNnj86ePauNGzdKkmrUqKHg4GC1a9dOdevW1X333adJkyYpNTVVzz33nBISEjjyAgAACsxjl2b37dtXb731Vq725cuXq3Xr1pKk3bt3a+DAgVqxYoWCgoLUp08fTZgwQWXKFDxjcWk2AADWU5i/3x6/z4ynEWYAALCeEnGfGQAAgMJAmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJZGmAEAAJbmsTAzbtw4tWrVSmXLllVoaGiefWw2W67pgw8+8FRJAACgBCrjqRVnZWWpW7duiouL0+zZsy/Yb86cOWrfvr1r/kLBBwAAIC8eCzNjxoyRJCUlJeXbLzQ0VBEREZ4qAwAAlHBeHzOTkJCgSpUqqUWLFnrzzTdljMm3f2ZmpjIyMtwmAABQennsyExBvPDCC7rllltUtmxZLV68WI888oiOHz+uwYMHX/A1iYmJrqM+AAAANnOxQyHnGT58uCZOnJhvny1btqh27dqu+aSkJA0ZMkRpaWkXXf/IkSM1Z84c7d2794J9MjMzlZmZ6ZrPyMhQZGSk0tPTFRIScvGdAAAAXpeRkSGHw1Eov9+XdGRm2LBh6tu3b759YmNjL7uYli1b6sUXX1RmZqbsdnuefex2+wWXAQCA0ueSwkxYWJjCwsI8VYs2btyo8uXLE1YAAECBeWzMzJ49e3TkyBHt2bNHZ8+e1caNGyVJNWrUUHBwsL744gsdOHBA1113nQICArRkyRKNHz9eTzzxhKdKAgAAJZDHwszIkSP11ltvueYbN24sSVq+fLlat24tPz8/vf7663r88cdljFGNGjU0ZcoU9evXz1MlAQCAEuiSBgAXR4U5gAgAABSNwvz99vp9ZgAAAK4EYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFgaYQYAAFiax8LMrl279OCDDyomJkaBgYGqXr26Ro0apaysLLd+P//8s2688UYFBAQoMjJSkyZN8lRJAACgBCrjqRVv3bpVOTk5mjlzpmrUqKFNmzapX79+OnHihF566SVJUkZGhtq1a6e2bdtqxowZ+uWXX/TAAw8oNDRU/fv391RpAACgBLEZY0xRbWzy5MmaPn26fv/9d0nS9OnT9eyzzyo1NVX+/v6SpOHDh2vBggXaunVrgdaZkZEhh8Oh9PR0hYSEeKx2AABQeArz99tjR2bykp6ergoVKrjmk5OTddNNN7mCjCTFx8dr4sSJOnr0qMqXL59rHZmZmcrMzHRbp+R8UwAAgDWc+90ujGMqRRZmduzYoVdffdV1ikmSUlNTFRMT49avcuXKrmV5hZnExESNGTMmV3tkZGQhVwwAADzt8OHDcjgcV7SOSw4zw4cP18SJE/Pts2XLFtWuXds1v2/fPrVv317dunVTv379Lr3K84wYMUJDhw51zaelpSk6Olp79uy54jfDSjIyMhQZGam9e/eWqtNr7Df7XRqw3+x3aZCenq6oqCi3MzaX65LDzLBhw9S3b998+8TGxrr+/Oeff6pNmzZq1aqVZs2a5dYvIiJCBw4ccGs7Nx8REZHnuu12u+x2e652h8NRqr4E54SEhLDfpQj7Xbqw36VLad1vH58rv7D6ksNMWFiYwsLCCtR33759atOmjZo2bao5c+bkKjguLk7PPvuszpw5Iz8/P0nSkiVLVKtWrTxPMQEAAPydx+4zs2/fPrVu3VpRUVF66aWXdOjQIaWmpio1NdXV55577pG/v78efPBBbd68WfPmzdO///1vt9NIAAAA+fHYAOAlS5Zox44d2rFjh66++mq3ZedGLjscDi1evFgJCQlq2rSpKlWqpJEjR17SPWbsdrtGjRqV56mnkoz9Zr9LA/ab/S4N2O8r3+8ivc8MAABAYePZTAAAwNIIMwAAwNIIMwAAwNIIMwAAwNIIMwAAwNIsG2Z27dqlBx98UDExMQoMDFT16tU1atQoZWVlufX7+eefdeONNyogIECRkZGaNGmSlyouPOPGjVOrVq1UtmxZhYaG5tnHZrPlmj744IOiLbSQFWS/9+zZo44dO6ps2bIKDw/Xk08+qezs7KIt1MOqVauW67OdMGGCt8sqdK+//rqqVaumgIAAtWzZUmvXrvV2SR43evToXJ/t+Y+GKSlWrVqlTp06qWrVqrLZbFqwYIHbcmOMRo4cqSpVqigwMFBt27bV9u3bvVNsIbrYfvft2zfX59++fXvvFFtIEhMT1bx5c5UrV07h4eHq0qWLtm3b5tbn9OnTSkhIUMWKFRUcHKy77ror19MBLsayYWbr1q3KycnRzJkztXnzZk2dOlUzZszQM8884+qTkZGhdu3aKTo6WuvXr9fkyZM1evToXI9VsJqsrCx169ZNAwcOzLffnDlztH//ftfUpUuXoinQQy6232fPnlXHjh2VlZWl77//Xm+99ZaSkpI0cuTIIq7U81544QW3z/bRRx/1dkmFat68eRo6dKhGjRqlH3/8UQ0bNlR8fLwOHjzo7dI87tprr3X7bL/99ltvl1ToTpw4oYYNG+r111/Pc/mkSZP0yiuvaMaMGVqzZo2CgoIUHx+v06dPF3Glheti+y1J7du3d/v833///SKssPCtXLlSCQkJWr16tZYsWaIzZ86oXbt2OnHihKvP448/ri+++EIfffSRVq5cqT///FN33nnnpW3IlCCTJk0yMTExrvlp06aZ8uXLm8zMTFfb008/bWrVquWN8grdnDlzjMPhyHOZJDN//vwiraeoXGi/v/zyS+Pj42NSU1NdbdOnTzchISFu3wGri46ONlOnTvV2GR7VokULk5CQ4Jo/e/asqVq1qklMTPRiVZ43atQo07BhQ2+XUaT+/m9VTk6OiYiIMJMnT3a1paWlGbvdbt5//30vVOgZef0b3adPH9O5c2ev1FNUDh48aCSZlStXGmOcn62fn5/56KOPXH22bNliJJnk5OQCr9eyR2bykp6e7vb0zeTkZN10003y9/d3tcXHx2vbtm06evSoN0osUgkJCapUqZJatGihN99803Xn5ZIqOTlZ9evXV+XKlV1t8fHxysjI0ObNm71YWeGbMGGCKlasqMaNG2vy5Mkl6lRaVlaW1q9fr7Zt27rafHx81LZtWyUnJ3uxsqKxfft2Va1aVbGxsbr33nu1Z88eb5dUpFJSUpSamur2+TscDrVs2bJUfP4rVqxQeHi4atWqpYEDB+rw4cPeLqlQpaenS5Lrt3r9+vU6c+aM2+ddu3ZtRUVFXdLn7bHHGRS1HTt26NVXX9VLL73kaktNTVVMTIxbv3M/dKmpqSX6YZYvvPCCbrnlFpUtW1aLFy/WI488ouPHj2vw4MHeLs1jUlNT3YKM5P55lxSDBw9WkyZNVKFCBX3//fcaMWKE9u/frylTpni7tELx119/6ezZs3l+llu3bvVSVUWjZcuWSkpKUq1atbR//36NGTNGN954ozZt2qRy5cp5u7wice7val6ff0n6e5yX9u3b684771RMTIx27typZ555Rh06dFBycrJ8fX29Xd4Vy8nJ0ZAhQ3T99derXr16kpyft7+/f65xkJf6eRe7IzPDhw/Pc/Dq+dPf/0Hbt2+f2rdvr27duqlfv35eqvzKXM5+5+f555/X9ddfr8aNG+vpp5/WU089pcmTJ3twDy5PYe+3VV3K+zB06FC1bt1aDRo00MMPP6x//etfevXVV5WZmenlvcCV6tChg7p166YGDRooPj5eX375pdLS0vThhx96uzQUgZ49e+qOO+5Q/fr11aVLFy1cuFA//PCDVqxY4e3SCkVCQoI2bdrkkYtRit2RmWHDhqlv37759omNjXX9+c8//1SbNm3UqlWrXAN7IyIico2IPjcfERFROAUXkkvd70vVsmVLvfjii8rMzCxWDzMrzP2OiIjIdcVLcf28/+5K3oeWLVsqOztbu3btUq1atTxQXdGqVKmSfH198/y7W9w/x8IWGhqqa665Rjt27PB2KUXm3Gd84MABValSxdV+4MABNWrUyEtVeUdsbKwqVaqkHTt26NZbb/V2OVdk0KBBWrhwoVatWuX28OmIiAhlZWUpLS3N7ejMpf59L3ZhJiwsTGFhYQXqu2/fPrVp00ZNmzbVnDlz5OPjfqApLi5Ozz77rM6cOSM/Pz9Jzqd516pVq9idYrqU/b4cGzduVPny5YtVkJEKd7/j4uI0btw4HTx4UOHh4ZKcn3dISIjq1q1bKNvwlCt5HzZu3CgfHx/XPludv7+/mjZtqqVLl7quwMvJydHSpUs1aNAg7xZXxI4fP66dO3fqvvvu83YpRSYmJkYRERFaunSpK7xkZGRozZo1F72Cs6T5448/dPjwYbdQZzXGGD366KOaP3++VqxYkWvoR9OmTeXn56elS5fqrrvukiRt27ZNe/bsUVxc3CVtyJL++OMPU6NGDXPrrbeaP/74w+zfv981nZOWlmYqV65s7rvvPrNp0ybzwQcfmLJly5qZM2d6sfIrt3v3brNhwwYzZswYExwcbDZs2GA2bNhgjh07Zowx5vPPPzf/+c9/zC+//GK2b99upk2bZsqWLWtGjhzp5cqvzMX2Ozs729SrV8+0a9fObNy40SxatMiEhYWZESNGeLnywvP999+bqVOnmo0bN5qdO3ead99914SFhZnevXt7u7RC9cEHHxi73W6SkpLMr7/+avr3729CQ0PdrlQriYYNG2ZWrFhhUlJSzHfffWfatm1rKlWqZA4ePOjt0grVsWPHXH9/JZkpU6aYDRs2mN27dxtjjJkwYYIJDQ01n332mfn5559N586dTUxMjDl16pSXK78y+e33sWPHzBNPPGGSk5NNSkqK+eabb0yTJk1MzZo1zenTp71d+mUbOHCgcTgcZsWKFW6/0ydPnnT1efjhh01UVJRZtmyZWbdunYmLizNxcXGXtB3Lhpk5c+YYSXlO5/vpp5/MDTfcYOx2u7nqqqvMhAkTvFRx4enTp0+e+718+XJjjDFfffWVadSokQkODjZBQUGmYcOGZsaMGebs2bPeLfwKXWy/jTFm165dpkOHDiYwMNBUqlTJDBs2zJw5c8Z7RRey9evXm5YtWxqHw2ECAgJMnTp1zPjx4y39j92FvPrqqyYqKsr4+/ubFi1amNWrV3u7JI/r0aOHqVKlivH39zdXXXWV6dGjh9mxY4e3yyp0y5cvz/Pvcp8+fYwxzsuzn3/+eVO5cmVjt9vNrbfearZt2+bdogtBfvt98uRJ065dOxMWFmb8/PxMdHS06devn+UD/IV+p+fMmePqc+rUKfPII4+Y8uXLm7Jly5quXbu6HZgoCNv/bQwAAMCSit3VTAAAAJeCMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACyNMAMAACzt/wGu+GzR77QZxgAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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", 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", 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", 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", + "image/png": 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", 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", + "image/png": 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", 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", + "image/png": 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" ] @@ -1223,21 +1223,6 @@ "# plt.title('Description of Transformation')\n", "# plt.show()\n", "\n", - "# quiver = plt.quiver([0, 0], # Sets the x-cordinate of the start of the vectors\n", - "# [0, 0], # Sets the y-cordinate of the start of the vectors\n", - "# [rand1[0, 0], a1[0, 0]], \n", - "# [rand1[1, 0], a1[1, 0]], \n", - "# angles='xy', \n", - "# scale_units='xy', \n", - "# scale=1,\n", - "# color=['r','b'])\n", - "# plt.xlim(-10, 10)\n", - "# plt.ylim(-10, 10)\n", - "# plt.quiverkey(quiver, 1, 1, 1, 'orginal vector', coordinates='data')\n", - "# plt.quiverkey(quiver, -1, -1.5, 1, 'transformed vector', coordinates='data')\n", - "# plt.title('Transforming a1 by rand1')\n", - "# plt.show()\n", - "\n", "matrices = [a1, a2, a3, b1, b2, b3, c1, c2, d1, d2, d3]\n", "matrices_strings = [\"a1\", \"a2\", \"a3\", \"b1\", \"b2\", \"b3\", \"c1\", \"c2\", \"d1\", \"d2\", \"d3\"]\n", "randoms = [rand1, rand2]\n", @@ -1247,12 +1232,31 @@ " curr_matrix = matrices[i]\n", " curr_rand = randoms[j]\n", " transformed = curr_matrix @ curr_rand\n", + " \n", + " # quiver = plt.quiver(\n", + " # [0, 0], [0, 0], \n", + " # [curr_rand[0, 0], transformed[0, 0]], \n", + " # [curr_rand[1, 0], transformed[1, 0]], \n", + " # angles=\"xy\", scale_units=\"xy\", scale=1,\n", + " # color=[\"r\", \"b\"]\n", + " # )\n", + "\n", " quiver = plt.quiver(\n", " [0, 0], [0, 0], \n", - " [curr_rand[0, 0], transformed[0, 0]], \n", - " [curr_rand[1, 0], transformed[1, 0]], \n", + " transformed[0, 0], \n", + " transformed[1, 0], \n", " angles=\"xy\", scale_units=\"xy\", scale=1,\n", - " color=[\"r\", \"b\"]\n", + " color=\"b\",\n", + " label=\"Original\"\n", + " )\n", + "\n", + " quiver = plt.quiver(\n", + " [0, 0], [0, 0], \n", + " curr_rand[0, 0], \n", + " curr_rand[1, 0], \n", + " angles=\"xy\", scale_units=\"xy\", scale=1,\n", + " color=\"r\",\n", + " label=\"Transformed\"\n", " )\n", "\n", " plt.xlim(-20, 20)\n", @@ -1262,6 +1266,7 @@ " plt.quiverkey(quiver, 6, 8, 1, \"\", coordinates=\"data\")\n", "\n", " plt.title(f\"rand{j+1} transformed by {matrices_strings[i]}\")\n", + " plt.legend()\n", " plt.show()\n", "\n" ] @@ -1286,14 +1291,93 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 159, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Live transposed: \n", + " [[-1.16892322 -5.17937728]\n", + " [-0.13395911 5.57947295]] \n", + "\n", + "This thows an error becuase L has a shape of (2,6) and I has a shape of (6,3) \n", + "if you tranpose them then you are trying to perform matrix multiplication on two matrices \n", + "where the inner shapes do not match. L transposed has a shape of (6,2) and I transposed has \n", + "a shape of (3,6). You cannot perform matrix multiplication a (6,2) and a (3,6) matrix.\n", + "\n", + "Evil individually transposed \n", + " [[-1.16892322 -5.17937728]\n", + " [-0.13395911 5.57947295]]\n", + "You can see that this has the same result as live_transposed.\n", + "\n", + "Squares live transposed: \n", + " [[ 1.81055355 -0.64153822]\n", + " [-1.45006407 0.50700586]] \n", + "\n", + "Live squares individual transposed: \n", + " [[-0.76817289 -2.26482368]\n", + " [-0.38549051 -1.1205261 ]]\n", + "This does not match squares_transposed or evil_squares_individual. Although it does \n", + "not throw an error like the other set above because regardless of whether or not you transpose \n", + "you can still perform matrix multiplication on all of the differen square matrices. \n", + "\n", + "Evil squares individually transposed: \n", + " [[ 1.81055355 -0.64153822]\n", + " [-1.45006407 0.50700586]]\n", + "You can see that this has the same result as squares_transposed.\n", + "\n" + ] + } + ], "source": [ - "L = np.random.rand(2,6)\n", - "I = np.random.rand(6,3)\n", - "V = np.random.rand(3,5)\n", - "E = np.random.rand(5,2)" + "# 1\n", + "L = np.random.randn(2,6)\n", + "I = np.random.randn(6,3)\n", + "V = np.random.randn(3,5)\n", + "E = np.random.randn(5,2)\n", + "\n", + "# 2\n", + "live_transposed = (L @ I @ V @ E).T\n", + "print(\"Live transposed:\", \"\\n\",live_transposed, \"\\n\")\n", + "\n", + "# 3\n", + "try:\n", + " live_individual_transposed = L.T @ I.T @ V.T @ E.T\n", + " print()\n", + "except:\n", + " print(\"This thows an error becuase L has a shape of (2,6) and I has a shape of (6,3) \\n\" \\\n", + " \"if you tranpose them then you are trying to perform matrix multiplication on two matrices \\n\" \\\n", + " \"where the inner shapes do not match. L transposed has a shape of (6,2) and I transposed has \\n\" \\\n", + " \"a shape of (3,6). You cannot perform matrix multiplication a (6,2) and a (3,6) matrix.\\n\")\n", + "\n", + "# 4\n", + "evil_individual = E.T @ V.T @ I.T @ L.T \n", + "print(\"Evil individually transposed\", \"\\n\", evil_individual)\n", + "print(\"You can see that this has the same result as live_transposed.\\n\")\n", + "\n", + "# 5 (1)\n", + "L_square = np.random.randn(2,2)\n", + "I_square = np.random.randn(2,2)\n", + "V_square = np.random.randn(2,2)\n", + "E_square = np.random.randn(2,2)\n", + "\n", + "# 5 (2)\n", + "squares_transposed = (L_square @ I_square @ V_square @ E_square).T\n", + "print(\"Squares live transposed: \", \"\\n\", squares_transposed, \"\\n\")\n", + "\n", + "#5 (3)\n", + "live_squares_individual_transposed = L_square.T @ I_square.T @ V_square.T @ E_square.T\n", + "print(\"Live squares individual transposed: \\n\", live_squares_individual_transposed)\n", + "print(\"This does not match squares_transposed or evil_squares_individual. Although it does \\n\" \\\n", + "\"not throw an error like the other set above because regardless of whether or not you transpose \\n\" \\\n", + "\"you can still perform matrix multiplication on all of the differen square matrices. \\n\")\n", + "\n", + "#5 (4)\n", + "evil_squares_individual = E_square.T @ V_square.T @ I_square.T @ L_square.T \n", + "print(\"Evil squares individually transposed: \", \"\\n\", evil_squares_individual)\n", + "print(\"You can see that this has the same result as squares_transposed.\\n\")\n" ] } ], From 43a9643f3c835393a32904e84c7021cc6ff2aa08 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Sat, 21 Feb 2026 11:55:01 -0800 Subject: [PATCH 54/62] initial commit --- week_8_joining.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/week_8_joining.ipynb b/week_8_joining.ipynb index 4b2d34a..fd6e25b 100644 --- a/week_8_joining.ipynb +++ b/week_8_joining.ipynb @@ -99,7 +99,8 @@ "outputs": [], "source": [ "import pandas as pd\n", - "import numpy as np" + "import numpy as np\n", + "x=5" ] }, { From 425e969635888b03fb0f4b9ce13a4639a11557d2 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 24 Feb 2026 01:21:14 -0800 Subject: [PATCH 55/62] finished problem 1 --- week_8_joining.ipynb | 357 +++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 343 insertions(+), 14 deletions(-) diff --git a/week_8_joining.ipynb b/week_8_joining.ipynb index fd6e25b..c61af23 100644 --- a/week_8_joining.ipynb +++ b/week_8_joining.ipynb @@ -94,20 +94,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", - "import numpy as np\n", - "x=5" + "import numpy as np" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ints threes\n", + "0 0 0.0\n", + "1 1 0.0\n", + "2 2 0.0\n", + "3 3 3.0\n", + "4 4 3.0\n", + ".. ... ...\n", + "95 95 93.0\n", + "96 96 96.0\n", + "97 97 96.0\n", + "98 98 96.0\n", + "99 99 99.0\n", + "\n", + "[100 rows x 2 columns] \n", + "\n", + "\n", + " ints evens threes\n", + "-10 -10 -20 -12.0\n", + "-9 -9 -18 -9.0\n", + "-8 -8 -16 -9.0\n", + "-7 -7 -14 -9.0\n", + "-6 -6 -12 -6.0\n", + "-5 -5 -10 -6.0\n", + "-4 -4 -8 -6.0\n", + "-3 -3 -6 -3.0\n", + "-2 -2 -4 -3.0\n", + "-1 -1 -2 -3.0\n", + "0 0 0 0.0\n", + "1 1 2 0.0\n", + "2 2 4 0.0\n", + "3 3 6 3.0\n", + "4 4 8 3.0\n", + "5 5 10 3.0\n", + "6 6 12 6.0\n", + "7 7 14 6.0\n", + "8 8 16 6.0\n", + "9 9 18 9.0\n" + ] + } + ], "source": [ "df_1 = pd.DataFrame({\"ints\": range(100)})\n", "df_2 = pd.DataFrame({\"ints\": range(-10, 10)}, index=range(-10, 10))\n", @@ -116,7 +159,10 @@ "df_1['threes'] = np.floor(df_1['ints']/3) * 3\n", "\n", "df_2['evens'] = df_2['ints']*2\n", - "df_2['threes'] = np.floor(df_2['ints']/3) * 3" + "df_2['threes'] = np.floor(df_2['ints']/3) * 3\n", + "\n", + "print(df_1,\"\\n\\n\")\n", + "print(df_2)" ] }, { @@ -132,17 +178,300 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ints ints_left threes_left ints_right evens threes_right\n", + "0 0 0 0.0 0.0 0.0 0.0\n", + "1 1 1 0.0 1.0 2.0 0.0\n", + "2 2 2 0.0 2.0 4.0 0.0\n", + "3 3 3 3.0 3.0 6.0 3.0\n", + "4 4 4 3.0 4.0 8.0 3.0\n", + ".. ... ... ... ... ... ...\n", + "95 95 95 93.0 NaN NaN NaN\n", + "96 96 96 96.0 NaN NaN NaN\n", + "97 97 97 96.0 NaN NaN NaN\n", + "98 98 98 96.0 NaN NaN NaN\n", + "99 99 99 99.0 NaN NaN NaN\n", + "\n", + "[100 rows x 6 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_4 = df_1.join(df_2, on=\"ints\", how=\"left\", lsuffix=\"_left\", rsuffix=\"_right\")\n", + "df_4" + ] }, { "cell_type": "markdown", @@ -353,7 +682,7 @@ ], "metadata": { "kernelspec": { - "display_name": "AD_450_env", + "display_name": "ml-env", "language": "python", "name": "python3" }, @@ -367,7 +696,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.10.18" } }, "nbformat": 4, From c16fdb1a7f3a693861bf968ba06118cc1a9cb230 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 24 Feb 2026 16:24:49 -0800 Subject: [PATCH 56/62] completed through problem 5 --- week_8_joining.ipynb | 1281 +++++++++++++++++++++++++++++++++++++----- 1 file changed, 1139 insertions(+), 142 deletions(-) diff --git a/week_8_joining.ipynb b/week_8_joining.ipynb index c61af23..3b7cff1 100644 --- a/week_8_joining.ipynb +++ b/week_8_joining.ipynb @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -104,13 +104,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "df_1: \n", " ints threes\n", "0 0 0.0\n", "1 1 0.0\n", @@ -127,6 +128,7 @@ "[100 rows x 2 columns] \n", "\n", "\n", + "df_2: \n", " ints evens threes\n", "-10 -10 -20 -12.0\n", "-9 -9 -18 -9.0\n", @@ -161,7 +163,9 @@ "df_2['evens'] = df_2['ints']*2\n", "df_2['threes'] = np.floor(df_2['ints']/3) * 3\n", "\n", + "print(\"df_1: \")\n", "print(df_1,\"\\n\\n\")\n", + "print(\"df_2: \")\n", "print(df_2)" ] }, @@ -309,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -347,8 +351,8 @@ " 0\n", " 0\n", " 0.0\n", - " 0.0\n", - " 0.0\n", + " 0\n", + " 0\n", " 0.0\n", " \n", " \n", @@ -356,8 +360,8 @@ " 1\n", " 1\n", " 0.0\n", - " 1.0\n", - " 2.0\n", + " 1\n", + " 2\n", " 0.0\n", " \n", " \n", @@ -365,8 +369,8 @@ " 2\n", " 2\n", " 0.0\n", - " 2.0\n", - " 4.0\n", + " 2\n", + " 4\n", " 0.0\n", " \n", " \n", @@ -374,8 +378,8 @@ " 3\n", " 3\n", " 3.0\n", - " 3.0\n", - " 6.0\n", + " 3\n", + " 6\n", " 3.0\n", " \n", " \n", @@ -383,197 +387,1164 @@ " 4\n", " 4\n", " 3.0\n", - " 4.0\n", - " 8.0\n", + " 4\n", + " 8\n", + " 3.0\n", + " \n", + " \n", + " 5\n", + " 5\n", + " 5\n", + " 3.0\n", + " 5\n", + " 10\n", " 3.0\n", " \n", " \n", + " 6\n", + " 6\n", + " 6\n", + " 6.0\n", + " 6\n", + " 12\n", + " 6.0\n", + " \n", + " \n", + " 7\n", + " 7\n", + " 7\n", + " 6.0\n", + " 7\n", + " 14\n", + " 6.0\n", + " \n", + " \n", + " 8\n", + " 8\n", + " 8\n", + " 6.0\n", + " 8\n", + " 16\n", + " 6.0\n", + " \n", + " \n", + " 9\n", + " 9\n", + " 9\n", + " 9.0\n", + " 9\n", + " 18\n", + " 9.0\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " ints ints_left threes_left ints_right evens threes_right\n", + "0 0 0 0.0 0 0 0.0\n", + "1 1 1 0.0 1 2 0.0\n", + "2 2 2 0.0 2 4 0.0\n", + "3 3 3 3.0 3 6 3.0\n", + "4 4 4 3.0 4 8 3.0\n", + "5 5 5 3.0 5 10 3.0\n", + "6 6 6 6.0 6 12 6.0\n", + "7 7 7 6.0 7 14 6.0\n", + "8 8 8 6.0 8 16 6.0\n", + "9 9 9 9.0 9 18 9.0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_4 = df_1.join(df_2, on=\"ints\", how=\"inner\", lsuffix=\"_left\", rsuffix=\"_right\")\n", + "\n", + "df_4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem 2:\n", + "\n", + "Next you will perform the same merge as above three times with the following modifications:\n", + "\n", + "* You want to keep all rows in `df_1` even if there is no match found in `df_2`\n", + "* You want to keep all rows in `df_2` even if there is no match found in `df_1`\n", + "* You want to keep all rows in `df_1` and `df_2` even if there is no match found in the other dataframe\n", + "\n", + "\n", + "How many rows do you end up with in each case? \n", + "\n", + "Think through a scenario where you might want to do this and add it as a comment above each merge. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" ], "text/plain": [ - " ints ints_left threes_left ints_right evens threes_right\n", - "0 0 0 0.0 0.0 0.0 0.0\n", - "1 1 1 0.0 1.0 2.0 0.0\n", - "2 2 2 0.0 2.0 4.0 0.0\n", - "3 3 3 3.0 3.0 6.0 3.0\n", - "4 4 4 3.0 4.0 8.0 3.0\n", - ".. ... ... ... ... ... ...\n", - "95 95 95 93.0 NaN NaN NaN\n", - "96 96 96 96.0 NaN NaN NaN\n", - "97 97 97 96.0 NaN NaN NaN\n", - "98 98 98 96.0 NaN NaN NaN\n", - "99 99 99 99.0 NaN NaN NaN\n", + " ints threes_x evens threes_y\n", + "0 -10 NaN -20.0 -12.0\n", + "1 -9 NaN -18.0 -9.0\n", + "2 -8 NaN -16.0 -9.0\n", + "3 -7 NaN -14.0 -9.0\n", + "4 -6 NaN -12.0 -6.0\n", + ".. ... ... ... ...\n", + "105 95 93.0 NaN NaN\n", + "106 96 96.0 NaN NaN\n", + "107 97 96.0 NaN NaN\n", + "108 98 96.0 NaN NaN\n", + "109 99 99.0 NaN NaN\n", "\n", - "[100 rows x 6 columns]" + "[110 rows x 4 columns]" ] }, - "execution_count": 14, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_4 = df_1.join(df_2, on=\"ints\", how=\"left\", lsuffix=\"_left\", rsuffix=\"_right\")\n", - "df_4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Problem 2:\n", - "\n", - "Next you will perform the same merge as above three times with the following modifications:\n", - "\n", - "* You want to keep all rows in `df_1` even if there is no match found in `df_2`\n", - "* You want to keep all rows in `df_2` even if there is no match found in `df_1`\n", - "* You want to keep all rows in `df_1` and `df_2` even if there is no match found in the other dataframe\n", - "\n", - "\n", - "How many rows do you end up with in each case? \n", - "\n", - "Think through a scenario where you might want to do this and add it as a comment above each merge. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem 3\n", - "\n", - "Now we are going to merge on columns that are not the same. Merge on the following:\n", - "\n", - "* Merge `df_1` and `df_2` where `df_1.ints = df_2.evens`, only keep rows where there is a value for either dataframe\n", - "* Merge `df_1` and `df_2` where `df_1.ints = df_2.threes`, only keep rows where there is a value for either dataframe\n", - "* Merge `df_1` and `df_2` where `df_1.ints = df_2.threes`, keep all rows from `df_1` even if there is no match found in `df_2`\n", - "* Merge `df_1` and `df_2` where `df_1.threes = df_2.threes`, only keep rows where there is a value for either dataframe\n", - "\n", - "\n", - "How many rows do you end up with in each case? Are there any duplications? (try: value_count)\n", - "\n", - "Think through a scenario where you might want to do this and add it as a comment above each merge. " + "df_5 = pd.merge(df_1, df_2, on=\"ints\", how=\"outer\")\n", + "df_5" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", + "execution_count": 10, "metadata": {}, - "source": [ - "### Problem 4\n", - "\n", - "Add a new the column to `df_2` called `threes_string` that is the `threes` column converted to a string. Attempt to merge `df_1` and `df_2` where `df_1.threes = df_2.threes_string` with an inner join. What happens? Why?" + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ints threes evens\n", + "0 -10 -12.0 -20.0\n", + "1 -9 -9.0 -18.0\n", + "2 -8 -9.0 -16.0\n", + "3 -7 -9.0 -14.0\n", + "4 -6 -6.0 -12.0\n", + ".. ... ... ...\n", + "105 95 93.0 NaN\n", + "106 96 96.0 NaN\n", + "107 97 96.0 NaN\n", + "108 98 96.0 NaN\n", + "109 99 99.0 NaN\n", + "\n", + "[110 rows x 3 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_7 = pd.merge(df_1, df_2, on=\"ints\", how=\"outer\")\n", + "df_7\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 3\n", + "\n", + "Now we are going to merge on columns that are not the same. Merge on the following:\n", + "\n", + "* Merge `df_1` and `df_2` where `df_1.ints = df_2.evens`, only keep rows where there is a value for either dataframe\n", + "* Merge `df_1` and `df_2` where `df_1.ints = df_2.threes`, only keep rows where there is a value for either dataframe\n", + "* Merge `df_1` and `df_2` where `df_1.ints = df_2.threes`, keep all rows from `df_1` even if there is no match found in `df_2`\n", + "* Merge `df_1` and `df_2` where `df_1.threes = df_2.threes`, only keep rows where there is a value for either dataframe\n", + "\n", + "\n", + "How many rows do you end up with in each case? Are there any duplications? (try: value_count)\n", + "\n", + "Think through a scenario where you might want to do this and add it as a comment above each merge. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " ints_x threes ints_y evens\n", + "0 NaN -12.0 -10.0 -20.0\n", + "1 NaN -9.0 -9.0 -18.0\n", + "2 NaN -9.0 -8.0 -16.0\n", + "3 NaN -9.0 -7.0 -14.0\n", + "4 NaN -6.0 -6.0 -12.0\n", + ".. ... ... ... ...\n", + "123 95.0 93.0 NaN NaN\n", + "124 96.0 96.0 NaN NaN\n", + "125 97.0 96.0 NaN NaN\n", + "126 98.0 96.0 NaN NaN\n", + "127 99.0 99.0 NaN NaN\n", + "\n", + "[128 rows x 4 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_11 = pd.merge(df_1, df_2, left_on=\"threes\", right_on=\"threes\", how=\"outer\")\n", + "df_11" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem 4\n", + "\n", + "Add a new the column to `df_2` called `threes_string` that is the `threes` column converted to a string. Attempt to merge `df_1` and `df_2` where `df_1.threes = df_2.threes_string` with an inner join. What happens? Why?" + ] + }, + { + "cell_type": "code", + "execution_count": 40, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "\"\"\" \n", + "You cannot merge these two dataframes on df_1.threes and df_2.threes_string as they are \n", + "incompatible data types. \n", + "\"\"\";\n", + "\n", + "df_2[\"threes_string\"] = df_2[\"threes\"].astype(str)\n", + "# df_12 = pd.merge(df_1,df_2, left_on=\"threes\", right_on=\"threes_string\", how=\"inner\")\n", + "# df_12\n", + "\n" + ] }, { "cell_type": "markdown", @@ -590,10 +1561,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ints threes evens\n", + "0 0 0.0 NaN\n", + "1 1 0.0 NaN\n", + "2 2 0.0 NaN\n", + "3 3 3.0 NaN\n", + "4 4 3.0 NaN\n", + ".. ... ... ...\n", + "5 5 3.0 10.0\n", + "6 6 6.0 12.0\n", + "7 7 6.0 14.0\n", + "8 8 6.0 16.0\n", + "9 9 9.0 18.0\n", + "\n", + "[120 rows x 3 columns]\n", + "------------------------------------------------\n" + ] + } + ], + "source": [ + "df_concat = pd.concat([df_1, df_2], ignore_index=False)\n", + "print(df_concat)\n", + "print(\"------------------------------------------------\")" + ] }, { "cell_type": "markdown", From 56b6fff36828d2132411528c8a5a0363592a06b5 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Tue, 24 Feb 2026 16:48:25 -0800 Subject: [PATCH 57/62] completed problem 6 --- week_8_joining.ipynb | 29 ++++++++++++++++++++++++++--- 1 file changed, 26 insertions(+), 3 deletions(-) diff --git a/week_8_joining.ipynb b/week_8_joining.ipynb index 3b7cff1..8478f66 100644 --- a/week_8_joining.ipynb +++ b/week_8_joining.ipynb @@ -1611,10 +1611,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The matrix ranks are equal\n" + ] + } + ], + "source": [ + "matrix_1 = np.arange(12).reshape((3,4))\n", + "matrix_1_rank = np.linalg.matrix_rank(matrix_1)\n", + "\n", + "matrix_3 = np.arange(8).reshape(4,matrix_1_rank)\n", + "matrix_4 = np.arange(8).reshape(matrix_1_rank,4)\n", + "\n", + "matrix_5 = matrix_3 @ matrix_4\n", + "matrix_5_rank = np.linalg.matrix_rank(matrix_5)\n", + "\n", + "try:\n", + " if matrix_1_rank == matrix_5_rank:\n", + " print(\"The matrix ranks are equal\")\n", + "except: \n", + " print(\"The matrix ranks are NOT equal.\")" + ] }, { "cell_type": "markdown", From f696dd2d96c472749fec739e14ab4fa48e72b5dc Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Wed, 25 Feb 2026 12:49:03 -0800 Subject: [PATCH 58/62] completed problem 7 --- week_8_joining.ipynb | 414 +++++++++++++++++++++++++++---------------- 1 file changed, 265 insertions(+), 149 deletions(-) diff --git a/week_8_joining.ipynb b/week_8_joining.ipynb index 8478f66..4f36a0b 100644 --- a/week_8_joining.ipynb +++ b/week_8_joining.ipynb @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -313,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -454,7 +454,7 @@ "9 9 9 9.0 9 18 9.0" ] }, - "execution_count": 16, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -485,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -518,38 +518,38 @@ " \n", " \n", " 0\n", - " -10\n", - " NaN\n", - " -20.0\n", - " -12.0\n", + " 0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", " \n", " \n", " 1\n", - " -9\n", - " NaN\n", - " -18.0\n", - " -9.0\n", + " 1\n", + " 0.0\n", + " 2.0\n", + " 0.0\n", " \n", " \n", " 2\n", - " -8\n", - " NaN\n", - " -16.0\n", - " -9.0\n", + " 2\n", + " 0.0\n", + " 4.0\n", + " 0.0\n", " \n", " \n", " 3\n", - " -7\n", - " NaN\n", - " -14.0\n", - " -9.0\n", + " 3\n", + " 3.0\n", + " 6.0\n", + " 3.0\n", " \n", " \n", " 4\n", - " -6\n", - " NaN\n", - " -12.0\n", - " -6.0\n", + " 4\n", + " 3.0\n", + " 8.0\n", + " 3.0\n", " \n", " \n", " ...\n", @@ -559,35 +559,35 @@ " ...\n", " \n", " \n", - " 105\n", + " 95\n", " 95\n", " 93.0\n", " NaN\n", " NaN\n", " \n", " \n", - " 106\n", + " 96\n", " 96\n", " 96.0\n", " NaN\n", " NaN\n", " \n", " \n", - " 107\n", + " 97\n", " 97\n", " 96.0\n", " NaN\n", " NaN\n", " \n", " \n", - " 108\n", + " 98\n", " 98\n", " 96.0\n", " NaN\n", " NaN\n", " \n", " \n", - " 109\n", + " 99\n", " 99\n", " 99.0\n", " NaN\n", @@ -595,39 +595,39 @@ " \n", " \n", "\n", - "

110 rows × 4 columns

\n", + "

100 rows × 4 columns

\n", "" ], "text/plain": [ - " ints threes_x evens threes_y\n", - "0 -10 NaN -20.0 -12.0\n", - "1 -9 NaN -18.0 -9.0\n", - "2 -8 NaN -16.0 -9.0\n", - "3 -7 NaN -14.0 -9.0\n", - "4 -6 NaN -12.0 -6.0\n", - ".. ... ... ... ...\n", - "105 95 93.0 NaN NaN\n", - "106 96 96.0 NaN NaN\n", - "107 97 96.0 NaN NaN\n", - "108 98 96.0 NaN NaN\n", - "109 99 99.0 NaN NaN\n", + " ints threes_x evens threes_y\n", + "0 0 0.0 0.0 0.0\n", + "1 1 0.0 2.0 0.0\n", + "2 2 0.0 4.0 0.0\n", + "3 3 3.0 6.0 3.0\n", + "4 4 3.0 8.0 3.0\n", + ".. ... ... ... ...\n", + "95 95 93.0 NaN NaN\n", + "96 96 96.0 NaN NaN\n", + "97 97 96.0 NaN NaN\n", + "98 98 96.0 NaN NaN\n", + "99 99 99.0 NaN NaN\n", "\n", - "[110 rows x 4 columns]" + "[100 rows x 4 columns]" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_5 = pd.merge(df_1, df_2, on=\"ints\", how=\"outer\")\n", + "df_5 = pd.merge(df_1, df_2, on=\"ints\", how=\"left\")\n", "df_5" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -652,8 +652,8 @@ " \n", " \n", " ints\n", - " evens\n", " threes_x\n", + " evens\n", " threes_y\n", " \n", " \n", @@ -661,115 +661,184 @@ " \n", " 0\n", " -10\n", - " -20.0\n", - " -12.0\n", " NaN\n", + " -20\n", + " -12.0\n", " \n", " \n", " 1\n", " -9\n", - " -18.0\n", - " -9.0\n", " NaN\n", + " -18\n", + " -9.0\n", " \n", " \n", " 2\n", " -8\n", - " -16.0\n", - " -9.0\n", " NaN\n", + " -16\n", + " -9.0\n", " \n", " \n", " 3\n", " -7\n", - " -14.0\n", - " -9.0\n", " NaN\n", + " -14\n", + " -9.0\n", " \n", " \n", " 4\n", " -6\n", - " -12.0\n", - " -6.0\n", " NaN\n", + " -12\n", + " -6.0\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 5\n", + " -5\n", + " NaN\n", + " -10\n", + " -6.0\n", " \n", " \n", - " 105\n", - " 95\n", - " NaN\n", + " 6\n", + " -4\n", " NaN\n", - " 93.0\n", + " -8\n", + " -6.0\n", " \n", " \n", - " 106\n", - " 96\n", - " NaN\n", + " 7\n", + " -3\n", " NaN\n", - " 96.0\n", + " -6\n", + " -3.0\n", " \n", " \n", - " 107\n", - " 97\n", - " NaN\n", + " 8\n", + " -2\n", " NaN\n", - " 96.0\n", + " -4\n", + " -3.0\n", " \n", " \n", - " 108\n", - " 98\n", - " NaN\n", + " 9\n", + " -1\n", " NaN\n", - " 96.0\n", + " -2\n", + " -3.0\n", " \n", " \n", - " 109\n", - " 99\n", - " NaN\n", - " NaN\n", - " 99.0\n", + " 10\n", + " 0\n", + " 0.0\n", + " 0\n", + " 0.0\n", + " \n", + " \n", + " 11\n", + " 1\n", + " 0.0\n", + " 2\n", + " 0.0\n", + " \n", + " \n", + " 12\n", + " 2\n", + " 0.0\n", + " 4\n", + " 0.0\n", + " \n", + " \n", + " 13\n", + " 3\n", + " 3.0\n", + " 6\n", + " 3.0\n", + " \n", + " \n", + " 14\n", + " 4\n", + " 3.0\n", + " 8\n", + " 3.0\n", + " \n", + " \n", + " 15\n", + " 5\n", + " 3.0\n", + " 10\n", + " 3.0\n", + " \n", + " \n", + " 16\n", + " 6\n", + " 6.0\n", + " 12\n", + " 6.0\n", + " \n", + " \n", + " 17\n", + " 7\n", + " 6.0\n", + " 14\n", + " 6.0\n", + " \n", + " \n", + " 18\n", + " 8\n", + " 6.0\n", + " 16\n", + " 6.0\n", + " \n", + " \n", + " 19\n", + " 9\n", + " 9.0\n", + " 18\n", + " 9.0\n", " \n", " \n", "\n", - "

110 rows × 4 columns

\n", "" ], "text/plain": [ - " ints evens threes_x threes_y\n", - "0 -10 -20.0 -12.0 NaN\n", - "1 -9 -18.0 -9.0 NaN\n", - "2 -8 -16.0 -9.0 NaN\n", - "3 -7 -14.0 -9.0 NaN\n", - "4 -6 -12.0 -6.0 NaN\n", - ".. ... ... ... ...\n", - "105 95 NaN NaN 93.0\n", - "106 96 NaN NaN 96.0\n", - "107 97 NaN NaN 96.0\n", - "108 98 NaN NaN 96.0\n", - "109 99 NaN NaN 99.0\n", - "\n", - "[110 rows x 4 columns]" + " ints threes_x evens threes_y\n", + "0 -10 NaN -20 -12.0\n", + "1 -9 NaN -18 -9.0\n", + "2 -8 NaN -16 -9.0\n", + "3 -7 NaN -14 -9.0\n", + "4 -6 NaN -12 -6.0\n", + "5 -5 NaN -10 -6.0\n", + "6 -4 NaN -8 -6.0\n", + "7 -3 NaN -6 -3.0\n", + "8 -2 NaN -4 -3.0\n", + "9 -1 NaN -2 -3.0\n", + "10 0 0.0 0 0.0\n", + "11 1 0.0 2 0.0\n", + "12 2 0.0 4 0.0\n", + "13 3 3.0 6 3.0\n", + "14 4 3.0 8 3.0\n", + "15 5 3.0 10 3.0\n", + "16 6 6.0 12 6.0\n", + "17 7 6.0 14 6.0\n", + "18 8 6.0 16 6.0\n", + "19 9 9.0 18 9.0" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_6 = pd.merge(df_2, df_1, on=\"ints\", how=\"outer\")\n", + "df_6 = pd.merge(df_1, df_2, on=\"ints\", how=\"right\")\n", "df_6" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -794,100 +863,112 @@ " \n", " \n", " ints\n", - " threes\n", + " threes_x\n", " evens\n", + " threes_y\n", " \n", " \n", " \n", " \n", " 0\n", " -10\n", - " -12.0\n", + " NaN\n", " -20.0\n", + " -12.0\n", " \n", " \n", " 1\n", " -9\n", - " -9.0\n", + " NaN\n", " -18.0\n", + " -9.0\n", " \n", " \n", " 2\n", " -8\n", - " -9.0\n", + " NaN\n", " -16.0\n", + " -9.0\n", " \n", " \n", " 3\n", " -7\n", - " -9.0\n", + " NaN\n", " -14.0\n", + " -9.0\n", " \n", " \n", " 4\n", " -6\n", - " -6.0\n", + " NaN\n", " -12.0\n", + " -6.0\n", " \n", " \n", " ...\n", " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 105\n", " 95\n", " 93.0\n", " NaN\n", + " NaN\n", " \n", " \n", " 106\n", " 96\n", " 96.0\n", " NaN\n", + " NaN\n", " \n", " \n", " 107\n", " 97\n", " 96.0\n", " NaN\n", + " NaN\n", " \n", " \n", " 108\n", " 98\n", " 96.0\n", " NaN\n", + " NaN\n", " \n", " \n", " 109\n", " 99\n", " 99.0\n", " NaN\n", + " NaN\n", " \n", " \n", "\n", - "

110 rows × 3 columns

\n", + "

110 rows × 4 columns

\n", "" ], "text/plain": [ - " ints threes evens\n", - "0 -10 -12.0 -20.0\n", - "1 -9 -9.0 -18.0\n", - "2 -8 -9.0 -16.0\n", - "3 -7 -9.0 -14.0\n", - "4 -6 -6.0 -12.0\n", - ".. ... ... ...\n", - "105 95 93.0 NaN\n", - "106 96 96.0 NaN\n", - "107 97 96.0 NaN\n", - "108 98 96.0 NaN\n", - "109 99 99.0 NaN\n", + " ints threes_x evens threes_y\n", + "0 -10 NaN -20.0 -12.0\n", + "1 -9 NaN -18.0 -9.0\n", + "2 -8 NaN -16.0 -9.0\n", + "3 -7 NaN -14.0 -9.0\n", + "4 -6 NaN -12.0 -6.0\n", + ".. ... ... ... ...\n", + "105 95 93.0 NaN NaN\n", + "106 96 96.0 NaN NaN\n", + "107 97 96.0 NaN NaN\n", + "108 98 96.0 NaN NaN\n", + "109 99 99.0 NaN NaN\n", "\n", - "[110 rows x 3 columns]" + "[110 rows x 4 columns]" ] }, - "execution_count": 17, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -918,7 +999,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1060,7 +1141,7 @@ "[110 rows x 5 columns]" ] }, - "execution_count": 23, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1072,7 +1153,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1214,7 +1295,7 @@ "[116 rows x 5 columns]" ] }, - "execution_count": 21, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1226,7 +1307,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1368,7 +1449,7 @@ "[106 rows x 5 columns]" ] }, - "execution_count": 24, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1380,7 +1461,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1510,7 +1591,7 @@ "[128 rows x 4 columns]" ] }, - "execution_count": 25, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1531,7 +1612,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1561,27 +1642,27 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " ints threes evens\n", - "0 0 0.0 NaN\n", - "1 1 0.0 NaN\n", - "2 2 0.0 NaN\n", - "3 3 3.0 NaN\n", - "4 4 3.0 NaN\n", - ".. ... ... ...\n", - "5 5 3.0 10.0\n", - "6 6 6.0 12.0\n", - "7 7 6.0 14.0\n", - "8 8 6.0 16.0\n", - "9 9 9.0 18.0\n", + " ints threes evens threes_string\n", + "0 0 0.0 NaN NaN\n", + "1 1 0.0 NaN NaN\n", + "2 2 0.0 NaN NaN\n", + "3 3 3.0 NaN NaN\n", + "4 4 3.0 NaN NaN\n", + ".. ... ... ... ...\n", + "5 5 3.0 10.0 3.0\n", + "6 6 6.0 12.0 6.0\n", + "7 7 6.0 14.0 6.0\n", + "8 8 6.0 16.0 6.0\n", + "9 9 9.0 18.0 9.0\n", "\n", - "[120 rows x 3 columns]\n", + "[120 rows x 4 columns]\n", "------------------------------------------------\n" ] } @@ -1611,7 +1692,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1649,10 +1730,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Each matrix has the same rank as its transpose. Marix multipliation of A @ A.T and A.T @ A all have the same rank as the orignial matrix.\n" + ] + } + ], + "source": [ + "matrix_6 = np.random.randint(1,20,(7,2))\n", + "matrix_7 = np.random.randint(1,20,(3,7))\n", + "matrix_8 = np.random.randint(1,20,(3,4))\n", + "matrix_9 = np.random.randint(1,20,(4,5))\n", + "matrix_10 = np.random.randint(1,20,(5,3))\n", + "matrix_11 = np.random.randint(1,20,(3,6))\n", + "\n", + "def same_rank(arr: np.array) -> bool:\n", + " assert np.linalg.matrix_rank(arr) == np.linalg.matrix_rank(arr.T)\n", + " assert np.linalg.matrix_rank(arr) == np.linalg.matrix_rank(arr.T @ arr)\n", + " assert np.linalg.matrix_rank(arr) == np.linalg.matrix_rank((arr @ arr.T))\n", + " return True\n", + "\n", + "def all_ranks_are_equal() -> None:\n", + " assert same_rank(matrix_6) == True\n", + " assert same_rank(matrix_7) == True\n", + " assert same_rank(matrix_8) == True\n", + " assert same_rank(matrix_9) == True\n", + " assert same_rank(matrix_10) == True\n", + " assert same_rank(matrix_11) == True\n", + " print(\"Each matrix has the same rank as its transpose. Marix multipliation of A @ A.T and A.T @ A \" \\\n", + " \"all have the same rank as the orignial matrix.\")\n", + "\n", + "all_ranks_are_equal()\n", + "\n", + "\n" + ] }, { "cell_type": "markdown", From 3aad17418961dcef2ed5fbea654ccabca8301fa7 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Wed, 25 Feb 2026 19:08:36 -0800 Subject: [PATCH 59/62] finished problem 8 --- week_8_joining.ipynb | 121 +++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 116 insertions(+), 5 deletions(-) diff --git a/week_8_joining.ipynb b/week_8_joining.ipynb index 4f36a0b..994f3b1 100644 --- a/week_8_joining.ipynb +++ b/week_8_joining.ipynb @@ -1730,7 +1730,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1784,12 +1784,119 @@ "Then repeat this exercise using matrix multiplication instead of addition." ] }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rank_zero_1 = np.array([ [1,2,3], \n", + " [2,4,6] ])\n", + "rank_zero_2 = rank_zero_1 * -1\n", + "\n", + "rank_one_1 = np.array([ [3,4,5], \n", + " [6,8,10] ])\n", + "rank_one_2 = rank_one_1 * 3\n", + "\n", + "rank_two_1 = np.array([ [7,8,9], \n", + " [14,16,18] ])\n", + "rank_two_2 = np.array( [ [1,4,5],\n", + " [3,12,15]])\n", + "\n", + "\n", + "\n", + "def rank(arr: np.array) -> int:\n", + " return np.linalg.matrix_rank(arr)\n", + "\n", + "def matrix_addition(arr1: np.array, arr2: np.array) -> np.array:\n", + " return arr1 + arr2\n", + "\n", + "def marix_addition_rank_rule(arr1: np.array, arr2: np.array) -> bool:\n", + " assert (rank(arr1 + arr2)) <= (rank(arr1) + rank(arr2))\n", + " return True\n", + "\n", + "display(marix_addition_rank_rule(rank_zero_1, rank_zero_2))\n", + "display(marix_addition_rank_rule(rank_one_1, rank_one_2))\n", + "display(marix_addition_rank_rule(rank_two_1, rank_two_2))\n", + "\n", + "\n" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# I created a separate box for the matrix multiplication part of problem # 8\n", + "\n", + "def matrix_multiplication(arr1: np.array, arr2: np.array) -> np.array:\n", + " return arr1 @ arr2\n", + "\n", + "def marix_multiplication_rank_rule(arr1: np.array, arr2: np.array) -> bool:\n", + " assert (rank(arr1 @ arr2.T)) <= min(rank(arr1), rank(arr2))\n", + " return True\n", + "\n", + "display(marix_multiplication_rank_rule(rank_one_1, rank_one_2))\n", + "display(marix_multiplication_rank_rule(rank_zero_1, rank_zero_2))\n", + "display(marix_multiplication_rank_rule(rank_two_1, rank_two_2))\n" + ] }, { "cell_type": "markdown", @@ -1810,10 +1917,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "A = np.random.randint(1,20,(4,3))\n", + "v = np.random.randint(1,20,(4))\n", + "\n" + ] } ], "metadata": { From e88a92f5078ceddf8af9f72f6ecbac471f4da834 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Wed, 25 Feb 2026 22:36:31 -0800 Subject: [PATCH 60/62] completed problem #9 --- week_8_joining.ipynb | 41 +++++++++++++++++++++++++++++++++++++++-- 1 file changed, 39 insertions(+), 2 deletions(-) diff --git a/week_8_joining.ipynb b/week_8_joining.ipynb index 994f3b1..2e77223 100644 --- a/week_8_joining.ipynb +++ b/week_8_joining.ipynb @@ -1917,12 +1917,49 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "A = np.random.randint(1,20,(4,3))\n", - "v = np.random.randint(1,20,(4))\n", + "v = np.random.randint(1,20,(4,1))\n", + "\n", + "# check if a vector (v) is in the column space of a matrix (A)\n", + "def in_column_space(A: np.array, v: np.array) -> bool:\n", + " A_rank = rank(A)\n", + " Av_rank = rank(A * v)\n", + " if A_rank == Av_rank:\n", + " return True \n", + " elif A_rank < Av_rank:\n", + " return False \n", + " else:\n", + " return False\n", + "\n", + "for i in range(1000000):\n", + " x = in_column_space(A,v)\n", + " if x == True:\n", + " continue\n", + " elif x == False:\n", + " print(\"it's false\")\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "B = np.random.randint(1,20,(4,4))\n", + "v2 = np.random.randint(1,20,(4,1))\n", + "\n", + "for i in range(1000000):\n", + " x = in_column_space(B,v2)\n", + " if x == True:\n", + " continue\n", + " elif x == False:\n", + " print(\"it's false\")\n", "\n" ] } From 88c0f291550ae0932c2c8aa070c83c32f7b3a231 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Thu, 26 Feb 2026 02:37:36 -0800 Subject: [PATCH 61/62] finished comments on problem #8 --- week_8_joining.ipynb | 82 ++++++++++++++++++++++++++++++++++---------- 1 file changed, 64 insertions(+), 18 deletions(-) diff --git a/week_8_joining.ipynb b/week_8_joining.ipynb index 2e77223..a59e3ac 100644 --- a/week_8_joining.ipynb +++ b/week_8_joining.ipynb @@ -1917,49 +1917,95 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 320, "metadata": {}, "outputs": [], "source": [ "A = np.random.randint(1,20,(4,3))\n", "v = np.random.randint(1,20,(4,1))\n", + "A_v = np.hstack([A,v])\n", "\n", "# check if a vector (v) is in the column space of a matrix (A)\n", "def in_column_space(A: np.array, v: np.array) -> bool:\n", " A_rank = rank(A)\n", - " Av_rank = rank(A * v)\n", + " Av = np.hstack([A,v])\n", + " Av_rank = rank(Av)\n", " if A_rank == Av_rank:\n", " return True \n", " elif A_rank < Av_rank:\n", " return False \n", - " else:\n", - " return False\n", "\n", - "for i in range(1000000):\n", - " x = in_column_space(A,v)\n", - " if x == True:\n", - " continue\n", - " elif x == False:\n", - " print(\"it's false\")\n", + "\n", + "def cp_test1(arr: np.array, vector: np.array) -> None:\n", + " for i in range(1000000):\n", + " cp = in_column_space(arr, vector)\n", + " assert cp == False\n", + "\n", + "# display(cp_test1(A,v))\n", + "\n", + "\"\"\" \n", + "I ran the above test (1 million loops, takes around 16 seconds) and not once did the assertion fail. \n", + "Thus regardless of the random distribution of A and v, v is NEVER in the column space of A for this test. \n", + "In order for this to fail, all 3 columns of A would need to lie in a 2D or 1D subspace.\n", + "\n", + "A has a rank of 3 as it contains 4 rows and 3 columns, thus resulting in a rank of 3. While \n", + "the augmented matrix, A_v has a rank of 4 since as it contains 4 rows and 4 columns, resulting \n", + "in a rank of 4. \n", + "\n", + "Initiallly I did not have the cp_test1 function above and was running the for loop by return \n", + "True/False in an if/elif conditional. After running it many times I noticed that it always \n", + "failed. So, I created the above test so I could run it many times and would only generate an \n", + "output if it failed. I did the same thing below but change the assert to checking for a return \n", + "value of True.\n", + "\n", + "The overall pattern I found is that if a vector has more rows than a matrix has columns then \n", + "v will NOT be in the matrixes column space.\n", + "\"\"\";\n", "\n", "\n" ] }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 319, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My final conclusion is that in a random distribution if a matrix M (of shape j x n) has linearly \n", + "independent columns and vector v (of shape j x 1) is not a linear combination of any of the column vectors within M then \n", + "v is not in the column space of M. If j == n and M has linearly independent columns then v will be within the column \n", + "space of M.\n" + ] + } + ], "source": [ "B = np.random.randint(1,20,(4,4))\n", "v2 = np.random.randint(1,20,(4,1))\n", + "B_v2 = np.hstack([B, v2])\n", + "\n", + "def cp_test2(arr: np.array, vector: np.array) -> None:\n", + " for i in range(1000000):\n", + " cp = in_column_space(arr, vector)\n", + " assert cp == True\n", + "\n", + "# uncomment the line below if you would like to run the test, it will take around 16 seconds\n", + "# display(cp_test2(B,v2))\n", + "\n", + "\"\"\" \n", + "I ran the above test (1 million loops, takes around 16 seconds) and not once did the \n", + "assertion fail. Regardless of the random distribution of B and v2, v2 is ALWAYS \n", + "in the column space of B during this test. There is the very minimal possiblity of \n", + "linear dependence within the resulting matrix. If that were to occur, it would result \n", + "in failure of the test. \n", + "\"\"\";\n", "\n", - "for i in range(1000000):\n", - " x = in_column_space(B,v2)\n", - " if x == True:\n", - " continue\n", - " elif x == False:\n", - " print(\"it's false\")\n", + "print(\"My final conclusion is that in a random distribution if a matrix M (of shape j x n) has linearly \\n\" \\\n", + "\"independent columns and vector v (of shape j x 1) is not a linear combination of any of the column vectors within M then \\n\" \\\n", + "\"v is not in the column space of M. If j == n and M has linearly independent columns then v will be within the column \\n\" \\\n", + "\"space of M.\")\n", "\n" ] } From 99cc78a4cb3546a1fab0a0fe955380f4e040e224 Mon Sep 17 00:00:00 2001 From: Joe Klinck Date: Thu, 26 Feb 2026 02:39:50 -0800 Subject: [PATCH 62/62] fixed comment issue --- week_8_joining.ipynb | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/week_8_joining.ipynb b/week_8_joining.ipynb index a59e3ac..327bc8c 100644 --- a/week_8_joining.ipynb +++ b/week_8_joining.ipynb @@ -1917,7 +1917,7 @@ }, { "cell_type": "code", - "execution_count": 320, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1957,9 +1957,6 @@ "failed. So, I created the above test so I could run it many times and would only generate an \n", "output if it failed. I did the same thing below but change the assert to checking for a return \n", "value of True.\n", - "\n", - "The overall pattern I found is that if a vector has more rows than a matrix has columns then \n", - "v will NOT be in the matrixes column space.\n", "\"\"\";\n", "\n", "\n"