From 40463bf916abda797a91c6b16a00ba0beaf777bb Mon Sep 17 00:00:00 2001
From: Ricardo Mendes <ricardomendes@Ricardos-MacBook-Air.local>
Date: Mon, 15 May 2023 01:01:49 +0100
Subject: [PATCH] msg

---
 your-code/main.ipynb | 1782 ++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 1716 insertions(+), 66 deletions(-)

diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a0a5b66..16e2e9d 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -11,11 +11,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "# Libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import stats"
    ]
   },
   {
@@ -29,11 +33,116 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Rolls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Rolls\n",
+       "0      5\n",
+       "1      4\n",
+       "2      2\n",
+       "3      4\n",
+       "4      5\n",
+       "5      1\n",
+       "6      4\n",
+       "7      3\n",
+       "8      3\n",
+       "9      4"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def roll_dice():\n",
+    "    rolls = []\n",
+    "    for i in range(10):\n",
+    "        rolls.append(random.choice(range(1, 7)))\n",
+    "    df = pd.DataFrame({'Rolls': rolls})\n",
+    "    return df\n",
+    "\n",
+    "roll_dice()"
    ]
   },
   {
@@ -45,18 +154,123 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Rolls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Rolls\n",
+       "0      1\n",
+       "2      1\n",
+       "7      2\n",
+       "9      2\n",
+       "1      3\n",
+       "5      4\n",
+       "3      5\n",
+       "4      5\n",
+       "6      5\n",
+       "8      6"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "df = roll_dice()\n",
+    "sorted_df = df.sort_values(by=['Rolls'], ascending=True)\n",
+    "sorted_df"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 38,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words."
+    "df.plot(kind='bar', x=None, y='Rolls', legend=None)\n",
+    "plt.show()"
    ]
   },
   {
@@ -64,8 +278,36 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "# your code here"
+    "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "counts = df['Rolls'].value_counts().sort_index()\n",
+    "counts.plot(kind='bar', x=None, y=None, legend=None)\n",
+    "plt.show()"
    ]
   },
   {
@@ -75,7 +317,13 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "The relationship between this plot and the plot above is that they both show the distribution of the dice rolls,\n",
+    "but in different ways. The first plot shows the actual roll values sorted in ascending order,\n",
+    "while the second plot shows the frequency of occurrence of each value. \n",
+    "The first plot is useful for analyzing the actual roll sequence, while the second plot is useful\n",
+    "for analyzing the overall distribution of the rolls. For example, in the first plot, we can see if there are \n",
+    "any patterns or trends in the roll sequence, while in the second plot, we can see if the rolls are evenly\n",
+    "distributed or skewed towards certain values.\n",
     "\"\"\""
    ]
   },
@@ -91,11 +339,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.4"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def calculate_mean(x):\n",
+    "    return sum(x)/len(x)\n",
+    "\n",
+    "calculate_mean(df[\"Rolls\"])\n"
    ]
   },
   {
@@ -107,11 +370,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 2, 3: 1, 5: 3, 4: 1, 2: 2, 6: 1}"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def calculate_freq_dist(x):\n",
+    "    x = list(x)\n",
+    "    return {element:x.count(element) for element in x}\n",
+    "\n",
+    "calculate_freq_dist(df[\"Rolls\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{1: 2, 3: 1, 5: 3, 4: 1, 2: 2, 6: 1}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3.4"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def calculate_mean_freq_dist(freq_dist):\n",
+    "    sum_elements = 0\n",
+    "    for key,value in freq_dist.items():\n",
+    "        sum_elements += int(key)*int(value)\n",
+    "    return sum_elements/sum(freq_dist.values())\n",
+    "\n",
+    "freq_dist = calculate_freq_dist(df[\"Rolls\"])\n",
+    "\n",
+    "print(freq_dist)\n",
+    "\n",
+    "calculate_mean_freq_dist(freq_dist)"
    ]
   },
   {
@@ -124,11 +440,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.5"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "def calculate_median(x):\n",
+    "    sorted_data = sorted(data)\n",
+    "    length = len(sorted_data)\n",
+    "    if length % 2 == 0:\n",
+    "        median = (sorted_data[length//2 - 1] + sorted_data[length//2]) / 2\n",
+    "    else:\n",
+    "        median = sorted_data[length//2]\n",
+    "    return median\n",
+    "\n",
+    "calculate_median(df[\"Rolls\"])"
    ]
   },
   {
@@ -140,11 +478,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2.5, 2.5, 2.5)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "def calculate_quartiles(x):\n",
+    "    sorted_data = sorted(data)\n",
+    "    length = len(sorted_data)\n",
+    "    if length % 2 == 0:\n",
+    "        median_index = length // 2\n",
+    "        q1 = calculate_median(sorted_data[:median_index])\n",
+    "        q3 = calculate_median(sorted_data[median_index:])\n",
+    "    else:\n",
+    "        median_index = length // 2\n",
+    "        q1 = calculate_median(sorted_data[:median_index])\n",
+    "        q3 = calculate_median(sorted_data[median_index+1:])\n",
+    "    return q1, calculate_median(sorted_data), q3\n",
+    "\n",
+    "calculate_quartiles(df[\"Rolls\"])"
    ]
   },
   {
@@ -158,11 +522,370 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>13</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>16</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>19</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0  roll  value\n",
+       "0            0     0      1\n",
+       "1            1     1      2\n",
+       "2            2     2      6\n",
+       "3            3     3      1\n",
+       "4            4     4      6\n",
+       "5            5     5      5\n",
+       "6            6     6      2\n",
+       "7            7     7      2\n",
+       "8            8     8      4\n",
+       "9            9     9      1\n",
+       "10          10    10      5\n",
+       "11          11    11      6\n",
+       "12          12    12      5\n",
+       "13          13    13      4\n",
+       "14          14    14      5\n",
+       "15          15    15      4\n",
+       "16          16    16      4\n",
+       "17          17    17      6\n",
+       "18          18    18      2\n",
+       "19          19    19      4"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "df = pd.read_csv('/Users/ricardomendes/Desktop/LABS/Week4/Descriptive-Stats/data/roll_the_dice_hundred.csv')\n",
+    "df.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100, 3)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>47</td>\n",
+       "      <td>47</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>56</td>\n",
+       "      <td>56</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>73</td>\n",
+       "      <td>73</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>24</td>\n",
+       "      <td>24</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>21</td>\n",
+       "      <td>21</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>99</td>\n",
+       "      <td>99</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0  roll  value\n",
+       "0            0     0      1\n",
+       "47          47    47      1\n",
+       "56          56    56      1\n",
+       "9            9     9      1\n",
+       "73          73    73      1\n",
+       "..         ...   ...    ...\n",
+       "17          17    17      6\n",
+       "11          11    11      6\n",
+       "24          24    24      6\n",
+       "21          21    21      6\n",
+       "99          99    99      6\n",
+       "\n",
+       "[100 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.sort_values(by=\"value\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 3500x550 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(35,5.5)) \n",
+    "plt.bar(df[\"roll\"].astype(str),df[\"value\"])\n",
+    "plt.xlabel(\"roll\")\n",
+    "plt.ylabel(\"value\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -185,11 +908,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "calculate_mean(df[\"value\"])"
    ]
   },
   {
@@ -201,11 +937,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 12, 2: 17, 6: 23, 5: 12, 4: 22, 3: 14}"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "calculate_freq_dist(df[\"value\"])"
    ]
   },
   {
@@ -217,11 +966,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "plt.hist(df[\"value\"])\n",
+    "plt.xlabel(\"Result\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -244,11 +1009,215 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>15</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>19</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0  roll  value\n",
+       "0            0     0      5\n",
+       "1            1     1      6\n",
+       "2            2     2      1\n",
+       "3            3     3      6\n",
+       "4            4     4      5\n",
+       "5            5     5      2\n",
+       "6            6     6      6\n",
+       "7            7     7      5\n",
+       "8            8     8      6\n",
+       "9            9     9      6\n",
+       "10          10    10      4\n",
+       "11          11    11      3\n",
+       "12          12    12      5\n",
+       "13          13    13      6\n",
+       "14          14    14      1\n",
+       "15          15    15      3\n",
+       "16          16    16      1\n",
+       "17          17    17      1\n",
+       "18          18    18      1\n",
+       "19          19    19      1"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "df = pd.read_csv('/Users/ricardomendes/Desktop/LABS/Week4/Descriptive-Stats/data/roll_the_dice_thousand.csv')\n",
+    "df.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(df['value'], bins=range(1, 8))\n",
+    "plt.show()"
    ]
   },
   {
@@ -258,7 +1227,8 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "The results are very balanced what is to be expected\n",
+    "\n",
     "\"\"\""
    ]
   },
@@ -274,11 +1244,226 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>68.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>44.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>53.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>46.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    observation\n",
+       "0          68.0\n",
+       "1          12.0\n",
+       "2          45.0\n",
+       "3          38.0\n",
+       "4          49.0\n",
+       "5          27.0\n",
+       "6          39.0\n",
+       "7          12.0\n",
+       "8          42.0\n",
+       "9          33.0\n",
+       "10         30.0\n",
+       "11         25.0\n",
+       "12         30.0\n",
+       "13         44.0\n",
+       "14         53.0\n",
+       "15         46.0\n",
+       "16         50.0\n",
+       "17         22.0\n",
+       "18          6.0\n",
+       "19         29.0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "df = pd.read_csv('/Users/ricardomendes/Desktop/LABS/Week4/Descriptive-Stats/data/ages_population.csv')\n",
+    "df.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 17.,  59., 115., 204., 261., 194.,  99.,  36.,  14.,   1.]),\n",
+       " array([ 1. ,  9.1, 17.2, 25.3, 33.4, 41.5, 49.6, 57.7, 65.8, 73.9, 82. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation\n",
+       "39.0           45\n",
+       "41.0           36\n",
+       "30.0           34\n",
+       "35.0           33\n",
+       "43.0           32\n",
+       "42.0           32\n",
+       "36.0           31\n",
+       "38.0           30\n",
+       "37.0           30\n",
+       "32.0           30\n",
+       "45.0           29\n",
+       "34.0           29\n",
+       "40.0           27\n",
+       "29.0           26\n",
+       "27.0           25\n",
+       "31.0           24\n",
+       "44.0           23\n",
+       "46.0           23\n",
+       "26.0           23\n",
+       "33.0           22\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "freq_dist = df.value_counts()\n",
+    "freq_dist.head(20)"
    ]
   },
   {
@@ -290,11 +1475,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    36.56\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "df.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    12.8165\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.std()"
    ]
   },
   {
@@ -304,7 +1524,8 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "My guess was mean between 35 and 45 and the standard deviation 10.\n",
+    "I was right as the real mean is 36.56\n",
     "\"\"\""
    ]
   },
@@ -317,11 +1538,224 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    observation\n",
+       "0          25.0\n",
+       "1          31.0\n",
+       "2          29.0\n",
+       "3          31.0\n",
+       "4          29.0\n",
+       "5          29.0\n",
+       "6          28.0\n",
+       "7          30.0\n",
+       "8          24.0\n",
+       "9          26.0\n",
+       "10         30.0\n",
+       "11         29.0\n",
+       "12         24.0\n",
+       "13         26.0\n",
+       "14         30.0\n",
+       "15         25.0\n",
+       "16         25.0\n",
+       "17         30.0\n",
+       "18         21.0\n",
+       "19         29.0"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "df = pd.read_csv('/Users/ricardomendes/Desktop/LABS/Week4/Descriptive-Stats/data/ages_population2.csv')\n",
+    "df.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation\n",
+       "28.0           139\n",
+       "27.0           125\n",
+       "26.0           120\n",
+       "29.0           115\n",
+       "25.0            98\n",
+       "30.0            90\n",
+       "24.0            78\n",
+       "31.0            61\n",
+       "23.0            41\n",
+       "22.0            35\n",
+       "32.0            31\n",
+       "33.0            22\n",
+       "21.0            17\n",
+       "20.0            13\n",
+       "34.0             7\n",
+       "35.0             3\n",
+       "19.0             3\n",
+       "36.0             2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "freq_dist = df.value_counts()\n",
+    "freq_dist.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 16.,  52., 119.,  98., 245., 254.,  90.,  92.,  29.,   5.]),\n",
+       " array([19. , 20.7, 22.4, 24.1, 25.8, 27.5, 29.2, 30.9, 32.6, 34.3, 36. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(df)"
    ]
   },
   {
@@ -338,7 +1772,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "\n",
     "\"\"\""
    ]
   },
@@ -351,11 +1785,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    27.155\n",
+      "dtype: float64\n",
+      "observation    2.969814\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "print(df.mean())\n",
+    "print(df.std())"
    ]
   },
   {
@@ -365,7 +1812,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "\n",
     "\"\"\""
    ]
   },
@@ -381,11 +1828,183 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>54.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>52.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>37.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>69.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>36.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>64.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>76.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>73.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>52.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>67.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    observation\n",
+       "0          21.0\n",
+       "1          21.0\n",
+       "2          24.0\n",
+       "3          31.0\n",
+       "4          54.0\n",
+       "5          52.0\n",
+       "6          37.0\n",
+       "7          69.0\n",
+       "8          36.0\n",
+       "9          30.0\n",
+       "10         64.0\n",
+       "11         30.0\n",
+       "12         76.0\n",
+       "13         73.0\n",
+       "14         42.0\n",
+       "15         52.0\n",
+       "16         67.0\n",
+       "17         28.0\n",
+       "18         24.0\n",
+       "19         50.0"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "df = pd.read_csv('/Users/ricardomendes/Desktop/LABS/Week4/Descriptive-Stats/data/ages_population3.csv')\n",
+    "df.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  8.,  33.,  78., 158., 187., 174., 133.,  57., 117.,  55.]),\n",
+       " array([ 1. ,  8.6, 16.2, 23.8, 31.4, 39. , 46.6, 54.2, 61.8, 69.4, 77. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(df)"
    ]
   },
   {
@@ -397,11 +2016,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    41.989\n",
+      "dtype: float64\n",
+      "observation    16.144706\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "print(df.mean())\n",
+    "print(df.std())"
    ]
   },
   {
@@ -424,11 +2057,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first quartile is 30.0\n",
+      "the second quartile is 40.0\n",
+      "the third quartile is 53.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "q1 = np.quantile(df, 0.25)\n",
+    "print(f\"the first quartile is {q1}\")\n",
+    "q2 = np.quantile(df, 0.50)\n",
+    "print(f\"the second quartile is {q2}\")\n",
+    "q3 = np.quantile(df, 0.75)\n",
+    "print(f\"the third quartile is {q3}\")"
    ]
   },
   {
@@ -500,9 +2150,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ironhack-3.7",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "ironhack-3.7"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -514,7 +2164,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,