diff --git a/your-code/.ipynb_checkpoints/Central Limit Theorem-checkpoint.ipynb b/your-code/.ipynb_checkpoints/Central Limit Theorem-checkpoint.ipynb new file mode 100644 index 0000000..8ec76a4 --- /dev/null +++ b/your-code/.ipynb_checkpoints/Central Limit Theorem-checkpoint.ipynb @@ -0,0 +1,376 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9b887533", + "metadata": {}, + "source": [ + "with a sample, will try to infer statistics to the population.\n", + "\n", + "often, we need to ask questions about and collect data for an entire group of people,objects or events a population does not have a fixed definition, it is up to the researcher, but it comprises of all individuals of interest to study a certain effect very often it can be impractical/impossble to collect data for all elements of a population, but we still need to take conclusions on the population as a whole. to do that we have to sample our population and use that sample as a proxy to infer information about the whole population. in the relatively rare cases where we do collect data for the whole population we call that a census. population'" + ] + }, + { + "cell_type": "markdown", + "id": "8743b44b", + "metadata": {}, + "source": [ + "the porportion of a sample does not matter, the size is what matters.\n", + "\n", + "note: perfectly balanced sampling does not eliminate variation due to sampling! \n", + "note 2: the proportion of the population sampled (other than 100%) does not reduce variation due to sampling, only the size of the sample does (a sample of 100 from a population of 1m is just as useful as a sample of 100 from a population of 10k. \n", + "\n", + "you are measuring against the unkown so only what you have counts\n", + "\n", + "Very minimum for any type of data, have at least 30 data points.\n", + "\n", + "Measure of a population are parameters.\n", + "Measures of a sample is statistics\n", + "\n", + "DDOF=1 is the deltas degrees of freedom for samples\n", + "deltas degrees of freedom 0 is for population. Dont need to add it but doesnt make a difference for population\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "923d2eef", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cd6bb54a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([93, 44, 57, ..., 51, 25, 90])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# randomly generating a population ages of people. AND THIS IS THE POPULATION, it has 10000 people\n", + "\n", + "population = np.random.randint(0,95,10000)\n", + "population" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2296e1f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "46.7625" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#mean of ages\n", + "np.mean(population)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f0f89375", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(population)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "207818a4", + "metadata": {}, + "source": [ + "### Sampple Mean Variation" + ] + }, + { + "cell_type": "raw", + "id": "70bbbf8a", + "metadata": {}, + "source": [ + "pop_sample=np.random.choice(population, 10)\n", + "print(pop_sample.mean())\n", + "print(pop_sample)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "47b56340", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[49.07, 45.23, 50.085, 47.265, 45.22, 46.05, 41.435, 48.17, 45.4, 46.715]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#bigger sample -> less variation due to sampling \n", + "\n", + "samples_means=[]\n", + "\n", + "#create 10 samples from our population save it in a mean\n", + "for i in range(10):\n", + " pop_sample=np.random.choice(population, 200)\n", + " samples_means.append(pop_sample.mean())\n", + " \n", + "samples_means" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78d4123c", + "metadata": {}, + "outputs": [], + "source": [ + "### Sample Standard Deviation" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "f775df15", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "27.261608421918176" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "true_sigma=population.std(ddof=0)\n", + "true_sigma" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "2ae035ce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[24.74424736737365,\n", + " 28.774216853905088,\n", + " 28.5238691469287,\n", + " 14.849242404917497,\n", + " 32.21524828745267,\n", + " 26.047819273192314,\n", + " 24.004860618910772,\n", + " 24.73504037235885,\n", + " 27.07274151860748,\n", + " 30.000925911637385]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Now let's compute 10 small samples\n", + "\n", + "sample_std = []\n", + "\n", + "for i in range(10):\n", + " pop_sample = np.random.choice(population, 10) #10 here is we are collecting 10 samples\n", + " std_sample = pop_sample.std(ddof=1) #here i'm computing std for a sample -> ddof=1\n", + " sample_std.append(std_sample)\n", + "\n", + "sample_std" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "09169220", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[26.964614351695786,\n", + " 26.397776668979656,\n", + " 26.861713614202973,\n", + " 26.85490092176078,\n", + " 27.878944613521853,\n", + " 26.44131645891335,\n", + " 27.351958304947342,\n", + " 28.95270098751344,\n", + " 26.858570588740417,\n", + " 26.769381275430085]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Now let's compute 10 bigger samples\n", + "\n", + "sample_std = []\n", + "\n", + "for i in range(10):\n", + " pop_sample = np.random.choice(population, 200) #10 here is we are collecting 10 samples\n", + " std_sample = pop_sample.std(ddof=1) #here i'm computing std for a sample -> ddof=1\n", + " sample_std.append(std_sample)\n", + "\n", + "sample_std" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "f7c5f7bb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(population)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "cfa7dcca", + "metadata": {}, + "outputs": [], + "source": [ + "samples_means =[]\n", + "for i in range(100):\n", + " pop_sample = np.random.choice(population, 200)\n", + " samples_means.append(pop_sample.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "550bd049", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(samples_means)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd33dcff", + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"Whaat we obrserve from the two histograms, (pop) and It does not matter the shape of your sample\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e5cc4ed", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97950eeb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/your-code/.ipynb_checkpoints/Probability-checkpoint.ipynb b/your-code/.ipynb_checkpoints/Probability-checkpoint.ipynb new file mode 100644 index 0000000..d0420a3 --- /dev/null +++ b/your-code/.ipynb_checkpoints/Probability-checkpoint.ipynb @@ -0,0 +1,312 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c6d92927", + "metadata": {}, + "source": [ + "# Probability" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d994b8fd", + "metadata": {}, + "outputs": [], + "source": [ + "from PIL import Image" + ] + }, + { + "cell_type": "markdown", + "id": "aa7b6bef", + "metadata": {}, + "source": [ + "SET THEORY\n", + "\n", + "Data Squad | EDA\n", + "\n", + "Data set is a collection of some items (elements), the order doesn't matter.\n", + "Often used capital letters to denote a set.\n", + "\n", + "A = { x | x condition} or A = { x : x condition} | and : are \"such that\"\n", + "\n", + "A is a Subset of set B if every element of A is also an element of B. B is the superset.\n", + "\n", + "Null set / Empty set is the set with no elements {}.\n", + "\n", + "Two sets are mutually exclusive or disjoint if they have no element in common.\n", + "\n", + "Universal set is the set of all things we could possibily consider in the context." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "66aecd3f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from PIL import Image\n", + "from IPython.display import display\n", + "image = Image.open('C:/Users/Elsio Miranda/Downloads/probability_set_diagram1.png')\n", + "display(image)" + ] + }, + { + "cell_type": "markdown", + "id": "5cafbd94", + "metadata": {}, + "source": [ + "## Cartesian Product, Inclusion-Exclusion" + ] + }, + { + "cell_type": "markdown", + "id": "a3ca3423", + "metadata": {}, + "source": [ + "Mathematical equation that combines every element from A & B" + ] + }, + { + "cell_type": "markdown", + "id": "b59425a1", + "metadata": {}, + "source": [ + "CARTESIAN PRODUCT, INCLUSION-EXCLUSION\n", + "\n", + "Data Squad | EDA\n", + "\n", + "A Cartesian Product of two sets A and B, written in A x B, is the set containing ordered pairs\n", + "from A and B. That is, if C = A x B, then each element of C is of the form (x,y), where:\n", + "x ∈ A and y ∈ B.\n", + "\n", + "A x B = {(x,y) | x ∈ A and y ∈ B}\n", + "\n", + "Inclusion-Exclusion principle: |A U B| = |A| + |B| - |A ∩ B|\n", + "\n", + "Cardinality= not interested in the elements but the amount of elements. \n", + "example 1:\n", + "S={1,2,3,4,5,6,7,8}\n", + "A={1,2,4}\n", + "B={2,6,8}\n", + "|AUB| = 3+3-1=5\n", + "\n", + "example 2:\n", + "3 sets -->A U B U C= |A|+|B|+|C|-A n B|| - |A n C| -|B n C| + (need to add intersection or wont be captured) |A n B n C|\n", + "formula for 3 sets different to capture intersection \n", + "\n", + "EXERCISES\n", + " EXERCISES\n", + "\n", + "Data Squad | EDA\n", + "\n", + "1 - In a party,\n", + "- there are 10 ppl with white shirts and 8 ppl with red shirts;\n", + "- 4 ppl have black shoes and white shirts;\n", + "- 3 ppl have black shoes and red shirts;\n", + "- the total number of ppl with white or red shirts or black shoes is 21.\n", + "How many ppl have black shoes?\n", + "\n", + "|W| no. of people with White Shirts\n", + "|W U R U B| =21 \n", + "21=|W|+|R|+|B|-|WnR|-|WnB|-|RnB|+|WnRnB|\n", + "21=10+8+|B|-0-4-3+0\n", + "|B|=10 \n", + "there are no intersection between White and Red\n", + " \n", + "2 - Write all the possible partitions of S = {1,2,3}\n", + "\n", + "3 - Let A, B, C be three sets. For each of the following sets, draw a Venn diagram and shade\n", + "the area representing the given set.\n", + "- A U B U C - A - (B ∩ C)\n", + "- A ∩ B ∩ C - A U not (B ∩ C)\n", + "- A U (B ∩ C)\n", + "\n", + "\n", + "\n", + "Infinite / finite set: difference between countable VS uncountable set ( N VS R)" + ] + }, + { + "cell_type": "markdown", + "id": "37c07f56", + "metadata": {}, + "source": [ + "## Random Experiments and Probability" + ] + }, + { + "cell_type": "markdown", + "id": "bab5437f", + "metadata": {}, + "source": [ + "P(A)>=0, for all A (A is events)\n", + "\n", + "We assign a probability measure P(A) to an event. This is a value between 0 and 1 that shows how\n", + "likely the event is.\n", + "- P(A) close to 0 -> very unlikely\n", + "- P(A) close to 1 -> very likely\n", + "\n", + "Stuff to remember:\n", + "- P(A) >= 0 , for all A\n", + "- P (S) = 1\n", + "- The sum probability of all disjoint probability is their sum.\n", + "\n", + "Additional rule:\n", + "Probability of A or, Probability of A and B happening, the p\n", + "\n", + "multiplicity rule\n", + "Couple of events happening at the same time and we know nothing of the event\n", + "probability of A and B P(A u B) =>\n", + "Probability of A happening at the same time as P(AnB)\n", + "is given by probability of an event, A happening giving that B already happened times the probability of B (conditional probability) = P(A|B) * P(B). This is interchangeable with P(A|B) *P(B)\n", + "Multiplicative Rule comes from Bayesian Theorem\n", + "P(AnB)=P(A|B)*P(B)\n", + "P(AnB)=P(B|A)*P(A)\n", + "\n", + "Conditional Probability\n", + "P(A|B) = (P(AnB))/p(B)\n", + "\n", + "Exercise\n", + "John is going to university, the probabilty that he chooses algebra subject is 0.70 and the math subject 0.30. The probablity that John chooses math knowing that he already choose Algebra is 0.40. What is the probability that John picks both algebra and math? \n", + "P(A) =0.70\n", + "P(M)=0.30\n", + "P(M|A)=0.40\n", + "looking for P(AnM)\n", + "using formula P(AnM)=P(M|A)*P(A)\n", + "0.28=0.4*0.7\n", + "\n", + "What is the probability of John choosing either Algebra or Math?\n", + "looking for probability of math and algebra P(M U A) \n", + "the formula is:\n", + "P(M U A) = P(M) + P(A) -P(MnA)(need to add M & A to avoid overcounting)\n", + "So what we are doing now is P(M) + P(A) -P(MnA)= P(M U A)\n", + "0.30 + 0.70 -0.28=0.72\n", + "\n", + "Are they independent? never assume. Only if you know\n", + "so first test it...\n", + "P(A|B) = P(A) * P(B) \n", + "P(A|B) = P(A) If A and B are independent, means that the probability of A knowing that B happened means the probability of A \n", + "if this does not add up then it shows that it is independent\n", + "\n", + "DISJOINT VS INDEPENDANCE\n", + "\n", + "Data Squad | Bayesian Probability\n", + "\n", + "Disjoint:\n", + "A and B cannot occur at the same time e.g. night and day. It looks like 2 circles that have no intersection. it is dependant though. No information in A means it is in B\n", + "- A ∩ B = 0\n", + "- P (A U B) = P(A) + P(B)\n", + "\n", + "Independant:\n", + "A does not give any information on B\n", + "- P(A|B) = P(A), P(B|A) = P(B)\n", + "- P(A ∩ B) = P(A)P(B)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Independant:\n", + "A does not give any information on B\n", + "- P(A|B) = P(A), P(B|A) = P(B)\n", + "- P(A ∩ B) = P(A)P(B)\n", + "\n", + "\n", + "Summary\n", + "\n", + "Universal manipulations (always true)\n", + "\n", + "P(S) = 1\n", + "P({}) = 0\n", + "P(A∪B) = P(A)+P(B)-P(A∩B) (Inclusion-exclusion principle)\n", + "P(Ā) = 1-P(A)\n", + "P(A|B) = P(A∩B)/P(B) (Definition)\n", + "P(A|B) = P(B|A)P(A)/P(B) (Bayes Law)\n", + "P(B) = P(B|A)P(A) + P(B|Ā)P(Ā) (Law of total probability)\n", + "\n", + "If A,B are independent (written A ᅭ B)\n", + "\n", + "P(A∩B) = P(A)*P(B) (Definition)\n", + "P(A|B) = P(A)\n", + "\n", + "Laplace’s rule (Only if samples equiprobable) what is the probability that an element of A will be in sample S\n", + "P(A) = |A| / |S|\n", + "example\n", + "A={1,2,3,}\n", + "S={1,2,3,4,5,6,7,8,9,10}\n", + "3/10\n", + "\n", + "\n", + "Exercise\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fa0c796", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a0ba5a1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ace1150e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4726f53d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/your-code/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/your-code/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/your-code/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/your-code/.ipynb_checkpoints/main-Copy1-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main-Copy1-checkpoint.ipynb new file mode 100644 index 0000000..4ab3ff4 --- /dev/null +++ b/your-code/.ipynb_checkpoints/main-Copy1-checkpoint.ipynb @@ -0,0 +1,671 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding Descriptive Statistics\n", + "\n", + "Import the necessary libraries here:" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy import stats\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 1\n", + "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n", + "**Hint**: you can use the *choices* function from module *random* to help you with the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4, 2, 5, 5, 4, 4, 2, 1, 2, 4])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "def roll_dice_simulation():\n", + " roll_dataset = np.random.randint(1, 6, size = 10)\n", + " return roll_dataset\n", + " \n", + "rolls_1=roll_dice_simulation()\n", + "rolls_1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Plot the results sorted by value." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "datasorted=np.sort(rolls_1)\n", + "datasorted\n", + "plt.hist(datasorted)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 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": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ModeResult(mode=4, count=4)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"mode is 3 and count is 4\"\"\"\n", + "rolls1_plot=stats.mode(rolls_1)\n", + "rolls1_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(rolls1_plot)\n", + "plt.show()\n", + "\"\"\"this histogram highlighted solely the most frequent, 4. the previous histogram provided other options\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 2\n", + "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n", + "\n", + "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.3" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "def mean_rolls(array):\n", + " mean_rolls=sum(array)/len(array)\n", + " return mean_rolls\n", + "\n", + "mean_rolls(rolls_1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.0" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rolls1_plot=stats.mode(rolls_1)\n", + "mean_rolls(rolls1_plot)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n", + "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.0" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def median_rolls(data):\n", + " n = len(datasorted)\n", + " \n", + " if n % 2 != 0: # If the number of observations is odd\n", + " median_index = n // 2\n", + " median = datasorted[median_index]\n", + " else: # If the number of observations is even\n", + " upper_median_index = n // 2\n", + " lower_median_index = upper_median_index - 1\n", + " median = (datasorted[lower_median_index] + datasorted[upper_median_index]) / 2\n", + " \n", + " return median\n", + "\n", + "median_rolls(rolls_1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. " + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the first quartile is 2.0\n", + "the second quartile is 4.0\n", + "the third quartile is 4.0\n", + "[4 2 5 5 4 4 2 1 2 4]\n" + ] + } + ], + "source": [ + "q1 = np.quantile(rolls_1, 0.25)\n", + "print(f\"the first quartile is {q1}\")\n", + "\n", + "q2 = np.quantile(rolls_1, 0.50)\n", + "print(f\"the second quartile is {q2}\")\n", + "\n", + "q3 = np.quantile(rolls_1, 0.75)\n", + "print(f\"the third quartile is {q3}\")\n", + "print(rolls_1) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 3\n", + "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n", + "#### 1.- Sort the values and plot them. What do you see?" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'roll_the_dice_hundred.csv'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[73], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m data\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mroll_the_dice_hundred.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:912\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 899\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 900\u001b[0m dialect,\n\u001b[0;32m 901\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 908\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 909\u001b[0m )\n\u001b[0;32m 910\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 912\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:577\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 574\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 576\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 577\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 579\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 580\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1407\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1404\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1406\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1407\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_engine(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine)\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1661\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1659\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m 1660\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1661\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m get_handle(\n\u001b[0;32m 1662\u001b[0m f,\n\u001b[0;32m 1663\u001b[0m mode,\n\u001b[0;32m 1664\u001b[0m encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m 1665\u001b[0m compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m 1666\u001b[0m memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmemory_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[0;32m 1667\u001b[0m is_text\u001b[38;5;241m=\u001b[39mis_text,\n\u001b[0;32m 1668\u001b[0m errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding_errors\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m 1669\u001b[0m storage_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage_options\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m 1670\u001b[0m )\n\u001b[0;32m 1671\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1672\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\io\\common.py:859\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 854\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m 855\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 856\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 857\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m 858\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 859\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[0;32m 860\u001b[0m handle,\n\u001b[0;32m 861\u001b[0m ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[0;32m 862\u001b[0m encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[0;32m 863\u001b[0m errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m 864\u001b[0m newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 865\u001b[0m )\n\u001b[0;32m 866\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 867\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m 868\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'roll_the_dice_hundred.csv'" + ] + } + ], + "source": [ + "data=pd.read_csv(\"roll_the_dice_hundred.csv\")\n", + "da" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now, calculate the frequency distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 4\n", + "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n", + "\n", + "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- What do you see? Is there any difference with the frequency distribution in step 1?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 5\n", + "Now is the turn of `ages_population3.csv`.\n", + "\n", + "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus challenge\n", + "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb new file mode 100644 index 0000000..47612e2 --- /dev/null +++ b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb @@ -0,0 +1,2008 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding Descriptive Statistics\n", + "\n", + "Import the necessary libraries here:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy import stats\n", + "import seaborn as sns\n", + "from collections import Counter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 1\n", + "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n", + "**Hint**: you can use the *choices* function from module *random* to help you with the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4, 3, 2, 1, 2, 4, 5, 1, 5, 3])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "def roll_dice_simulation():\n", + " roll_dataset = np.random.randint(1, 6, size = 10)\n", + " return roll_dataset\n", + " \n", + "rolls_1=roll_dice_simulation()\n", + "rolls_1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Plot the results sorted by value." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "datasorted=np.sort(rolls_1)\n", + "datasorted\n", + "plt.hist(datasorted)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 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": "markdown", + "metadata": {}, + "source": [ + "#### Calculation of frequency distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "hist_rolls={}\n", + "\n", + "for a in range(1,7):\n", + " hist_rolls[a]=0\n", + "\n", + "for i in rolls_1:\n", + " hist_rolls[i]+=1\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 2, 2: 2, 3: 2, 4: 2, 5: 2, 6: 0}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"output\"\"\"\n", + "hist_rolls" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(hist_rolls.values())\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 2\n", + "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n", + "\n", + "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### function to calculate mean" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "def x_rolls(f):\n", + " x_rolls=sum(f)/len(f)\n", + " return x_rolls\n", + "\n", + "x_rolls(rolls_1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### To calculate the frequency distribution I first will convert rolls_1 which is an array to a list" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[4, 3, 2, 1, 2, 4, 5, 1, 5, 3]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_list = list(rolls_1)\n", + "data_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### will now calculate the frequency distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Value Frequency\n", + "0 1 2\n", + "1 2 2\n", + "2 3 2\n", + "3 4 2\n", + "4 5 2\n", + "5 6 0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency={}\n", + "for a in range(1,7):\n", + " frequency[a]=0\n", + " \n", + "for i in data_list:\n", + " frequency[i]+=1\n", + "\n", + "frequency_df = pd.DataFrame.from_dict(frequency, orient='index').reset_index()\n", + "frequency_df.columns = ['Value', 'Frequency']\n", + "\n", + "frequency_df\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### here we calculated the mean using the values of the frequency distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "#mean = sum(frequency_df['Value'] * frequency_df['Frequency']) / sum(frequency_df['Frequency'])\n", + "#print(\"Mean:\", mean)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 3.0\n" + ] + } + ], + "source": [ + "def x_rolls(f):\n", + " x_rolls=sum(f)/len(f)\n", + " return x_rolls\n", + "\n", + "mean2=x_rolls(rolls_1)\n", + "print(\"Mean:\", mean2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n", + "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def median_rolls(x):\n", + " n = len(datasorted)\n", + " \n", + " if n % 2 != 0: # If the number of observations is odd\n", + " median_index = n // 2\n", + " median = datasorted[median_index]\n", + " else: # If the number of observations is even\n", + " upper_median_index = n // 2\n", + " lower_median_index = upper_median_index - 1\n", + " median = (datasorted[lower_median_index] + datasorted[upper_median_index]) / 2\n", + " \n", + " return median\n", + "\n", + "median_rolls(rolls_1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the first quartile is 2.0\n", + "the second quartile is 3.0\n", + "the third quartile is 4.0\n", + "[4 3 2 1 2 4 5 1 5 3]\n" + ] + } + ], + "source": [ + "q1 = np.quantile(rolls_1, 0.25)\n", + "print(f\"the first quartile is {q1}\")\n", + "\n", + "q2 = np.quantile(rolls_1, 0.50)\n", + "print(f\"the second quartile is {q2}\")\n", + "\n", + "q3 = np.quantile(rolls_1, 0.75)\n", + "print(f\"the third quartile is {q3}\")\n", + "print(rolls_1) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 3\n", + "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n", + "#### 1.- Sort the values and plot them. What do you see?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "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", + ".. ... ... ...\n", + "95 95 95 4\n", + "96 96 96 6\n", + "97 97 97 1\n", + "98 98 98 3\n", + "99 99 99 6\n", + "\n", + "[100 rows x 3 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data=pd.read_csv(\"roll_the_dice_hundred.csv\")\n", + "datasorted_1=np.sort(data[\"value\"])\n", + "datasorted_1\n", + "plt.hist(datasorted_1)\n", + "plt.show()\n", + "data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'numbers 1and 5 rolled the least times, numbers 4 and 6 rolled the most times'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"numbers 1and 5 rolled the least times, numbers 4 and 6 rolled the most times\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean is 3.74\n" + ] + } + ], + "source": [ + "mean_2=x_rolls(data[\"value\"])\n", + "print(\"Mean is \",x_rolls(data[\"value\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now, calculate the frequency distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Value Frequency\n", + "0 1 12\n", + "1 2 17\n", + "2 3 14\n", + "3 4 22\n", + "4 5 12\n", + "5 6 23" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency={}\n", + "for a in range(1,7):\n", + " frequency[a]=0\n", + " \n", + "for i in data[\"value\"]:\n", + " frequency[i]+=1\n", + "\n", + "frequency_df_2 = pd.DataFrame.from_dict(frequency, orient='index').reset_index()\n", + "frequency_df_2.columns = ['Value', 'Frequency']\n", + "\n", + "frequency_df_2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "frequency_df_2.plot(kind=\"bar\", y=\"Frequency\", x=\"Value\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "the mean may be connected by calculating the weighted mean and plotting a line with the value in the histogram\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "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", + ".. ... ... ...\n", + "995 995 995 1\n", + "996 996 996 4\n", + "997 997 997 4\n", + "998 998 998 3\n", + "999 999 999 6\n", + "\n", + "[1000 rows x 3 columns]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_1=pd.read_csv(\"roll_the_dice_thousand.csv\")\n", + "datasorted_2=np.sort(data_1[\"value\"])\n", + "datasorted_2\n", + "data_1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Will now calculate frequency distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Value Frequency\n", + "0 1 175\n", + "1 2 167\n", + "2 3 175\n", + "3 4 168\n", + "4 5 149\n", + "5 6 166" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency={}\n", + "for a in range(1,7):\n", + " frequency[a]=0\n", + " \n", + "for i in data_1[\"value\"]:\n", + " frequency[i]+=1\n", + "\n", + "frequency_df_3 = pd.DataFrame.from_dict(frequency, orient='index').reset_index()\n", + "frequency_df_3.columns = ['Value', 'Frequency']\n", + "\n", + "frequency_df_3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Now will plot Frequency Distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "
" + ], + "text/plain": [ + " Value Frequency\n", + "0 39.0 45\n", + "1 41.0 36\n", + "2 30.0 34\n", + "3 35.0 33\n", + "4 43.0 32\n", + ".. ... ...\n", + "67 73.0 1\n", + "68 82.0 1\n", + "69 70.0 1\n", + "70 71.0 1\n", + "71 69.0 1\n", + "\n", + "[72 rows x 2 columns]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency_series = data_2[\"observation\"].value_counts()\n", + "frequency_series\n", + "frequency_df_4 = frequency_series.reset_index()\n", + "frequency_df_4.columns = ['Value', 'Frequency']\n", + "frequency_df_4" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "frequency_df_4.plot(kind=\"bar\", y=\"Frequency\", x=\"Value\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### my guess on range of mean and standard deviation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\"\"\"mean around 39 with standard deviation of 12 years\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is standard_deviation observation 12.8165\n", + "dtype: float64\n", + "This is the mean observation 36.56\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "mean=data_2.mean()\n", + "standard_dev=data_2.std()\n", + "print(\"This is standard_deviation \", standard_dev)\n", + "print(\"This is the mean \", mean)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "no I guessed higher\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([19., 19., 19., 20., 20., 20., 20., 20., 20., 20., 20., 20., 20.,\n", + " 20., 20., 20., 21., 21., 21., 21., 21., 21., 21., 21., 21., 21.,\n", + " 21., 21., 21., 21., 21., 21., 21., 22., 22., 22., 22., 22., 22.,\n", + " 22., 22., 22., 22., 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n", + " 22., 22., 22., 22., 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n", + " 22., 22., 22., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23.,\n", + " 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23.,\n", + " 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23.,\n", + " 23., 23., 23., 23., 23., 24., 24., 24., 24., 24., 24., 24., 24.,\n", + " 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24.,\n", + " 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24.,\n", + " 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24.,\n", + " 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24.,\n", + " 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24.,\n", + " 24., 24., 24., 24., 24., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31.,\n", + " 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31.,\n", + " 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31.,\n", + " 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31.,\n", + " 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 32.,\n", + " 32., 32., 32., 32., 32., 32., 32., 32., 32., 32., 32., 32., 32.,\n", + " 32., 32., 32., 32., 32., 32., 32., 32., 32., 32., 32., 32., 32.,\n", + " 32., 32., 32., 32., 33., 33., 33., 33., 33., 33., 33., 33., 33.,\n", + " 33., 33., 33., 33., 33., 33., 33., 33., 33., 33., 33., 33., 33.,\n", + " 34., 34., 34., 34., 34., 34., 34., 35., 35., 35., 36., 36.])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_3=pd.read_csv(\"ages_population2.csv\")\n", + "data_3\n", + "datasorted_3=np.sort(data_3[\"observation\"])\n", + "datasorted_3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Calculate the frequency distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "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", + "19.0 3\n", + "35.0 3\n", + "36.0 2\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency_series = data_3[\"observation\"].value_counts()\n", + "frequency_series\n", + "frequency_df_5 = frequency_series\n", + "frequency_df_5.columns = ['Value', 'Frequency']\n", + "frequency_df_5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot the frequency distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "frequency_df_4.plot(kind=\"bar\", y=\"Frequency\", x=\"Value\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- What do you see? Is there any difference with the frequency distribution in step 1?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "not much\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is standard_deviation observation 2.969814\n", + "dtype: float64\n", + "This is the mean observation 27.155\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "mean=data_3.mean()\n", + "standard_dev=data_3.std()\n", + "print(\"This is standard_deviation \", standard_dev)\n", + "print(\"This is the mean \", mean)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "the standard deviation has decreased by 10 years compared to that of step 2. \n", + "Mean has decreased by 9 years\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 5\n", + "Now is the turn of `ages_population3.csv`.\n", + "\n", + "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "0 68.0\n", + "1 12.0\n", + "2 45.0\n", + "3 38.0\n", + "4 49.0\n", + ".. ...\n", + "995 27.0\n", + "996 47.0\n", + "997 53.0\n", + "998 33.0\n", + "999 31.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_4=pd.read_csv(\"ages_population.csv\")\n", + "datasorted_4=np.sort(data_4[\"observation\"])\n", + "datasorted_4\n", + "data_4" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "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", + " ..\n", + "73.0 1\n", + "82.0 1\n", + "70.0 1\n", + "71.0 1\n", + "69.0 1\n", + "Name: count, Length: 72, dtype: int64" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency_series = data_4[\"observation\"].value_counts()\n", + "frequency_series\n", + "frequency_df_6 = frequency_series\n", + "frequency_df_6.columns = ['Value', 'Frequency']\n", + "frequency_df_6" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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rjAAwBbI+Sfnt3vYDgJPhzggAALAVyQgAALAVyQgAALAVyQgAALAVyQgAALAVyQgAALAVyQgAALAV64wAqDGsQQKgMrgzAgAAbEUyAgAAbEUyAgAAbEUyAgAAbEUyAgAAbEUyAgAAbEUyAgAAbMU6IwBs4W8NEtYnAf5cuDMCAABsRTICAABsRTICAABsVeVkZPHixdKtWzdJSUkRwzBk1qxZHuWqKoWFhZKSkiIul0tyc3OlpKSkuuIFAABnmConIwcPHpRzzz1XJk6c6LV83LhxMn78eJk4caIsX75ckpKSJC8vT/bv33/KwQIAgDNPlT9NU1BQIAUFBV7LVFUmTJggI0eOlKuvvlpERKZPny6JiYnyxhtvyO23335q0QIAgDNOtb5nZNOmTVJaWir5+fnmNqfTKZ06dZIlS5Z43aesrEz27dvn8QAAAH8e1brOSGlpqYiIJCYmemxPTEyULVu2eN1nzJgxMnr06OoMA8AZrPwaJBXXH/FXBqD2qpFP0xiG4fG7qlq2uT344IOyd+9e87F169aaCAkAANRS1XpnJCkpSURO3CFJTk42t+/cudNyt8TN6XSK0+mszjAAAEAdUq13RjIyMiQpKUkWLFhgbjty5Ih89tlnkpOTU52HAgAAZ4gq3xk5cOCA/PDDD+bvmzZtktWrV0v9+vWlYcOGMnToUCkqKpLMzEzJzMyUoqIiCQ8Plz59+lRr4AAA4MxQ5WRkxYoVcskll5i/Dxs2TERE+vfvLy+//LIMHz5cDh8+LHfeeafs2bNH2rdvL/Pnz5eoqKjqixoAAJwxqpyM5Obmiqr6LDcMQwoLC6WwsPBU4gIAAH8S1foGVgCorfjYL1B78UV5AADAViQjAADAViQjAADAViQjAADAViQjAADAViQjAADAViQjAADAVqwzAuBPz98aJL7Kym/3th+AyuPOCAAAsBXJCAAAsBXJCAAAsBXJCAAAsBXJCAAAsBXJCAAAsBXJCAAAsBXrjABANfO3Bkl1lLGmCc403BkBAAC2IhkBAAC2IhkBAAC2IhkBAAC2IhkBAAC2IhkBAAC2IhkBAAC2Yp0RAKjjWIMEdR13RgAAgK1IRgAAgK1IRgAAgK1IRgAAgK1IRgAAgK1IRgAAgK1IRgAAgK1YZwQAzmCsQYK6gDsjAADAViQjAADAViQjAADAViQjAADAViQjAADAViQjAADAVny0FwD+pPx97NdXWfnt3vYDAsGdEQAAYCuSEQAAYCuSEQAAYCuSEQAAYCuSEQAAYCuSEQAAYCuSEQAAYCvWGQEAVAt/a5BURxlrmpy5uDMCAABsRTICAABsRTICAABsRTICAABsRTICAABsRTICAABsRTICAABsxTojAIA6yd8aJNVdVtNrqHiL5c+EOyMAAMBWJCMAAMBWJCMAAMBWJCMAAMBWJCMAAMBWJCMAAMBWJCMAAMBWrDMCAEAtczrXUPFXVpV1UtaM6CiB4s4IAACwFckIAACwFckIAACwVY0lI88995xkZGRIWFiYtG3bVj7//POaOhQAAKjDaiQZ+fe//y1Dhw6VkSNHyqpVq6Rjx45SUFAgP/30U00cDgAA1GE1koyMHz9ebrnlFhk4cKBkZWXJhAkTJC0tTSZNmlQThwMAAHVYtX+098iRI7Jy5Up54IEHPLbn5+fLkiVLLM8vKyuTsrIy8/e9e/eKiMi+ffvkeNkhj+fu27fP/LmyZeW3n+6y6og/0DLaffrLaPfpL6Pdp7+Mdp/+srrWblWVKtNqtm3bNhUR/e9//+ux/bHHHtOmTZtanj9q1CgVER48ePDgwYPHGfDYunVrlXOHGlv0zDAMj99V1bJNROTBBx+UYcOGmb8fP35cdu/eLXFxcWIYhuzbt0/S0tJk69atEh0d7bFvdZedzmPVprLaEgftpt20m3bT7rrb7qioKNm/f7+kpKRIVVV7MhIfHy9BQUFSWlrqsX3nzp2SmJhoeb7T6RSn0+mxLTY21vK86OhoSyfUVNnpPFZtKqstcZzustoSx+kuqy1xnO6y2hLH6S6rLXGc7rLaEsfpLrMrjpiYGK/lJ1Ptb2ANDQ2Vtm3byoIFCzy2L1iwQHJycqr7cAAAoI6rkX/TDBs2TG666SZp166dZGdnywsvvCA//fST3HHHHTVxOAAAUIfVSDJy/fXXy65du+Thhx+W7du3S8uWLeXDDz+URo0aVbkup9Mpo0aNsvwrpybKTuexalNZbYnjdJfVljhOd1ltieN0l9WWOE53WW2J43SX1ZY4TndZbYmjqgzVQD6DAwAAUD34bhoAAGArkhEAAGArkhEAAGArkhEAAGArkhEAAGCrGlsOHgBgn2PHjslvv/0mhmFIXFycBAUF2R3SaVET7Q60zuqO5Ux+TevMnZHly5dL3759JSMjQ1wul4SHh0tGRob07dtXVqxYUe11Tp8+vdqPV90x+osj0LYF2u66cLyuXbtK165dqzXGmiir7vFVF8b5maAmzlGBmDlzplx00UUSHh4uKSkpkpycLOHh4XLRRRfJE088USfGcmX68tixY7Jjxw7ZuXOnHDt2LOB2+6rvVPryiSeeqNZY/MUxa9asU+rHmnhtAlHr1hlZvny5TJgwQZYsWSKlpaViGIZERkbKrl27pH379tKrVy9JTEwUVZWdO3fK/PnzZeHChfLYY4/JqlWrPPZLTEyUnJwcyc/Pl/nz51e6zldffVW+/vprOf/88+Wmm26yHM+9tP1PP/1UqWMFWhZouwNtW6DtTktLky+//FI6d+4sXbp0qZXHW7hwoUybNk1ERP76179K586dTznGmiir7vFVF8Z5XSpr1qyZiIisX7++0mPydPZleHi47NmzR6666iq58cYbPeJ4/vnn5aOPPpJzzjlHBg4cWGvHcmX6skmTJrJx40b5448/ROTEF7MeP35c8vPz5Y477qh0u33VFxwcLKmpqfLTTz/JwIEDLXH4q/PFF1+UkpIS6dq1q9x+++2nHIu/tn300Ucybdo0ueeee2Tz5s1Vun7UxGuzcOFCeeutt6RHjx5VvvbXqmRk1qxZ0qtXL7nssss8GnrPPfdIdHS0bN682WtD+/XrJ6+++qp07dq10hcnf3W2bNlS0tLS5KeffpKSkhJLjNdee62EhYXJo48+WqMTOtB2B9q2QNv9j3/8Qw4fPizvvvuuJcbacryWLVvKjTfeKIZhyCuvvGIpCyTGmiir7vFVF8Z5XSnzl9D6G5Onsy+HDBkiSUlJ8v3333t9vZs3by6rVq2SH3/8sdaOZX99OXnyZBk0aJBER0fLSy+9ZO53zTXXSLNmzWTp0qXyzDPPyK233lqpdvuqb+fOndK/f385dOiQPPfccx71nazOJk2aSJs2beTbb7/12l9VjcVf20REBg8eLM8++2yVrgM18dqIiDz++OOW82ulaS1yzjnn6JgxYyzbnU6nrl+/XseOHastWrSwlGdmZmpQUJDPOrt27WrZz1+dTqdT586dq06n02t9w4YN81nm7ViBlgXa7kDbFmi7nU6n3nPPPV5jrC3Hc/fJd99957UskBhroqy6x1ddGOd1pcx9fvLVl77G5Onsy7CwMP3uu+98xvjhhx9qWFiYpb7aNJb99eXZZ5+tjz76qKVOd7unTp2qjRs3rnS7fdXnrvOxxx6z1HeyOsPCwvTDDz/02V9VjcVf21RVmzRposHBwV6P5Wvu18Rro6pez6+VVauSEXfnVdSiRQt9/PHHfTY0ODjY64vkrtPbZPFXZ4sWLbSgoECzsrK81nfvvff6LKvOCR1ouwNtW6DtbtGihd57771eY6wtx3P3yeOPP+61LJAYa6KsusdXXRjndaXMX0Lrb0yezr5s27atDhs2zGeMHTt21LZt21rqq01j2V9fhoWFea3T3e5vv/3WcqH3125f9bnrHDBggNfEwV+dbdu21Y4dO/rsr6rG4q9tqqpBQUF6zjnneD2Wr7lfE6+Nqno9v1ZWrfo0zdlnny2zZs2S4cOHe2x/+OGHpXfv3jJt2jSJjY2VGTNmiGEYUlpaKgsWLJA//vhDLrroIp91Pv3009K4ceNK1xkWFiZz586V888/X4qLiyUxMdEsCwkJkfHjx8vbb79d6WMFWhZouwNtW6Dtfvjhh6VXr17icrmqVOfpPF779u3lgQceEBGRAQMGVEuf1ERZdY+vujDO60qZ+/wkIl772deYPJ19+dRTT8kVV1whr732mkRERMjYsWPNOPbv3y/r1q2TCy+8sFaPZX996XQ6vdbpbvf06dMlOjq60u32VZ+7zry8PAkKCpK7777bYz9/dYaEhMjnn38uqampVdovkLYtWLBAVFU6duzotR99zf2aeG0WLFgg8+fPlxkzZlj2q4xa9Z6Rd999V3r37i35+fmSn5/v0dC33npLvvzyS4mLi5N9+/aJiEhSUpJkZ2dL69atZdSoUV73e+WVV8z/f/Xr16/SdV566aWyaNEi+fLLL6W0tNQsS01NlWXLlkmXLl2qdKxAywJtdyBtC7TdCxYskHnz5smFF14oW7durXSdp/t4mZmZIiKyYcOGaomxJspqYnzVhXFeF8o++ugjefnll0XkREKbn59fqTF5uvty9uzZ8tlnn0nz5s3NN0G6X++//OUv5niorWPZX1+effbZsnTpUsnIyLDUOWfOHNm8ebO0aNFCDh8+XKl2+6tvwYIFsmnTJunRo4ds27bN0m5/fdmrVy9ZtmyZLF26tNL7Bdq2Ro0ayV133VWl60BNvDbZ2dkyZMgQyc7OlkDUqmREROTLL7+U4uJiry+wv4b628/fRAqk8wI9VqBlgbb7VAaG3cey43i1RU2Mr7owzutCmb+EtibOT6fz9a4JNXFe3rx5s0yaNMnrhf6OO+6Q9PT0KsVY3fWdikBjCeRceTqvmZVR65IRAADw51JnFj3zZ8SIEXLzzTeftjpr4niBCDSOQNt2Jh+vJmI83e0ORG2J40xXW/qyf//+cumll3otqytjOZA6A213TdRZ3bH4q8+f2vLauNWpZMRXQ7dt2yabN2+u8n6B1umv7HRO6EDbHWjbAm13XTheTcR4utt9po7zulIW6Jg8nTE2aNBAGjVqVOUYa9NY9lenrwtzoO32d6EPtM7qjsVffSKBzf2aeG1OplZ9muZktm3bJlu3brVsnz59ekD7BVqnv7JAjlUTMQa6X020uy4cryZiPN3tPlPHeV0pC3RMns4Yi4qKfMZRV8ayvzpTUlLE4bD+jR1ou33Vdyp1BrpfgwYNPGJRVTEMw299IoHN/Zp4bU6G94wAAODF9u3bZdKkSfLFF1/I9u3bJSgoSDIyMqRnz54yYMAAW7+oLjQ0VL755hvJysqyLYbqdEYkIzt27JDJkyfLP//5zyrtt2vXLlmzZo2ce+65Ur9+ffntt99k6tSpUlZWJtddd51ERUVJbGysREZGeux39OhR+fLLL+Xiiy+uzmac1NGjR2XOnDmyYcMGSU5OlquuukoiIiK8PvfgwYPyxhtvWL5b4KKLLpKePXvK+++/b5lgR48elQcffNDvLT9vfv75ZwkLC5P4+HgREfn888/l+eefl59++kkaNWokgwYNsrz7unHjxjJ9+nTJysqq0n5uH3zwgaxYsUK6du0q2dnZsmjRInnyySfl+PHjcvXVV8ttt91m6bPt27eL0+mU7t27W/a58sorJTw83OtJ57LLLrMcv3HjxvLRRx9J/fr1vY6hRYsWyX333SedO3f22292j6+NGzfK0KFDJS4uzjJObrjhBp/jC1YnO5+crovGM888IytWrJArrrhCevXqJa+++qqMGTPGY27Ex8d7jLunnnpKevToIb/88ovXcefrfJKVlSXXX3+9ec6oyhz2p7Lzu7zDhw/Lhx9+KK+//rq89957HmW///67vPXWW9KvXz9zm3sOuz8hJSKyZ88emT59umzYsEGOHz8ub7zxhjRu3FhcLpcsW7ZM+vbtK0eOHJGPPvpIsrKy5KOPPpKoqCiPY61atUqOHj0q8+bNk3/+85/y2muvyaRJk8w+GTx4sPTu3VtEPM9PCxYskHvvvVfy8/M96hs2bJjXPiouLpYbb7xR4uLiRERk/PjxXp9X3eeZw4cPy5tvvlnpc2WlBbRUWg06dOiQTp06Vf/6179q165d9YorrtDBgwfrxx9/7HOf1atXq2EY+sILL+iAAQO0a9euWlBQoAMGDNApU6bogQMHzOceOXJEZ86cqYMHD9bw8HA1DEPr1aunK1as0IyMDM3MzNT09HQ1DEMdDocGBQVpv379dP/+/aqqunXrVi0pKVGHw6GqqosXL9Y+ffpohw4dtG/fvrpkyRLdunWr+Xy3J598Ujds2KCfffaZ1za8//77+s9//lOXLFmiqqoLFy7U2NhYvfTSS3Xy5Mm6c+dObdWqlYaGhmpmZqaGhYVpw4YN9eeff7bU1aBBA01ISNDY2Fjt0aOH3nbbbXrrrbdqjx49NCoqSoOCgjQ2NlaTk5PVMAy94oortH379ioiahiGXnbZZTpjxgwtKyur1GuWnZ2tr7/+uo4ePVpnzZqlDodDu3fvrvfff7+2bt1aHQ6H3nrrrVpcXGw+goKCtEGDBnr77bdrcXGxZb+rrrpKg4OD9bHHHtNdu3apquqvv/6qY8eO1SuvvFKDg4O1bdu2Gh0dra+99ppGRUXpwIEDNTExUcPCwnTChAkefXbWWWepiGhoaKhGRUV57NO7d281DEMjIiIsfWIYhrZp00bHjx9vib9///7qcrm8jiERURHR9u3bW/ryl19+0QsuuMDr+FJV/fHHH9UwDMtYfvzxx3Xz5s3m8yo79tzjvuLYKykpMful4jiJjY3VBg0aaElJibn/zJkzddy4cfrqq696zCm3jIwM/f777/W3337TRYsWWV630aNH67p163zGuHfvXh0+fHil535GRobef//9Hn1SFaWlpTp69GhL+4qLi3328/XXX68jRoywxDhx4kSNiYnxOhaaNGmioaGheuutt3rM74KCAu3SpYveeeedlrlfUFCgzZo106KiIp/xe+vnvLw8dTqdmp+fr0lJSTp27FiNi4vTRx99VB944AENDg5WwzAs4869TUQs47WkpERTUlK8nk+CgoI0Li5OS0pKvM7hkJAQ/eCDD7zG/+OPP2q3bt0s47xv374+5/ftt9+uLpdLJ0yY4FHX+vXrtVGjRmoYhoqIdurUSX/55RdVVS0uLtZHH31UDcOwzOHIyEgtKirS4uJi3bhxoyYlJWlSUpLm5eVpaGioOp1O/fbbb1VV9dVXX9X27durquru3bu1TZs2etddd1nadd555+mUKVPU4XDolClT1OVy6V133aWTJk3S5ORkjYiI0KlTp1rO6e5zRkZGho4dO1a3b99uvjZt2rTR3Nxcj4dhGHrBBRdobm6udujQwTIWRowYoQ0aNKjyeabiNbO8DRs2aGpqqoaHh1vOlUFBQXrdddfp0aNHfY5Zf2pVMrJhwwZt1KiRxsXFeTS0VatW6nA4NC8vT1euXKnffPONx+Opp55SEfE5WZKTk7WkpMTjxXe5XBoUFKSpqan60EMPaWpqqg4cOFBVVfv166dnnXWWdurUSRcsWKDt2rXTtm3b6u7du80Lr2EYlslXUFDgM4nxN9knTZrkdfKJiPbr109dLpdmZ2drmzZtzAH622+/aUZGhrZv395jghUXF6uIaFZWlj711FNaXFzs0cddunTRJk2aaKdOnVRVdcyYMVpQUGDGGBcXp82aNdOQkBCNi4vTIUOG6Nq1a/2+blFRUTpnzhx1OBzavn17HTt2rFlmGIbGxsZqaGiopqenmw/DMNQwDG3QoIHZjvL7LVu2TF0ul4qI5eQeEhKiISEhunLlSl20aJGGhYXps88+ax6vuLhYs7Ky9NZbbzX7LCsrS8ePH685OTlaUFDgsU9BQYHm5uZq8+bNvfaJO3mrGL/T6dTIyEht1KiRPvHEEx5jyDAM7dixoyYlJVn6sl+/fnrhhRfq8uXLLeOrpKREk5KSvCYI/k78vsaee44sWrRIDcPwmDft2rXTnJwcNQzD8ppeeOGFes0112hubq7lpBkcHKz16tXT0aNHVylB85fku09yIlKlhFBE1OFwaNOmTaucQC9evFgdDoelfYZh6FlnnaU///yzRz/fdtttGh4eriKi9evXtyTyGRkZunv3bstYmDRpkjkPKl5cO3bsqCKiaWlpljL3xclb25YtW+Y1+QkJCdGkpCR1uVz65ptvalBQkL722muqeuK81rRpU01NTbWMO8MwdMKECSoilvGam5urvXv39tq3UVFR2q1bN83NzbXMYVXVZ555Rs877zzLfv4SYYfDobGxsVpSUmKZ37Nnz9a77rpLU1NTdfbs2eajffv2esEFF+gjjzyihmFot27dNCMjQ7ds2aKGYWhKSoqKiGUOu/s+IyNDe/furbm5uXrw4EFVVXW5XHrJJZfotddeq6qqx44d05CQEC0tLdVvvvlGn3/+eT3rrLMs1yN3vA6HQ8877zydPHmy2W7DMPS5557TFi1aeJyf3GUtWrTQFi1aaHx8vIaEhGj37t21X79+mpGRoQsXLvTow+DgYC0pKfE5FqKiotTpdKrT6dTnnnuu0ueZin+IlFdQUKDXXnutec4of678/vvvNT09XUeNGuVr2vlVq5KRgoICvf322/XYsWOq+n8NdZ/A3JPTfSGruN3bZDEMQ6+66irNzc31ePHr1aunS5Ys0ZycHB0wYIA6HA5dtmyZqqqmpKToK6+8og0aNFBV1d9//1179Oihbdq00cjISP3qq6+8Xnj79eun6enp2qxZsypN9qysLH3hhRdUVT0mn2EYumPHDp02bZqGhobqf/7zH0vbgoKCPCZYenq6iogmJCRoenq6ZmRkeOwTHh6u//nPf9TlcqmqallZmYaEhOhvv/2mhmHoyy+/rOnp6bpjxw59/PHHtXnz5upwOPScc84x/3qrOPmioqJ03Lhx6nA4NCEhQb/55hvzeLfddptmZWVZvlMhODhYo6KizOdW3K9z587aq1cvdblclpO7y+XS66+/Xnv27KmqqiEhIWbCZBiGLl++XMPDw7Vp06Zmn7lcLt2yZYt+8sknmp6e7rFPeHi4Llq0SMPDwy19ctttt2lGRoampKRY4o+OjtZ169ap6om/rMuPIcMw9OOPP9YGDRpY+jIkJEQffPBB3bdvn2V8dejQQXv27Ok1QfB34vc19txzxH3iLT933PPGfZev4tj69NNP1eVyeT1phoaGamRkZJUSNH9JfkFBgfbr10/dN2srmxCKiMbFxWl4eLhlXlUcp+Uf7jup7rt25dsXGRmp559/vt58880e/ew+PxUXF+t5553nEWNMTIympKToqFGjLGMhKytLH3roIW3QoIHl4pqVlaUDBgzQrKwsr4n1LbfcopGRkZa2de7cWQcOHKj79u3z6Gf3OL/lllu0Z8+eGhISov/zP/+jqifOa7NmzTLHeflxZxiGrl27Vh0Oh2W8OhwOLSwsNMdreTExMfrOO++oy+WyzOFvvvlG//Of/2hYWJil//0lwi6XS7t37665ubmqap3f7nO+t7HsLldVvfPOO7Vhw4bau3dvbdmypeVY7rtEO3bsUFW1XPAbNWqkkydP1tTUVFU9cUfTMAw9dOiQzzgqzquEhARdvXq1x7xy/6FV/vzkLnvvvfc0PT1djxw5ov/+97+1S5cuGhQUpPHx8Vq/fn295ZZb9MiRI2b8JSUlPsdCSkqKLlu2zBwLlT3PrFixQrt27art2rXzmmhNmDDB7OPy50pV1VmzZml6erqlzsqoVclIeHi4fv/99+bv7obGxcXp1KlT9YUXXtDU1FTdvHmzx8PpdHrtVFXPk2r5Fz8iIkI3bdpkXpwiIyP1xx9/NMs+/fRTjwvo0aNHtWfPnupwOPSdd97xeuFNSUnRd999N6DJPmbMGHOyuyefYRi6c+dO3bRpk4qIJVO94YYb1DAM84LoJiL6zDPPeO2PlJQUffLJJ82L6549e9QwDN23b58ahqFfffWV5UuQFi9ebE6wk02+Ll26WO7G3HnnnRocHOwRU3BwsF5yySX6wAMPqKpa9qtXr54+/PDDmpmZaTm5p6am6osvvqgNGjTQbdu2qWEYOmfOHPP1njVrlqampmpCQoLZZ6mpqbp48WLdvHmzhoaGeuyTkpJijq2KfaKq+vzzz1v6NDg4WF0ul27atMncVn4MGYahK1eutCRhixcvNveNiIgwt7vHl2EYOnPmTK8Jgr8Tv6+xFxQUpE8//bQuX75cDcPwmDeJiYn60EMP+UxGpk2bpikpKZaT5m233aZNmjSpcoLmL8kPDw/XJUuWeD3J+UsIy19MKs4rb2PV25it2L6YmBh98cUXNT093aOf3eenH374QcPDwz1ijIiI0MmTJ5sn4vJjweVy6ZIlS8yxUP7i6nK59IsvvjBfN1+JdcW2BQUF6ejRo3Xfvn0e/ZyRkaFz587Vr7/+WhMTE9XhcOhbb72lqifOay+88ILHxcI97kREP/nkE8tYWLx4sbpcLnU6nR7j1a179+569dVXa0pKimUOl+9rf/1fUWpqqr788svqcrks8zslJUUfeeQRc666RUVF6bp163TVqlUedQ4ePFhTU1P1scce8zqH3edYd93uxE1VdciQIeZd4kWLFukll1xiJkjx8fF69913a8OGDS3Xo6uuukrz8/PV4XDoddddpw899JBHn4wYMUJbtWrlcX5yl61YscJy/t2yZYuOGjVK09LSVES0devWumbNGg0JCdGSkhKtV6+e1zkXERGh33//vX799dfmnKvMeaayiZaq9Vy5cePGM+Nbe1NSUnTlypXm7+6GXnbZZfrII4/4bKj7dp837r/2U1JSPF785s2b68KFC81k5j//+Y8eOnRIVVVbtWqlRUVFlgF/9OhRTUpK0ujoaK8X3oiICH300Uc1MzPTY5+TTfbw8HANCwvTiIgIj8lnGIZefvnl2rFjRzUMQz/88EOPfb/88kuNiYnRtLQ0j0lmGIZGRUXpE088oatXr9bt27draWmprl69Wtu2bavBwcE6ePBg3bhxo15//fXmbVSHw6EzZ87UtLQ0Sz/Gx8frxIkTdcyYMZbJ9/HHH2t0dLSKiD7yyCMaGRmpN954oz722GPar18/dTqdOn78eL300ku1a9euun37dg0ODtb3339f4+LitF+/fpb9goODNTQ0VKdNm6aqnif3QYMGaUZGhgYHB+tf/vIX7d+/vzZv3lznzp1rtr1Ro0Zar149s88GDRqkmZmZevvtt2twcLDHPp07d9aIiAi9+uqrLX2iqvrpp59qcnKyJf7GjRt7/CVVfgw5HA798MMPLWPIPb5eeeUV825Y+bESFham8fHxXk8S/k78vsZeQkKCJiYm6po1ayx1jho1SiMjI1VELOPEMAyNiYnR0aNHW06aqqqTJ0+ucoLmL8kPDg7WGTNm+DzJ+UoIyycj5S1evFidTqf5L9mKY9YwDH3jjTfMPyrKt6979+76t7/9TZ1Op0c/u89PU6ZM0czMTI8Ymzdvrq+99pp5fio/FlJTU/X555/X1NRUy8U1NTVVn376aa9l7gtGxTHkLaF19/PIkSP1rLPOMt8H9eCDD2rDhg110qRJ2qBBA42Li9O7777bo76jR4+qiJjvL6ho1KhRGhMTo9dee61lnNx7771qGIaee+65ljnsdDo1ODhYn3jiCUv/+0uEBw0apMnJyRoVFWWZ3+3bt9eEhAS9+eabPfa54IIL9JVXXjHHbsX6YmNj1TAMyxw2DENbtWql5513nkZGRup7771n7rd//3695JJLzItyTk6Obty4UVVP/PHUv39/M9Erb9u2bea/hYYNG6Yul0s7dOigt956q1lXdna2x/nJ/XrPmTNHExMTLXWqqh4/flznz5+vb775pplolpSUmH9Yu7nHQqtWrfSdd97RLVu2WOacv/NMfHy8Dho0SBMSEiyv2zXXXKOtWrVSwzB8niu9XT8qo1YlI/3799dOnTrpt99+69HQ9957T1999VWfDR0+fLh5O9/bSTU4OFizsrI8XvzCwkJ988039csvv7S8+MOHD9eMjAy9+uqrLcdas2aNhoaGer3wxsbGanBwsHkBdTvZZB80aJCeffbZ2qNHD4/Jl5eXp3l5eVqvXj3NzMy0DPx7771Xu3Tpoj///LNlkt19993m/97dd18Mw9CEhARt2LChuT09PV2//vprVT0xGV588UV9+umnLTF26dJFH3nkEZ+v3QcffKAiolFRUWYWHRISojk5OTpz5kxVPTGZioqKNCkpSYOCgrSkpER/+OEH7d27t2U/l8tlvrlQ1fPkfuDAAe3evbsGBwfrHXfcoUeOHNEnnnjCfF2SkpK0d+/eOmDAALPPDhw4oAMHDtT4+HhNS0uz7BMdHe21T1RV3377bX366act8Q8aNEjffPNNr/1hGIYOGTLE6xgaPny45ufne93vH//4h3mSrMqJ39fYe/vtt7Vt27basGFDnxca94m6/DgREW3evLleddVVlpOm6olEOD4+vkoJmr8kPy0tzbzDWZWE0H2n0ZsuXbroQw89ZEn6VE+8Ph06dDDfk1S+fevWrdOYmBgNCwvz6OfzzjtPExMTNSQkRMeNG+cRY2Fhof7jH//wen4aNGiQ1q9fX1u0aGG5uHbr1k1DQ0P1/PPPt5Q5HA7NysqyXHhVT/wx9f7775ttc/fzH3/8oY8++qhedNFFGh0draqqb775pqalpWlYWJimpKR4fXOiYRjapUsXr2NEVXXs2LFezyfJyck6fPhwr3M4NjZW+/Tp47U+f4nwY489pqGhoZqQkOB1frdq1crymhcVFWlBQYEeOHBAP/30U8vx/va3v6lhGF7ncGFhofmYN2+ex3733nuvXnfddZY3XLuvSb5s2rRJL7/8cm3RooWGhYVpaGioNmrUSBs3bqxXXnmlDhgwwOP8pKqanp6ugwYN0i5duvis123r1q06a9YsPXDggPmHtZt7LLjPM0uXLrXMOX/nmaZNm2pYWJjH+ddtx44d2rp1a/PuiK9zZSBqVTKyY8cOvfDCC096UXA7fvy4+bOvyeJyubRt27ZeX3zV/7ugl6/z6NGjun37dv399989nus+3vr16/XKK6+0TL6UlBRt06aN17b5m+zuC2XLli0tk88wDM3NzfWYfO44Dhw4oIcPHza3VbzQq564bbZkyRJdsmSJ+Req6ok3G61du7bS73w+2eTbvXu3vvzyy3r8+HEtLS3VX375xfzfZkUrVqzQ8ePH6+7duz3a5N6vrKzMTBZ9GTFihOVCf/jwYa//1y6vfJ+pqh48eNDcp2KflB9fFeOfMGGCR/zeHDx40OsYOnr0qO7du9fnfkVFRZqQkFClE7+/sXf06FHt3r27zwuNquc42bhxozlfTjZvqpKg+Uvyt23bpvXq1TvpSa7i8XzdGVH1P2YHDBigN9xwg3bo0MFr+wYOHKhJSUke/RwcHKyRkZEe56fyd3J9nYgPHDigAwYM0HPOOcfrxTU5OVmbN2/utSwnJ8dr+wKZHycbd3/88cdJP5lUcZyUV3Hun+yc4SsRTk5O1scff9zy/MrM78qq7Bw+3SqenyrD11hwv97exoKq7/NMvXr19Prrr/d5vN27d+vYsWOrdP2ojFq5zsiGDRukrKxMmjdvLsHBvheJ9bboy6ZNmzy+ZTAjI8PvsQ4ePChBQUESFhbms05fx1NV2blzpxw/flzi4+PFMAw5dOiQREdHez3WsWPH5Oeff670Oh6///67HD161PI5dn8xrly5Ur744gvp16+f1KtXr9L7nW5V6WdvDh06JEFBQeJ0Om2NozqPV5G/sVzTY+9kKs6br7/+Wj7//HOv487tjz/+kN9++03q1avn9XU7duyYfP755xIfH3/Sue9vnFcHd/ucTqdHP4eEhFT6/HQyvub3ycoqo7rmx+lQ1XM2quZkY6G29H+tWw7+22+/laVLl0pOTo4EBwfLd999J9dff70cO3ZMmjdvLg0bNjSfe+zYMRk7dqzExcXJrl27JDc3V3JyciQ7O1u+++47GTdunJSVlUnHjh1FRCQnJ0eaNWt20jqvueYacblcIiLSqVMny/FEREpLS+Xpp58263v44Yc9jpWdnS3NmzeX7777ToqLiytdVtkYK7bbXWdERIS89NJLMmXKFJ/7xcTEyI4dO2TUqFHSokULj/7fs2ePPPXUU9KnTx9L2erVq2XixIly7733WuJ3LxJXv359j/2GDRsmf/zxh3z//fce26vSz8XFxZU+nq82uBcOqhhLZeIIpL/8tftkdZZfoCk2NtZchCklJUX69esnaWlpYhiGhIaGWsrcyUj5xZvi4+Pl3HPPlebNm0tF/l7v8nGUry85OVn69+8vaWlpZh2LFy+WDRs2yLPPPmspq7ifr5PiL7/8Iq+88oq89NJLlrKtW7fKqFGjzLK2bdtK27ZtvZb52s9fGyravXu3uV9iYqJHWVhYmIwZM0aeeuopsz5Vlb59+8pFF10kIuJ3oavycaSkpEj//v0lNTXVcoywsLCA49+1a5elT1atWiWxsbHmxaZ8jHFxcXLzzTfLXXfdVW1lFdvt77UpfwFctWqVHDp0SKZOnSovvfRSler0xl+7A6nvVOqsiVj+/ve/S69evcxrSHm+FsDbvn27XHnllTJt2jTJyMjw6H9/9dVE/KZqu8dSDebOnauhoaFav359DQsL07lz55pvTnXfHj333HMti76431BTcb/OnTtrmzZtzPcyVLZOd1lsbKxlkZmmTZuat2wre6xAywJtt7/9WrVqpU6n07wdXn5xoPXr15trPVQsc782ImKJPzs72+NjdeX3MwxDW7ZsqSJi6ctA+9nf8Xy1wTBOrFWRk5PjEcvJ4gi0v/y121+dycnJum7dOnU4HLpx40ZNTk42F2FyOBwaFRVlvqeq/AJNvspycnI81uOobPwV46jMsU5WlpqaqjExMeYiUhWtXr3a57+SAilLTk7WTz/91GsbAoklOTlZf/vtN/O9aOXrCwkJ0YiICP32228tC11FRESYC11VJY7qjv+8887TRYsWqapaYjzrrLM0LCxMp06dWm1lQ4cO1cjISJ06dWqVXjd/i4adrE5v/LU7kPpOpc6aiMV9HszMzPRYLO3hhx/WqKgoveaaaywL4LnPe/Xr1/fYx199NRW/W61KRrKzs3XkyJGqeuJNV/Xq1dMRI0ZoUVGRZmRkaJ8+fTQvL898vvtz1r72c9eZk5OjeXl5la5z6NChPheZadOmTZWPFWhZoO32t99ll12mV155pf7666+6YcMGj8WBevbsqXl5eWoYhqUsOztbhw4dqg6HwxJ/z549zYXUKu5XVFRkvmG2Yl8G2s/+juerDe73K7g/ll3ZOALtL3/t9lenYfzfR8ArLsJkGIbm5eXptddeW+mynj176uWXX675+fnatWvXSsdf3XGoqr7zzjvm+hLlF6yaPXu2jhgxQm+55RY1DKPaygzD0IcffthrGwKJxTAMnT59uv7rX/8yk0x3feHh4XrZZZfptdde63ehq6rEEWj8s2fP1n/961+WC314eLhu2bJFVdUSY3h4uBYXF2uLFi2qrWz27Nk6bNgwTUtLq9Lr5nQ69Z///KfXRcNUVV9//XVt0aKFVpa/dgdS36nUWROxGMaJNY2GDBnisVhaUlKSvv3226p6IvErvwCeYRhaWFioMTExHvt88MEHPuv74IMPaiR+t1qVjERHR+uGDRtU9cRqd8HBweYbxL766itNT0/X8PBwy6Iv/vaLjo42Py5VlTq/+uorbdq0qd5zzz0eZZGRkQEdK9CyQNvta7+4uDhds2aNR7+7FweKi4uzfPzYXRYZGalffvmlOhwOy7ESEhL03Xff9fhUknu/H3/8UefOnasiYunLQPvZ3/H8tWHmzJl69tlnq4hUOo5A+8tfu/3VWT4JqJgguT/enZqaWumyhIQEXbNmjcc76isTf3XH4S472foFNVHmqw1VjaX8NhHxqC8uLk5ffvllc30bXwtdBRJHVeN3PyomI3FxcbpixQpzXJSPMS4uTmfNmmWuYVMdZaf6entbNExV9YcffjAXbawMf+0OpL5TqbMmYin/Ju7yi6XJ/y5+OWLECN2wYYPHAniGcWJNE/f1ofwCayKiQ4YM0Q0bNljKHA6H/vWvf9UNGzZUW/xu3r8buRZwOBwSFhYmsbGxIiJywQUXyJw5c+T333+Xdu3aydq1a8UwjJPuJyISEREhe/furVKdF1xwgaxcuVJ+/fVXn8eryrECLQu03b72+/333y1vunv22Wele/fusnv3bstXQ7vLDh48KJs2bfJ6rMOHD0tMTIzs3bvXsl+nTp0kIiJCDMPw2peB9LO/4/lrw9///nd59tlnRUQqHUeg/eWv3f7qVFWzn8vKyizvV0hISJBff/210mWHDx+W4OBgSUxMlF9//bXS8Vd3HCIiycnJMmnSJHE6nXL8+HGPR0pKiowfP14cDke1lRmGIQsXLvTahkBiMQxDdu7cKV9//bWIiEd9BQUF8uGHH8qvv/4qnTp1knfeecejT2bPni1NmjSpUhyBxn/8+HEzxvIKCgpk0qRJIiKWGAsKCuTRRx+VJk2aVFtZcnKy9OnTR1q1alWl1+3GG2+Unj17eo1TROStt96SJk2aWNrni792B1LfqdRZE7GUFxISIr169ZJ58+ZJWlqa5OXlyeuvvy5NmzaVY8eOybp168znrl+/XhISEjz22bhxo4iIvPvuu9KsWTNLWcuWLeXtt9+WZs2aVX/8AaUwNaR169Y6d+5c8/eKHx36/PPPNSMjw7Loi7/9WrdurU8++aS5LHpl6yyvfFnTpk0DOlagZYG229d+LVu21FdeecVr/yckJGh4eLjX/+PGxcVpRESEWVb+WBdccIGOHDnSsvS86v8tOOTerzr62d/x/LWhfCyVjSPQ/vLXbn91yv/+RSj/+z6W8oswGYahjRs31pCQkEqXuReD+uyzz8xVGCsTf3XHoararVs37devnyUOd9ltt91m+bfWqZQZhmF+AVl1xOJ+v0/z5s1VRDzq27ZtmyYlJWloaKjPha7OPvvsKsURaPyq6nXxr23btml6erpefPHFlhjd36/TsmXLaiurX7++OhwOcxG3yvSxO05fi4ZdfPHFGhoa6rVOX/y1O5D6TqXOmoil/J2R8twL4N1yyy2alJTksQCeYZz4vp6KC+C56ystLdX58+f7jL9Vq1bVFr9brUpGJk2aZPn+lfLc/2dU9Vz0xd9+kyZN0l69epn7VbbOitxlEyZMCOhYgZYF2m5f+xUWFprfp1GRr/c5uGO8/PLLvZYVFRXp2Wef7TN+94JDbqfaz/6O568NFWOpTByB9lfFY1W2zsLCQm3Xrp2KiGURpsLCQs3JydGWLVtWusy9GNS9996rvXv3rnT81R2H6omVQ6+55hpLHO6ymTNnel2wKtCywsJCHTFihA4YMKBaYnEvjOWus+ICWYMHD9asrCzLQletWrXS2267zevCWv7iCDR+VfW5+NeePXv0/vvvt8TYp08fXbRoUbWWde7c2fLVECfrY7eff/5Zb7jhBq/HW758udd9/PHX7kDqO5U6qzuW9PR087thynMvgHfllVea36/kXgDP/T4kb+c+X/XVVPxutXKdEQAA8OdRa98zAgAA/hxIRgAAgK1IRgAAgK1IRgAAgK1IRoA/mU8//VQMw5D/9//+n92hVJsBAwaYa1MAqHtq3RflAYAvmzdvloyMDFm1apW0adPG3F5cXCx8MBCou0hGANS4I0eOSGhoaI3VHxMTU2N1A6h5/JsGOAOVlZXJXXfdJQkJCRIWFiYdOnSQ5cuXezznv//9r5x77rkSFhYm7du3l7Vr15plW7ZskW7dukm9evUkIiJCzjnnHPnwww/N8nXr1snll18ukZGRkpiYKDfddJP89ttvZnlubq4MHjxYhg0bJvHx8ZKXlyc33HCD5evFjx49KvHx8TJt2jQREZk3b5506NBBYmNjJS4uTq688kr58ccfzee7v7r8vPPOE8MwJDc3V0Ss/6Y5Wfvd/6pauHChtGvXTsLDwyUnJ0fWr18fYI8DOBUkI8AZaPjw4fLuu+/K9OnT5euvv5YmTZpIly5dZPfu3eZz7rvvPnnyySdl+fLlkpCQIN27d5ejR4+KiMigQYOkrKxMFi9eLGvXrpXHH39cIiMjRURk+/bt0qlTJ2nTpo2sWLFC5s2bJzt27JBevXp5xDB9+nQJDg6W//73vzJ58mTp27evvP/++3LgwAHzOR999JEcPHhQrrnmGhEROXjwoAwbNkyWL18uCxcuFIfDIVdddZUcP35cRES++uorERH5+OOPZfv27fLee+8F3H4RkZEjR8pTTz0lK1askODgYLn55ptPpdsBBCrgtVsB1EoHDhzQkJAQff31181tR44c0ZSUFB03bpx+8sknKiI6Y8YMs3zXrl3qcrn03//+t6qqtmrVSgsLC73W/49//EPz8/M9tm3dulVFRNevX6+qqp06ddI2bdp4POfIkSMaHx/v8b08N9xwg1533XU+27Jz504VEV27dq2qqm7atElFRFetWuXxvP79+2uPHj0q1X5VNfvg448/Np8zZ84cFRE9fPiwz3gA1AzujABnmB9//FGOHj0qF110kbktJCRE/vKXv8i3335rbsvOzjZ/rl+/vjRr1swsv+uuu+TRRx+Viy66SEaNGiVr1qwxn7ty5Ur55JNPJDIy0nw0b97cPLZbu3btPOIKCQmR6667Tl5//XUROXEXZPbs2dK3b1+P2Pv06SONGzeW6Oho898yP/30U7W3X0SkdevW5s/JyckiIrJz585KHwtA9SAZAc4w+r+fKjEMw7K94raK3OUDBw6UjRs3yk033SRr166Vdu3ayTPPPCMiIsePH5du3brJ6tWrPR4bNmyQiy++2KwrIiLCUn/fvn3l448/lp07d8qsWbMkLCxMCgoKzPJu3brJrl27ZMqUKbJs2TJZtmyZiJx4A2xNtD8kJMTSdve/hACcPiQjwBmmSZMmEhoaKl988YW57ejRo7JixQrJysoyty1dutT8ec+ePfL999+bdzhERNLS0uSOO+6Q9957T+655x6ZMmWKiIicf/75UlJSIunp6dKkSROPh7cEpLycnBxJS0uTf//73/L666/LddddZ37KZteuXfLtt9/KQw89JJdddplkZWXJnj17PPZ3P/fYsWOn3H4AtQcf7QXOMBEREfK3v/1N7rvvPqlfv740bNhQxo0bJ4cOHZJbbrlFvvnmGxERefjhhyUuLk4SExNl5MiREh8fb34iZejQoVJQUCBNmzaVPXv2yKJFi8wL+aBBg2TKlClyww03yH333Sfx8fHyww8/yIwZM2TKlCkSFBTkMzbDMKRPnz7y/PPPy/fffy+ffPKJWVavXj2Ji4uTF154QZKTk+Wnn36SBx54wGP/hIQEcblcMm/ePElNTZWwsDDLx3pP1n4AtQ93RoAz0NixY+Waa66Rm266Sc4//3z54Ycf5KOPPpJ69ep5PGfIkCHStm1b2b59u7z//vsedx4GDRokWVlZ0rVrV2nWrJk899xzIiKSkpIi//3vf+XYsWPSpUsXadmypQwZMkRiYmLE4Tj5KaVv376ybt06adCggcf7OhwOh8yYMUNWrlwpLVu2lLvvvlueeOIJj32Dg4Pl6aeflsmTJ0tKSor06NEj4PYDqD0MVZYtBAAA9uHOCAAAsBXJCAAAsBXJCAAAsBXJCAAAsBXJCAAAsBXJCAAAsBXJCAAAsBXJCAAAsBXJCAAAsBXJCAAAsBXJCAAAsNX/B0QKtlUmkGaNAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "frequency_df_6.plot(kind=\"bar\", y=\"Frequency\", x=\"Value\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is standard_deviation observation 12.8165\n", + "dtype: float64\n", + "This is the mean observation 36.56\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "mean=data_4.mean()\n", + "standard_dev=data_4.std()\n", + "print(\"This is standard_deviation \", standard_dev)\n", + "print(\"This is the mean \", mean)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "it makes sense\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the first quartile is 28.0\n", + "the second quartile is 37.0\n", + "the third quartile is 45.0\n" + ] + } + ], + "source": [ + "q1 = np.quantile(data_4, 0.25)\n", + "print(f\"the first quartile is {q1}\")\n", + "\n", + "q2 = np.quantile(data_4, 0.50)\n", + "print(f\"the second quartile is {q2}\")\n", + "\n", + "q3 = np.quantile(data_4, 0.75)\n", + "print(f\"the third quartile is {q3}\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "it seems reasonable\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning." + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Q0 is 1.0\n", + "Q4 is 82.0\n" + ] + } + ], + "source": [ + "print(\"Q0 is\", np.min(data_4))\n", + "print (\"Q4 is \", np.max(data_4))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "give a better view of the data\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus challenge\n", + "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/ages_population.csv b/your-code/ages_population.csv new file mode 100644 index 0000000..64d8a0a --- /dev/null +++ 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+973,973,1 +974,974,5 +975,975,4 +976,976,1 +977,977,6 +978,978,2 +979,979,2 +980,980,2 +981,981,5 +982,982,3 +983,983,4 +984,984,4 +985,985,4 +986,986,4 +987,987,1 +988,988,2 +989,989,6 +990,990,4 +991,991,6 +992,992,4 +993,993,1 +994,994,3 +995,995,1 +996,996,4 +997,997,4 +998,998,3 +999,999,6 diff --git a/your-code/main.ipynb b/your-code/main.ipynb index a0a5b66..47612e2 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,11 +11,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "# Libraries" + "import random\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy import stats\n", + "import seaborn as sns\n", + "from collections import Counter" ] }, { @@ -29,11 +35,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([4, 3, 2, 1, 2, 4, 5, 1, 5, 3])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "\n", + "def roll_dice_simulation():\n", + " roll_dataset = np.random.randint(1, 6, size = 10)\n", + " return roll_dataset\n", + " \n", + "rolls_1=roll_dice_simulation()\n", + "rolls_1" ] }, { @@ -45,11 +68,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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Nm7V582a7SwEAADcJvksIAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABjPdmBpaGjQwoULlZycLIfDoX379l31mvr6emVlZSk2NlZ33323du7c2avG5/MpPT1dTqdT6enp2rt3r92lAQCAKGU7sHR3dysjI0Pbtm0bVH1ra6vmz5+vvLw8NTc3a926dXrmmWfk8/lCNY2NjSoqKlJxcbGOHTum4uJiLVmyREeOHLG7PAAAEIVi7F5QWFiowsLCQdfv3LlT48ePV1lZmSRp6tSpampq0saNG/XII49IksrKyjR37lx5PB5JksfjUX19vcrKylRZWWl3iQAAIMpc9/ewNDY2qqCgIGxs3rx5ampq0jfffDNgzeHDh/udt6enR8FgMOwAAADR6boHlkAgoISEhLCxhIQEXbhwQadOnRqwJhAI9DtvaWmpXC5X6EhJSbn2iwcAAEa4IZ8ScjgcYeeWZfUa76vmyrHv8ng86urqCh3t7e3XcMUAAMAktt/DYldiYmKvOyWdnZ2KiYnR7bffPmDNlXddvsvpdMrpdF77BQMAAONc9zssbrdbtbW1YWM1NTXKzs7WqFGjBqzJzc293ssDAAARwPYdlrNnz+rkyZOh89bWVrW0tCg+Pl7jx4+Xx+NRR0eHKioqJEkrVqzQtm3bVFJSop/85CdqbGxUeXl52Kd/Vq9erVmzZmnDhg1atGiR9u/fr4MHD+rDDz+8Bi0CAIBIZ/sOS1NTkzIzM5WZmSlJKikpUWZmpn72s59Jkvx+v9ra2kL1aWlpqq6uVl1dnX74wx/qF7/4hbZu3Rr6SLMk5ebmavfu3XrjjTd0zz33yOv1qqqqSjNnzvy+/QEAgChg+w7L7NmzQ2+a7YvX6+019sADD+jjjz8ecN7Fixdr8eLFdpcDAABuAnyXEAAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAw3pACy2uvvaa0tDTFxsYqKytLH3zwQb+1jz/+uBwOR69j2rRpoRqv19tnzblz54ayPAAAEGVsB5aqqiqtWbNGL7zwgpqbm5WXl6fCwkK1tbX1Wb9lyxb5/f7Q0d7ervj4eP3oRz8Kq4uLiwur8/v9io2NHVpXAAAgqtgOLJs2bdKyZcu0fPlyTZ06VWVlZUpJSdGOHTv6rHe5XEpMTAwdTU1N+uqrr/TEE0+E1TkcjrC6xMTEoXUEAACijq3Acv78eR09elQFBQVh4wUFBTp8+PCg5igvL9ecOXOUmpoaNn727FmlpqZq3LhxWrBggZqbmwecp6enR8FgMOwAAADRyVZgOXXqlC5evKiEhISw8YSEBAUCgate7/f79d5772n58uVh41OmTJHX69WBAwdUWVmp2NhY3XfffTpx4kS/c5WWlsrlcoWOlJQUO60AAIAIMqQ33TocjrBzy7J6jfXF6/Xq1ltv1cMPPxw2npOTo0cffVQZGRnKy8vT22+/rUmTJunVV1/tdy6Px6Ourq7Q0d7ePpRWAABABIixU3zHHXdo5MiRve6mdHZ29rrrciXLsvT666+ruLhYo0ePHrB2xIgRmjFjxoB3WJxOp5xO5+AXDwAAIpatOyyjR49WVlaWamtrw8Zra2uVm5s74LX19fU6efKkli1bdtV/x7IstbS0KCkpyc7yAABAlLJ1h0WSSkpKVFxcrOzsbLndbu3atUttbW1asWKFpG9fquno6FBFRUXYdeXl5Zo5c6amT5/ea87169crJydHEydOVDAY1NatW9XS0qLt27cPsS0AABBNbAeWoqIiffnll3rllVfk9/s1ffp0VVdXhz714/f7e/1Nlq6uLvl8Pm3ZsqXPOU+fPq0nn3xSgUBALpdLmZmZamho0L333juElgAAQLSxHVgkaeXKlVq5cmWfj3m93l5jLpdLX3/9db/zbd68WZs3bx7KUgAAwE2A7xICAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYbUmB57bXXlJaWptjYWGVlZemDDz7ot7aurk4Oh6PX8fnnn4fV+Xw+paeny+l0Kj09XXv37h3K0gAAQBSyHViqqqq0Zs0avfDCC2publZeXp4KCwvV1tY24HXHjx+X3+8PHRMnTgw91tjYqKKiIhUXF+vYsWMqLi7WkiVLdOTIEfsdAQCAqGM7sGzatEnLli3T8uXLNXXqVJWVlSklJUU7duwY8LqxY8cqMTExdIwcOTL0WFlZmebOnSuPx6MpU6bI4/EoPz9fZWVlthsCAADRx1ZgOX/+vI4ePaqCgoKw8YKCAh0+fHjAazMzM5WUlKT8/HwdOnQo7LHGxsZec86bN2/AOXt6ehQMBsMOAAAQnWwFllOnTunixYtKSEgIG09ISFAgEOjzmqSkJO3atUs+n0979uzR5MmTlZ+fr4aGhlBNIBCwNacklZaWyuVyhY6UlBQ7rQAAgAgSM5SLHA5H2LllWb3GLps8ebImT54cOne73Wpvb9fGjRs1a9asIc0pSR6PRyUlJaHzYDBIaAEAIErZusNyxx13aOTIkb3ufHR2dva6QzKQnJwcnThxInSemJhoe06n06m4uLiwAwAARCdbgWX06NHKyspSbW1t2Hhtba1yc3MHPU9zc7OSkpJC5263u9ecNTU1tuYEAADRy/ZLQiUlJSouLlZ2drbcbrd27dqltrY2rVixQtK3L9V0dHSooqJC0refALrrrrs0bdo0nT9/Xm+++aZ8Pp98Pl9oztWrV2vWrFnasGGDFi1apP379+vgwYP68MMPr1GbAAAgktkOLEVFRfryyy/1yiuvyO/3a/r06aqurlZqaqokye/3h/1NlvPnz+u5555TR0eHbrnlFk2bNk3vvvuu5s+fH6rJzc3V7t279eKLL+qll17ShAkTVFVVpZkzZ16DFgEAQKQb0ptuV65cqZUrV/b5mNfrDTtfu3at1q5de9U5Fy9erMWLFw9lOQAAIMrxXUIAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHhDCiyvvfaa0tLSFBsbq6ysLH3wwQf91u7Zs0dz587VnXfeqbi4OLndbr3//vthNV6vVw6Ho9dx7ty5oSwPAABEGduBpaqqSmvWrNELL7yg5uZm5eXlqbCwUG1tbX3WNzQ0aO7cuaqurtbRo0f14IMPauHChWpubg6ri4uLk9/vDztiY2OH1hUAAIgqMXYv2LRpk5YtW6bly5dLksrKyvT+++9rx44dKi0t7VVfVlYWdv7LX/5S+/fv1zvvvKPMzMzQuMPhUGJiot3lAACAm4CtOyznz5/X0aNHVVBQEDZeUFCgw4cPD2qOS5cu6cyZM4qPjw8bP3v2rFJTUzVu3DgtWLCg1x2YK/X09CgYDIYdAAAgOtkKLKdOndLFixeVkJAQNp6QkKBAIDCoOX7961+ru7tbS5YsCY1NmTJFXq9XBw4cUGVlpWJjY3XffffpxIkT/c5TWloql8sVOlJSUuy0AgAAIsiQ3nTrcDjCzi3L6jXWl8rKSr388suqqqrS2LFjQ+M5OTl69NFHlZGRoby8PL399tuaNGmSXn311X7n8ng86urqCh3t7e1DaQUAAEQAW+9hueOOOzRy5Mhed1M6Ozt73XW5UlVVlZYtW6bf/va3mjNnzoC1I0aM0IwZMwa8w+J0OuV0Oge/eAAAELFs3WEZPXq0srKyVFtbGzZeW1ur3Nzcfq+rrKzU448/rrfeeksPPfTQVf8dy7LU0tKipKQkO8sDAABRyvanhEpKSlRcXKzs7Gy53W7t2rVLbW1tWrFihaRvX6rp6OhQRUWFpG/DymOPPaYtW7YoJycndHfmlltukcvlkiStX79eOTk5mjhxooLBoLZu3aqWlhZt3779WvUJAAAimO3AUlRUpC+//FKvvPKK/H6/pk+frurqaqWmpkqS/H5/2N9k+c1vfqMLFy5o1apVWrVqVWh86dKl8nq9kqTTp0/rySefVCAQkMvlUmZmphoaGnTvvfd+z/YAAEA0sB1YJGnlypVauXJln49dDiGX1dXVXXW+zZs3a/PmzUNZCgAAuAnwXUIAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHhDCiyvvfaa0tLSFBsbq6ysLH3wwQcD1tfX1ysrK0uxsbG6++67tXPnzl41Pp9P6enpcjqdSk9P1969e4eyNAAAEIVsB5aqqiqtWbNGL7zwgpqbm5WXl6fCwkK1tbX1Wd/a2qr58+crLy9Pzc3NWrdunZ555hn5fL5QTWNjo4qKilRcXKxjx46puLhYS5Ys0ZEjR4beGQAAiBq2A8umTZu0bNkyLV++XFOnTlVZWZlSUlK0Y8eOPut37typ8ePHq6ysTFOnTtXy5cv14x//WBs3bgzVlJWVae7cufJ4PJoyZYo8Ho/y8/NVVlY25MYAAED0iLFTfP78eR09elTPP/982HhBQYEOHz7c5zWNjY0qKCgIG5s3b57Ky8v1zTffaNSoUWpsbNSzzz7bq2agwNLT06Oenp7QeVdXlyQpGAzaaWlQLvV8fc3nvN6ux/NwvUXi8yxF5nMdiSLx5yMSfzZ4nm8Mnufe81qWNWCdrcBy6tQpXbx4UQkJCWHjCQkJCgQCfV4TCAT6rL9w4YJOnTqlpKSkfmv6m1OSSktLtX79+l7jKSkpg20nqrnKhnsFNw+ea/SHn40bg+f5xrjez/OZM2fkcrn6fdxWYLnM4XCEnVuW1WvsavVXjtud0+PxqKSkJHR+6dIl/c///I9uv/32Aa+zKxgMKiUlRe3t7YqLi7tm85ok2nukv8gX7T3SX+SL9h6vZ3+WZenMmTNKTk4esM5WYLnjjjs0cuTIXnc+Ojs7e90huSwxMbHP+piYGN1+++0D1vQ3pyQ5nU45nc6wsVtvvXWwrdgWFxcXlT+E3xXtPdJf5Iv2Hukv8kV7j9erv4HurFxm6023o0ePVlZWlmpra8PGa2trlZub2+c1bre7V31NTY2ys7M1atSoAWv6mxMAANxcbL8kVFJSouLiYmVnZ8vtdmvXrl1qa2vTihUrJH37Uk1HR4cqKiokSStWrNC2bdtUUlKin/zkJ2psbFR5ebkqKytDc65evVqzZs3Shg0btGjRIu3fv18HDx7Uhx9+eI3aBAAAkcx2YCkqKtKXX36pV155RX6/X9OnT1d1dbVSU1MlSX6/P+xvsqSlpam6ulrPPvustm/fruTkZG3dulWPPPJIqCY3N1e7d+/Wiy++qJdeekkTJkxQVVWVZs6ceQ1a/H6cTqd+/vOf93r5KZpEe4/0F/mivUf6i3zR3qMJ/Tmsq32OCAAAYJjxXUIAAMB4BBYAAGA8AgsAADAegQUAABjvpg8sDQ0NWrhwoZKTk+VwOLRv376rXlNfX6+srCzFxsbq7rvv1s6dO6//QofIbn91dXVyOBy9js8///zGLNim0tJSzZgxQ2PGjNHYsWP18MMP6/jx41e9LlL2cCj9Rdoe7tixQ/fcc0/oD1K53W699957A14TKfsn2e8v0vbvSqWlpXI4HFqzZs2AdZG0h981mP4ibQ9ffvnlXmtNTEwc8Jrh2L+bPrB0d3crIyND27ZtG1R9a2ur5s+fr7y8PDU3N2vdunV65pln5PP5rvNKh8Zuf5cdP35cfr8/dEycOPE6rfD7qa+v16pVq/TRRx+ptrZWFy5cUEFBgbq7u/u9JpL2cCj9XRYpezhu3Dj96le/UlNTk5qamvRXf/VXWrRokT799NM+6yNp/yT7/V0WKfv3Xb/73e+0a9cu3XPPPQPWRdoeXjbY/i6LpD2cNm1a2Fp///vf91s7bPtnIUSStXfv3gFr1q5da02ZMiVs7O/+7u+snJyc67iya2Mw/R06dMiSZH311Vc3ZE3XWmdnpyXJqq+v77cmkvdwMP1F+h5almXddttt1j/90z/1+Vgk799lA/UXqft35swZa+LEiVZtba31wAMPWKtXr+63NhL30E5/kbaHP//5z62MjIxB1w/X/t30d1jsamxsVEFBQdjYvHnz1NTUpG+++WaYVnXtZWZmKikpSfn5+Tp06NBwL2fQurq6JEnx8fH91kTyHg6mv8sicQ8vXryo3bt3q7u7W263u8+aSN6/wfR3WaTt36pVq/TQQw9pzpw5V62NxD20099lkbSHJ06cUHJystLS0vS3f/u3+uKLL/qtHa79G9K3Nd/MAoFAry9lTEhI0IULF3Tq1CklJSUN08qujaSkJO3atUtZWVnq6enRv/zLvyg/P191dXWaNWvWcC9vQJZlqaSkRPfff7+mT5/eb12k7uFg+4vEPfz9738vt9utc+fO6S/+4i+0d+9epaen91kbiftnp79I3L/du3fr448/1u9+97tB1UfaHtrtL9L2cObMmaqoqNCkSZP0X//1X/rHf/xH5ebm6tNPPw19SfF3Ddf+EViGwOFwhJ1b///Hgq8cj0STJ0/W5MmTQ+dut1vt7e3auHGjkf+jfddTTz2lTz75ZFDfQRWJezjY/iJxDydPnqyWlhadPn1aPp9PS5cuVX19fb+/1CNt/+z0F2n7197ertWrV6umpkaxsbGDvi5S9nAo/UXaHhYWFob++wc/+IHcbrcmTJigf/7nf1ZJSUmf1wzH/vGSkE2JiYkKBAJhY52dnYqJiekziUaDnJwcnThxYriXMaCnn35aBw4c0KFDhzRu3LgBayNxD+301xfT93D06NH6y7/8S2VnZ6u0tFQZGRnasmVLn7WRuH92+uuLyft39OhRdXZ2KisrSzExMYqJiVF9fb22bt2qmJgYXbx4sdc1kbSHQ+mvLybv4ZX+/M//XD/4wQ/6Xe9w7R93WGxyu9165513wsZqamqUnZ2tUaNGDdOqrq/m5mbjbtFeZlmWnn76ae3du1d1dXVKS0u76jWRtIdD6a8vJu9hXyzLUk9PT5+PRdL+9Weg/vpi8v7l5+f3+kTJE088oSlTpuinP/2pRo4c2euaSNrDofTXF5P38Eo9PT367LPPlJeX1+fjw7Z/1/UtvRHgzJkzVnNzs9Xc3GxJsjZt2mQ1Nzdbf/zjHy3Lsqznn3/eKi4uDtV/8cUX1p/92Z9Zzz77rPUf//EfVnl5uTVq1CjrX//1X4erhQHZ7W/z5s3W3r17rf/8z/+0/v3f/916/vnnLUmWz+cbrhYG9Pd///eWy+Wy6urqLL/fHzq+/vrrUE0k7+FQ+ou0PfR4PFZDQ4PV2tpqffLJJ9a6deusESNGWDU1NZZlRfb+WZb9/iJt//py5adoIn0Pr3S1/iJtD//hH/7Bqqurs7744gvro48+shYsWGCNGTPG+sMf/mBZljn7d9MHlssfP7vyWLp0qWVZlrV06VLrgQceCLumrq7OyszMtEaPHm3ddddd1o4dO278wgfJbn8bNmywJkyYYMXGxlq33Xabdf/991vvvvvu8Cx+EPrqTZL1xhtvhGoieQ+H0l+k7eGPf/xjKzU11Ro9erR15513Wvn5+aFf5pYV2ftnWfb7i7T968uVv9AjfQ+vdLX+Im0Pi4qKrKSkJGvUqFFWcnKy9Td/8zfWp59+GnrclP1zWNb/v1MGAADAULzpFgAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADj/R9AXWa6QpVDNwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "datasorted=np.sort(rolls_1)\n", + "datasorted\n", + "plt.hist(datasorted)\n", + "plt.show()\n" ] }, { @@ -59,24 +96,68 @@ "#### 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": "markdown", + "metadata": {}, + "source": [ + "#### Calculation of frequency distribution" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "hist_rolls={}\n", + "\n", + "for a in range(1,7):\n", + " hist_rolls[a]=0\n", + "\n", + "for i in rolls_1:\n", + " hist_rolls[i]+=1\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 2, 2: 2, 3: 2, 4: 2, 5: 2, 6: 0}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" + "\"\"\"output\"\"\"\n", + "hist_rolls" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Fu971rhg2bNhOr0689NJLO72KsUOlUolKpTLg2MEHH1zmtKW0tbXtl18sb7a/r9H6mt/+vkbra377+xrrtb7dvVKxQ6k3b44YMSKmT58e69evH3B8/fr1cfLJJ5ebHQCw3yn9o5CFCxfG3Llz44QTToiZM2fG8uXL47nnnosrrriiHvMDAJpI6bC46KKL4u9//3t85StfiW3btsW0adPinnvuiYkTJ9Zjfm9bpVKJL3/5yzv92GV/sr+v0fqa3/6+Rutrfvv7GhthfS3Fnn5vBADgbfJZIQBAGmEBAKQRFgBAGmEBAKRp6LAo+/HsGzdujOnTp8fIkSPj3e9+d3z/+9/facyaNWti6tSpUalUYurUqbF27dp6TX+Pyqzv9ttvj3PPPTcOPfTQaGtri5kzZ8bPf/7zAWNWrlwZLS0tO93+/e9/13spu1RmfRs2bNjl3P/4xz8OGNdI1y+i3BovueSSXa7x6KOP7h/TSNdw06ZNMWfOnBg/fny0tLTEHXfcscfHNNMeLLu+ZtuDZdfXjHuw7BqbbQ92dnbGiSeeGKNGjYqxY8fGBRdcEE8++eQeH7ev92HDhkXZj2d/9tln4yMf+Uicdtpp0dXVFddee2189rOfjTVr1vSPeeCBB+Kiiy6KuXPnxu9///uYO3duXHjhhfHggw/urWX1K7u+TZs2xbnnnhv33HNPbN26Nc4666yYM2dOdHV1DRjX1tYW27ZtG3AbOXLk3ljSAGXXt8OTTz45YO7vfe97+/+tka5fRPk13nDDDQPW1t3dHaNHj45PfOITA8Y1yjXs6+uLY489Nm688ca3Nb7Z9mDZ9TXbHiy7vh2aaQ+WXWOz7cGNGzfG/PnzY/PmzbF+/fp47bXXYtasWdHX1zfoYxpiHxYN6oMf/GBxxRVXDDg2ZcqUYtGiRbscf8011xRTpkwZcOwzn/lMMWPGjP77F154YfHhD394wJjzzjuvuPjii5Nm/faVXd+uTJ06tViyZEn//Ztvvrlob2/PmuKQlF3ffffdV0RE8c9//nPQ52yk61cUQ7+Ga9euLVpaWoq//OUv/cca6Rq+WUQUa9eu3e2YZtuDb/Z21rcrjbwH3+ztrK8Z9+Cb1XINm2kPFkVRvPTSS0VEFBs3bhx0TCPsw4Z8xWLHx7PPmjVrwPHdfTz7Aw88sNP48847L7Zs2RL//e9/dztmsOesl1rW91ZvvPFGbN++PUaPHj3g+KuvvhoTJ06MCRMmxEc/+tGd/m9qbxjK+o477rgYN25cnH322XHfffcN+LdGuX4ROddwxYoVcc455+z0x+Ua4RrWopn2YIZG3oND0Sx7MEOz7cGenp6IiJ2+5t6sEfZhQ4ZFLR/P/sILL+xy/GuvvRYvv/zybscM9pz1Usv63uqb3/xm9PX1xYUXXth/bMqUKbFy5cq46667YtWqVTFy5Mg45ZRT4qmnnkqd/57Usr5x48bF8uXLY82aNXH77bfH5MmT4+yzz45Nmzb1j2mU6xcx9Gu4bdu2uPfee+Pyyy8fcLxRrmEtmmkPZmjkPViLZtuDQ9Vse7Aoili4cGGceuqpMW3atEHHNcI+rOlj0/eWMh/PPtj4tx4v+5z1VOtcVq1aFdddd13ceeedMXbs2P7jM2bMiBkzZvTfP+WUU+L444+P7373u/Gd73wnb+JvU5n1TZ48OSZPntx/f+bMmdHd3R3f+MY34vTTT6/pOfeGWuezcuXKOPjgg+OCCy4YcLzRrmFZzbYHa9Use7CMZt2DtWq2PXjVVVfFI488Er/5zW/2OHZf78OGfMWilo9nP/zww3c5vrW1NcaMGbPbMYM9Z73Usr4dbr311rjsssvipz/9aZxzzjm7HXvAAQfEiSeeuNdLeyjre7MZM2YMmHujXL+Ioa2xKIr40Y9+FHPnzo0RI0bsduy+uoa1aKY9OBTNsAezNPIeHIpm24NXX3113HXXXXHffffFhAkTdju2EfZhQ4ZFLR/PPnPmzJ3G/+IXv4gTTjghhg8fvtsxe/sj32v9+PlVq1bFJZdcErfcckucf/75ezxPURTx8MMPx7hx44Y85zJqXd9bdXV1DZh7o1y/iKGtcePGjfH000/HZZddtsfz7KtrWItm2oO1apY9mKWR9+BQNMseLIoirrrqqrj99tvjV7/6VUyaNGmPj2mIfZjyFtA6WL16dTF8+PBixYoVxeOPP14sWLCgOOigg/rfvbto0aJi7ty5/eOfeeaZ4h3veEfx+c9/vnj88ceLFStWFMOHDy9+9rOf9Y/57W9/WwwbNqxYunRp8cQTTxRLly4tWltbi82bNzf8+m655ZaitbW1uOmmm4pt27b131555ZX+Mdddd12xbt264s9//nPR1dVVXHrppUVra2vx4IMPNvz6vvWtbxVr164t/vSnPxV/+MMfikWLFhURUaxZs6Z/TCNdv6Iov8YdPv3pTxcnnXTSLp+zka7h9u3bi66urqKrq6uIiOL6668vurq6ir/+9a9FUTT/Hiy7vmbbg2XX14x7sOwad2iWPXjllVcW7e3txYYNGwZ8zf3rX//qH9OI+7Bhw6IoiuKmm24qJk6cWIwYMaI4/vjjB/yKzbx584ozzjhjwPgNGzYUxx13XDFixIjiyCOPLJYtW7bTc952223F5MmTi+HDhxdTpkwZsGn2tjLrO+OMM4qI2Ok2b968/jELFiwojjjiiGLEiBHFoYceWsyaNau4//779+KKBiqzvq9//evFUUcdVYwcObI45JBDilNPPbW4++67d3rORrp+RVH+a/SVV14pDjzwwGL58uW7fL5GuoY7fv1wsK+5Zt+DZdfXbHuw7PqacQ/W8jXaTHtwV2uLiOLmm2/uH9OI+9DHpgMAaRryPRYAQHMSFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAmv8DgsTvLW+JinwAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(hist_rolls.values())\n", + "plt.show()" ] }, { @@ -89,13 +170,36 @@ "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### function to calculate mean" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3.0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "\n", + "def x_rolls(f):\n", + " x_rolls=sum(f)/len(f)\n", + " return x_rolls\n", + "\n", + "x_rolls(rolls_1)\n" ] }, { @@ -105,13 +209,172 @@ "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### To calculate the frequency distribution I first will convert rolls_1 which is an array to a list" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[4, 3, 2, 1, 2, 4, 5, 1, 5, 3]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_list = list(rolls_1)\n", + "data_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### will now calculate the frequency distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Value Frequency\n", + "0 1 2\n", + "1 2 2\n", + "2 3 2\n", + "3 4 2\n", + "4 5 2\n", + "5 6 0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency={}\n", + "for a in range(1,7):\n", + " frequency[a]=0\n", + " \n", + "for i in data_list:\n", + " frequency[i]+=1\n", + "\n", + "frequency_df = pd.DataFrame.from_dict(frequency, orient='index').reset_index()\n", + "frequency_df.columns = ['Value', 'Frequency']\n", + "\n", + "frequency_df\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### here we calculated the mean using the values of the frequency distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "#mean = sum(frequency_df['Value'] * frequency_df['Frequency']) / sum(frequency_df['Frequency'])\n", + "#print(\"Mean:\", mean)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 3.0\n" + ] + } + ], + "source": [ + "def x_rolls(f):\n", + " x_rolls=sum(f)/len(f)\n", + " return x_rolls\n", + "\n", + "mean2=x_rolls(rolls_1)\n", + "print(\"Mean:\", mean2)" ] }, { @@ -124,11 +387,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3.0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "def median_rolls(x):\n", + " n = len(datasorted)\n", + " \n", + " if n % 2 != 0: # If the number of observations is odd\n", + " median_index = n // 2\n", + " median = datasorted[median_index]\n", + " else: # If the number of observations is even\n", + " upper_median_index = n // 2\n", + " lower_median_index = upper_median_index - 1\n", + " median = (datasorted[lower_median_index] + datasorted[upper_median_index]) / 2\n", + " \n", + " return median\n", + "\n", + "median_rolls(rolls_1)" ] }, { @@ -140,11 +427,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the first quartile is 2.0\n", + "the second quartile is 3.0\n", + "the third quartile is 4.0\n", + "[4 3 2 1 2 4 5 1 5 3]\n" + ] + } + ], "source": [ - "# your code here" + "q1 = np.quantile(rolls_1, 0.25)\n", + "print(f\"the first quartile is {q1}\")\n", + "\n", + "q2 = np.quantile(rolls_1, 0.50)\n", + "print(f\"the second quartile is {q2}\")\n", + "\n", + "q3 = np.quantile(rolls_1, 0.75)\n", + "print(f\"the third quartile is {q3}\")\n", + "print(rolls_1) " ] }, { @@ -158,22 +464,166 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ], + "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", + ".. ... ... ...\n", + "95 95 95 4\n", + "96 96 96 6\n", + "97 97 97 1\n", + "98 98 98 3\n", + "99 99 99 6\n", + "\n", + "[100 rows x 3 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data=pd.read_csv(\"roll_the_dice_hundred.csv\")\n", + "datasorted_1=np.sort(data[\"value\"])\n", + "datasorted_1\n", + "plt.hist(datasorted_1)\n", + "plt.show()\n", + "data\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'numbers 1and 5 rolled the least times, numbers 4 and 6 rolled the most times'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" + "\"\"\"numbers 1and 5 rolled the least times, numbers 4 and 6 rolled the most times\"\"\"" ] }, { @@ -185,11 +635,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean is 3.74\n" + ] + } + ], "source": [ - "# your code here" + "mean_2=x_rolls(data[\"value\"])\n", + "print(\"Mean is \",x_rolls(data[\"value\"]))" ] }, { @@ -201,11 +662,96 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " Value Frequency\n", + "0 1 12\n", + "1 2 17\n", + "2 3 14\n", + "3 4 22\n", + "4 5 12\n", + "5 6 23" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "frequency={}\n", + "for a in range(1,7):\n", + " frequency[a]=0\n", + " \n", + "for i in data[\"value\"]:\n", + " frequency[i]+=1\n", + "\n", + "frequency_df_2 = pd.DataFrame.from_dict(frequency, orient='index').reset_index()\n", + "frequency_df_2.columns = ['Value', 'Frequency']\n", + "\n", + "frequency_df_2" ] }, { @@ -217,11 +763,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "frequency_df_2.plot(kind=\"bar\", y=\"Frequency\", x=\"Value\")\n" ] }, { @@ -231,7 +798,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "the mean may be connected by calculating the weighted mean and plotting a line with the value in the histogram\n", "\"\"\"" ] }, @@ -244,11 +811,272 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "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", + ".. ... ... ...\n", + "995 995 995 1\n", + "996 996 996 4\n", + "997 997 997 4\n", + "998 998 998 3\n", + "999 999 999 6\n", + "\n", + "[1000 rows x 3 columns]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data_1=pd.read_csv(\"roll_the_dice_thousand.csv\")\n", + "datasorted_2=np.sort(data_1[\"value\"])\n", + "datasorted_2\n", + "data_1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Will now calculate frequency distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + " Value Frequency\n", + "0 1 175\n", + "1 2 167\n", + "2 3 175\n", + "3 4 168\n", + "4 5 149\n", + "5 6 166" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency={}\n", + "for a in range(1,7):\n", + " frequency[a]=0\n", + " \n", + "for i in data_1[\"value\"]:\n", + " frequency[i]+=1\n", + "\n", + "frequency_df_3 = pd.DataFrame.from_dict(frequency, orient='index').reset_index()\n", + "frequency_df_3.columns = ['Value', 'Frequency']\n", + "\n", + "frequency_df_3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Now will plot Frequency Distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ValueFrequency
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\n", + "
" + ], + "text/plain": [ + " Value Frequency\n", + "0 39.0 45\n", + "1 41.0 36\n", + "2 30.0 34\n", + "3 35.0 33\n", + "4 43.0 32\n", + ".. ... ...\n", + "67 73.0 1\n", + "68 82.0 1\n", + "69 70.0 1\n", + "70 71.0 1\n", + "71 69.0 1\n", + "\n", + "[72 rows x 2 columns]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency_series = data_2[\"observation\"].value_counts()\n", + "frequency_series\n", + "frequency_df_4 = frequency_series.reset_index()\n", + "frequency_df_4.columns = ['Value', 'Frequency']\n", + "frequency_df_4" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "frequency_df_4.plot(kind=\"bar\", y=\"Frequency\", x=\"Value\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### my guess on range of mean and standard deviation" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "\n", + "\"\"\"mean around 39 with standard deviation of 12 years\"\"\"" ] }, { @@ -290,11 +1381,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is standard_deviation observation 12.8165\n", + "dtype: float64\n", + "This is the mean observation 36.56\n", + "dtype: float64\n" + ] + } + ], "source": [ - "# your code here" + "mean=data_2.mean()\n", + "standard_dev=data_2.std()\n", + "print(\"This is standard_deviation \", standard_dev)\n", + "print(\"This is the mean \", mean)" ] }, { @@ -304,7 +1409,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "no I guessed higher\n", "\"\"\"" ] }, @@ -317,11 +1422,179 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([19., 19., 19., 20., 20., 20., 20., 20., 20., 20., 20., 20., 20.,\n", + " 20., 20., 20., 21., 21., 21., 21., 21., 21., 21., 21., 21., 21.,\n", + " 21., 21., 21., 21., 21., 21., 21., 22., 22., 22., 22., 22., 22.,\n", + " 22., 22., 22., 22., 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n", + " 22., 22., 22., 22., 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n", + " 22., 22., 22., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23.,\n", + " 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23.,\n", + " 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23.,\n", + " 23., 23., 23., 23., 23., 24., 24., 24., 24., 24., 24., 24., 24.,\n", + " 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24.,\n", + " 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24.,\n", + " 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24.,\n", + " 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24.,\n", + " 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24., 24.,\n", + " 24., 24., 24., 24., 24., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25.,\n", + " 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 25., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26., 26.,\n", + " 26., 26., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 27.,\n", + " 27., 27., 27., 27., 27., 27., 27., 27., 27., 27., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28., 28.,\n", + " 28., 28., 28., 28., 28., 28., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,\n", + " 29., 29., 29., 29., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30., 30.,\n", + " 30., 30., 30., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31.,\n", + " 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31.,\n", + " 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31.,\n", + " 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31.,\n", + " 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 32.,\n", + " 32., 32., 32., 32., 32., 32., 32., 32., 32., 32., 32., 32., 32.,\n", + " 32., 32., 32., 32., 32., 32., 32., 32., 32., 32., 32., 32., 32.,\n", + " 32., 32., 32., 32., 33., 33., 33., 33., 33., 33., 33., 33., 33.,\n", + " 33., 33., 33., 33., 33., 33., 33., 33., 33., 33., 33., 33., 33.,\n", + " 34., 34., 34., 34., 34., 34., 34., 35., 35., 35., 36., 36.])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data_3=pd.read_csv(\"ages_population2.csv\")\n", + "data_3\n", + "datasorted_3=np.sort(data_3[\"observation\"])\n", + "datasorted_3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Calculate the frequency distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "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", + "19.0 3\n", + "35.0 3\n", + "36.0 2\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency_series = data_3[\"observation\"].value_counts()\n", + "frequency_series\n", + "frequency_df_5 = frequency_series\n", + "frequency_df_5.columns = ['Value', 'Frequency']\n", + "frequency_df_5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot the frequency distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "frequency_df_4.plot(kind=\"bar\", y=\"Frequency\", x=\"Value\")\n", + "plt.show()" ] }, { @@ -338,7 +1611,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "not much\n", "\"\"\"" ] }, @@ -351,11 +1624,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is standard_deviation observation 2.969814\n", + "dtype: float64\n", + "This is the mean observation 27.155\n", + "dtype: float64\n" + ] + } + ], "source": [ - "# your code here" + "mean=data_3.mean()\n", + "standard_dev=data_3.std()\n", + "print(\"This is standard_deviation \", standard_dev)\n", + "print(\"This is the mean \", mean)" ] }, { @@ -365,7 +1652,8 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "the standard deviation has decreased by 10 years compared to that of step 2. \n", + "Mean has decreased by 9 years\n", "\"\"\"" ] }, @@ -381,11 +1669,167 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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observation
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" + ], + "text/plain": [ + " observation\n", + "0 68.0\n", + "1 12.0\n", + "2 45.0\n", + "3 38.0\n", + "4 49.0\n", + ".. ...\n", + "995 27.0\n", + "996 47.0\n", + "997 53.0\n", + "998 33.0\n", + "999 31.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data_4=pd.read_csv(\"ages_population.csv\")\n", + "datasorted_4=np.sort(data_4[\"observation\"])\n", + "datasorted_4\n", + "data_4" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "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", + " ..\n", + "73.0 1\n", + "82.0 1\n", + "70.0 1\n", + "71.0 1\n", + "69.0 1\n", + "Name: count, Length: 72, dtype: int64" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency_series = data_4[\"observation\"].value_counts()\n", + "frequency_series\n", + "frequency_df_6 = frequency_series\n", + "frequency_df_6.columns = ['Value', 'Frequency']\n", + "frequency_df_6" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "frequency_df_6.plot(kind=\"bar\", y=\"Frequency\", x=\"Value\")\n", + "plt.show()" ] }, { @@ -397,11 +1841,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is standard_deviation observation 12.8165\n", + "dtype: float64\n", + "This is the mean observation 36.56\n", + "dtype: float64\n" + ] + } + ], "source": [ - "# your code here" + "mean=data_4.mean()\n", + "standard_dev=data_4.std()\n", + "print(\"This is standard_deviation \", standard_dev)\n", + "print(\"This is the mean \", mean)" ] }, { @@ -411,7 +1869,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "it makes sense\n", "\"\"\"" ] }, @@ -424,11 +1882,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 101, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the first quartile is 28.0\n", + "the second quartile is 37.0\n", + "the third quartile is 45.0\n" + ] + } + ], "source": [ - "# your code here" + "q1 = np.quantile(data_4, 0.25)\n", + "print(f\"the first quartile is {q1}\")\n", + "\n", + "q2 = np.quantile(data_4, 0.50)\n", + "print(f\"the second quartile is {q2}\")\n", + "\n", + "q3 = np.quantile(data_4, 0.75)\n", + "print(f\"the third quartile is {q3}\")\n", + " " ] }, { @@ -438,7 +1914,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "it seems reasonable\n", "\"\"\"" ] }, @@ -451,11 +1927,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 104, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Q0 is 1.0\n", + "Q4 is 82.0\n" + ] + } + ], "source": [ - "# your code here" + "print(\"Q0 is\", np.min(data_4))\n", + "print (\"Q4 is \", np.max(data_4))" ] }, { @@ -465,7 +1951,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "give a better view of the data\n", "\"\"\"" ] }, @@ -500,9 +1986,9 @@ ], "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 +2000,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/your-code/roll_the_dice_hundred.csv b/your-code/roll_the_dice_hundred.csv new file mode 100644 index 0000000..50975a2 --- /dev/null +++ b/your-code/roll_the_dice_hundred.csv @@ -0,0 +1,101 @@ +,roll,value +0,0,1 +1,1,2 +2,2,6 +3,3,1 +4,4,6 +5,5,5 +6,6,2 +7,7,2 +8,8,4 +9,9,1 +10,10,5 +11,11,6 +12,12,5 +13,13,4 +14,14,5 +15,15,4 +16,16,4 +17,17,6 +18,18,2 +19,19,4 +20,20,4 +21,21,6 +22,22,3 +23,23,6 +24,24,6 +25,25,4 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