diff --git a/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb new file mode 100644 index 0000000..d55bc77 --- /dev/null +++ b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb @@ -0,0 +1,1217 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding Descriptive Statistics\n", + "\n", + "Import the necessary libraries here:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from scipy import stats #JIC we want to calculate the mode" + ] + }, + { + "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": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 6, 4, 2, 5, 2, 2, 6, 6, 3])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Let's use random module to simulate the dice roll\n", + "dice=np.random.randint(1,7,10)\n", + "dice" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Plot the results sorted by value." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dice_series = pd.Series(dice)\n", + "\n", + "sorted_dice = dice_series.sort_values(ascending=False)\n", + "\n", + "plt.plot(sorted_dice, marker='o')\n", + "plt.xlabel('Rolls')\n", + "plt.ylabel('Value')\n", + "plt.title('Sorted Dice Roll Results')\n", + "plt.show()" + ] + }, + { + "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": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "values, counts = np.unique(dice, return_counts=True)\n", + "\n", + "plt.bar(values, counts)\n", + "plt.xlabel('Dice Value')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Dice Roll Frequency Distribution')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nThe frecuency distribution aggregates the potential values for the dice rolling, \\nwhile in the sorted series plot we can spot the values and the roll number they were obtained,\\nso we get visibility on the process'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "The frecuency distribution aggregates the potential values for the dice rolling, \n", + "while in the sorted series plot we can spot the values and the roll number they were obtained,\n", + "so we get visibility on the process\"\"\"" + ] + }, + { + "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": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.8\n" + ] + } + ], + "source": [ + "# Define our mean function\n", + "def mean_rustic(result):\n", + " total=0\n", + " for element in result:\n", + " total+=element\n", + " return total/len(result)\n", + "\n", + "#Apply\n", + "dice_mean=mean_rustic(dice)\n", + "print(dice_mean)" + ] + }, + { + "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": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean from Frequency Distribution: 3.8\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "\n", + "def mean_calculation(result):\n", + "\n", + " values, counts = np.unique(dice, return_counts=True)\n", + "\n", + " total_sum = 0\n", + " total_count = 0\n", + "\n", + "\n", + " for value, count in zip(values, counts):\n", + " total_sum += value * count\n", + " total_count += count\n", + "\n", + " mean_from_distribution = total_sum / total_count\n", + " \n", + " return mean_from_distribution\n", + "\n", + "\n", + "print(\"Mean from Frequency Distribution:\", mean_from_distribution)\n", + "\n" + ] + }, + { + "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": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.5\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "def median(result):\n", + " sorted_result = np.sort(result)\n", + " middle = len(sorted_result) // 2\n", + " if len(sorted_result) % 2 == 0:\n", + " return (sorted_result[middle - 1] + sorted_result[middle]) / 2\n", + " else:\n", + " return sorted_result[middle]\n", + "\n", + " \n", + "#Apply the function\n", + "dice_median=median(dice)\n", + "print(dice_median)" + ] + }, + { + "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": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The q1 is 2, the median and q2 is 3.5 and the q3 is 6\n" + ] + } + ], + "source": [ + "# your code here\n", + "def calculate_quartiles(result):\n", + " \n", + " sorted_result = np.sort(result)\n", + " n = len(sorted_result)\n", + " \n", + " middle = len(sorted_result) // 2\n", + " if len(sorted_result) % 2 == 0:\n", + " q2=(sorted_result[middle - 1] + sorted_result[middle]) / 2\n", + " else:\n", + " q2=sorted_result[middlle]\n", + " \n", + " q1_index = int(n * 0.25)\n", + " q1 = sorted_result[q1_index]\n", + " \n", + " # Calculate the third quartile (Q3)\n", + " q3_index = int(n * 0.75)\n", + " q3 = sorted_result[q3_index]\n", + "\n", + "\n", + " return q1, q2, q3\n", + "\n", + "\n", + "q1, q2, q3 = calculate_quartiles(dice)\n", + "\n", + "print(f\"The q1 is {q1}, the median and q2 is {q2} and the q3 is {q3}\")\n", + "\n" + ] + }, + { + "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": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current Working Directory: /Users/gonzalo/Desktop/DAPT/Descriptive-Stats/your-code\n" + ] + } + ], + "source": [ + "#Check current directory\n", + "import os\n", + "\n", + "current_directory = os.getcwd()\n", + "print(\"Current Working Directory:\", current_directory)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "file_path = \"/Users/gonzalo/Desktop/DAPT/Descriptive-Stats/data/roll_the_dice_hundred.csv\"\n", + "\n", + "data=pd.read_csv(file_path)\n", + "\n", + "\n", + "\n", + "data_values=data.sort_values(by='value')\n", + "\n", + "display(data_values.head())\n", + "\n", + "print(data_values.value_counts('value'))\n", + "\n", + "plt.hist(data_values['value'],bins=[1, 2, 3, 4, 5, 6, 7])\n", + "#plot(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "The data is slightly negatively skewed, which is not expected since \n", + "if you roll a fair, 6-sided die, there is an equal probability that the die will land on any given side\n", + "could be due to low volumen (100 tries)\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": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.8\n" + ] + } + ], + "source": [ + "# your code here\n", + "dice_mean=mean_calculation(data['value'])\n", + "\n", + "print(dice_mean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now, calculate the frequency distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 12\n", + "2 17\n", + "3 14\n", + "4 22\n", + "5 12\n", + "6 23\n", + "Name: value, dtype: int64\n" + ] + } + ], + "source": [ + "# your code here\n", + "frequency_distribution = data['value'].value_counts().sort_index()\n", + "\n", + "print(frequency_distribution)" + ] + }, + { + "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": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([12., 17., 14., 22., 12., 23.]),\n", + " array([1., 2., 3., 4., 5., 6., 7.]),\n", + " )" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "plt.hist(data['value'],bins=[1,2,3,4,5,6,7])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "[Previous comment] The data is slightly negatively skewed, which is not expected since \n", + "if you roll a fair, 6-sided die, there is an equal probability that the die will land on any given side\n", + "could be due to low volumen (100 tries). I guess we will see witht the 1k rolls\n", + "\n", + "\n", + "Following up on my comments above, the mean is 3.8 and reflects how the data is negatively skewed\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": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([175., 0., 167., 0., 175., 0., 168., 0., 149., 166.]),\n", + " array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. ]),\n", + " )" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "\n", + "file_path = \"/Users/gonzalo/Desktop/DAPT/Descriptive-Stats/data/roll_the_dice_thousand.csv\"\n", + "\n", + "data2=pd.read_csv(file_path)\n", + "\n", + "data2.head()\n", + "\n", + "plt.hist(data2['value'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "We can spot diffences in terms of the number of occurrences for each possible outcome. \n", + "The more times you roll the die, the closer the observed frequency distribution will be \n", + "to the theoretical probability distribution for a fair six-sided die. \n", + "\n", + "A larger sample size (1000 versus 100) typically results in a smoother distribution with smaller fluctuations \n", + "in the observed frequencies\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": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 17., 59., 115., 204., 261., 194., 99., 36., 14., 1.]),\n", + " array([ 1. , 9.1, 17.2, 25.3, 33.4, 41.5, 49.6, 57.7, 65.8, 73.9, 82. ]),\n", + " )" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "file_path = \"/Users/gonzalo/Desktop/DAPT/Descriptive-Stats/data/ages_population.csv\"\n", + "\n", + "ages=pd.read_csv(file_path)\n", + "\n", + "ages.head()\n", + "plt.hist(ages)\n", + "\n", + "\"\"\"The mean is around 35, so the standard deviation is where the 68% of the data lies, \n", + "around 15 would be my Estimation \"\"\"" + ] + }, + { + "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": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " The mean is observation 36.56\n", + "dtype: float64\n", + " The standard deviation is observation 12.81009\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/gonzalo/anaconda3/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3462: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n", + " return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n" + ] + } + ], + "source": [ + "# your code here\n", + "print(f\" The mean is {np.mean(ages)}\")\n", + "print(f\" The standard deviation is {np.std(ages)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "My guess was 20% off, maybe I should go to the casino xD\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": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 16., 52., 119., 98., 245., 254., 90., 92., 29., 5.]),\n", + " array([19. , 20.7, 22.4, 24.1, 25.8, 27.5, 29.2, 30.9, 32.6, 34.3, 36. ]),\n", + " )" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "file_path = \"/Users/gonzalo/Desktop/DAPT/Descriptive-Stats/data/ages_population3.csv\"\n", + "\n", + "ages3=pd.read_csv(file_path)\n", + "\n", + "display(ages3.head())\n", + "plt.hist(ages3)" + ] + }, + { + "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": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " The mean is observation 41.989\n", + "dtype: float64\n", + " The standard deviation is observation 16.136632\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/gonzalo/anaconda3/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3462: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n", + " return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n" + ] + } + ], + "source": [ + "# your code here\n", + "print(f\" The mean is {np.mean(ages3)}\")\n", + "print(f\" The standard deviation is {np.std(ages3)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "We get a negative skew distribution(left-skewed) with a mean of 41 and standard deviation of 16\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": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([30.]), array([40.]), array([53.]))" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "ages3_sorted=ages3.sort_values(by='observation',ascending=True)\n", + "calculate_quartiles(ages3_sorted)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "The is not much difference between the median 40 and the mean 41.2\"\"\"" + ] + }, + { + "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": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Other percentiles (10th and 90th percentiles): 22.0 67.0\n" + ] + } + ], + "source": [ + "# 10th, 90th percentiles\n", + "\n", + "p10 = np.percentile(ages3_sorted['observation'], 10)\n", + "p90 = np.percentile(ages3_sorted['observation'], 90)\n", + "\n", + "print(\"Other percentiles (10th and 90th percentiles):\", p10, p90)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'your comments here'" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "The 10th percentile (P10) indicates the value below which 10% of your data falls. \n", + "A high 10th percentile can suggest a right-skewed distribution.\n", + "We have a high value for the 90th percentile (P90), which can suggest that out distribution left-skewed.\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": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Dataset 3')" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "import seaborn as sns\n", + "\n", + "# Figure tabular with 3 columns\n", + "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 6))\n", + "\n", + "# Plot the histograms for the \"ages\"\n", + "sns.histplot(data=ages, kde=True, ax=axes[0])\n", + "axes[0].set_title('First population')\n", + "\n", + "sns.histplot(data=ages2, kde=True, ax=axes[1])\n", + "axes[1].set_title('Second population')\n", + "\n", + "sns.histplot(data=ages3, kde=True, ax=axes[2])\n", + "axes[2].set_title('Third population')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "Firs population/neighborhood has less vairability compared with the second population, as we can see with the\n", + "probability density function\n", + "\n", + "Thrid neighborhood is left skewed with a valley around the 90th percentile\"\"\"" + ] + } + ], + "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": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 5759add..d55bc77 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -1,522 +1,1217 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Understanding Descriptive Statistics\n", - "\n", - "Import the necessary libraries here:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Libraries" - ] - }, - { - "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": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.- Plot the results sorted by value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "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": 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 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": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "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": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "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": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "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": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "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": 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": [ - "#### 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": "ironhack-3.7", - "language": "python", - "name": "ironhack-3.7" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding Descriptive Statistics\n", + "\n", + "Import the necessary libraries here:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from scipy import stats #JIC we want to calculate the mode" + ] + }, + { + "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": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 6, 4, 2, 5, 2, 2, 6, 6, 3])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Let's use random module to simulate the dice roll\n", + "dice=np.random.randint(1,7,10)\n", + "dice" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Plot the results sorted by value." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dice_series = pd.Series(dice)\n", + "\n", + "sorted_dice = dice_series.sort_values(ascending=False)\n", + "\n", + "plt.plot(sorted_dice, marker='o')\n", + "plt.xlabel('Rolls')\n", + "plt.ylabel('Value')\n", + "plt.title('Sorted Dice Roll Results')\n", + "plt.show()" + ] + }, + { + "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": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "values, counts = np.unique(dice, return_counts=True)\n", + "\n", + "plt.bar(values, counts)\n", + "plt.xlabel('Dice Value')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Dice Roll Frequency Distribution')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nThe frecuency distribution aggregates the potential values for the dice rolling, \\nwhile in the sorted series plot we can spot the values and the roll number they were obtained,\\nso we get visibility on the process'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "The frecuency distribution aggregates the potential values for the dice rolling, \n", + "while in the sorted series plot we can spot the values and the roll number they were obtained,\n", + "so we get visibility on the process\"\"\"" + ] + }, + { + "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": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.8\n" + ] + } + ], + "source": [ + "# Define our mean function\n", + "def mean_rustic(result):\n", + " total=0\n", + " for element in result:\n", + " total+=element\n", + " return total/len(result)\n", + "\n", + "#Apply\n", + "dice_mean=mean_rustic(dice)\n", + "print(dice_mean)" + ] + }, + { + "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": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean from Frequency Distribution: 3.8\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "\n", + "def mean_calculation(result):\n", + "\n", + " values, counts = np.unique(dice, return_counts=True)\n", + "\n", + " total_sum = 0\n", + " total_count = 0\n", + "\n", + "\n", + " for value, count in zip(values, counts):\n", + " total_sum += value * count\n", + " total_count += count\n", + "\n", + " mean_from_distribution = total_sum / total_count\n", + " \n", + " return mean_from_distribution\n", + "\n", + "\n", + "print(\"Mean from Frequency Distribution:\", mean_from_distribution)\n", + "\n" + ] + }, + { + "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": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.5\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "def median(result):\n", + " sorted_result = np.sort(result)\n", + " middle = len(sorted_result) // 2\n", + " if len(sorted_result) % 2 == 0:\n", + " return (sorted_result[middle - 1] + sorted_result[middle]) / 2\n", + " else:\n", + " return sorted_result[middle]\n", + "\n", + " \n", + "#Apply the function\n", + "dice_median=median(dice)\n", + "print(dice_median)" + ] + }, + { + "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": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The q1 is 2, the median and q2 is 3.5 and the q3 is 6\n" + ] + } + ], + "source": [ + "# your code here\n", + "def calculate_quartiles(result):\n", + " \n", + " sorted_result = np.sort(result)\n", + " n = len(sorted_result)\n", + " \n", + " middle = len(sorted_result) // 2\n", + " if len(sorted_result) % 2 == 0:\n", + " q2=(sorted_result[middle - 1] + sorted_result[middle]) / 2\n", + " else:\n", + " q2=sorted_result[middlle]\n", + " \n", + " q1_index = int(n * 0.25)\n", + " q1 = sorted_result[q1_index]\n", + " \n", + " # Calculate the third quartile (Q3)\n", + " q3_index = int(n * 0.75)\n", + " q3 = sorted_result[q3_index]\n", + "\n", + "\n", + " return q1, q2, q3\n", + "\n", + "\n", + "q1, q2, q3 = calculate_quartiles(dice)\n", + "\n", + "print(f\"The q1 is {q1}, the median and q2 is {q2} and the q3 is {q3}\")\n", + "\n" + ] + }, + { + "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": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current Working Directory: /Users/gonzalo/Desktop/DAPT/Descriptive-Stats/your-code\n" + ] + } + ], + "source": [ + "#Check current directory\n", + "import os\n", + "\n", + "current_directory = os.getcwd()\n", + "print(\"Current Working Directory:\", current_directory)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "file_path = \"/Users/gonzalo/Desktop/DAPT/Descriptive-Stats/data/roll_the_dice_hundred.csv\"\n", + "\n", + "data=pd.read_csv(file_path)\n", + "\n", + "\n", + "\n", + "data_values=data.sort_values(by='value')\n", + "\n", + "display(data_values.head())\n", + "\n", + "print(data_values.value_counts('value'))\n", + "\n", + "plt.hist(data_values['value'],bins=[1, 2, 3, 4, 5, 6, 7])\n", + "#plot(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "The data is slightly negatively skewed, which is not expected since \n", + "if you roll a fair, 6-sided die, there is an equal probability that the die will land on any given side\n", + "could be due to low volumen (100 tries)\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": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.8\n" + ] + } + ], + "source": [ + "# your code here\n", + "dice_mean=mean_calculation(data['value'])\n", + "\n", + "print(dice_mean)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now, calculate the frequency distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 12\n", + "2 17\n", + "3 14\n", + "4 22\n", + "5 12\n", + "6 23\n", + "Name: value, dtype: int64\n" + ] + } + ], + "source": [ + "# your code here\n", + "frequency_distribution = data['value'].value_counts().sort_index()\n", + "\n", + "print(frequency_distribution)" + ] + }, + { + "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": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([12., 17., 14., 22., 12., 23.]),\n", + " array([1., 2., 3., 4., 5., 6., 7.]),\n", + " )" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBq2+PjZz34W48ePj+OPPz7OPffc+P3vfz9cpwIA6siwxMeDDz4Y8+fPj7vuuiv+8pe/xEUXXRQdHR3x6quvDsfpAIA6Mizxcd9998W3v/3tuP766+OMM86I+++/P9rb22Pp0qXDcToAoI40VPqABw8ejK1bt8Ydd9zRb/vMmTPj2WefHbB/b29v9Pb29q3v3bs3IiJ6enoqPVpERBzq/X/DclwAqBfD8TP28DGLovif+1Y8Pt5444149913o7m5ud/25ubm6O7uHrB/V1dX/PCHPxywvb29vdKjAQAR0XT/8B1737590dTU9L77VDw+DiuVSv3Wi6IYsC0iYsGCBdHZ2dm3fujQofjXv/4VJ5100hH3/zB6enqivb09du3aFSNHjqzosY82rtXguVaD51oNjes1eK7V4A3XtSqKIvbt2xdtbW3/c9+Kx8fJJ58cxxxzzIC7HLt37x5wNyQiolwuR7lc7rftE5/4RKXH6mfkyJG+OQfJtRo812rwXKuhcb0Gz7UavOG4Vv/rjsdhFX/g9Ljjjotzzz031q9f32/7+vXrY9q0aZU+HQBQZ4blbZfOzs74xje+EZMnT46pU6fGsmXL4tVXX40bb7xxOE4HANSRYYmPq666Kvbs2RM/+tGP4vXXX4+JEyfGY489FmPHjh2O0w1auVyOH/zgBwPe5mEg12rwXKvBc62GxvUaPNdq8GrhWpWKwfxODABAhfhsFwAglfgAAFKJDwAglfgAAFJ9JOJj48aNMXv27Ghra4tSqRRr1qyp9kg1q6urK84777xobGyM0aNHxxVXXBEvvvhitceqSUuXLo2zzz677w/1TJ06NR5//PFqj1UXurq6olQqxfz586s9Ss1ZuHBhlEqlfktLS0u1x6pZ//znP+PrX/96nHTSSXHCCSfEZz/72di6dWu1x6pJ48aNG/C9VSqVYt68eemzfCTi48CBAzFp0qRYsmRJtUepeRs2bIh58+bFpk2bYv369fHOO+/EzJkz48CBA9UereaMGTMmFi9eHFu2bIktW7bEpZdeGl/4whdi+/bt1R6tpm3evDmWLVsWZ599drVHqVlnnnlmvP76633L888/X+2RatKbb74ZF1xwQRx77LHx+OOPx1//+te49957h/2vZNerzZs39/u+OvzHQK+88sr0WYbts11qSUdHR3R0dFR7jLrwxBNP9FtfsWJFjB49OrZu3RoXX3xxlaaqTbNnz+63/uMf/ziWLl0amzZtijPPPLNKU9W2/fv3x9e+9rVYvnx53H333dUep2Y1NDS42zEIP/nJT6K9vT1WrFjRt23cuHHVG6jGnXLKKf3WFy9eHKeddlpMnz49fZaPxJ0PPri9e/dGRMSoUaOqPElte/fdd2P16tVx4MCBmDp1arXHqVnz5s2LWbNmxec+97lqj1LTduzYEW1tbTF+/Pi4+uqr4+WXX672SDXpkUceicmTJ8eVV14Zo0ePjnPOOSeWL19e7bHqwsGDB+OXv/xlXHfddRX/ENfBEB/8V0VRRGdnZ1x44YUxceLEao9Tk55//vn4+Mc/HuVyOW688cZ46KGH4jOf+Uy1x6pJq1evjj//+c/R1dVV7VFq2pQpU2LlypWxdu3aWL58eXR3d8e0adNiz5491R6t5rz88suxdOnSmDBhQqxduzZuvPHG+O53vxsrV66s9mg1b82aNfHWW2/FtddeW5XzfyTeduGDufnmm+O5556LP/zhD9UepWZ9+tOfjm3btsVbb70Vv/nNb2Lu3LmxYcMGAfIeu3btiltvvTXWrVsXxx9/fLXHqWn/+RbxWWedFVOnTo3TTjstHnjggejs7KziZLXn0KFDMXny5Fi0aFFERJxzzjmxffv2WLp0aXzzm9+s8nS17ec//3l0dHREW1tbVc7vzgdHdMstt8QjjzwSTz/9dIwZM6ba49Ss4447Lj75yU/G5MmTo6urKyZNmhQ//elPqz1Wzdm6dWvs3r07zj333GhoaIiGhobYsGFD/N///V80NDTEu+++W+0Ra9aJJ54YZ511VuzYsaPao9Sc1tbWAaF/xhlnxKuvvlqlierD3//+93jyySfj+uuvr9oM7nzQT1EUccstt8RDDz0UzzzzTIwfP77aI9WVoiiit7e32mPUnMsuu2zAb2x861vfitNPPz2+//3vxzHHHFOlyWpfb29vvPDCC3HRRRdVe5Sac8EFFwz4UwB/+9vfqv4hprXu8C8SzJo1q2ozfCTiY//+/fHSSy/1re/cuTO2bdsWo0aNilNPPbWKk9WeefPmxapVq+Lhhx+OxsbG6O7ujoiIpqamGDFiRJWnqy133nlndHR0RHt7e+zbty9Wr14dzzzzzIDfGCKisbFxwHNDJ554Ypx00kmeJ3qP2267LWbPnh2nnnpq7N69O+6+++7o6emJuXPnVnu0mvO9730vpk2bFosWLYqvfOUr8ac//SmWLVsWy5Ytq/ZoNevQoUOxYsWKmDt3bjQ0VDEBio+Ap59+uoiIAcvcuXOrPVrNOdJ1iohixYoV1R6t5lx33XXF2LFji+OOO6445ZRTissuu6xYt25dtceqG9OnTy9uvfXWao9Rc6666qqitbW1OPbYY4u2trZizpw5xfbt26s9Vs363e9+V0ycOLEol8vF6aefXixbtqzaI9W0tWvXFhFRvPjii1Wdo1QURVGd7AEAPoo8cAoApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAECq/w+X69u0+19IIwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "plt.hist(data['value'],bins=[1,2,3,4,5,6,7])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "[Previous comment] The data is slightly negatively skewed, which is not expected since \n", + "if you roll a fair, 6-sided die, there is an equal probability that the die will land on any given side\n", + "could be due to low volumen (100 tries). I guess we will see witht the 1k rolls\n", + "\n", + "\n", + "Following up on my comments above, the mean is 3.8 and reflects how the data is negatively skewed\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": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([175., 0., 167., 0., 175., 0., 168., 0., 149., 166.]),\n", + " array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. ]),\n", + " )" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "\n", + "file_path = \"/Users/gonzalo/Desktop/DAPT/Descriptive-Stats/data/roll_the_dice_thousand.csv\"\n", + "\n", + "data2=pd.read_csv(file_path)\n", + "\n", + "data2.head()\n", + "\n", + "plt.hist(data2['value'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "We can spot diffences in terms of the number of occurrences for each possible outcome. \n", + "The more times you roll the die, the closer the observed frequency distribution will be \n", + "to the theoretical probability distribution for a fair six-sided die. \n", + "\n", + "A larger sample size (1000 versus 100) typically results in a smoother distribution with smaller fluctuations \n", + "in the observed frequencies\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": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 17., 59., 115., 204., 261., 194., 99., 36., 14., 1.]),\n", + " array([ 1. , 9.1, 17.2, 25.3, 33.4, 41.5, 49.6, 57.7, 65.8, 73.9, 82. ]),\n", + " )" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "file_path = \"/Users/gonzalo/Desktop/DAPT/Descriptive-Stats/data/ages_population.csv\"\n", + "\n", + "ages=pd.read_csv(file_path)\n", + "\n", + "ages.head()\n", + "plt.hist(ages)\n", + "\n", + "\"\"\"The mean is around 35, so the standard deviation is where the 68% of the data lies, \n", + "around 15 would be my Estimation \"\"\"" + ] + }, + { + "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": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " The mean is observation 36.56\n", + "dtype: float64\n", + " The standard deviation is observation 12.81009\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/gonzalo/anaconda3/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3462: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n", + " return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n" + ] + } + ], + "source": [ + "# your code here\n", + "print(f\" The mean is {np.mean(ages)}\")\n", + "print(f\" The standard deviation is {np.std(ages)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "My guess was 20% off, maybe I should go to the casino xD\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": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 16., 52., 119., 98., 245., 254., 90., 92., 29., 5.]),\n", + " array([19. , 20.7, 22.4, 24.1, 25.8, 27.5, 29.2, 30.9, 32.6, 34.3, 36. ]),\n", + " )" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdSIOlAMHDujBBx+U1+uVw+HQW2+9FXa/MUZVVVXyer1KSUnR9OnTdeLEibAxwWBQpaWlysjI0PDhwzV//nydPn36tp4IAACIHREHysWLFzVhwgStW7eux/tfeuklrVmzRuvWrdPhw4fl8Xj0wAMP6MKFC6ExZWVl2rlzp7Zt26aDBw+qs7NT8+bN09WrV2/9mQAAgJiRGOkDiouLVVxc3ON9xhi98sorev7557VgwQJJ0qZNm+R2u7V161Y9/fTT8vv92rBhgzZv3qzZs2dLkrZs2aLs7Gzt3btXRUVFt/F0AABALBjQ96C0trbK5/OpsLAwtM/pdGratGlqbm6WJB05ckRXrlwJG+P1epWXlxca010wGFQgEAjbAABA7BrQQPH5fJIkt9sdtt/tdofu8/l8Sk5O1ogRI3od011NTY1cLldoy87OHshpAwAAywzKp3gcDkfYbWPMTfu662tMZWWl/H5/aGtraxuwuQIAAPtE/B6Uvng8HknXr5JkZWWF9nd0dISuqng8HnV1dencuXNhV1E6OjpUUFDQ43GdTqecTudAThW4JXdV1Ed7CgAQFwb0Ckpubq48Ho8aGhpC+7q6utTU1BSKj/z8fCUlJYWNaW9v1/Hjx3sNFAAAEF8ivoLS2dmpTz75JHS7tbVVR48eVXp6usaMGaOysjJVV1dr7NixGjt2rKqrqzVs2DAtXrxYkuRyubR06VKtXLlSI0eOVHp6ulatWqXx48eHPtUDAADiW8SB8sEHH2jGjBmh2+Xl5ZKkkpIS1dXV6dlnn9Xly5e1bNkynTt3TpMnT9aePXuUmpoaeszatWuVmJiohQsX6vLly5o1a5bq6uqUkJAwAE8JAAAMdQ5jjIn2JCIVCATkcrnk9/uVlpYW7engFvF+DsSSU7Vzoz0FwHqR/Pzmu3gAAIB1CBQAAGAdAgUAAFiHQAEAANYZ0D/UBgAYWobim9V5Q3J84AoKAACwDoECAACsw694AGAADMVflQA24woKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsM+CBUlVVJYfDEbZ5PJ7Q/cYYVVVVyev1KiUlRdOnT9eJEycGehoAAGAISxyMg37/+9/X3r17Q7cTEhJC//3SSy9pzZo1qqur07hx4/Tiiy/qgQce0MmTJ5WamjoY04kLd1XUR3sKAAAMmEH5FU9iYqI8Hk9oGzVqlKTrV09eeeUVPf/881qwYIHy8vK0adMmXbp0SVu3bh2MqQAAgCFoUAKlpaVFXq9Xubm5euyxx/TPf/5TktTa2iqfz6fCwsLQWKfTqWnTpqm5ubnX4wWDQQUCgbANAADErgEPlMmTJ+u1117TX//6V/3hD3+Qz+dTQUGBzp49K5/PJ0lyu91hj3G73aH7elJTUyOXyxXasrOzB3raAADAIgMeKMXFxXr00Uc1fvx4zZ49W/X1198bsWnTptAYh8MR9hhjzE37vqqyslJ+vz+0tbW1DfS0AQCARQb9Y8bDhw/X+PHj1dLSEvo0T/erJR0dHTddVfkqp9OptLS0sA0AAMSuQQ+UYDCojz/+WFlZWcrNzZXH41FDQ0Po/q6uLjU1NamgoGCwpwIAAIaIAf+Y8apVq/Tggw9qzJgx6ujo0IsvvqhAIKCSkhI5HA6VlZWpurpaY8eO1dixY1VdXa1hw4Zp8eLFAz0VAAAwRA14oJw+fVqPP/64Pv/8c40aNUpTpkzRoUOHlJOTI0l69tlndfnyZS1btkznzp3T5MmTtWfPHv4GCgAACHEYY0y0JxGpQCAgl8slv9/P+1H+H3+oDUC8OFU7N9pTwC2K5Oc338UDAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6yRGewIAAETiror6aE8hYqdq50Z7CkMOV1AAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUSoz0BG91VUR/tKQAAENe4ggIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOskRnsCAADEursq6qM9hYidqp0b1X+fKygAAMA6BAoAALBOVAPlt7/9rXJzc3XnnXcqPz9f7777bjSnAwAALBG1QNm+fbvKysr0/PPP68MPP9SPf/xjFRcX67PPPovWlAAAgCWiFihr1qzR0qVL9eSTT+p73/ueXnnlFWVnZ2v9+vXRmhIAALBEVD7F09XVpSNHjqiioiJsf2FhoZqbm28aHwwGFQwGQ7f9fr8kKRAIDMr8rgUvDcpxAQAYKgbjZ+yNYxpj+h0blUD5/PPPdfXqVbnd7rD9brdbPp/vpvE1NTX6xS9+cdP+7OzsQZsjAADxzPXK4B37woULcrlcfY6J6t9BcTgcYbeNMTftk6TKykqVl5eHbl+7dk3//e9/NXLkyB7HDwWBQEDZ2dlqa2tTWlpatKcTVazFdazDdazD/7AW17EO18XCOhhjdOHCBXm93n7HRiVQMjIylJCQcNPVko6OjpuuqkiS0+mU0+kM2/ftb397MKf4jUlLSxuyL7SBxlpcxzpcxzr8D2txHetw3VBfh/6unNwQlTfJJicnKz8/Xw0NDWH7GxoaVFBQEI0pAQAAi0TtVzzl5eV64oknNHHiRE2dOlW///3v9dlnn+mZZ56J1pQAAIAlohYoixYt0tmzZ/XLX/5S7e3tysvL0+7du5WTkxOtKX2jnE6nXnjhhZt+dRWPWIvrWIfrWIf/YS2uYx2ui7d1cJiv81kfAACAbxDfxQMAAKxDoAAAAOsQKAAAwDoECgAAsA6BMgBqamo0adIkpaamKjMzUw8//LBOnjwZNsYYo6qqKnm9XqWkpGj69Ok6ceJEv8d+8803de+998rpdOree+/Vzp07B+tp3Lb+1uHKlSt67rnnNH78eA0fPlxer1c/+clPdObMmT6PW1dXJ4fDcdP2xRdfDPZTumVf5zWxZMmSm57TlClT+j12LL0mJPX4v63D4dCvf/3rXo871F4T69ev1w9+8IPQH9iaOnWq/vKXv4Tuj4fzww19rUU8nSP6e03Ew/mhXwa3raioyGzcuNEcP37cHD161MydO9eMGTPGdHZ2hsbU1taa1NRU8+abb5pjx46ZRYsWmaysLBMIBHo9bnNzs0lISDDV1dXm448/NtXV1SYxMdEcOnTom3haEetvHc6fP29mz55ttm/fbv7xj3+Y999/30yePNnk5+f3edyNGzeatLQ0097eHrbZ7Ou8JkpKSsycOXPCntPZs2f7PG6svSaMMTf97/qnP/3JOBwO8+mnn/Z63KH2mti1a5epr683J0+eNCdPnjSrV682SUlJ5vjx48aY+Dg/3NDXWsTTOaK/10Q8nB/6Q6AMgo6ODiPJNDU1GWOMuXbtmvF4PKa2tjY05osvvjAul8v87ne/6/U4CxcuNHPmzAnbV1RUZB577LHBmfgA674OPfn73/9uJJl//etfvY7ZuHGjcblcgzDDb05Pa1FSUmIeeuihiI4TD6+Jhx56yMycObPP48TCa2LEiBHmj3/8Y9yeH77qxlr0JF7OEcaEr0M8nh+641c8g8Dv90uS0tPTJUmtra3y+XwqLCwMjXE6nZo2bZqam5t7Pc77778f9hhJKioq6vMxNum+Dr2NcTgc/X63Umdnp3JycjR69GjNmzdPH3744UBOddD1thb79+9XZmamxo0bp6eeekodHR19HifWXxP//ve/VV9fr6VLl/Z7rKH6mrh69aq2bdumixcvaurUqXF7fpBuXouexMM5ord1iLfzQ3cEygAzxqi8vFz33Xef8vLyJCn0pYjdvwjR7Xbf9IWJX+Xz+SJ+jC16WofuvvjiC1VUVGjx4sV9fvHVPffco7q6Ou3atUtvvPGG7rzzTv3oRz9SS0vLYE1/QPW2FsXFxXr99dfV2Niol19+WYcPH9bMmTMVDAZ7PVasvyY2bdqk1NRULViwoM9jDcXXxLFjx/Stb31LTqdTzzzzjHbu3Kl77703Ls8Pva1Fd7F+juhrHeLt/NCj6F7AiT3Lli0zOTk5pq2tLbTvvffeM5LMmTNnwsY++eSTpqioqNdjJSUlma1bt4bt27Jli3E6nQM76UHQ0zp8VVdXl3nooYfMD3/4Q+P3+yM69tWrV82ECRNMaWnpQEx10PW3FjecOXPGJCUlmTfffLPXMbH8mjDGmLvvvtusWLEi4mMPhddEMBg0LS0t5vDhw6aiosJkZGSYEydOxOX5obe1+Kp4OEd8nXW4IdbPDz2J2nfxxKLS0lLt2rVLBw4c0OjRo0P7PR6PpOt1m5WVFdrf0dFxU+1+lcfjual8+3uMDXpbhxuuXLmihQsXqrW1VY2NjRF/bfgdd9yhSZMmWf/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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "file_path = \"/Users/gonzalo/Desktop/DAPT/Descriptive-Stats/data/ages_population3.csv\"\n", + "\n", + "ages3=pd.read_csv(file_path)\n", + "\n", + "display(ages3.head())\n", + "plt.hist(ages3)" + ] + }, + { + "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": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " The mean is observation 41.989\n", + "dtype: float64\n", + " The standard deviation is observation 16.136632\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/gonzalo/anaconda3/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3462: FutureWarning: In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'\n", + " return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n" + ] + } + ], + "source": [ + "# your code here\n", + "print(f\" The mean is {np.mean(ages3)}\")\n", + "print(f\" The standard deviation is {np.std(ages3)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "We get a negative skew distribution(left-skewed) with a mean of 41 and standard deviation of 16\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": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([30.]), array([40.]), array([53.]))" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "ages3_sorted=ages3.sort_values(by='observation',ascending=True)\n", + "calculate_quartiles(ages3_sorted)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "The is not much difference between the median 40 and the mean 41.2\"\"\"" + ] + }, + { + "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": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Other percentiles (10th and 90th percentiles): 22.0 67.0\n" + ] + } + ], + "source": [ + "# 10th, 90th percentiles\n", + "\n", + "p10 = np.percentile(ages3_sorted['observation'], 10)\n", + "p90 = np.percentile(ages3_sorted['observation'], 90)\n", + "\n", + "print(\"Other percentiles (10th and 90th percentiles):\", p10, p90)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'your comments here'" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "The 10th percentile (P10) indicates the value below which 10% of your data falls. \n", + "A high 10th percentile can suggest a right-skewed distribution.\n", + "We have a high value for the 90th percentile (P90), which can suggest that out distribution left-skewed.\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": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Dataset 3')" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "import seaborn as sns\n", + "\n", + "# Figure tabular with 3 columns\n", + "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 6))\n", + "\n", + "# Plot the histograms for the \"ages\"\n", + "sns.histplot(data=ages, kde=True, ax=axes[0])\n", + "axes[0].set_title('First population')\n", + "\n", + "sns.histplot(data=ages2, kde=True, ax=axes[1])\n", + "axes[1].set_title('Second population')\n", + "\n", + "sns.histplot(data=ages3, kde=True, ax=axes[2])\n", + "axes[2].set_title('Third population')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "Firs population/neighborhood has less vairability compared with the second population, as we can see with the\n", + "probability density function\n", + "\n", + "Thrid neighborhood is left skewed with a valley around the 90th percentile\"\"\"" + ] + } + ], + "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": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}