diff --git a/your-code/[lab-understanding-descriptive-stats] - David Morazzo.ipynb b/your-code/[lab-understanding-descriptive-stats] - David Morazzo.ipynb new file mode 100644 index 0000000..50fa418 --- /dev/null +++ b/your-code/[lab-understanding-descriptive-stats] - David Morazzo.ipynb @@ -0,0 +1,1998 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding Descriptive Statistics\n", + "\n", + "Import the necessary libraries here:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Libraries\n", + "import random\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "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": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 3.432\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "def dice_throw(n):\n", + " throws = random.choices([1,2,3,4,5,6], k=n)\n", + " return pd.DataFrame(throws)\n", + "\n", + "dice_throw(1000).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "df = dice_throw(50).sort_values(by=0).reset_index()[0]\n", + "plt.plot(range(len(df)), df, color = 'green')" + ] + }, + { + "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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([10., 7., 5., 10., 11., 7.]),\n", + " array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5]),\n", + " )" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "plt.hist(df, bins= 6, range=(0.5, 6.5), color= 'green')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Histogram plot is showing the number of times each dice number ocurred.\n" + ] + } + ], + "source": [ + "print('Histogram plot is showing the number of times each dice number ocurred.')" + ] + }, + { + "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": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "def mean_calculation(x):\n", + " return sum(x) / len(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.52" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_df = mean_calculation(df)\n", + "mean_df" + ] + }, + { + "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": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "# First, calculate the frequency distribution\n", + "def freq_calc(x):\n", + " dic = {}\n", + " for n in x:\n", + " if n not in dic:\n", + " dic[n] = 0\n", + " else:\n", + " dic[n] += 1\n", + " return dic" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: 9, 2: 6, 3: 4, 4: 9, 5: 10, 6: 6}\n" + ] + } + ], + "source": [ + "print(freq_calc(df))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.5" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Then, calculate the mean using the values of the frequency distribution you've just computed.\n", + "list1 = {1: 5, 2: 7, 3: 11, 4: 7, 5: 6, 6: 8}\n", + "list1_mu = sum(list1)/len(list1)\n", + "list1_mu\n", + "\n", + "# Not entirely sure if it's this what the exercise requests?" + ] + }, + { + "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": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "def calc_median(x):\n", + " n = len(x)\n", + " index = n // 2\n", + " if n % 2:\n", + " return sorted(x)[index]\n", + " return sum(sorted(x)[index - 1:index + 1]) / 2 " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "median_df = calc_median(df)\n", + "median_df" + ] + }, + { + "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": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "def quartile_calc(x):\n", + " middle = calc_median(x)\n", + " Q1 = calc_median(middle)[0]" + ] + }, + { + "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": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Unnamed: 0 roll value\n", + "0 0 0 1\n", + "47 47 47 1\n", + "56 56 56 1\n", + "9 9 9 1\n", + "73 73 73 1" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "roll_hundred = pd.read_csv('../data/roll_the_dice_hundred.csv')\n", + "roll_hundred = roll_hundred.sort_values('value')\n", + "roll_hundred.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Dice value for each throw')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "roll_hundred[['value']].plot(kind='bar', color = 'green')\n", + "plt.xticks([])\n", + "plt.ylabel('Dice Value')\n", + "plt.xlabel('Dice value for each throw')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conclusions? Values between 1 and 6 rolled 100x. It shows how many times the values between 1 and 6 were rolled but it's not very perceptive since we couldn't see the numbers so I decided to exclude them from the axis.\n" + ] + } + ], + "source": [ + "print(f\"Conclusions? Values between 1 and 6 rolled 100x. It shows how many times the values between 1 and 6 were rolled but it's not very perceptive since we couldn't see the numbers so I decided to exclude them from the axis.\")\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": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.74" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "mean_calculation(roll_hundred['value'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now, calculate the frequency distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 11, 2: 16, 3: 13, 4: 21, 5: 11, 6: 22}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "freq_calc(list(roll_hundred['value']))" + ] + }, + { + "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": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "plt.hist(roll_hundred['value'], color ='green')\n", + "plt.vlines(roll_hundred['value'].mean(), ymin = 0, ymax = 30, linestyles =\"dotted\", colors =\"k\") \n", + "\n", + "# It was actually quite nice to learn the dot line" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The median amount is 3.74, exactly where the line is showed in the plot\n" + ] + } + ], + "source": [ + "print(\"The median amount is 3.74, exactly where the line is showed in the plot\")" + ] + }, + { + "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": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0rollvalue
5645645641
9229229221
5605605601
2132132131
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............
8558558556
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9999999996
\n", + "

1000 rows × 3 columns

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" + ], + "text/plain": [ + " Unnamed: 0 roll value\n", + "564 564 564 1\n", + "922 922 922 1\n", + "560 560 560 1\n", + "213 213 213 1\n", + "214 214 214 1\n", + ".. ... ... ...\n", + "855 855 855 6\n", + "360 360 360 6\n", + "857 857 857 6\n", + "388 388 388 6\n", + "999 999 999 6\n", + "\n", + "[1000 rows x 3 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "roll_thousand = pd.read_csv('../data/roll_the_dice_thousand.csv')\n", + "roll_thousand = roll_thousand.sort_values('value')\n", + "roll_thousand" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "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": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(roll_thousand['value'], color = 'green')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "With this plot we can see that the distribution is more uniform. Basically since we have more samples the distribution changed and tends to be more similar. It makes perfectly sense.\n" + ] + } + ], + "source": [ + "print(\"With this plot we can see that the distribution is more uniform. Basically since we have more samples the distribution changed and tends to be more similar. It makes perfectly sense.\")" + ] + }, + { + "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": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "489 1.0\n", + "209 1.0\n", + "301 2.0\n", + "451 2.0\n", + "338 4.0\n", + ".. ...\n", + "523 69.0\n", + "437 70.0\n", + "493 71.0\n", + "339 73.0\n", + "363 82.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "ages_population = pd.read_csv('../data/ages_population.csv')\n", + "ages_population = ages_population.sort_values('observation')\n", + "ages_population" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "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": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(ages_population['observation'], color = 'green')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "count 1000.0000\n", + "mean 36.5600\n", + "std 12.8165\n", + "min 1.0000\n", + "25% 28.0000\n", + "50% 37.0000\n", + "75% 45.0000\n", + "max 82.0000" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ages_population.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The mean is 36.5 and the standard deviation is 12.8.\n" + ] + } + ], + "source": [ + "print(f'The mean is 36.5 and the standard deviation is 12.8.')" + ] + }, + { + "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": 239, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 36.56\n", + "dtype: float64\n", + "observation 12.8165\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# your code here\n", + "print(ages_population.mean())\n", + "print(ages_population.std())" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nNothing to add. Already did it previously.\\n'" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Nothing to add. Already did it previously.\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": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "327 19.0\n", + "998 19.0\n", + "96 19.0\n", + "897 20.0\n", + "272 20.0\n", + ".. ...\n", + "616 35.0\n", + "186 35.0\n", + "263 35.0\n", + "288 36.0\n", + "525 36.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "ages_population2 = pd.read_csv('../data/ages_population2.csv')\n", + "ages_population2 = ages_population2.sort_values('observation')\n", + "ages_population2" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "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": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(ages_population2['observation'], color = 'green')" + ] + }, + { + "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": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It seems that we have a narrower range of ages. The mean value should be around 27 and the standard deviation around 2.\n" + ] + } + ], + "source": [ + "print(f'It seems that we have a narrower range of ages. The mean value should be around 27 and the standard deviation around 2.')" + ] + }, + { + "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": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 27.155\n", + "dtype: float64\n", + "observation 2.969814\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# your code here\n", + "print(ages_population2.mean())\n", + "print(ages_population2.std())\n", + "\n", + "# There you go" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nNothing to add.\\n'" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Nothing to add.\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": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "263 1.0\n", + "924 2.0\n", + "415 2.0\n", + "639 4.0\n", + "698 4.0\n", + ".. ...\n", + "76 75.0\n", + "323 75.0\n", + "12 76.0\n", + "937 77.0\n", + "218 77.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "ages_population3 = pd.read_csv('../data/ages_population3.csv')\n", + "ages_population3 = ages_population3.sort_values('observation')\n", + "ages_population3" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 8., 33., 78., 158., 187., 174., 133., 57., 117., 55.]),\n", + " array([ 1. , 8.6, 16.2, 23.8, 31.4, 39. , 46.6, 54.2, 61.8, 69.4, 77. ]),\n", + " )" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(ages_population3['observation'], color = 'green')" + ] + }, + { + "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": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 41.989\n", + "dtype: float64\n", + "observation 16.144706\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# your code here\n", + "print(ages_population3.mean())\n", + "print(ages_population3.std())" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We can see that the distribution now has two big centers. One around age 35 and the other around age 60.\n" + ] + } + ], + "source": [ + "print(f'We can see that the distribution now has two big centers. One around age 35 and the other around age 60.')" + ] + }, + { + "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": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30.0\n", + "40.0\n", + "53.0\n" + ] + } + ], + "source": [ + "# your code here\n", + "print(ages_population3.observation.quantile(.25))\n", + "print(ages_population3.observation.quantile(.5))\n", + "print(ages_population3.observation.quantile(.75))" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nNothing to add.\\n'" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Nothing to add.\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": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "64.0\n" + ] + }, + { + "data": { + "text/plain": [ + "32.0" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "import statistics\n", + "\n", + "# calculate a new percentile\n", + "print(ages_population3['observation'].quantile(0.85))\n", + "\n", + "# calculate the mode\n", + "statistics.mode(ages_population3['observation'])" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n'" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": 127, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n'" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/main.ipynb b/your-code/main.ipynb index a0a5b66..01254cb 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,11 +11,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "# Libraries" + "# Libraries\n", + "import random\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" ] }, { @@ -29,11 +33,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 3.566\n", + "dtype: float64" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "def dice_throw(n):\n", + " throws = random.choices([1,2,3,4,5,6], k=n)\n", + " return pd.DataFrame(throws)\n", + "\n", + "dice_throw(1000).mean()" ] }, { @@ -45,11 +66,302 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " R\n", + "0 6\n", + "1 3\n", + "2 4\n", + "3 1\n", + "4 1\n", + "5 1\n", + "6 2\n", + "7 6\n", + "8 1\n", + "9 2\n", + "10 2\n", + "11 2\n", + "12 1\n", + "13 2\n", + "14 5\n", + "15 3\n", + "16 4\n", + "17 4\n", + "18 6\n", + "19 4\n", + "20 6\n", + "21 4\n", + "22 1\n", + "23 1\n", + "24 4\n", + "25 6\n", + "26 2\n", + "27 4\n", + "28 2\n", + "29 2\n", + "30 4\n", + "31 6\n", + "32 2\n", + "33 1\n", + "34 3\n", + "35 5\n", + "36 4\n", + "37 3\n", + "38 3\n", + "39 5\n", + "40 1\n", + "41 6\n", + "42 6\n", + "43 6\n", + "44 2\n", + "45 4\n", + "46 5\n", + "47 3\n", + "48 1\n", + "49 3" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "df = (dice_throw(50))\n", + "df.columns=list('R')\n", + "df" ] }, { @@ -61,22 +373,87 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "# your code here\n", + "df = dice_throw(50).sort_values(by=0).reset_index()[0]\n", + "plt.plot(range(len(df)), df, color = 'green')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 8., 9., 5., 8., 6., 14.]),\n", + " array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5]),\n", + " )" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" + "plt.hist(df, bins= 6, range=(0.5, 6.5), color= 'green')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Histogram plot is showing the number of times each dice number ocurred.\n" + ] + } + ], + "source": [ + "print('Histogram plot is showing the number of times each dice number ocurred.')" ] }, { @@ -91,11 +468,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "def mean_calculation(x):\n", + " return sum(x) / len(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.74" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_df = mean_calculation(df)\n", + "mean_df" ] }, { @@ -107,11 +507,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "def freq_calc(x):\n", + " dic = {}\n", + " for n in x:\n", + " if n not in dic:\n", + " dic[n] = 0\n", + " else:\n", + " dic[n] += 1\n", + " return dic" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: 7, 2: 8, 3: 4, 4: 7, 5: 5, 6: 13}\n" + ] + } + ], + "source": [ + "print(freq_calc(df))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.5" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list1 = {1: 5, 2: 7, 3: 11, 4: 7, 5: 6, 6: 8}\n", + "list1_mu = sum(list1)/len(list1)\n", + "list1_mu\n", + "\n", + "# Not entirely sure if it's this what the exercise requests?" ] }, { @@ -124,11 +573,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "4.0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "def calc_median(x):\n", + " n = len(x)\n", + " index = n // 2\n", + " if n % 2:\n", + " return sorted(x)[index]\n", + " return sum(sorted(x)[index - 1:index + 1]) / 2 \n", + "\n", + "median_df = calc_median(df)\n", + "median_df" ] }, { @@ -140,11 +609,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "def quartile_calc(x):\n", + " middle = calc_median(x)\n", + " Q1 = calc_median(middle)[0]" ] }, { @@ -158,22 +630,141 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 roll value\n", + "0 0 0 1\n", + "47 47 47 1\n", + "56 56 56 1\n", + "9 9 9 1\n", + "73 73 73 1" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "roll_hundred = pd.read_csv('../data/roll_the_dice_hundred.csv')\n", + "roll_hundred = roll_hundred.sort_values('value')\n", + "roll_hundred.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Dice value for each throw')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" + "roll_hundred[['value']].plot(kind='bar', color = 'green')\n", + "plt.xticks([])\n", + "plt.ylabel('Dice Value')\n", + "plt.xlabel('Dice value for each throw')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conclusions? Values between 1 and 6 rolled 100x. It shows how many times the values between 1 and 6 were rolled but it's not very perceptive since we couldn't see the numbers so I decided to exclude them from the axis.\n" + ] + } + ], + "source": [ + "print(f\"Conclusions? Values between 1 and 6 rolled 100x. It shows how many times the values between 1 and 6 were rolled but it's not very perceptive since we couldn't see the numbers so I decided to exclude them from the axis.\")" ] }, { @@ -185,11 +776,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3.74" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "mean_calculation(roll_hundred['value'])" ] }, { @@ -201,11 +804,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 11, 2: 16, 3: 13, 4: 21, 5: 11, 6: 22}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "freq_calc(list(roll_hundred['value']))" ] }, { @@ -217,22 +832,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "# your code here\n", + "plt.hist(roll_hundred['value'], color ='green')\n", + "plt.vlines(roll_hundred['value'].mean(), ymin = 0, ymax = 30, linestyles =\"dotted\", colors =\"k\") \n", + "# It was actually quite nice to learn the dot line" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The median amount is 3.74, exactly where the line is showed in the plot\n" + ] + } + ], "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" + "print(\"The median amount is 3.74, exactly where the line is showed in the plot\")" ] }, { @@ -244,22 +891,185 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Unnamed: 0 roll value\n", + "564 564 564 1\n", + "922 922 922 1\n", + "560 560 560 1\n", + "213 213 213 1\n", + "214 214 214 1\n", + ".. ... ... ...\n", + "855 855 855 6\n", + "360 360 360 6\n", + "857 857 857 6\n", + "388 388 388 6\n", + "999 999 999 6\n", + "\n", + "[1000 rows x 3 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "roll_thousand = pd.read_csv('../data/roll_the_dice_thousand.csv')\n", + "roll_thousand = roll_thousand.sort_values('value')\n", + "roll_thousand" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "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": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" + "plt.hist(roll_thousand['value'], color = 'green')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "With this plot we can see that the distribution is more uniform. Basically since we have more samples the distribution changed and tends to be more similar. It makes perfectly sense.\n" + ] + } + ], + "source": [ + "print(\"With this plot we can see that the distribution is more uniform. Basically since we have more samples the distribution changed and tends to be more similar. It makes perfectly sense.\")" ] }, { @@ -274,11 +1084,110 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "489 1.0\n", + "209 1.0\n", + "301 2.0\n", + "451 2.0\n", + "338 4.0\n", + ".. ...\n", + "523 69.0\n", + "437 70.0\n", + "493 71.0\n", + "339 73.0\n", + "363 82.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "ages_population = pd.read_csv('../data/ages_population.csv')\n", + "ages_population = ages_population.sort_values('observation')\n", + "ages_population" ] }, { @@ -290,22 +1199,141 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "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": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "# your code here\n", + "plt.hist(ages_population['observation'], color = 'green')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "count 1000.0000\n", + "mean 36.5600\n", + "std 12.8165\n", + "min 1.0000\n", + "25% 28.0000\n", + "50% 37.0000\n", + "75% 45.0000\n", + "max 82.0000" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" + "ages_population.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The mean is 36.5 and the standard deviation is 12.8.\n" + ] + } + ], + "source": [ + "print(f'The mean is 36.5 and the standard deviation is 12.8.')" ] }, { @@ -317,11 +1345,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 36.56\n", + "dtype: float64\n", + "observation 12.8165\n", + "dtype: float64\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "print(ages_population.mean())\n", + "print(ages_population.std())" ] }, { @@ -333,12 +1374,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nNothing to add. Already did it previously.\\n'" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", - "your comments here\n", + "Nothing to add. Already did it previously.\n", "\"\"\"" ] }, @@ -351,22 +1403,161 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "327 19.0\n", + "998 19.0\n", + "96 19.0\n", + "897 20.0\n", + "272 20.0\n", + ".. ...\n", + "616 35.0\n", + "186 35.0\n", + "263 35.0\n", + "288 36.0\n", + "525 36.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "ages_population2 = pd.read_csv('../data/ages_population2.csv')\n", + "ages_population2 = ages_population2.sort_values('observation')\n", + "ages_population2" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "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": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ebu8aUMuG7+dljguA3wa+VVXzA8ZOah/PXLyqJMlPX6yT6T6W+9Y85cfyoJqn8lgeaBLv4m7kA/h1Fv9b9AxwrHvcBvws8ATwYvd8Xdf/jcBjPeNvY/FKim8DH5lwzSdZnM+72PY3S2tm8V36b3aP41NQ898Bz3btR4Et07CfB9Xbrfss8MdL+k/DPv4V4F+7mp+ju5Jnyo/lQTVP87E8qOapPJYHPbz9gCQ1qPlpGUl6PTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoP+F9nvTgxdES/8AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "\"\"\"\n", - "your comments here\n", - "\"\"\"" + "plt.hist(ages_population2['observation'], color = 'green')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It seems that we have a narrower range of ages. The mean value should be around 27 and the standard deviation around 2.\n" + ] + } + ], + "source": [ + "print(f'It seems that we have a narrower range of ages. The mean value should be around 27 and the standard deviation around 2.')" ] }, { @@ -381,11 +1572,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 27.155\n", + "dtype: float64\n", + "observation 2.969814\n", + "dtype: float64\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "print(ages_population2.mean())\n", + "print(ages_population2.std())\n", + "# There you go" ] }, { @@ -500,9 +1705,9 @@ ], "metadata": { "kernelspec": { - "display_name": "ironhack-3.7", + "display_name": "Python 3", "language": "python", - "name": "ironhack-3.7" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -514,7 +1719,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.3" } }, "nbformat": 4,