{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise Solutions\n", "\n", "## Exercise 6.1\n", "* Try to plot the Distance, Velocity, and Acceleration vs. Time - Usain Bolt's 100 meter olympic record (2008).\n", "* You should draw the following elements:\n", " * Plot distance, velocity and acceleration as a function of time.\n", " * Add a title.\n", " * Add legends to your axes.\n", " * Add a legend to the plot.\n", "* Change the styling of your Usain Bolt 100m plot, e.g.:\n", " * Draw some lines with dashes.\n", " * Add data point marker symbols on some lines.\n", " * Change the color of lines and marker symbols.\n", " \n", "Here is the raw data:\n", "\n", "```python\n", "time = [0, 1.85, 2.87, 3.78, 4.65, 5.50, 6.31, 7.14, 7.96, 8.79, 9.69]\n", "distance = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n", "velocity = [0, 5.41, 9.80, 10.99, 11.49, 11.76, 12.35, 12.05, 12.20, 12.05, 11.11]\n", "acceleration = [0, 2.92, 4.30, 1.31, 0.57, 0.32, 0.73, -0.36, 0.18, -0.18, -1.04]\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Solution in procedural mode" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "time = [0, 1.85, 2.87, 3.78, 4.65, 5.50, 6.31, 7.14, 7.96, 8.79, 9.69]\n", "distance = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n", "velocity = [0, 5.41, 9.80, 10.99, 11.49, 11.76, 12.35, 12.05, 12.20, 12.05, 11.11]\n", "acceleration = [0, 2.92, 4.30, 1.31, 0.57, 0.32, 0.73, -0.36, 0.18, -0.18, -1.04]\n", "\n", "# Adjust plot size to make it bigger than the default.\n", "plt.figure(figsize=(10, 5))\n", "\n", "# Create a new plot with the distance, velocity and acceleration vs. time data.\n", "plt.plot(time, distance, '--s', label='distance [m]', markersize=5)\n", "plt.plot(time, velocity, '-o', label='velocity [m/s]', color='teal', markersize=5)\n", "plt.plot(time, acceleration, '--', label='acceleration [m/s^2]', color='darkorange')\n", "\n", "# Add X and Y axis labels.\n", "plt.xlabel('Time [s]')\n", "plt.ylabel('Distance [m]')\n", "\n", "# Set a title and a legend for the figure.\n", "# Note the use of the \"fontsize\" and \"markerscale\" arguments to adjust the size of the legend.\n", "plt.title('Usain Bold 100m gold medal sprint - Beijing 2008')\n", "plt.legend(fontsize=12, markerscale=1.2)\n", "\n", "# Display the figure.\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Solution in object-oriented mode" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "time = [0, 1.85, 2.87, 3.78, 4.65, 5.50, 6.31, 7.14, 7.96, 8.79, 9.69]\n", "distance = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n", "velocity = [0, 5.41, 9.80, 10.99, 11.49, 11.76, 12.35, 12.05, 12.20, 12.05, 11.11]\n", "acceleration = [0, 2.92, 4.30, 1.31, 0.57, 0.32, 0.73, -0.36, 0.18, -0.18, -1.04]\n", "\n", "# Create a new figure object, adjust the size.\n", "fig = plt.figure(figsize=(10, 5))\n", "\n", "# Create an axis object (a subplot). Add the distance, velocity and acceleration vs. time data.\n", "ax = fig.add_subplot()\n", "ax.plot(time, distance, '--s', label='distance [m]', markersize=5)\n", "ax.plot(time, velocity, '-o', label='velocity [m/s]', color='teal', markersize=5)\n", "ax.plot(time, acceleration, '--', label='acceleration [m/s^2]', color='darkorange')\n", "\n", "# Add X and Y axis labels.\n", "ax.set_xlabel('Time [s]')\n", "ax.set_ylabel('Distance [m]')\n", "\n", "# Set a title and a legend for the figure.\n", "# Note the use of the \"fontsize\" and \"markerscale\" arguments to adjust the size of the legend.\n", "ax.set_title('Usain Bold 100m gold medal sprint - Beijing 2008')\n", "ax.legend(fontsize=12, markerscale=1.2)\n", "\n", "# Display the figure.\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "### Exercise 6.2:\n", "* Given the a lists of height, weight and gender below:\n", " * Create a scatter plot of height (y) as a function of weight (x).\n", " * Give different colors to females and males (see `gender` list below). \n", " Specifically, use \"teal\" color for males, and \"darkorange\" for females.\n", " * Add a title, and axes labels.\n", "\n", "```\n", "height = [150, 152, 152, 152, 152, 152, 152, 152, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 157, 157, 157, 157, 157, 157, 157, 157, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 165, 165, 165, 165, 165, 165, 165, 165, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 170, 170, 170, 170, 170, 170, 170, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 175, 175, 175, 175, 175, 175, 175, 178, 178, 178, 178, 178, 178, 178, 180, 180, 180, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 185, 188, 188, 191, 193]\n", "\n", "weight = [73, 72, 45, 59, 95, 62, 54, 68, 77, 92, 83, 56, 47, 77, 63, 81, 52, 110, 54, 59, 61, 90, 81, 63, 63, 59, 52, 77, 50, 63, 58, 72, 63, 65, 69, 59, 72, 67, 59, 59, 65, 77, 61, 111, 93, 74, 68, 59, 68, 50, 77, 74, 65, 72, 77, 86, 61, 65, 53, 60, 67, 99, 61, 70, 97, 54, 88, 99, 68, 70, 56, 86, 63, 65, 72, 81, 68, 79, 70, 81, 79, 85, 99, 77, 71, 81, 89, 72, 106, 56, 90, 81, 87, 81, 94, 83, 77, 94, 86, 95, 77, 77, 92, 77, 77, 131, 97, 101, 108, 99, 90, 91, 86, 113, 126, 104, 81, 77, 117, 63, 68, 101]\n", "\n", "gender = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n", "```\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np # This is only needed for the \"Bonus\" section at the end.\n", " # Not needed for the actual exercise.\n", "\n", "\n", "height = [150, 152, 152, 152, 152, 152, 152, 152, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 157, 157, 157, 157, 157, 157, 157, 157, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 165, 165, 165, 165, 165, 165, 165, 165, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 170, 170, 170, 170, 170, 170, 170, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 175, 175, 175, 175, 175, 175, 175, 178, 178, 178, 178, 178, 178, 178, 180, 180, 180, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 185, 188, 188, 191, 193]\n", "weight = [73, 72, 45, 59, 95, 62, 54, 68, 77, 92, 83, 56, 47, 77, 63, 81, 52, 110, 54, 59, 61, 90, 81, 63, 63, 59, 52, 77, 50, 63, 58, 72, 63, 65, 69, 59, 72, 67, 59, 59, 65, 77, 61, 111, 93, 74, 68, 59, 68, 50, 77, 74, 65, 72, 77, 86, 61, 65, 53, 60, 67, 99, 61, 70, 97, 54, 88, 99, 68, 70, 56, 86, 63, 65, 72, 81, 68, 79, 70, 81, 79, 85, 99, 77, 71, 81, 89, 72, 106, 56, 90, 81, 87, 81, 94, 83, 77, 94, 86, 95, 77, 77, 92, 77, 77, 131, 97, 101, 108, 99, 90, 91, 86, 113, 126, 104, 81, 77, 117, 63, 68, 101]\n", "gender = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n", "\n", "# Generate a marker color list, with \"teal\" color for males (gender == 1) \n", "# and \"darkorange\" for females (gender == 2)\n", "# Method 1: using a for loop:\n", "marker_color = []\n", "for x in gender:\n", " if x == 1:\n", " marker_color.append('teal')\n", " elif x == 2:\n", " marker_color.append('darkorange')\n", "\n", "# Method 2: using list comprehension:\n", "marker_color = [\"teal\" if x == 1 else \"darkorange\" for x in gender]\n", "\n", "# Plot height and weight as a scatter plot.\n", "plt.figure(figsize=(8, 5))\n", "plt.scatter(weight, height, c=marker_color)\n", "plt.title(\"Height vs Weight\")\n", "plt.xlabel('Weight [kg]')\n", "plt.ylabel('Height [cm]')\n", "\n", "\n", "# ************************************************************************************************\n", "# Bonus section: display centroids of male and female ditributions with \"vlines()\" and \"hlines()\".\n", "for gender_code in [1, 2]:\n", " sub_weight = [weight[index] for index, sex in enumerate(gender) if sex == gender_code]\n", " sub_height = [height[index] for index, sex in enumerate(gender) if sex == gender_code]\n", " mean_weight = np.mean(sub_weight)\n", " mean_height = np.mean(sub_height)\n", " plt.vlines(x=mean_weight, ymin=min(sub_height), ymax=max(sub_height), \n", " color=\"blue\" if gender_code == 1 else \"red\", linestyle=\"dashed\")\n", " plt.hlines(y=mean_height, xmin=min(sub_weight), xmax=max(sub_weight), \n", " color=\"blue\" if gender_code == 1 else \"red\", linestyle=\"dashed\")\n", "# ************************************************************************************************\n", " \n", "\n", "# Render plot.\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
\n", "\n", "# Additional Exercises\n", "\n", "## Exercise 6.3\n", "Given a dictionary with the popularity of each programming language, plot the popularity of the different languages in the best way." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "code_languages = {'Java': 17.2, 'Python': 22.2, \n", " 'PHP': 8.8, 'JavaScript':8, \n", " 'R':9.3, 'C++': 6.7}\n", "\n", "plt.bar(code_languages.keys(), code_languages.values(), color='green')\n", "plt.ylabel('Popularity')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "## Exercise 6.4\n", "Grab the most recent data on:\n", " * [number of hospitalised person](https://raw.githubusercontent.com/daenuprobst/covid19-cases-switzerland/master/covid19_hospitalized_switzerland_openzh.csv)\n", "\n", "1. download and read this data file as pandas `DataFrame`\n", "\n", "> import pandas as pd\n", "\n", "> datHospit = pd.read_csv(file.csv)\n", "\n", "2. replace all the nan values by 0\n", "> df.fillna(0)\n", "3. plot the distribution of one canton, to see how it was evolving througth time" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Date AG AI AR BE BL BS FR GE GL ... SO SZ \\\n", "0 2020-02-25 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "1 2020-02-26 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 ... 0.0 0.0 \n", "2 2020-02-27 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 ... 0.0 0.0 \n", "3 2020-02-28 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 ... 0.0 0.0 \n", "4 2020-02-29 0.0 0.0 0.0 0.0 1.0 0.0 0.0 3.0 0.0 ... 0.0 0.0 \n", "\n", " TG TI UR VD VS ZG ZH CH \n", "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 \n", "3 0.0 0.0 0.0 4.0 5.0 0.0 0.0 12.0 \n", "4 0.0 0.0 0.0 4.0 5.0 0.0 0.0 14.0 \n", "\n", "[5 rows x 28 columns]\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "\n", "datHospit = pd.read_csv(\"../solutions/covid19_hospitalized_switzerland_openzh.csv\")\n", "\n", "datHospit = datHospit.fillna(0)\n", "print(datHospit.head())\n", "\n", "fig = plt.figure(figsize=(20,10))\n", "\n", "plt.bar(datHospit['Date'],datHospit['VD'])\n", "\n", "plt.xticks(range(len(datHospit['VD'])), datHospit['Date'], rotation=45)\n", "plt.show()" ] } ], "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": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 4 }