{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sharing visualizations with others\n", "> This chapter shows you how to share your visualizations with others - how to save your figures as files, how to adjust their look and feel, and how to automate their creation based on input data. This is the Summary of lecture \"Introduction to Data Visualization with Matplotlib\", via datacamp.\n", "\n", "- toc: true \n", "- badges: true\n", "- comments: true\n", "- author: Chanseok Kang\n", "- categories: [Python, Datacamp, Visualization]\n", "- image: images/sports_weights.png" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "plt.rcParams['figure.figsize'] = (10, 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparing your figures to share with others\n", "- Guidelines for choosing plotting style\n", " - Dark backgrounds are usually less visible\n", " - If color is important, consider choosing colorblind-friendly options `\"seaborn-colorblind\"` or `\"tableau-colorblind10\"`\n", " - If you think that someone will want to print your gure, use less ink\n", " - If it will be printed in black-and-white, use the \"grayscale\" sty" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "seattle_weather = pd.read_csv('./dataset/seattle_weather.csv', index_col='DATE')\n", "austin_weather = pd.read_csv('./dataset/austin_weather.csv', index_col='DATE')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Use the \"ggplot\" style and create new Figure/Axes\n", "plt.style.use('ggplot')\n", "fig, ax = plt.subplots()\n", "ax.plot(seattle_weather[\"MONTH\"], seattle_weather[\"MLY-TAVG-NORMAL\"]);" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Use the \"Solarize_Light2\" style and create new Figure/Axes\n", "plt.style.use('Solarize_Light2')\n", "fig, ax = plt.subplots()\n", "ax.plot(austin_weather[\"MONTH\"], austin_weather[\"MLY-TAVG-NORMAL\"]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Automating figures from data\n", "- Why automate?\n", " - Ease and speed\n", " - Flexibility\n", " - Robustness\n", " - Reproducibility" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Unique values of a column\n", "One of the main strengths of Matplotlib is that it can be automated to adapt to the data that it receives as input. For example, if you receive data that has an unknown number of categories, you can still create a bar plot that has bars for each category.\n", "\n", "In this exercise and the next, you will be visualizing the weight of medalis in the 2016 summer Olympic Games again, from a dataset that has some unknown number of branches of sports in it. This will be loaded into memory as a Pandas DataFrame object called `summer_2016_medals`, which has a column called `\"Sport\"` that tells you to which branch of sport each row corresponds. There is also a `\"Weight\"` column that tells you the weight of each athlete." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "summer_2016_medals = pd.read_csv('./dataset/summer_2016_medals.csv', index_col=0)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Rowing' 'Taekwondo' 'Handball' 'Wrestling' 'Gymnastics' 'Swimming'\n", " 'Basketball' 'Boxing' 'Volleyball' 'Athletics' 'Rugby Sevens' 'Judo'\n", " 'Rhythmic Gymnastics' 'Weightlifting' 'Equestrianism' 'Badminton'\n", " 'Water Polo' 'Football' 'Fencing' 'Shooting' 'Sailing' 'Beach Volleyball'\n", " 'Canoeing' 'Hockey' 'Cycling' 'Tennis' 'Diving' 'Table Tennis'\n", " 'Triathlon' 'Archery' 'Synchronized Swimming' 'Modern Pentathlon'\n", " 'Trampolining' 'Golf']\n" ] } ], "source": [ "# Extract the \"Sport\" column\n", "sports_column = summer_2016_medals['Sport']\n", "\n", "# Find the unique values of the \"Sport\" column\n", "sports = sports_column.unique()\n", "\n", "# Print out the unique sports values\n", "print(sports)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Automate your visualization\n", "One of the main strengths of Matplotlib is that it can be automated to adapt to the data that it receives as input. For example, if you receive data that has an unknown number of categories, you can still create a bar plot that has bars for each category.\n", "\n", "This is what you will do in this exercise. You will be visualizing data about medal winners in the 2016 summer Olympic Games again, but this time you will have a dataset that has some unknown number of branches of sports in it. This will be loaded into memory as a Pandas DataFrame object called `summer_2016_medals`, which has a column called `\"Sport\"` that tells you to which branch of sport each row corresponds. There is also a `\"Weight\"` column that tells you the weight of each athlete." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "# Loop over the different sports branches\n", "for sport in sports:\n", " # Extract the rows only for this sport\n", " sport_df = summer_2016_medals[summer_2016_medals[\"Sport\"] == sport]\n", " # Add a bar for the \"Weight\" mean with std y error bar\n", " ax.bar(sport, sport_df[\"Weight\"].mean(), yerr=sport_df[\"Weight\"].std());\n", "\n", "ax.set_ylabel(\"Weight\");\n", "ax.set_xticklabels(sports, rotation=90);\n", "\n", "# Save the figure to file\n", "fig.savefig(\"../images/sports_weights.png\")" ] } ], "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }