{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "![](images/EscUpmPolit_p.gif \"UPM\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "# Course Notes for Learning Intelligent Systems" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## [Introduction to Visualization](00_Intro_Visualization.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Hierarchical charts\n", "Charts are used to show the parts of a variable.\n", "\n", "Most popular: pie chart, donut chart, and bar chart." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "
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total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
\n", "
" ], "text/plain": [ " total_bill tip sex smoker day time size\n", "0 16.99 1.01 Female No Sun Dinner 2\n", "1 10.34 1.66 Male No Sun Dinner 3\n", "2 21.01 3.50 Male No Sun Dinner 3" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "\n", "df = sns.load_dataset('tips')\n", "df.head(3)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We can represent them with **pandas** or **mathplotlib**.\n", "\n", "A pie plot is a proportional representation of the numerical data in a column." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.tip.groupby(df.sex, observed=False).sum().plot(kind='pie') # If observed False: show all values for categorical groupers" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "For other graph types, we can use *Dash plot.ly*.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Male', 'Female']\n", "[157, 87]\n" ] } ], "source": [ "import plotly as py\n", "import plotly.graph_objects as go\n", "\n", "labels = list(df['sex'].value_counts().index)\n", "values = list(df['sex'].value_counts().values)\n", "\n", "print(labels)\n", "print(values)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Donut chart\n", "fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "# References\n", "* [Data Preprocessing for Machine learning in Python, GeeksForGeeks](https://www.geeksforgeeks.org/data-preprocessing-machine-learning-python/)\n", "* [Plotly](https://plot.ly/python/pie-charts/) library" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Licence\n", "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n", "\n", "© Carlos A. Iglesias, Universidad Politécnica de Madrid." ] } ], "metadata": { "datacleaner": { "position": { "height": "790.33px", "left": "1413.33px", "right": "20px", "top": "50px", "width": "700px" }, "python": { "varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])" }, "window_display": false }, "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": "3.10.13" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 4 }