{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Scatterplot with Categories" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A function to quickly produce a scatter plot colored by categories from a pandas `DataFrame` or NumPy `ndarray` object." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> from mlxtend.general_plotting import category_scatter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### References\n", "\n", "- -" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1 - Category Scatter from Pandas DataFrames" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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labelxy
0class110.08.04
1class110.57.30
2class28.35.50
3class28.15.90
4class33.53.50
5class33.85.10
\n", "
" ], "text/plain": [ " label x y\n", "0 class1 10.0 8.04\n", "1 class1 10.5 7.30\n", "2 class2 8.3 5.50\n", "3 class2 8.1 5.90\n", "4 class3 3.5 3.50\n", "5 class3 3.8 5.10" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from io import StringIO\n", "\n", "csvfile = \"\"\"label,x,y\n", "class1,10.0,8.04\n", "class1,10.5,7.30\n", "class2,8.3,5.5\n", "class2,8.1,5.9\n", "class3,3.5,3.5\n", "class3,3.8,5.1\"\"\"\n", "\n", "df = pd.read_csv(StringIO(csvfile))\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting the data where the categories are determined by the unique values in the label column `label_col`. The `x` and `y` values are simply the column names of the DataFrame that we want to plot." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from mlxtend.plotting import category_scatter\n", "\n", "fig = category_scatter(x='x', y='y', label_col='label', \n", " data=df, legend_loc='upper left')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2 - Category Scatter from NumPy Arrays" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 10. , 8.04],\n", " [ 1. , 10.5 , 7.3 ],\n", " [ 2. , 8.3 , 5.5 ],\n", " [ 2. , 8.1 , 5.9 ],\n", " [ 3. , 3.5 , 3.5 ],\n", " [ 3. , 3.8 , 5.1 ]])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "from io import BytesIO\n", "\n", "csvfile = \"\"\"1,10.0,8.04\n", "1,10.5,7.30\n", "2,8.3,5.5\n", "2,8.1,5.9\n", "3,3.5,3.5\n", "3,3.8,5.1\"\"\"\n", "\n", "ary = np.genfromtxt(BytesIO(csvfile.encode()), delimiter=',')\n", "ary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, pretending that the first column represents the labels, and the second and third column represent the `x` and `y` values, respectively." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from mlxtend.plotting import category_scatter\n", "\n", "fix = category_scatter(x=1, y=2, label_col=0, \n", " data=ary, legend_loc='upper left')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## API" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "## category_scatter\n", "\n", "*category_scatter(x, y, label_col, data, markers='sxo^v', colors=('blue', 'green', 'red', 'purple', 'gray', 'cyan'), alpha=0.7, markersize=20.0, legend_loc='best')*\n", "\n", "Scatter plot to plot categories in different colors/markerstyles.\n", "\n", "**Parameters**\n", "\n", "- `x` : str or int\n", "\n", " DataFrame column name of the x-axis values or\n", " integer for the numpy ndarray column index.\n", "\n", "- `y` : str\n", "\n", " DataFrame column name of the y-axis values or\n", " integer for the numpy ndarray column index\n", "\n", "- `data` : Pandas DataFrame object or NumPy ndarray.\n", "\n", "\n", "- `markers` : str\n", "\n", " Markers that are cycled through the label category.\n", "\n", "- `colors` : tuple\n", "\n", " Colors that are cycled through the label category.\n", "\n", "- `alpha` : float (default: 0.7)\n", "\n", " Parameter to control the transparency.\n", "\n", "- `markersize` : float (default` : 20.0)\n", "\n", " Parameter to control the marker size.\n", "\n", "- `legend_loc` : str (default: 'best')\n", "\n", " Location of the plot legend\n", " {best, upper left, upper right, lower left, lower right}\n", " No legend if legend_loc=False\n", "\n", "**Returns**\n", "\n", "- `fig` : matplotlig.pyplot figure object\n", "\n", "\n", "**Examples**\n", "\n", "For usage examples, please see\n", " [http://rasbt.github.io/mlxtend/user_guide/plotting/category_scatter/](http://rasbt.github.io/mlxtend/user_guide/plotting/category_scatter/)\n", "\n", "\n" ] } ], "source": [ "with open('../../api_modules/mlxtend.plotting/category_scatter.md', 'r') as f:\n", " print(f.read())" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.6.4" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }