{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pandas Visualization" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['bmh',\n", " 'classic',\n", " 'dark_background',\n", " 'fivethirtyeight',\n", " 'ggplot',\n", " 'grayscale',\n", " 'seaborn-bright',\n", " 'seaborn-colorblind',\n", " 'seaborn-dark-palette',\n", " 'seaborn-dark',\n", " 'seaborn-darkgrid',\n", " 'seaborn-deep',\n", " 'seaborn-muted',\n", " 'seaborn-notebook',\n", " 'seaborn-paper',\n", " 'seaborn-pastel',\n", " 'seaborn-poster',\n", " 'seaborn-talk',\n", " 'seaborn-ticks',\n", " 'seaborn-white',\n", " 'seaborn-whitegrid',\n", " 'seaborn',\n", " '_classic_test']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# see the pre-defined styles provided.\n", "plt.style.available" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# use the 'seaborn-colorblind' style\n", "plt.style.use('seaborn-colorblind')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DataFrame.plot" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", " | A | \n", "B | \n", "C | \n", "
---|---|---|---|
2017-01-01 | \n", "-1.085631 | \n", "20.059291 | \n", "-20.230904 | \n", "
2017-01-02 | \n", "-0.088285 | \n", "21.803332 | \n", "-16.659325 | \n", "
2017-01-03 | \n", "0.194693 | \n", "20.835588 | \n", "-17.055481 | \n", "
2017-01-04 | \n", "-1.311601 | \n", "21.255156 | \n", "-17.093802 | \n", "
2017-01-05 | \n", "-1.890202 | \n", "21.462083 | \n", "-19.518638 | \n", "