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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Creating A Time Series Plot With Seaborne And Pandas\n",
      "\n",
      "- **Author:** [Chris Albon](http://www.chrisalbon.com/), [@ChrisAlbon](https://twitter.com/chrisalbon)\n",
      "- **Date:** -\n",
      "- **Repo:** [Python 3 code snippets for data science](https://github.com/chrisalbon/code_py)\n",
      "- **Note:**"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Preliminaries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import seaborn as sns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = {'date': ['2014-05-01 18:47:05.069722', '2014-05-01 18:47:05.119994', '2014-05-02 18:47:05.178768', '2014-05-02 18:47:05.230071', '2014-05-02 18:47:05.230071', '2014-05-02 18:47:05.280592', '2014-05-03 18:47:05.332662', '2014-05-03 18:47:05.385109', '2014-05-04 18:47:05.436523', '2014-05-04 18:47:05.486877'], \n",
      "        'deaths_regiment_1': [34, 43, 14, 15, 15, 14, 31, 25, 62, 41],\n",
      "        'deaths_regiment_2': [52, 66, 78, 15, 15, 5, 25, 25, 86, 1],\n",
      "        'deaths_regiment_3': [13, 73, 82, 58, 52, 87, 26, 5, 56, 75],\n",
      "        'deaths_regiment_4': [44, 75, 26, 15, 15, 14, 54, 25, 24, 72],\n",
      "        'deaths_regiment_5': [25, 24, 25, 15, 57, 68, 21, 27, 62, 5],\n",
      "        'deaths_regiment_6': [84, 84, 26, 15, 15, 14, 26, 25, 62, 24],\n",
      "        'deaths_regiment_7': [46, 57, 26, 15, 15, 14, 26, 25, 62, 41]}\n",
      "df = pd.DataFrame(data, columns = ['date', 'battle_deaths', 'deaths_regiment_1', 'deaths_regiment_2',\n",
      "                                   'deaths_regiment_3', 'deaths_regiment_4', 'deaths_regiment_5',\n",
      "                                   'deaths_regiment_6', 'deaths_regiment_7'])\n",
      "df = df.set_index(df.date)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Time Series Plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.tsplot([df.deaths_regiment_1, df.deaths_regiment_2, df.deaths_regiment_3, df.deaths_regiment_4,\n",
      "            df.deaths_regiment_5, df.deaths_regiment_6, df.deaths_regiment_7], color=\"indianred\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10bd2c350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6ciWxDRsDja1X38x6oyX9a40LISZm49mjhwNdV8nUKhYzUkpk6aETH+9qH5mW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\n2hjlxBUKhaKNUU5coVAo2pj/H5rU9iB2I0YIAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aba13d0>"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Time Series Splot With Confidence Interval Lines But No Lines"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.tsplot([df.deaths_regiment_1, df.deaths_regiment_2, df.deaths_regiment_3, df.deaths_regiment_4,\n",
      "            df.deaths_regiment_5, df.deaths_regiment_6, df.deaths_regiment_7], err_style=\"ci_bars\", interpolate=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10c9b7b50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10c9c7a50>"
       ]
      }
     ],
     "prompt_number": 68
    }
   ],
   "metadata": {}
  }
 ]
}