Visualization -- Learning to use pandas and DataFrames with matplotlib.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas and Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# same ol boilerplate, except no csv.DictReader\n",
    "from os import makedirs\n",
    "from os.path import join\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "# but now we got some pandas\n",
    "import pandas as pd\n",
    "\n",
    "DATA_FILENAME = join('data', 'climate', 'extracted', 'nasa-ghgases-co2-mean.csv')\n",
    "IMAGES_DIR = join('images', 'climate')\n",
    "makedirs(IMAGES_DIR, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing data via pandas readers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>co2_ppm_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1850</td>\n",
       "      <td>285.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1851</td>\n",
       "      <td>285.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1852</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1853</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1854</td>\n",
       "      <td>284.9</td>\n",
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      "text/plain": [
       "   year  co2_ppm_mean\n",
       "0  1850         285.2\n",
       "1  1851         285.1\n",
       "2  1852         285.0\n",
       "3  1853         285.0\n",
       "4  1854         284.9"
      ]
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "# Handy built-in output for notebooks\n",
    "df = pd.read_csv(DATA_FILENAME)\n",
    "## note the indexes\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>co2_ppm_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1852</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1853</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  co2_ppm_mean\n",
       "2  1852           285\n",
       "3  1853           285"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2:4]"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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