ch12-data-analysis-pandas-seaborn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Processing with Pandas and Seaborn\n",
    "- __Series__\n",
    "- __DataFrame__\n",
    "- __Time Series__\n",
    "- __Intro to Seaborn__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "#pd.set_option('display.mpl_style', 'default')\n",
    "plt.style.use('default')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series object\n",
    "\n",
    "* Feature: ability to index data with labels instead of integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     909976\n",
       "1    8615246\n",
       "2    2872086\n",
       "3    2273305\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([909976, 8615246, 2872086, 2273305])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dtype('int64'),\n",
       " RangeIndex(start=0, stop=4, step=1),\n",
       " array([ 909976, 8615246, 2872086, 2273305]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dtype, s.index, s.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stockholm     909976\n",
       "London       8615246\n",
       "Rome         2872086\n",
       "Paris        2273305\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index = [\"Stockholm\", \"London\", \"Rome\", \"Paris\"]\n",
    "s.name = \"Population\"\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = pd.Series([909976, 8615246, 2872086, 2273305], \n",
    "              index=[\"Stockholm\", \"London\", \"Rome\", \"Paris\"], name=\"Population\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8615246, 909976)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can access by index (label), or directly via attribute with same name\n",
    "s[\"London\"], s.Stockholm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paris    2273305\n",
       "Rome     2872086\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[[\"Paris\", \"Rome\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2572695.5, 3667653.25, 3399048.5005155364, 909976, 8615246)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive stats\n",
    "s.median(), s.mean(), s.std(), s.min(), s.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1932472.75, 2572695.5, 4307876.0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.quantile(q=0.25), s.quantile(q=0.5), s.quantile(q=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.000000e+00\n",
       "mean     3.667653e+06\n",
       "std      3.399049e+06\n",
       "min      9.099760e+05\n",
       "25%      1.932473e+06\n",
       "50%      2.572696e+06\n",
       "75%      4.307876e+06\n",
       "max      8.615246e+06\n",
       "Name: Population, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# attributes summary\n",
    "s.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization - line plot, bar plot, box plot, pic chart\n",
    "\n",
    "fig, axes = plt.subplots(1, 4, figsize=(12, 3))\n",
    "\n",
    "s.plot(ax=axes[0], kind='line', title=\"line\")\n",
    "s.plot(ax=axes[1], kind='bar', title=\"bar\")\n",
    "s.plot(ax=axes[2], kind='box', title=\"box\")\n",
    "s.plot(ax=axes[3], kind='pie', title=\"pie\")\n",
    "\n",
    "fig.tight_layout()\n",
    "#fig.savefig(\"ch12-series-plot.pdf\")\n",
    "#fig.savefig(\"ch12-series-plot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame object\n",
    "* Can be viewed as collection of Series objects with common index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>909976</td>\n",
       "      <td>8615246</td>\n",
       "      <td>2872086</td>\n",
       "      <td>2273305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>United kingdom</td>\n",
       "      <td>Italy</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0               1        2        3\n",
       "0  909976         8615246  2872086  2273305\n",
       "1  Sweden  United kingdom    Italy   France"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[909976, 8615246, 2872086, 2273305],\n",
    "                   [\"Sweden\", \"United kingdom\", \"Italy\", \"France\"]])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0               1\n",
       "0   909976          Sweden\n",
       "1  8615246  United kingdom\n",
       "2  2872086           Italy\n",
       "3  2273305          France"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[909976, \"Sweden\"],\n",
    "                   [8615246, \"United kingdom\"], \n",
    "                   [2872086, \"Italy\"],\n",
    "                   [2273305, \"France\"]])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Population           State\n",
       "Stockholm      909976          Sweden\n",
       "London        8615246  United kingdom\n",
       "Rome          2872086           Italy\n",
       "Paris         2273305          France"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# setup for labeled indexing, either columns or rows\n",
    "df.index = [\"Stockholm\", \"London\", \"Rome\", \"Paris\"]\n",
    "df.columns = [\"Population\", \"State\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Population           State\n",
       "Stockholm      909976          Sweden\n",
       "London        8615246  United kingdom\n",
       "Rome          2872086           Italy\n",
       "Paris         2273305          France"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# setup during initial creation\n",
    "df = pd.DataFrame([[909976, \"Sweden\"],\n",
    "                   [8615246, \"United kingdom\"], \n",
    "                   [2872086, \"Italy\"],\n",
    "                   [2273305, \"France\"]],\n",
    "                  index=[\"Stockholm\", \"London\", \"Rome\", \"Paris\"],\n",
    "                  columns=[\"Population\", \"State\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>909976</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <td>8615246</td>\n",
       "      <td>United kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Population           State\n",
       "Stockholm      909976          Sweden\n",
       "London        8615246  United kingdom\n",
       "Rome          2872086           Italy\n",
       "Paris         2273305          France"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternative: use dict objects to create a dataframe\n",
    "\n",
    "df = pd.DataFrame({\"Population\": [909976, 8615246, 2872086, 2273305],\n",
    "                   \"State\": [\"Sweden\", \"United kingdom\", \"Italy\", \"France\"]},\n",
    "                  index=[\"Stockholm\", \"London\", \"Rome\", \"Paris\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Stockholm', 'London', 'Rome', 'Paris'], dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Population', 'State'], dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[909976, 'Sweden'],\n",
       "       [8615246, 'United kingdom'],\n",
       "       [2872086, 'Italy'],\n",
       "       [2273305, 'France']], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stockholm     909976\n",
       "London       8615246\n",
       "Rome         2872086\n",
       "Paris        2273305\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stockholm     909976\n",
       "London       8615246\n",
       "Rome         2872086\n",
       "Paris        2273305\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Population\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extracting a column from a dataframe returns a series object\n",
    "type(df.Population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "909976"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Population.Stockholm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexing._LocIndexer"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access dataframe rows with the loc attribute.\n",
    "type(df.loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population    909976\n",
       "State         Sweden\n",
       "Name: Stockholm, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extracting column from df == new Series object\n",
    "df.loc[\"Stockholm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>2273305</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rome</th>\n",
       "      <td>2872086</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Population   State\n",
       "Paris     2273305  France\n",
       "Rome      2872086   Italy"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# passing list of row labels == new DataFrame\n",
    "df.loc[[\"Paris\", \"Rome\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paris    2273305\n",
       "Rome     2872086\n",
       "Name: Population, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting rows & cols simultaneously == new DataFrame, Series, or element value\n",
    "df.loc[[\"Paris\", \"Rome\"], \"Population\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population    3667653.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics - mean, std, median, min, max, ...\n",
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4 entries, Stockholm to Paris\n",
      "Data columns (total 2 columns):\n",
      "Population    4 non-null int64\n",
      "State         4 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 256.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Population     int64\n",
       "State         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datatypes for each column\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Larger datasets\n",
    "* example creates a dataframe from a CSV file.\n",
    "* head(), tail() = handy methods to show truncated dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>London[2]</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>8,615,246</td>\n",
       "      <td>1 June 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>3,437,916</td>\n",
       "      <td>31 May 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Spain</td>\n",
       "      <td>3,165,235</td>\n",
       "      <td>1 January 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2,872,086</td>\n",
       "      <td>30 September 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>2,273,305</td>\n",
       "      <td>1 January 2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank       City            State Population Date of census/estimate\n",
       "0     1  London[2]   United Kingdom  8,615,246             1 June 2014\n",
       "1     2     Berlin          Germany  3,437,916             31 May 2014\n",
       "2     3     Madrid            Spain  3,165,235          1 January 2014\n",
       "3     4       Rome            Italy  2,872,086       30 September 2014\n",
       "4     5      Paris           France  2,273,305          1 January 2013"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use head() to review first few records.\n",
    "df_pop = pd.read_csv(\"european_cities.csv\")\n",
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 105 entries, 0 to 104\n",
      "Data columns (total 5 columns):\n",
      "Rank                       105 non-null int64\n",
      "City                       105 non-null object\n",
      "State                      105 non-null object\n",
      "Population                 105 non-null object\n",
      "Date of census/estimate    105 non-null object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 4.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# additional args for read_csv()\n",
    "df_pop = pd.read_csv(\"european_cities.csv\", delimiter=\",\", encoding=\"utf-8\", header=0)\n",
    "df_pop.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Rank</th>\n",
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       "      <th>State</th>\n",
       "      <th>Population</th>\n",
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       "      <th>0</th>\n",
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       "      <td>London[2]</td>\n",
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       "      <td>1 June 2014</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>3,437,916</td>\n",
       "      <td>31 May 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Spain</td>\n",
       "      <td>3,165,235</td>\n",
       "      <td>1 January 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2,872,086</td>\n",
       "      <td>30 September 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>2,273,305</td>\n",
       "      <td>1 January 2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank       City            State Population Date of census/estimate\n",
       "0     1  London[2]   United Kingdom  8,615,246             1 June 2014\n",
       "1     2     Berlin          Germany  3,437,916             31 May 2014\n",
       "2     3     Madrid            Spain  3,165,235          1 January 2014\n",
       "3     4       Rome            Italy  2,872,086       30 September 2014\n",
       "4     5      Paris           France  2,273,305          1 January 2013"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' United Kingdom', ' Germany', ' Spain'], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply(): tool for transforming content in a column. returns new Series object.\n",
    "# remove commas from population fields & recast as integers\n",
    "\n",
    "df_pop[\"NumericPopulation\"] = df_pop.Population.apply(\n",
    "    lambda x: int(x.replace(\",\", \"\")))\n",
    "\n",
    "df_pop[\"State\"].values[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>8,615,246</td>\n",
       "      <td>1 June 2014</td>\n",
       "      <td>8615246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>3,437,916</td>\n",
       "      <td>31 May 2014</td>\n",
       "      <td>3437916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Spain</td>\n",
       "      <td>3,165,235</td>\n",
       "      <td>1 January 2014</td>\n",
       "      <td>3165235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2,872,086</td>\n",
       "      <td>30 September 2014</td>\n",
       "      <td>2872086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>2,273,305</td>\n",
       "      <td>1 January 2013</td>\n",
       "      <td>2273305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank       City           State Population Date of census/estimate  \\\n",
       "0     1  London[2]  United Kingdom  8,615,246             1 June 2014   \n",
       "1     2     Berlin         Germany  3,437,916             31 May 2014   \n",
       "2     3     Madrid           Spain  3,165,235          1 January 2014   \n",
       "3     4       Rome           Italy  2,872,086       30 September 2014   \n",
       "4     5      Paris          France  2,273,305          1 January 2013   \n",
       "\n",
       "   NumericPopulation  \n",
       "0            8615246  \n",
       "1            3437916  \n",
       "2            3165235  \n",
       "3            2872086  \n",
       "4            2273305  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove whitespace from state field\n",
    "\n",
    "df_pop[\"State\"] = df_pop[\"State\"].apply(\n",
    "    lambda x: x.strip())\n",
    "\n",
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                        int64\n",
       "City                       object\n",
       "State                      object\n",
       "Population                 object\n",
       "Date of census/estimate    object\n",
       "NumericPopulation           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# use city field as index, and sort\n",
    "df_pop2 = df_pop.set_index(\"City\")\n",
    "df_pop2 = df_pop2.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aarhus</th>\n",
       "      <td>92</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>326,676</td>\n",
       "      <td>1 October 2014</td>\n",
       "      <td>326676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alicante</th>\n",
       "      <td>86</td>\n",
       "      <td>Spain</td>\n",
       "      <td>334,678</td>\n",
       "      <td>1 January 2012</td>\n",
       "      <td>334678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amsterdam</th>\n",
       "      <td>23</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>813,562</td>\n",
       "      <td>31 May 2014</td>\n",
       "      <td>813562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antwerp</th>\n",
       "      <td>59</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>510,610</td>\n",
       "      <td>1 January 2014</td>\n",
       "      <td>510610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Athens</th>\n",
       "      <td>34</td>\n",
       "      <td>Greece</td>\n",
       "      <td>664,046</td>\n",
       "      <td>24 May 2011</td>\n",
       "      <td>664046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Rank        State Population Date of census/estimate  \\\n",
       "City                                                              \n",
       "Aarhus       92      Denmark    326,676          1 October 2014   \n",
       "Alicante     86        Spain    334,678          1 January 2012   \n",
       "Amsterdam    23  Netherlands    813,562             31 May 2014   \n",
       "Antwerp      59      Belgium    510,610          1 January 2014   \n",
       "Athens       34       Greece    664,046             24 May 2011   \n",
       "\n",
       "           NumericPopulation  \n",
       "City                          \n",
       "Aarhus                326676  \n",
       "Alicante              334678  \n",
       "Amsterdam             813562  \n",
       "Antwerp               510610  \n",
       "Athens                664046  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <th>Vienna</th>\n",
       "      <td>7</td>\n",
       "      <td>1,794,770</td>\n",
       "      <td>1 January 2015</td>\n",
       "      <td>1794770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Belgium</th>\n",
       "      <th>Antwerp</th>\n",
       "      <td>59</td>\n",
       "      <td>510,610</td>\n",
       "      <td>1 January 2014</td>\n",
       "      <td>510610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brussels[17]</th>\n",
       "      <td>16</td>\n",
       "      <td>1,175,831</td>\n",
       "      <td>1 January 2014</td>\n",
       "      <td>1175831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Bulgaria</th>\n",
       "      <th>Plovdiv</th>\n",
       "      <td>84</td>\n",
       "      <td>341,041</td>\n",
       "      <td>31 December 2013</td>\n",
       "      <td>341041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sofia</th>\n",
       "      <td>14</td>\n",
       "      <td>1,291,895</td>\n",
       "      <td>14 December 2014</td>\n",
       "      <td>1291895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Varna</th>\n",
       "      <td>85</td>\n",
       "      <td>335,819</td>\n",
       "      <td>31 December 2013</td>\n",
       "      <td>335819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <th>Zagreb</th>\n",
       "      <td>24</td>\n",
       "      <td>790,017</td>\n",
       "      <td>31 March 2011</td>\n",
       "      <td>790017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Rank Population Date of census/estimate  \\\n",
       "State    City                                                    \n",
       "Austria  Vienna           7  1,794,770          1 January 2015   \n",
       "Belgium  Antwerp         59    510,610          1 January 2014   \n",
       "         Brussels[17]    16  1,175,831          1 January 2014   \n",
       "Bulgaria Plovdiv         84    341,041        31 December 2013   \n",
       "         Sofia           14  1,291,895        14 December 2014   \n",
       "         Varna           85    335,819        31 December 2013   \n",
       "Croatia  Zagreb          24    790,017           31 March 2011   \n",
       "\n",
       "                       NumericPopulation  \n",
       "State    City                             \n",
       "Austria  Vienna                  1794770  \n",
       "Belgium  Antwerp                  510610  \n",
       "         Brussels[17]            1175831  \n",
       "Bulgaria Plovdiv                  341041  \n",
       "         Sofia                   1291895  \n",
       "         Varna                    335819  \n",
       "Croatia  Zagreb                   790017  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create hierarchical index via list of column names\n",
    "# sortlevel(0) = sort by state\n",
    "\n",
    "# sortlevel deprecated\n",
    "#df_pop3 = df_pop.set_index([\"State\", \"City\"]).sortlevel(0)\n",
    "\n",
    "df_pop3 = df_pop.set_index([\"State\", \"City\"]).sort_index(level=0)\n",
    "df_pop3.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gothenburg</th>\n",
       "      <td>53</td>\n",
       "      <td>528,014</td>\n",
       "      <td>31 March 2013</td>\n",
       "      <td>528014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malmö</th>\n",
       "      <td>102</td>\n",
       "      <td>309,105</td>\n",
       "      <td>31 March 2013</td>\n",
       "      <td>309105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>20</td>\n",
       "      <td>909,976</td>\n",
       "      <td>31 January 2014</td>\n",
       "      <td>909976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rank Population Date of census/estimate  NumericPopulation\n",
       "City                                                                  \n",
       "Gothenburg    53    528,014           31 March 2013             528014\n",
       "Malmö        102    309,105           31 March 2013             309105\n",
       "Stockholm     20    909,976         31 January 2014             909976"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# partial indexing\n",
    "df_pop3.loc[\"Sweden\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                                  53\n",
       "Population                       528,014\n",
       "Date of census/estimate    31 March 2013\n",
       "NumericPopulation                 528014\n",
       "Name: (Sweden, Gothenburg), dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# complete indexing with tuple of all hierarchical indices\n",
    "df_pop3.loc[(\"Sweden\", \"Gothenburg\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Date of census/estimate</th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nottingham</th>\n",
       "      <td>103</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>308,735</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>308735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wirral</th>\n",
       "      <td>97</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>320,229</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>320229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coventry</th>\n",
       "      <td>94</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>323,132</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>323132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wakefield</th>\n",
       "      <td>91</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>327,627</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>327627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leicester</th>\n",
       "      <td>87</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>331,606</td>\n",
       "      <td>30 June 2012</td>\n",
       "      <td>331606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rank           State Population Date of census/estimate  \\\n",
       "City                                                                  \n",
       "Nottingham   103  United Kingdom    308,735            30 June 2012   \n",
       "Wirral        97  United Kingdom    320,229            30 June 2012   \n",
       "Coventry      94  United Kingdom    323,132            30 June 2012   \n",
       "Wakefield     91  United Kingdom    327,627            30 June 2012   \n",
       "Leicester     87  United Kingdom    331,606            30 June 2012   \n",
       "\n",
       "            NumericPopulation  \n",
       "City                           \n",
       "Nottingham             308735  \n",
       "Wirral                 320229  \n",
       "Coventry               323132  \n",
       "Wakefield              327627  \n",
       "Leicester              331606  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort by column other than index\n",
    "df_pop.set_index(\"City\").sort_values(by=[\"State\", \"NumericPopulation\"], \n",
    "                              ascending=[False, True]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Germany                     19\n",
       "United Kingdom              16\n",
       "Spain                       13\n",
       "Poland                      10\n",
       "Italy                       10\n",
       "France                       5\n",
       "Netherlands                  4\n",
       "Romania                      3\n",
       "Bulgaria                     3\n",
       "Sweden                       3\n",
       "Lithuania                    2\n",
       "Denmark                      2\n",
       "Greece                       2\n",
       "Czech Republic               2\n",
       "Belgium                      2\n",
       "Portugal                     1\n",
       "Slovakia Slovak Republic     1\n",
       "Austria                      1\n",
       "Finland                      1\n",
       "Hungary                      1\n",
       "Estonia                      1\n",
       "Latvia                       1\n",
       "Croatia                      1\n",
       "Ireland                      1\n",
       "Name: # cities in top 105, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarizing categorical data\n",
    "city_counts = df_pop.State.value_counts()\n",
    "city_counts.name = \"# cities in top 105\"\n",
    "city_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# how much population of all cities within a state?\n",
    "df_pop3 = df_pop[[\"State\", \"City\", \"NumericPopulation\"]].set_index([\"State\", \"City\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_pop4 = df_pop3.sum(level=\"State\").sort_values(by=\"NumericPopulation\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>16011877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>15119548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>10041639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>8764067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>6267409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                NumericPopulation\n",
       "State                            \n",
       "United Kingdom           16011877\n",
       "Germany                  15119548\n",
       "Spain                    10041639\n",
       "Italy                     8764067\n",
       "Poland                    6267409"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NumericPopulation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>16011877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>15119548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>10041639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>8764067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>6267409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                NumericPopulation\n",
       "State                            \n",
       "United Kingdom           16011877\n",
       "Germany                  15119548\n",
       "Spain                    10041639\n",
       "Italy                     8764067\n",
       "Poland                    6267409"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternative using groupby\n",
    "\n",
    "df_pop5 = (df_pop.drop(\"Rank\", axis=1)\n",
    "                 .groupby(\"State\").sum()\n",
    "                 .sort_values(by=\"NumericPopulation\", ascending=False))\n",
    "df_pop5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "city_counts.plot(kind='barh', ax=ax1)\n",
    "ax1.set_xlabel(\"# cities in top 105\")\n",
    "df_pop5.NumericPopulation.plot(kind='barh', ax=ax2)\n",
    "ax2.set_xlabel(\"Total pop. in top 105 cities\")\n",
    "\n",
    "fig.tight_layout()\n",
    "#fig.savefig(\"ch12-state-city-counts-sum.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time series\n",
    "* **Series** & **DataFrame** can support time series structures. Pandas has **DatetimeIndex** & **PeriodIndex** indexers to help."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04',\n",
       "               '2015-01-05', '2015-01-06', '2015-01-07', '2015-01-08',\n",
       "               '2015-01-09', '2015-01-10', '2015-01-11', '2015-01-12',\n",
       "               '2015-01-13', '2015-01-14', '2015-01-15', '2015-01-16',\n",
       "               '2015-01-17', '2015-01-18', '2015-01-19', '2015-01-20',\n",
       "               '2015-01-21', '2015-01-22', '2015-01-23', '2015-01-24',\n",
       "               '2015-01-25', '2015-01-26', '2015-01-27', '2015-01-28',\n",
       "               '2015-01-29', '2015-01-30', '2015-01-31'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate date range. default frequency = daily.\n",
    "pd.date_range(\"2015-1-1\", periods=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00',\n",
       "               '2015-01-01 02:00:00', '2015-01-01 03:00:00',\n",
       "               '2015-01-01 04:00:00', '2015-01-01 05:00:00',\n",
       "               '2015-01-01 06:00:00', '2015-01-01 07:00:00',\n",
       "               '2015-01-01 08:00:00', '2015-01-01 09:00:00',\n",
       "               '2015-01-01 10:00:00', '2015-01-01 11:00:00',\n",
       "               '2015-01-01 12:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate datetime range (starting, ending, hourly)\n",
    "pd.date_range(\"2015-1-1 00:00\", \"2015-1-1 12:00\", freq=\"H\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-01    0\n",
       "2015-01-02    1\n",
       "2015-01-03    2\n",
       "2015-01-04    3\n",
       "2015-01-05    4\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return instance of DatetimeIndex\n",
    "ts1 = pd.Series(\n",
    "    np.arange(31), \n",
    "    index=pd.date_range(\"2015-1-1\", periods=31))\n",
    "\n",
    "ts1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1[\"2015-1-3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-01-03 00:00:00', freq='D')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts1.index[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2015, 1, 3)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# time series elements\n",
    "ts1.index[2].year, ts1.index[2].month, ts1.index[2].day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Timestamps = nanosecond resolution\n",
    "# datetimes  = microsecond resolution\n",
    "ts1.index[2].nanosecond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 1, 3, 0, 0)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conversion to datetime\n",
    "ts1.index[2].to_pydatetime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-01    0.522910\n",
       "2015-02-01    0.502548\n",
       "dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2 = pd.Series(np.random.rand(2), \n",
    "                index=[datetime.datetime(2015, 1, 1), \n",
    "                       datetime.datetime(2015, 2, 1)])\n",
    "ts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# PeriodIndex - use to define sequences of time spans\n",
    "periods = pd.PeriodIndex(\n",
    "    [pd.Period('2015-01'), \n",
    "     pd.Period('2015-02'), \n",
    "     pd.Period('2015-03')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01    0.028349\n",
       "2015-02    0.781381\n",
       "2015-03    0.173739\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts3 = pd.Series(np.random.rand(3), periods)\n",
    "ts3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2015-01', '2015-02', '2015-03'], dtype='period[M]', freq='M')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts3.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01    0.522910\n",
       "2015-02    0.502548\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2015-01', '2015-02', '2015-03', '2015-04', '2015-05', '2015-06',\n",
       "             '2015-07', '2015-08', '2015-09', '2015-10', '2015-11', '2015-12'],\n",
       "            dtype='period[M]', freq='M')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(\"2015-1-1\", periods=12, freq=\"M\").to_period()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temperature time series example\n",
    "* indoor & outdoor datasets in TSV (tab-separated value) format\n",
    "* each with 2 columns: UNIX timestamp, temperature (celsius)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1388530986\t4.380000\n",
      "1388531586\t4.250000\n",
      "1388532187\t4.190000\n",
      "1388532787\t4.060000\n",
      "1388533388\t4.060000\n"
     ]
    }
   ],
   "source": [
    "!head -n 5 temperature_outdoor_2014.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(\n",
    "    'temperature_outdoor_2014.tsv', \n",
    "    delimiter=\"\\t\", \n",
    "    names=[\"time\", \"outdoor\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\n",
    "    'temperature_indoor_2014.tsv', \n",
    "    delimiter=\"\\t\", \n",
    "    names=[\"time\", \"indoor\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>outdoor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1388530986</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1388531586</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1388532187</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1388532787</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1388533388</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  outdoor\n",
       "0  1388530986     4.38\n",
       "1  1388531586     4.25\n",
       "2  1388532187     4.19\n",
       "3  1388532787     4.06\n",
       "4  1388533388     4.06"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>indoor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1388530986</td>\n",
       "      <td>21.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1388531586</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1388532187</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1388532787</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1388533388</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  indoor\n",
       "0  1388530986   21.94\n",
       "1  1388531586   22.00\n",
       "2  1388532187   22.00\n",
       "3  1388532787   22.00\n",
       "4  1388533388   22.00"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Convert UNIX timestampes to date & time objects\n",
    "* Also localize timestamps to a given timezone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1.time = (pd.to_datetime(df1.time.values, unit=\"s\")\n",
    "              .tz_localize('UTC').tz_convert('Europe/Stockholm'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = df1.set_index(\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2.time = (pd.to_datetime(df2.time.values, unit=\"s\")\n",
    "              .tz_localize('UTC').tz_convert('Europe/Stockholm'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = df2.set_index(\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outdoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:03:06+01:00</th>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:13:06+01:00</th>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:23:07+01:00</th>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:33:07+01:00</th>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:43:08+01:00</th>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           outdoor\n",
       "time                              \n",
       "2014-01-01 00:03:06+01:00     4.38\n",
       "2014-01-01 00:13:06+01:00     4.25\n",
       "2014-01-01 00:23:07+01:00     4.19\n",
       "2014-01-01 00:33:07+01:00     4.06\n",
       "2014-01-01 00:43:08+01:00     4.06"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2014-01-01 00:03:06+0100', tz='Europe/Stockholm')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n",
    "df1.plot(ax=ax)\n",
    "df2.plot(ax=ax)\n",
    "\n",
    "fig.tight_layout()\n",
    "#fig.savefig(\"ch12-timeseries-temperature-2014.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 49548 entries, 2014-01-01 00:03:06+01:00 to 2014-12-30 23:56:35+01:00\n",
      "Data columns (total 1 columns):\n",
      "outdoor    49548 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 774.2 KB\n"
     ]
    }
   ],
   "source": [
    "# almost 50K datapoints across 2014\n",
    "df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Common use case: selecting/extracting portions of a dataset\n",
    "* In this case, January data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_jan = df1[\n",
    "    (df1.index > \"2014-1-1\") & \n",
    "    (df1.index < \"2014-2-1\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, ..., False, False, False])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.index < \"2014-2-1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 4452 entries, 2014-01-01 00:03:06+01:00 to 2014-01-31 23:56:58+01:00\n",
      "Data columns (total 1 columns):\n",
      "outdoor    4452 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 69.6 KB\n"
     ]
    }
   ],
   "source": [
    "df1_jan.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_jan = df2[\"2014-1-1\":\"2014-1-31\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n",
    "\n",
    "df1_jan.plot(ax=ax)\n",
    "df2_jan.plot(ax=ax)\n",
    "\n",
    "fig.tight_layout()\n",
    "#fig.savefig(\"ch12-timeseries-selected-month.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Grouping data (in this case, by month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1_month = df1.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_month[\"month\"] = df1_month.time.apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>outdoor</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01 00:03:06+01:00</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01 00:13:06+01:00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01 00:23:07+01:00</td>\n",
       "      <td>4.19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01 00:33:07+01:00</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01 00:43:08+01:00</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       time  outdoor  month\n",
       "0 2014-01-01 00:03:06+01:00     4.38      1\n",
       "1 2014-01-01 00:13:06+01:00     4.25      1\n",
       "2 2014-01-01 00:23:07+01:00     4.19      1\n",
       "3 2014-01-01 00:33:07+01:00     4.06      1\n",
       "4 2014-01-01 00:43:08+01:00     4.06      1"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_month.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Now group DataFrame by new month field & aggregate grouped values with mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1_month = df1_month.groupby(\"month\").aggregate(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_month = df2.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_month[\"month\"] = df2_month.time.apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2_month = df2_month.groupby(\"month\").aggregate(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_month = df1_month.join(df2_month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outdoor</th>\n",
       "      <th>indoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.776646</td>\n",
       "      <td>19.862590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.231613</td>\n",
       "      <td>20.231507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.615437</td>\n",
       "      <td>19.597748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        outdoor     indoor\n",
       "month                     \n",
       "1     -1.776646  19.862590\n",
       "2      2.231613  20.231507\n",
       "3      4.615437  19.597748"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_month = pd.concat([df.to_period(\"M\").groupby(level=0).mean() for df in [df1, df2]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outdoor</th>\n",
       "      <th>indoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01</th>\n",
       "      <td>-1.776646</td>\n",
       "      <td>19.862590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02</th>\n",
       "      <td>2.231613</td>\n",
       "      <td>20.231507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03</th>\n",
       "      <td>4.615437</td>\n",
       "      <td>19.597748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          outdoor     indoor\n",
       "time                        \n",
       "2014-01 -1.776646  19.862590\n",
       "2014-02  2.231613  20.231507\n",
       "2014-03  4.615437  19.597748"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "df_month.plot(kind='bar', ax=axes[0])\n",
    "df_month.plot(kind='box', ax=axes[1])\n",
    "\n",
    "fig.tight_layout()\n",
    "#fig.savefig(\"ch12-grouped-by-month.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outdoor</th>\n",
       "      <th>indoor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01</th>\n",
       "      <td>-1.776646</td>\n",
       "      <td>19.862590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02</th>\n",
       "      <td>2.231613</td>\n",
       "      <td>20.231507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03</th>\n",
       "      <td>4.615437</td>\n",
       "      <td>19.597748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04</th>\n",
       "      <td>8.105193</td>\n",
       "      <td>22.149754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05</th>\n",
       "      <td>12.261396</td>\n",
       "      <td>26.332160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06</th>\n",
       "      <td>15.586955</td>\n",
       "      <td>28.687491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07</th>\n",
       "      <td>20.780314</td>\n",
       "      <td>30.605333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08</th>\n",
       "      <td>16.494823</td>\n",
       "      <td>28.099068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09</th>\n",
       "      <td>12.823905</td>\n",
       "      <td>26.950366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10</th>\n",
       "      <td>9.352000</td>\n",
       "      <td>23.379460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11</th>\n",
       "      <td>4.992142</td>\n",
       "      <td>20.610365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12</th>\n",
       "      <td>-0.058940</td>\n",
       "      <td>16.465674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           outdoor     indoor\n",
       "time                         \n",
       "2014-01  -1.776646  19.862590\n",
       "2014-02   2.231613  20.231507\n",
       "2014-03   4.615437  19.597748\n",
       "2014-04   8.105193  22.149754\n",
       "2014-05  12.261396  26.332160\n",
       "2014-06  15.586955  28.687491\n",
       "2014-07  20.780314  30.605333\n",
       "2014-08  16.494823  28.099068\n",
       "2014-09  12.823905  26.950366\n",
       "2014-10   9.352000  23.379460\n",
       "2014-11   4.992142  20.610365\n",
       "2014-12  -0.058940  16.465674"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 49548 entries, 2014-01-01 00:03:06+01:00 to 2014-12-30 23:56:35+01:00\n",
      "Data columns (total 1 columns):\n",
      "outdoor    49548 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 774.2 KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# resample(): 1st argument = string that represents a new data period.\n",
    "# ex: \"H\" = hourly\n",
    "\n",
    "df1_hour = df1.resample(\"H\").mean()\n",
    "df1_hour.columns = [\"outdoor (hourly avg.)\"]\n",
    "\n",
    "df1_day = df1.resample(\"D\").mean()\n",
    "df1_day.columns = [\"outdoor (daily avg.)\"]\n",
    "\n",
    "df1_week = df1.resample(\"W\").mean()\n",
    "df1_week.columns = [\"outdoor (weekly avg.)\"]\n",
    "\n",
    "df1_month = df1.resample(\"M\").mean()\n",
    "df1_month.columns = [\"outdoor (monthly avg.)\"]\n",
    "\n",
    "df_diff = (df1.resample(\"D\").mean().outdoor - df2.resample(\"D\").mean().indoor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6))\n",
    "\n",
    "df1_hour.plot(ax=ax1, alpha=0.25)\n",
    "df1_day.plot(ax=ax1)\n",
    "df1_week.plot(ax=ax1)\n",
    "df1_month.plot(ax=ax1)\n",
    "\n",
    "df_diff.plot(ax=ax2)\n",
    "ax2.set_title(\"temperature difference between outdoor and indoor\")\n",
    "\n",
    "fig.tight_layout()\n",
    "#fig.savefig(\"ch12-timeseries-resampled.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Resampling to 5-minute frequency with three aggregation methods\n",
    "* mean, ffill (forward fill), bfill (backward fill)\n",
    "* Some values are not filled (NaN) based on aggregation method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>None</th>\n",
       "      <th>ffill</th>\n",
       "      <th>bfill</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:00:00+01:00</th>\n",
       "      <td>4.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:05:00+01:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.38</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:10:00+01:00</th>\n",
       "      <td>4.25</td>\n",
       "      <td>4.38</td>\n",
       "      <td>4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:15:00+01:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:20:00+01:00</th>\n",
       "      <td>4.19</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           None  ffill  bfill\n",
       "time                                         \n",
       "2014-01-01 00:00:00+01:00  4.38    NaN   4.38\n",
       "2014-01-01 00:05:00+01:00   NaN   4.38   4.25\n",
       "2014-01-01 00:10:00+01:00  4.25   4.38   4.25\n",
       "2014-01-01 00:15:00+01:00   NaN   4.25   4.19\n",
       "2014-01-01 00:20:00+01:00  4.19   4.25   4.19"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(\n",
    "    [df1.resample(\"5min\").mean().rename(columns={\"outdoor\": 'None'}),\n",
    "     df1.resample(\"5min\").ffill().rename(columns={\"outdoor\": 'ffill'}),\n",
    "     df1.resample(\"5min\").bfill().rename(columns={\"outdoor\": 'bfill'})],\n",
    "    axis=1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seaborn statistical visualization library\n",
    "- Built on top of Matplotlib\n",
    "- Distribution plots, kernel density plots, joint distributions, factor plots, heatmaps, facet plots, many more.\n",
    "- Much better color usage (aesthetics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(\n",
    "    'temperature_outdoor_2014.tsv', \n",
    "    delimiter=\"\\t\", \n",
    "    names=[\"time\", \"outdoor\"])\n",
    "\n",
    "df1.time = pd.to_datetime(\n",
    "    df1.time.values, unit=\"s\").tz_localize('UTC').tz_convert('Europe/Stockholm')\n",
    "\n",
    "df1 = df1.set_index(\"time\").resample(\"10min\").mean()\n",
    "\n",
    "df2 = pd.read_csv(\n",
    "    'temperature_indoor_2014.tsv', \n",
    "    delimiter=\"\\t\", \n",
    "    names=[\"time\", \"indoor\"])\n",
    "\n",
    "df2.time = pd.to_datetime(\n",
    "    df2.time.values, unit=\"s\").tz_localize('UTC').tz_convert('Europe/Stockholm')\n",
    "\n",
    "df2 = df2.set_index(\"time\").resample(\"10min\").mean()\n",
    "\n",
    "df_temp = pd.concat([df1, df2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
    "df_temp.resample(\"D\").plot(y=[\"outdoor\", \"indoor\"], ax=ax)\n",
    "fig.tight_layout()\n",
    "#fig.savefig(\"ch12-seaborn-plot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribuion (histogram) plots, outdoor & indoor temp data\n",
    "sns.distplot(df_temp.to_period(\"M\")[\"outdoor\"][\"2014-04\"].dropna().values, bins=50);\n",
    "sns.distplot(df_temp.to_period(\"M\")[\"indoor\"][\"2014-04\"].dropna().values, bins=50);\n",
    "\n",
    "plt.savefig(\"ch12-seaborn-distplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method SeriesGroupBy.describe of <pandas.core.groupby.groupby.SeriesGroupBy object at 0x7f354ee2eac8>>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.resample(\"H\")[\"outdoor\"].describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n",
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# joint distribution plot - indoor/outdoor temp correlation, resampled to hourly averages\n",
    "# TODO: resolve \"non-tuple sequence for multi-d indexing is deprecated\" warning\n",
    "\n",
    "with sns.axes_style(\"white\"):\n",
    "    sns.jointplot(df_temp.resample(\"H\").mean()[\"outdoor\"].values,\n",
    "                  df_temp.resample(\"H\").mean()[\"indoor\"].values, kind=\"hex\");\n",
    "    \n",
    "#plt.savefig(\"ch12-seaborn-jointplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kernel Density Estimation (KDE) plot\n",
    "# TODO: resolve \"non-tuple sequence for multi-d indexing is deprecated\" warning\n",
    "\n",
    "sns.kdeplot(df_temp.resample(\"H\").mean()[\"outdoor\"].dropna().values,\n",
    "            df_temp.resample(\"H\").mean()[\"indoor\"].dropna().values, shade=False);\n",
    "\n",
    "#plt.savefig(\"ch12-seaborn-kdeplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# category data helpers:\n",
    "# box plot (viz for descriptive stats: min, max, median, quartiles)\n",
    "# violin plot (box plot with KDE data shown by box plot width)\n",
    "# TODO: resolve \"non-tuple sequence for multi-d indexing is deprecated\" warning\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "sns.boxplot(df_temp.dropna(), ax=ax1, palette=\"pastel\")\n",
    "sns.violinplot(df_temp.dropna(), ax=ax2, palette=\"pastel\")\n",
    "\n",
    "fig.tight_layout()\n",
    "#fig.savefig(\"ch12-seaborn-boxplot-violinplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bjpcjp/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#violin plot: outdoor temp data partitioned by month\n",
    "# shows distribution of temp for each month\n",
    "\n",
    "sns.violinplot(\n",
    "    x=df_temp.dropna().index.month, \n",
    "    y=df_temp.dropna().outdoor, \n",
    "    color=\"skyblue\");\n",
    "\n",
    "#plt.savefig(\"ch12-seaborn-violinplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# heatmaps: useful for categorical data with large # of categories.\n",
    "\n",
    "df_temp[\"month\"] = df_temp.index.month\n",
    "df_temp[\"hour\"] = df_temp.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outdoor</th>\n",
       "      <th>indoor</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:00:00+01:00</th>\n",
       "      <td>4.38</td>\n",
       "      <td>21.94</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:10:00+01:00</th>\n",
       "      <td>4.25</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:20:00+01:00</th>\n",
       "      <td>4.19</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:30:00+01:00</th>\n",
       "      <td>4.06</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:40:00+01:00</th>\n",
       "      <td>4.06</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           outdoor  indoor  month  hour\n",
       "time                                                   \n",
       "2014-01-01 00:00:00+01:00     4.38   21.94      1     0\n",
       "2014-01-01 00:10:00+01:00     4.25   22.00      1     0\n",
       "2014-01-01 00:20:00+01:00     4.19   22.00      1     0\n",
       "2014-01-01 00:30:00+01:00     4.06   22.00      1     0\n",
       "2014-01-01 00:40:00+01:00     4.06   22.00      1     0"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>hour</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>1</th>\n",
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       "      <td>-1.826649</td>\n",
       "      <td>-1.879086</td>\n",
       "      <td>-1.922527</td>\n",
       "      <td>-1.968065</td>\n",
       "      <td>-2.020914</td>\n",
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       "      <td>-2.101774</td>\n",
       "      <td>-2.001022</td>\n",
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       "      <td>-1.457849</td>\n",
       "      <td>-1.696935</td>\n",
       "      <td>-1.814194</td>\n",
       "      <td>-1.812258</td>\n",
       "      <td>-1.853297</td>\n",
       "      <td>-1.898432</td>\n",
       "      <td>-1.839730</td>\n",
       "      <td>-1.806486</td>\n",
       "      <td>-1.854462</td>\n",
       "      <td>-1.890811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.613690</td>\n",
       "      <td>1.521190</td>\n",
       "      <td>1.479405</td>\n",
       "      <td>1.464371</td>\n",
       "      <td>1.506407</td>\n",
       "      <td>1.485595</td>\n",
       "      <td>1.499167</td>\n",
       "      <td>1.516946</td>\n",
       "      <td>1.669226</td>\n",
       "      <td>2.067725</td>\n",
       "      <td>...</td>\n",
       "      <td>3.573593</td>\n",
       "      <td>3.360741</td>\n",
       "      <td>2.939390</td>\n",
       "      <td>2.501607</td>\n",
       "      <td>2.357425</td>\n",
       "      <td>2.236190</td>\n",
       "      <td>2.204458</td>\n",
       "      <td>2.137619</td>\n",
       "      <td>2.024671</td>\n",
       "      <td>1.896190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.192366</td>\n",
       "      <td>2.866774</td>\n",
       "      <td>2.628000</td>\n",
       "      <td>2.524140</td>\n",
       "      <td>2.384140</td>\n",
       "      <td>2.235538</td>\n",
       "      <td>2.243387</td>\n",
       "      <td>2.622258</td>\n",
       "      <td>3.419301</td>\n",
       "      <td>4.466290</td>\n",
       "      <td>...</td>\n",
       "      <td>7.790323</td>\n",
       "      <td>7.930914</td>\n",
       "      <td>7.595892</td>\n",
       "      <td>6.770914</td>\n",
       "      <td>5.731508</td>\n",
       "      <td>4.983784</td>\n",
       "      <td>4.437419</td>\n",
       "      <td>4.022312</td>\n",
       "      <td>3.657903</td>\n",
       "      <td>3.407258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.832738</td>\n",
       "      <td>5.336012</td>\n",
       "      <td>4.926667</td>\n",
       "      <td>4.597059</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>4.109769</td>\n",
       "      <td>4.123699</td>\n",
       "      <td>4.741437</td>\n",
       "      <td>5.878035</td>\n",
       "      <td>7.272299</td>\n",
       "      <td>...</td>\n",
       "      <td>12.175556</td>\n",
       "      <td>12.500059</td>\n",
       "      <td>12.494483</td>\n",
       "      <td>12.361156</td>\n",
       "      <td>11.989240</td>\n",
       "      <td>10.454881</td>\n",
       "      <td>8.857619</td>\n",
       "      <td>7.712619</td>\n",
       "      <td>6.974762</td>\n",
       "      <td>6.293512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.792204</td>\n",
       "      <td>9.369351</td>\n",
       "      <td>9.009839</td>\n",
       "      <td>8.670914</td>\n",
       "      <td>8.463387</td>\n",
       "      <td>8.446919</td>\n",
       "      <td>8.772324</td>\n",
       "      <td>9.562742</td>\n",
       "      <td>10.723622</td>\n",
       "      <td>12.047717</td>\n",
       "      <td>...</td>\n",
       "      <td>15.542581</td>\n",
       "      <td>15.744624</td>\n",
       "      <td>15.784839</td>\n",
       "      <td>15.799570</td>\n",
       "      <td>17.009892</td>\n",
       "      <td>15.685161</td>\n",
       "      <td>13.632796</td>\n",
       "      <td>12.216290</td>\n",
       "      <td>11.291237</td>\n",
       "      <td>10.622849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.209556</td>\n",
       "      <td>12.792889</td>\n",
       "      <td>12.382889</td>\n",
       "      <td>11.967889</td>\n",
       "      <td>11.735778</td>\n",
       "      <td>11.886667</td>\n",
       "      <td>12.503778</td>\n",
       "      <td>13.338167</td>\n",
       "      <td>14.343444</td>\n",
       "      <td>15.665475</td>\n",
       "      <td>...</td>\n",
       "      <td>18.630556</td>\n",
       "      <td>18.866292</td>\n",
       "      <td>18.680611</td>\n",
       "      <td>18.529832</td>\n",
       "      <td>20.057877</td>\n",
       "      <td>18.853389</td>\n",
       "      <td>16.969777</td>\n",
       "      <td>15.675111</td>\n",
       "      <td>14.658778</td>\n",
       "      <td>13.898167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17.956344</td>\n",
       "      <td>17.348641</td>\n",
       "      <td>16.793152</td>\n",
       "      <td>16.309892</td>\n",
       "      <td>16.001559</td>\n",
       "      <td>15.986774</td>\n",
       "      <td>16.506613</td>\n",
       "      <td>17.478226</td>\n",
       "      <td>18.850054</td>\n",
       "      <td>20.533763</td>\n",
       "      <td>...</td>\n",
       "      <td>24.598441</td>\n",
       "      <td>25.030000</td>\n",
       "      <td>24.869194</td>\n",
       "      <td>24.764409</td>\n",
       "      <td>26.155161</td>\n",
       "      <td>24.896505</td>\n",
       "      <td>22.550269</td>\n",
       "      <td>20.882649</td>\n",
       "      <td>19.699022</td>\n",
       "      <td>18.822634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.498205</td>\n",
       "      <td>13.960128</td>\n",
       "      <td>13.555128</td>\n",
       "      <td>12.995641</td>\n",
       "      <td>12.651410</td>\n",
       "      <td>12.485974</td>\n",
       "      <td>12.680130</td>\n",
       "      <td>13.403506</td>\n",
       "      <td>14.578780</td>\n",
       "      <td>16.170833</td>\n",
       "      <td>...</td>\n",
       "      <td>20.473810</td>\n",
       "      <td>20.292381</td>\n",
       "      <td>20.328795</td>\n",
       "      <td>19.642436</td>\n",
       "      <td>19.373846</td>\n",
       "      <td>18.713462</td>\n",
       "      <td>17.034872</td>\n",
       "      <td>15.843590</td>\n",
       "      <td>15.146154</td>\n",
       "      <td>14.596667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.133000</td>\n",
       "      <td>10.725667</td>\n",
       "      <td>10.362444</td>\n",
       "      <td>9.976833</td>\n",
       "      <td>9.729333</td>\n",
       "      <td>9.503944</td>\n",
       "      <td>9.357500</td>\n",
       "      <td>9.689778</td>\n",
       "      <td>10.600778</td>\n",
       "      <td>11.829106</td>\n",
       "      <td>...</td>\n",
       "      <td>16.336983</td>\n",
       "      <td>16.828268</td>\n",
       "      <td>17.031056</td>\n",
       "      <td>16.786983</td>\n",
       "      <td>15.853556</td>\n",
       "      <td>14.534637</td>\n",
       "      <td>13.350444</td>\n",
       "      <td>12.545278</td>\n",
       "      <td>11.954190</td>\n",
       "      <td>11.399056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.602011</td>\n",
       "      <td>8.490598</td>\n",
       "      <td>8.382486</td>\n",
       "      <td>8.257097</td>\n",
       "      <td>8.166774</td>\n",
       "      <td>8.140054</td>\n",
       "      <td>8.140161</td>\n",
       "      <td>8.148333</td>\n",
       "      <td>8.410914</td>\n",
       "      <td>9.054946</td>\n",
       "      <td>...</td>\n",
       "      <td>11.330323</td>\n",
       "      <td>11.189194</td>\n",
       "      <td>10.836865</td>\n",
       "      <td>10.361568</td>\n",
       "      <td>9.781022</td>\n",
       "      <td>9.373441</td>\n",
       "      <td>9.134570</td>\n",
       "      <td>8.956505</td>\n",
       "      <td>8.820270</td>\n",
       "      <td>8.623297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.847111</td>\n",
       "      <td>4.765922</td>\n",
       "      <td>4.815642</td>\n",
       "      <td>4.773240</td>\n",
       "      <td>4.809611</td>\n",
       "      <td>4.785833</td>\n",
       "      <td>4.741222</td>\n",
       "      <td>4.739778</td>\n",
       "      <td>4.794500</td>\n",
       "      <td>4.965389</td>\n",
       "      <td>...</td>\n",
       "      <td>5.526034</td>\n",
       "      <td>5.342753</td>\n",
       "      <td>5.081250</td>\n",
       "      <td>5.056629</td>\n",
       "      <td>4.959106</td>\n",
       "      <td>4.868111</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>4.774389</td>\n",
       "      <td>4.720722</td>\n",
       "      <td>4.699722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.366369</td>\n",
       "      <td>-0.390556</td>\n",
       "      <td>-0.447374</td>\n",
       "      <td>-0.370111</td>\n",
       "      <td>-0.353128</td>\n",
       "      <td>-0.319832</td>\n",
       "      <td>-0.358667</td>\n",
       "      <td>-0.410278</td>\n",
       "      <td>-0.483167</td>\n",
       "      <td>-0.344667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.738944</td>\n",
       "      <td>0.367056</td>\n",
       "      <td>0.152167</td>\n",
       "      <td>-0.106111</td>\n",
       "      <td>-0.182500</td>\n",
       "      <td>-0.244167</td>\n",
       "      <td>-0.290000</td>\n",
       "      <td>-0.305333</td>\n",
       "      <td>-0.302778</td>\n",
       "      <td>-0.325642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "hour          0          1          2          3          4          5   \\\n",
       "month                                                                     \n",
       "1      -1.692312  -1.750162  -1.826649  -1.879086  -1.922527  -1.968065   \n",
       "2       1.613690   1.521190   1.479405   1.464371   1.506407   1.485595   \n",
       "3       3.192366   2.866774   2.628000   2.524140   2.384140   2.235538   \n",
       "4       5.832738   5.336012   4.926667   4.597059   4.380000   4.109769   \n",
       "5       9.792204   9.369351   9.009839   8.670914   8.463387   8.446919   \n",
       "6      13.209556  12.792889  12.382889  11.967889  11.735778  11.886667   \n",
       "7      17.956344  17.348641  16.793152  16.309892  16.001559  15.986774   \n",
       "8      14.498205  13.960128  13.555128  12.995641  12.651410  12.485974   \n",
       "9      11.133000  10.725667  10.362444   9.976833   9.729333   9.503944   \n",
       "10      8.602011   8.490598   8.382486   8.257097   8.166774   8.140054   \n",
       "11      4.847111   4.765922   4.815642   4.773240   4.809611   4.785833   \n",
       "12     -0.366369  -0.390556  -0.447374  -0.370111  -0.353128  -0.319832   \n",
       "\n",
       "hour          6          7          8          9     ...             14  \\\n",
       "month                                                ...                  \n",
       "1      -2.020914  -2.035806  -2.101774  -2.001022    ...      -1.457849   \n",
       "2       1.499167   1.516946   1.669226   2.067725    ...       3.573593   \n",
       "3       2.243387   2.622258   3.419301   4.466290    ...       7.790323   \n",
       "4       4.123699   4.741437   5.878035   7.272299    ...      12.175556   \n",
       "5       8.772324   9.562742  10.723622  12.047717    ...      15.542581   \n",
       "6      12.503778  13.338167  14.343444  15.665475    ...      18.630556   \n",
       "7      16.506613  17.478226  18.850054  20.533763    ...      24.598441   \n",
       "8      12.680130  13.403506  14.578780  16.170833    ...      20.473810   \n",
       "9       9.357500   9.689778  10.600778  11.829106    ...      16.336983   \n",
       "10      8.140161   8.148333   8.410914   9.054946    ...      11.330323   \n",
       "11      4.741222   4.739778   4.794500   4.965389    ...       5.526034   \n",
       "12     -0.358667  -0.410278  -0.483167  -0.344667    ...       0.738944   \n",
       "\n",
       "hour          15         16         17         18         19         20  \\\n",
       "month                                                                     \n",
       "1      -1.696935  -1.814194  -1.812258  -1.853297  -1.898432  -1.839730   \n",
       "2       3.360741   2.939390   2.501607   2.357425   2.236190   2.204458   \n",
       "3       7.930914   7.595892   6.770914   5.731508   4.983784   4.437419   \n",
       "4      12.500059  12.494483  12.361156  11.989240  10.454881   8.857619   \n",
       "5      15.744624  15.784839  15.799570  17.009892  15.685161  13.632796   \n",
       "6      18.866292  18.680611  18.529832  20.057877  18.853389  16.969777   \n",
       "7      25.030000  24.869194  24.764409  26.155161  24.896505  22.550269   \n",
       "8      20.292381  20.328795  19.642436  19.373846  18.713462  17.034872   \n",
       "9      16.828268  17.031056  16.786983  15.853556  14.534637  13.350444   \n",
       "10     11.189194  10.836865  10.361568   9.781022   9.373441   9.134570   \n",
       "11      5.342753   5.081250   5.056629   4.959106   4.868111   4.833333   \n",
       "12      0.367056   0.152167  -0.106111  -0.182500  -0.244167  -0.290000   \n",
       "\n",
       "hour          21         22         23  \n",
       "month                                   \n",
       "1      -1.806486  -1.854462  -1.890811  \n",
       "2       2.137619   2.024671   1.896190  \n",
       "3       4.022312   3.657903   3.407258  \n",
       "4       7.712619   6.974762   6.293512  \n",
       "5      12.216290  11.291237  10.622849  \n",
       "6      15.675111  14.658778  13.898167  \n",
       "7      20.882649  19.699022  18.822634  \n",
       "8      15.843590  15.146154  14.596667  \n",
       "9      12.545278  11.954190  11.399056  \n",
       "10      8.956505   8.820270   8.623297  \n",
       "11      4.774389   4.720722   4.699722  \n",
       "12     -0.305333  -0.302778  -0.325642  \n",
       "\n",
       "[12 rows x 24 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.pivot_table(\n",
    "    df_temp, \n",
    "    values='outdoor', \n",
    "    index=['month'], \n",
    "    columns=['hour'], \n",
    "    aggfunc=np.mean); table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
    "sns.heatmap(table, ax=ax);\n",
    "\n",
    "fig.tight_layout()\n",
    "#fig.savefig(\"ch12-seaborn-heatmap.pdf\")"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}