pandas Cookbook Practice Chapter 4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Chapter 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%pylab inline  \n",
    "# or %matplotlib inline\n",
    "\n",
    "# fix the unicode encoding error\n",
    "import sys\n",
    "reload(sys)\n",
    "sys.setdefaultencoding('utf-8')\n",
    "\n",
    "# make plots nicer\n",
    "pd.set_option('display.mpl_style', 'default')\n",
    "plt.rcParams['figure.figsize'] = (15, 5)\n",
    "plt.rcParams['font.family'] = 'sans-serif' # change font\n",
    "\n",
    "# display all columns\n",
    "pd.set_option('display.width', 5000) \n",
    "pd.set_option('display.max_columns', 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Start new dataframe from a column and add date column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bikes = pd.read_csv('data/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3d7dc18>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc86cbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes['Berri 1'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create new dataframe using bike dataset.\n",
    "\n",
    "`.copy()` does not change the original dateframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "berri_bikes = bikes[['Berri 1']].copy() # this returns a dataframe\n",
    "berri_bikes_2 = bikes['Berri 1'].copy() # this returns a series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a columns for weekend using index information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.tseries.index.DatetimeIndex'>\n",
       "[2012-01-01, ..., 2012-11-05]\n",
       "Length: 310, Freq: None, Timezone: None"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use time series functionality to figure out day of the month and weekday\n",
    "   + day of mongth starts at 1 = 1st\n",
    "   + weekday starts at 0 = Monday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,\n",
       "       21, 22, 23, 24, 25, 26, 27, 28, 29,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
       "       26, 27, 28, 29, 30, 31,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n",
       "       29, 30,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,\n",
       "       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n",
       "       23, 24, 25, 26, 27, 28, 29, 30, 31,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
       "       26, 27, 28, 29, 30, 31,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n",
       "       29, 30,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,\n",
       "       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  1,\n",
       "        2,  3,  4,  5])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes.index.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
       "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
       "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "berri_bikes.index.weekday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `df.loc` to select by label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "berri_bikes.loc[:,'weekday'] = berri_bikes.index.weekday\n",
    "berri_bikes[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Add cyclist by weekday - .groupby()\n",
    "Use `df.groupby(key).agg(function)` to group and calculate sum.\n",
    "\n",
    "\n",
    "\n",
    "More options (could be found [here](http://pandas.pydata.org/pandas-docs/stable/groupby.html)):\n",
    "+ **Group by keys**  \n",
    "    `grouped = obj.groupby(key)`  \n",
    "    `grouped = obj.groupby(key, axis=1)`  \n",
    "    `grouped = obj.groupby([key1, key2])`  \n",
    "    \n",
    "+ **Aggregation**: computing a summary statistic (or statistics) about each group.   \n",
    "    `.agg(function)`  \n",
    "    Some examples:   group sums or means, group sizes / counts\n",
    "\n",
    "+ **Transformation**: perform some group-specific computations and return a like-indexed.   \n",
    "    `.transform(function)`  \n",
    "    Some examples:   standardizing data (zscore) within group, filling NAs within groups with a value derived from each group\n",
    "\n",
    "+ **Filtration**: discard some groups, according to a group-wise computation that evaluates True or False.   \n",
    "    `.filter(function)`  \n",
    "    Some examples:   discarding data that belongs to groups with only a few members, filtering out data based on the group sum or mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 134298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 135305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 152972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 160131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 141771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> 101578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>  99310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Berri 1\n",
       "weekday         \n",
       "0         134298\n",
       "1         135305\n",
       "2         152972\n",
       "3         160131\n",
       "4         141771\n",
       "5         101578\n",
       "6          99310"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts = berri_bikes.groupby('weekday').agg(sum)\n",
    "weekday_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now change the index (in the same order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Berri 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td> 134298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td> 135305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td> 152972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td> 160131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td> 141771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td> 101578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>  99310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Berri 1\n",
       "Monday      134298\n",
       "Tuesday     135305\n",
       "Wednesday   152972\n",
       "Thursday    160131\n",
       "Friday      141771\n",
       "Saturday    101578\n",
       "Sunday       99310"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "weekday_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcf5acf8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc85de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekday_counts.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Everything together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdd9a2e8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdb8deb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes = pd.read_csv('data/bikes.csv', \n",
    "                    sep=';', encoding='latin1', \n",
    "                    parse_dates=['Date'], dayfirst=True, \n",
    "                    index_col='Date')\n",
    "# Add the weekday column\n",
    "berri_bikes = bikes[['Berri 1']].copy()\n",
    "berri_bikes.loc[:,'weekday'] = berri_bikes.index.weekday\n",
    "\n",
    "# Add up the number of cyclists by weekday, and plot!\n",
    "weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)\n",
    "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "weekday_counts.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
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    "##Summary\n",
    "+ `df.index`  \n",
    "+ `timeseries.day`, `timeseries.weekday` to find day of the month and weekday  \n",
    "+ `df.loc[keys, keys]` to select by label  \n",
    "+ `df.[[keys]].copy()` to copy selected data into a new dataframe  \n",
    "+ `df.groupby(key).agg/transform/filter(function)` to apply function by group\n"
   ]
  }
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