{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-02-06T00:50:22.541960",
     "start_time": "2017-02-06T00:50:22.537775"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-01-19T06:02:58.766618",
     "start_time": "2017-01-19T06:02:58.747046"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = sns.load_dataset(\"iris\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-01-19T06:08:04.665538",
     "start_time": "2017-01-19T06:08:04.644876"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>250.3</td>\n",
       "      <td>171.4</td>\n",
       "      <td>73.1</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>296.8</td>\n",
       "      <td>138.5</td>\n",
       "      <td>213.0</td>\n",
       "      <td>66.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>329.4</td>\n",
       "      <td>148.7</td>\n",
       "      <td>277.6</td>\n",
       "      <td>101.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                         \n",
       "setosa             250.3        171.4          73.1         12.3\n",
       "versicolor         296.8        138.5         213.0         66.3\n",
       "virginica          329.4        148.7         277.6        101.3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby(iris.species).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-01-19T06:08:43.162185",
     "start_time": "2017-01-19T06:08:43.116384"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa']\n",
      "['setosa']\n",
      "['versicolor']\n",
      "['virginica']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</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>setosa</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>296.8</td>\n",
       "      <td>138.5</td>\n",
       "      <td>213.0</td>\n",
       "      <td>66.3</td>\n",
       "      <td>versicolorversicolorversicolorversicolorversic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>329.4</td>\n",
       "      <td>148.7</td>\n",
       "      <td>277.6</td>\n",
       "      <td>101.3</td>\n",
       "      <td>virginicavirginicavirginicavirginicavirginicav...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width  \\\n",
       "species                                                            \n",
       "setosa               0.0          0.0           0.0          0.0   \n",
       "versicolor         296.8        138.5         213.0         66.3   \n",
       "virginica          329.4        148.7         277.6        101.3   \n",
       "\n",
       "                                                      species  \n",
       "species                                                        \n",
       "setosa                                                      0  \n",
       "versicolor  versicolorversicolorversicolorversicolorversic...  \n",
       "virginica   virginicavirginicavirginicavirginicavirginicav...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(df):\n",
    "    print(df.species.unique())\n",
    "    return df[df.species != \"setosa\"].sum()\n",
    "\n",
    "iris.groupby(iris.species).apply(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-01-19T06:17:54.281463",
     "start_time": "2017-01-19T06:17:54.239422"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    5.1\n",
      "1    4.9\n",
      "Name: sepal_length, dtype: float64\n",
      "0    3.5\n",
      "1    3.0\n",
      "Name: sepal_width, dtype: float64\n",
      "0    1.4\n",
      "1    1.4\n",
      "Name: petal_length, dtype: float64\n",
      "0    0.2\n",
      "1    0.2\n",
      "Name: petal_width, dtype: float64\n",
      "   sepal_length  sepal_width  petal_length  petal_width\n",
      "0           5.1          3.5           1.4          0.2\n",
      "1           4.9          3.0           1.4          0.2\n",
      "50    7.0\n",
      "51    6.4\n",
      "Name: sepal_length, dtype: float64\n",
      "50    3.2\n",
      "51    3.2\n",
      "Name: sepal_width, dtype: float64\n",
      "50    4.7\n",
      "51    4.5\n",
      "Name: petal_length, dtype: float64\n",
      "50    1.4\n",
      "51    1.5\n",
      "Name: petal_width, dtype: float64\n",
      "100    6.3\n",
      "101    5.8\n",
      "Name: sepal_length, dtype: float64\n",
      "100    3.3\n",
      "101    2.7\n",
      "Name: sepal_width, dtype: float64\n",
      "100    6.0\n",
      "101    5.1\n",
      "Name: petal_length, dtype: float64\n",
      "100    2.5\n",
      "101    1.9\n",
      "Name: petal_width, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           NaN          NaN           NaN          NaN\n",
       "1           NaN          NaN           NaN          NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby(iris.species).transform(lambda x: print(x.head(2))).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-01-19T06:18:24.957929",
     "start_time": "2017-01-19T06:18:24.943867"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    5.1\n",
      "1    4.9\n",
      "Name: setosa, dtype: float64\n",
      "50    7.0\n",
      "51    6.4\n",
      "Name: versicolor, dtype: float64\n",
      "100    6.3\n",
      "101    5.8\n",
      "Name: virginica, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0   NaN\n",
       "1   NaN\n",
       "Name: sepal_length, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby(iris.species)[\"sepal_length\"].transform(lambda x: print(x.head(2))).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-01-19T06:22:56.339744",
     "start_time": "2017-01-19T06:22:56.314668"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>250.3</td>\n",
       "      <td>171.4</td>\n",
       "      <td>73.1</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>250.3</td>\n",
       "      <td>171.4</td>\n",
       "      <td>73.1</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>296.8</td>\n",
       "      <td>138.5</td>\n",
       "      <td>213.0</td>\n",
       "      <td>66.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>296.8</td>\n",
       "      <td>138.5</td>\n",
       "      <td>213.0</td>\n",
       "      <td>66.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>329.4</td>\n",
       "      <td>148.7</td>\n",
       "      <td>277.6</td>\n",
       "      <td>101.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>329.4</td>\n",
       "      <td>148.7</td>\n",
       "      <td>277.6</td>\n",
       "      <td>101.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "0           250.3        171.4          73.1         12.3\n",
       "1           250.3        171.4          73.1         12.3\n",
       "50          296.8        138.5         213.0         66.3\n",
       "51          296.8        138.5         213.0         66.3\n",
       "100         329.4        148.7         277.6        101.3\n",
       "101         329.4        148.7         277.6        101.3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby(iris.species).transform(np.sum).iloc[[0,1,50,51,100,101]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-01-19T06:23:24.820159",
     "start_time": "2017-01-19T06:23:24.790263"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>13.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>9.2</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>12.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "0             5.1          3.5           1.4          0.2\n",
       "1            10.0          6.5           2.8          0.4\n",
       "50            7.0          3.2           4.7          1.4\n",
       "51           13.4          6.4           9.2          2.9\n",
       "100           6.3          3.3           6.0          2.5\n",
       "101          12.1          6.0          11.1          4.4"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby(iris.species).transform(pd.Series.cumsum).iloc[[0,1,50,51,100,101]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-01-19T06:24:22.300777",
     "start_time": "2017-01-19T06:24:22.289503"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       5.1\n",
       "1      10.0\n",
       "50      7.0\n",
       "51     13.4\n",
       "100     6.3\n",
       "101    12.1\n",
       "Name: sepal_length, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby(iris.species)[\"sepal_length\"].transform(pd.Series.cumsum).iloc[[0,1,50,51,100,101]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-02-06T00:49:46.229172",
     "start_time": "2017-02-06T00:49:43.818957"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>method</th>\n",
       "      <th>Astrometry</th>\n",
       "      <th>Eclipse Timing Variations</th>\n",
       "      <th>Imaging</th>\n",
       "      <th>Microlensing</th>\n",
       "      <th>Orbital Brightness Modulation</th>\n",
       "      <th>Pulsar Timing</th>\n",
       "      <th>Pulsation Timing Variations</th>\n",
       "      <th>Radial Velocity</th>\n",
       "      <th>Transit</th>\n",
       "      <th>Transit Timing Variations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>553.000000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2011.50000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2009.131579</td>\n",
       "      <td>2009.782609</td>\n",
       "      <td>2011.666667</td>\n",
       "      <td>1998.40000</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2007.518987</td>\n",
       "      <td>2011.236776</td>\n",
       "      <td>2012.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.12132</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>2.781901</td>\n",
       "      <td>2.859697</td>\n",
       "      <td>1.154701</td>\n",
       "      <td>8.38451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.249052</td>\n",
       "      <td>2.077867</td>\n",
       "      <td>1.290994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2010.00000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>1992.00000</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1989.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2010.75000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>1992.00000</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2011.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2011.50000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>1994.00000</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>2012.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.25000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>2003.00000</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>2013.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2013.00000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>2011.00000</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "method  Astrometry  Eclipse Timing Variations      Imaging  Microlensing  \\\n",
       "count      2.00000                   9.000000    38.000000     23.000000   \n",
       "mean    2011.50000                2010.000000  2009.131579   2009.782609   \n",
       "std        2.12132                   1.414214     2.781901      2.859697   \n",
       "min     2010.00000                2008.000000  2004.000000   2004.000000   \n",
       "25%     2010.75000                2009.000000  2008.000000   2008.000000   \n",
       "50%     2011.50000                2010.000000  2009.000000   2010.000000   \n",
       "75%     2012.25000                2011.000000  2011.000000   2012.000000   \n",
       "max     2013.00000                2012.000000  2013.000000   2013.000000   \n",
       "\n",
       "method  Orbital Brightness Modulation  Pulsar Timing  \\\n",
       "count                        3.000000        5.00000   \n",
       "mean                      2011.666667     1998.40000   \n",
       "std                          1.154701        8.38451   \n",
       "min                       2011.000000     1992.00000   \n",
       "25%                       2011.000000     1992.00000   \n",
       "50%                       2011.000000     1994.00000   \n",
       "75%                       2012.000000     2003.00000   \n",
       "max                       2013.000000     2011.00000   \n",
       "\n",
       "method  Pulsation Timing Variations  Radial Velocity      Transit  \\\n",
       "count                           1.0       553.000000   397.000000   \n",
       "mean                         2007.0      2007.518987  2011.236776   \n",
       "std                             NaN         4.249052     2.077867   \n",
       "min                          2007.0      1989.000000  2002.000000   \n",
       "25%                          2007.0      2005.000000  2010.000000   \n",
       "50%                          2007.0      2009.000000  2012.000000   \n",
       "75%                          2007.0      2011.000000  2013.000000   \n",
       "max                          2007.0      2014.000000  2014.000000   \n",
       "\n",
       "method  Transit Timing Variations  \n",
       "count                    4.000000  \n",
       "mean                  2012.500000  \n",
       "std                      1.290994  \n",
       "min                   2011.000000  \n",
       "25%                   2011.750000  \n",
       "50%                   2012.500000  \n",
       "75%                   2013.250000  \n",
       "max                   2014.000000  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "planets = sns.load_dataset(\"planets\")\n",
    "planets.groupby('method')['year'].describe().unstack(level=0)  #.unstack(\"method\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-02-06T00:49:55.837230",
     "start_time": "2017-02-06T00:49:55.810815"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>6.262</td>\n",
       "      <td>2.872</td>\n",
       "      <td>4.906</td>\n",
       "      <td>1.676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.006</td>\n",
       "      <td>3.428</td>\n",
       "      <td>1.462</td>\n",
       "      <td>0.246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sepal_length  sepal_width  petal_length  petal_width\n",
       "other          6.262        2.872         4.906        1.676\n",
       "setosa         5.006        3.428         1.462        0.246"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = sns.load_dataset(\"iris\")\n",
    "print(iris.species.unique())\n",
    "iris.set_index(\"species\").groupby(\n",
    "    {\n",
    "        \"setosa\": \"setosa\",\n",
    "        \"versicolor\": \"other\",\n",
    "        \"virginica\": \"other\"\n",
    "    }).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-02-06T00:50:03.644430",
     "start_time": "2017-02-06T00:50:03.608807"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>6.262</td>\n",
       "      <td>2.872</td>\n",
       "      <td>4.906</td>\n",
       "      <td>1.676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>5.006</td>\n",
       "      <td>3.428</td>\n",
       "      <td>1.462</td>\n",
       "      <td>0.246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                      \n",
       "False           6.262        2.872         4.906        1.676\n",
       "True            5.006        3.428         1.462        0.246"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby(iris.species == \"setosa\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-02-06T00:50:26.808244",
     "start_time": "2017-02-06T00:50:26.800725"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('1 days 12:34:56')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_timedelta(\"1days 12:34:56\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-02-06T00:50:33.509220",
     "start_time": "2017-02-06T00:50:33.416660"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 59 entries, 0 to 58\n",
      "Data columns (total 2 columns):\n",
      "da    59 non-null datetime64[ns]\n",
      "de    59 non-null timedelta64[ns]\n",
      "dtypes: datetime64[ns](1), timedelta64[ns](1)\n",
      "memory usage: 1.0 KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>de</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>da</th>\n",
       "      <th>da</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-21</th>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2017-01-31</th>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-10</th>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-20</th>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               de\n",
       "da         da    \n",
       "2017-01-01 1   10\n",
       "2017-01-11 1   10\n",
       "2017-01-21 1   10\n",
       "2017-01-31 1    1\n",
       "           2    9\n",
       "2017-02-10 2   10\n",
       "2017-02-20 2    9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.DataFrame({\n",
    "    \"da\": pd.date_range(\"2017-01-01\", \"2017-02-28\"),\n",
    "    \"de\": pd.timedelta_range(\"1days 12:34:56.789012345\", freq=\"1H\", periods=31+28)\n",
    "})\n",
    "dates.info()\n",
    "dates.groupby(\n",
    "    [\n",
    "        pd.Grouper(freq=\"10d\", key=\"da\"),\n",
    "        dates.da.dt.month,\n",
    "    ]).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-02-06T00:50:41.765359",
     "start_time": "2017-02-06T00:50:41.743117"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>days</th>\n",
       "      <th>hours</th>\n",
       "      <th>minutes</th>\n",
       "      <th>seconds</th>\n",
       "      <th>milliseconds</th>\n",
       "      <th>microseconds</th>\n",
       "      <th>nanoseconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>34</td>\n",
       "      <td>56</td>\n",
       "      <td>789</td>\n",
       "      <td>12</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>34</td>\n",
       "      <td>56</td>\n",
       "      <td>789</td>\n",
       "      <td>12</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>34</td>\n",
       "      <td>56</td>\n",
       "      <td>789</td>\n",
       "      <td>12</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>56</td>\n",
       "      <td>789</td>\n",
       "      <td>12</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>34</td>\n",
       "      <td>56</td>\n",
       "      <td>789</td>\n",
       "      <td>12</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds\n",
       "0     1     12       34       56           789            12          345\n",
       "1     1     13       34       56           789            12          345\n",
       "2     1     14       34       56           789            12          345\n",
       "3     1     15       34       56           789            12          345\n",
       "4     1     16       34       56           789            12          345"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates.de.dt.components.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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