pandas_tricks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 new time-saving tricks in pandas ([video](https://www.youtube.com/watch?v=-NbY7E9hKxk&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=32))\n",
    "\n",
    "- [My pandas video series (30 videos)](http://www.dataschool.io/easier-data-analysis-with-pandas/)\n",
    "- [GitHub repository](https://github.com/justmarkham/pandas-videos)\n",
    "- [pandas release notes](http://pandas.pydata.org/pandas-docs/stable/whatsnew.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.22.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Create a datetime column from a DataFrame\n",
    "\n",
    "*New in 0.18.1*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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>month</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month  day  year  hour\n",
       "0     12   25  2017    10\n",
       "1      1   15  2018    11"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create an example DataFrame\n",
    "df = pd.DataFrame([[12, 25, 2017, 10], [1, 15, 2018, 11]],\n",
    "                  columns=['month', 'day', 'year', 'hour'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2017-12-25 10:00:00\n",
       "1   2018-01-15 11:00:00\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new: create a datetime column from the entire DataFrame\n",
    "pd.to_datetime(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2017-12-25\n",
       "1   2018-01-15\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new: create a datetime column from a subset of columns\n",
    "pd.to_datetime(df[['month', 'day', 'year']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>month</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-25</th>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-15</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            month  day  year  hour\n",
       "2017-12-25     12   25  2017    10\n",
       "2018-01-15      1   15  2018    11"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# overwrite the index\n",
    "df.index = pd.to_datetime(df[['month', 'day', 'year']])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [More information](http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#assembling-datetimes)\n",
    "- [Video: How do I work with dates and times in pandas?](https://www.youtube.com/watch?v=yCgJGsg0Xa4&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Create a category column during file reading\n",
    "\n",
    "*New in 0.19.0*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the drinks dataset into a DataFrame\n",
    "drinks = pd.read_csv('http://bit.ly/drinksbycountry')\n",
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                          object\n",
       "beer_servings                     int64\n",
       "spirit_servings                   int64\n",
       "wine_servings                     int64\n",
       "total_litres_of_pure_alcohol    float64\n",
       "continent                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data types are automatically detected\n",
    "drinks.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                           object\n",
       "beer_servings                      int64\n",
       "spirit_servings                    int64\n",
       "wine_servings                      int64\n",
       "total_litres_of_pure_alcohol     float64\n",
       "continent                       category\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# old way to create a category (after file reading)\n",
    "drinks['continent'] = drinks.continent.astype('category')\n",
    "drinks.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                           object\n",
       "beer_servings                      int64\n",
       "spirit_servings                    int64\n",
       "wine_servings                      int64\n",
       "total_litres_of_pure_alcohol     float64\n",
       "continent                       category\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new way to create a category (during file reading)\n",
    "drinks = pd.read_csv('http://bit.ly/drinksbycountry', dtype={'continent':'category'})\n",
    "drinks.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [More information](http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#read-csv-supports-parsing-categorical-directly)\n",
    "- [Video: How do I make my pandas DataFrame smaller and faster?](https://www.youtube.com/watch?v=wDYDYGyN_cw&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=21)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Convert the data type of multiple columns at once\n",
    "\n",
    "*New in 0.19.0*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                          object\n",
       "beer_servings                     int64\n",
       "spirit_servings                   int64\n",
       "wine_servings                     int64\n",
       "total_litres_of_pure_alcohol    float64\n",
       "continent                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the drinks dataset into a DataFrame\n",
    "drinks = pd.read_csv('http://bit.ly/drinksbycountry')\n",
    "drinks.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                          object\n",
       "beer_servings                   float64\n",
       "spirit_servings                 float64\n",
       "wine_servings                     int64\n",
       "total_litres_of_pure_alcohol    float64\n",
       "continent                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# old way to convert data types (one at a time)\n",
    "drinks['beer_servings'] = drinks.beer_servings.astype('float')\n",
    "drinks['spirit_servings'] = drinks.spirit_servings.astype('float')\n",
    "drinks.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                          object\n",
       "beer_servings                   float64\n",
       "spirit_servings                 float64\n",
       "wine_servings                     int64\n",
       "total_litres_of_pure_alcohol    float64\n",
       "continent                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new way to convert data types (all at once)\n",
    "drinks = pd.read_csv('http://bit.ly/drinksbycountry')\n",
    "drinks = drinks.astype({'beer_servings':'float', 'spirit_servings':'float'})\n",
    "drinks.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [More information](http://pandas.pydata.org/pandas-docs/stable/basics.html#astype)\n",
    "- [Video: How do I change the data type of a pandas Series?](https://www.youtube.com/watch?v=V0AWyzVMf54&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Apply multiple aggregations on a Series or DataFrame\n",
    "\n",
    "*New in 0.20.0*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa            61.471698\n",
       "Asia              37.045455\n",
       "Europe           193.777778\n",
       "North America    145.434783\n",
       "Oceania           89.687500\n",
       "South America    175.083333\n",
       "Name: beer_servings, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of a single aggregation function after a groupby\n",
    "drinks.groupby('continent').beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>61.471698</td>\n",
       "      <td>0.0</td>\n",
       "      <td>376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>37.045455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>247.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>193.777778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>145.434783</td>\n",
       "      <td>1.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>89.687500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>175.083333</td>\n",
       "      <td>93.0</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     mean   min    max\n",
       "continent                             \n",
       "Africa          61.471698   0.0  376.0\n",
       "Asia            37.045455   0.0  247.0\n",
       "Europe         193.777778   0.0  361.0\n",
       "North America  145.434783   1.0  285.0\n",
       "Oceania         89.687500   0.0  306.0\n",
       "South America  175.083333  93.0  333.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiple aggregation functions can be applied simultaneously\n",
    "drinks.groupby('continent').beer_servings.agg(['mean', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean    106.160622\n",
       "min       0.000000\n",
       "max     376.000000\n",
       "Name: beer_servings, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new: apply the same aggregations to a Series\n",
    "drinks.beer_servings.agg(['mean', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>376.000000</td>\n",
       "      <td>438.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>106.160622</td>\n",
       "      <td>80.994819</td>\n",
       "      <td>49.450777</td>\n",
       "      <td>4.717098</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  beer_servings  spirit_servings  wine_servings  \\\n",
       "max      Zimbabwe     376.000000       438.000000     370.000000   \n",
       "mean          NaN     106.160622        80.994819      49.450777   \n",
       "min   Afghanistan       0.000000         0.000000       0.000000   \n",
       "\n",
       "      total_litres_of_pure_alcohol      continent  \n",
       "max                      14.400000  South America  \n",
       "mean                      4.717098            NaN  \n",
       "min                       0.000000         Africa  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new: apply the same aggregations to a DataFrame\n",
    "drinks.agg(['mean', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>106.160622</td>\n",
       "      <td>80.994819</td>\n",
       "      <td>49.450777</td>\n",
       "      <td>4.717098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>101.143103</td>\n",
       "      <td>88.284312</td>\n",
       "      <td>79.697598</td>\n",
       "      <td>3.773298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>188.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>376.000000</td>\n",
       "      <td>438.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>14.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       beer_servings  spirit_servings  wine_servings  \\\n",
       "count     193.000000       193.000000     193.000000   \n",
       "mean      106.160622        80.994819      49.450777   \n",
       "std       101.143103        88.284312      79.697598   \n",
       "min         0.000000         0.000000       0.000000   \n",
       "25%        20.000000         4.000000       1.000000   \n",
       "50%        76.000000        56.000000       8.000000   \n",
       "75%       188.000000       128.000000      59.000000   \n",
       "max       376.000000       438.000000     370.000000   \n",
       "\n",
       "       total_litres_of_pure_alcohol  \n",
       "count                    193.000000  \n",
       "mean                       4.717098  \n",
       "std                        3.773298  \n",
       "min                        0.000000  \n",
       "25%                        1.300000  \n",
       "50%                        4.200000  \n",
       "75%                        7.200000  \n",
       "max                       14.400000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame describe method provides similar functionality but is less flexible\n",
    "drinks.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [More information](http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-aggregate)\n",
    "- [Video: When should I use a \"groupby\" in pandas?](https://www.youtube.com/watch?v=qy0fDqoMJx8&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Download the official pandas cheat sheet\n",
    "\n",
    "*New in 0.19.2*\n",
    "\n",
    "[Cheat sheet (PDF)](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf)"
   ]
  }
 ],
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