ch12.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "np.random.seed(12345)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "PREVIOUS_MAX_ROWS = pd.options.display.max_rows\n",
    "pd.options.display.max_rows = 20\n",
    "np.set_printoptions(precision=4, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Background and Motivation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "2     apple\n",
       "3     apple\n",
       "4     apple\n",
       "5    orange\n",
       "6     apple\n",
       "7     apple\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array(['apple', 'orange'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "apple     6\n",
       "orange    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np; import pandas as pd\n",
    "values = pd.Series(['apple', 'orange', 'apple',\n",
    "                    'apple'] * 2)\n",
    "values\n",
    "pd.unique(values)\n",
    "pd.value_counts(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "5    1\n",
       "6    0\n",
       "7    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = pd.Series([0, 1, 0, 0] * 2)\n",
    "dim = pd.Series(['apple', 'orange'])\n",
    "values\n",
    "dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "0     apple\n",
       "0     apple\n",
       "0     apple\n",
       "1    orange\n",
       "0     apple\n",
       "0     apple\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dim.take(values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Type in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>basket_id</th>\n",
       "      <th>fruit</th>\n",
       "      <th>count</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>apple</td>\n",
       "      <td>5</td>\n",
       "      <td>3.858058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>8</td>\n",
       "      <td>2.612708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>apple</td>\n",
       "      <td>4</td>\n",
       "      <td>2.995627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>apple</td>\n",
       "      <td>7</td>\n",
       "      <td>2.614279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>apple</td>\n",
       "      <td>12</td>\n",
       "      <td>2.990859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>orange</td>\n",
       "      <td>8</td>\n",
       "      <td>3.845227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>apple</td>\n",
       "      <td>5</td>\n",
       "      <td>0.033553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>apple</td>\n",
       "      <td>4</td>\n",
       "      <td>0.425778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   basket_id   fruit  count    weight\n",
       "0          0   apple      5  3.858058\n",
       "1          1  orange      8  2.612708\n",
       "2          2   apple      4  2.995627\n",
       "3          3   apple      7  2.614279\n",
       "4          4   apple     12  2.990859\n",
       "5          5  orange      8  3.845227\n",
       "6          6   apple      5  0.033553\n",
       "7          7   apple      4  0.425778"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits = ['apple', 'orange', 'apple', 'apple'] * 2\n",
    "N = len(fruits)\n",
    "df = pd.DataFrame({'fruit': fruits,\n",
    "                   'basket_id': np.arange(N),\n",
    "                   'count': np.random.randint(3, 15, size=N),\n",
    "                   'weight': np.random.uniform(0, 4, size=N)},\n",
    "                  columns=['basket_id', 'fruit', 'count', 'weight'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "2     apple\n",
       "3     apple\n",
       "4     apple\n",
       "5    orange\n",
       "6     apple\n",
       "7     apple\n",
       "Name: fruit, dtype: category\n",
       "Categories (2, object): ['apple', 'orange']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruit_cat = df['fruit'].astype('category')\n",
    "fruit_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.arrays.categorical.Categorical"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = fruit_cat.values\n",
    "type(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['apple', 'orange'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 0, 1, 0, 0], dtype=int8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.categories\n",
    "c.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     apple\n",
       "1    orange\n",
       "2     apple\n",
       "3     apple\n",
       "4     apple\n",
       "5    orange\n",
       "6     apple\n",
       "7     apple\n",
       "Name: fruit, dtype: category\n",
       "Categories (2, object): ['apple', 'orange']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fruit'] = df['fruit'].astype('category')\n",
    "df.fruit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'foo', 'bar']\n",
       "Categories (3, object): ['bar', 'baz', 'foo']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_categories = pd.Categorical(['foo', 'bar', 'baz', 'foo', 'bar'])\n",
    "my_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'foo', 'foo', 'bar']\n",
       "Categories (3, object): ['foo', 'bar', 'baz']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories = ['foo', 'bar', 'baz']\n",
    "codes = [0, 1, 2, 0, 0, 1]\n",
    "my_cats_2 = pd.Categorical.from_codes(codes, categories)\n",
    "my_cats_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'foo', 'foo', 'bar']\n",
       "Categories (3, object): ['foo' < 'bar' < 'baz']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_cat = pd.Categorical.from_codes(codes, categories,\n",
    "                                        ordered=True)\n",
    "ordered_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 'bar', 'baz', 'foo', 'foo', 'bar']\n",
       "Categories (3, object): ['foo' < 'bar' < 'baz']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_cats_2.as_ordered()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computations with Categoricals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.2047,  0.4789, -0.5194, -0.5557,  1.9658])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(12345)\n",
    "draws = np.random.randn(1000)\n",
    "draws[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.684, -0.0101], (-0.0101, 0.63], (-0.684, -0.0101], (-0.684, -0.0101], (0.63, 3.928], ..., (-0.0101, 0.63], (-0.684, -0.0101], (-2.9499999999999997, -0.684], (-0.0101, 0.63], (0.63, 3.928]]\n",
       "Length: 1000\n",
       "Categories (4, interval[float64]): [(-2.9499999999999997, -0.684] < (-0.684, -0.0101] < (-0.0101, 0.63] < (0.63, 3.928]]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = pd.qcut(draws, 4)\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Q2', 'Q3', 'Q2', 'Q2', 'Q4', ..., 'Q3', 'Q2', 'Q1', 'Q3', 'Q4']\n",
       "Length: 1000\n",
       "Categories (4, object): ['Q1' < 'Q2' < 'Q3' < 'Q4']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([1, 2, 1, 1, 3, 3, 2, 2, 3, 3], dtype=int8)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = pd.qcut(draws, 4, labels=['Q1', 'Q2', 'Q3', 'Q4'])\n",
    "bins\n",
    "bins.codes[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>quartile</th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q1</td>\n",
       "      <td>250</td>\n",
       "      <td>-2.949343</td>\n",
       "      <td>-0.685484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q2</td>\n",
       "      <td>250</td>\n",
       "      <td>-0.683066</td>\n",
       "      <td>-0.010115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q3</td>\n",
       "      <td>250</td>\n",
       "      <td>-0.010032</td>\n",
       "      <td>0.628894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q4</td>\n",
       "      <td>250</td>\n",
       "      <td>0.634238</td>\n",
       "      <td>3.927528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  quartile  count       min       max\n",
       "0       Q1    250 -2.949343 -0.685484\n",
       "1       Q2    250 -0.683066 -0.010115\n",
       "2       Q3    250 -0.010032  0.628894\n",
       "3       Q4    250  0.634238  3.927528"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = pd.Series(bins, name='quartile')\n",
    "results = (pd.Series(draws)\n",
    "           .groupby(bins)\n",
    "           .agg(['count', 'min', 'max'])\n",
    "           .reset_index())\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Q1\n",
       "1    Q2\n",
       "2    Q3\n",
       "3    Q4\n",
       "Name: quartile, dtype: category\n",
       "Categories (4, object): ['Q1' < 'Q2' < 'Q3' < 'Q4']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['quartile']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Better performance with categoricals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 10000000\n",
    "draws = pd.Series(np.random.randn(N))\n",
    "labels = pd.Series(['foo', 'bar', 'baz', 'qux'] * (N // 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = labels.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80000128"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "10000320"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.memory_usage()\n",
    "categories.memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 607 ms\n"
     ]
    }
   ],
   "source": [
    "%time _ = labels.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    d\n",
       "4    a\n",
       "5    b\n",
       "6    c\n",
       "7    d\n",
       "dtype: category\n",
       "Categories (4, object): ['a', 'b', 'c', 'd']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['a', 'b', 'c', 'd'] * 2)\n",
    "cat_s = s.astype('category')\n",
    "cat_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    2\n",
       "3    3\n",
       "4    0\n",
       "5    1\n",
       "6    2\n",
       "7    3\n",
       "dtype: int8"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd'], dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_s.cat.codes\n",
    "cat_s.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    d\n",
       "4    a\n",
       "5    b\n",
       "6    c\n",
       "7    d\n",
       "dtype: category\n",
       "Categories (5, object): ['a', 'b', 'c', 'd', 'e']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_categories = ['a', 'b', 'c', 'd', 'e']\n",
    "cat_s2 = cat_s.cat.set_categories(actual_categories)\n",
    "cat_s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    2\n",
       "c    2\n",
       "b    2\n",
       "a    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "d    2\n",
       "c    2\n",
       "b    2\n",
       "a    2\n",
       "e    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_s.value_counts()\n",
    "cat_s2.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "4    a\n",
       "5    b\n",
       "dtype: category\n",
       "Categories (4, object): ['a', 'b', 'c', 'd']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "4    a\n",
       "5    b\n",
       "dtype: category\n",
       "Categories (2, object): ['a', 'b']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_s3 = cat_s[cat_s.isin(['a', 'b'])]\n",
    "cat_s3\n",
    "cat_s3.cat.remove_unused_categories()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating dummy variables for modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_s = pd.Series(['a', 'b', 'c', 'd'] * 2, dtype='category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <th>6</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "</table>\n",
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       "   a  b  c  d\n",
       "0  1  0  0  0\n",
       "1  0  1  0  0\n",
       "2  0  0  1  0\n",
       "3  0  0  0  1\n",
       "4  1  0  0  0\n",
       "5  0  1  0  0\n",
       "6  0  0  1  0\n",
       "7  0  0  0  1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(cat_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced GroupBy Use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group Transforms and \"Unwrapped\" GroupBys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>key</th>\n",
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       "  </thead>\n",
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       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>4.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>c</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>6</th>\n",
       "      <td>a</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>c</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>a</td>\n",
       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>b</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>c</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  value\n",
       "0    a    0.0\n",
       "1    b    1.0\n",
       "2    c    2.0\n",
       "3    a    3.0\n",
       "4    b    4.0\n",
       "5    c    5.0\n",
       "6    a    6.0\n",
       "7    b    7.0\n",
       "8    c    8.0\n",
       "9    a    9.0\n",
       "10   b   10.0\n",
       "11   c   11.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'key': ['a', 'b', 'c'] * 4,\n",
    "                   'value': np.arange(12.)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key\n",
       "a    4.5\n",
       "b    5.5\n",
       "c    6.5\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = df.groupby('key').value\n",
    "g.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4.5\n",
       "1     5.5\n",
       "2     6.5\n",
       "3     4.5\n",
       "4     5.5\n",
       "5     6.5\n",
       "6     4.5\n",
       "7     5.5\n",
       "8     6.5\n",
       "9     4.5\n",
       "10    5.5\n",
       "11    6.5\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.transform(lambda x: x.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4.5\n",
       "1     5.5\n",
       "2     6.5\n",
       "3     4.5\n",
       "4     5.5\n",
       "5     6.5\n",
       "6     4.5\n",
       "7     5.5\n",
       "8     6.5\n",
       "9     4.5\n",
       "10    5.5\n",
       "11    6.5\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.transform('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.0\n",
       "1      2.0\n",
       "2      4.0\n",
       "3      6.0\n",
       "4      8.0\n",
       "5     10.0\n",
       "6     12.0\n",
       "7     14.0\n",
       "8     16.0\n",
       "9     18.0\n",
       "10    20.0\n",
       "11    22.0\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.transform(lambda x: x * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4.0\n",
       "1     4.0\n",
       "2     4.0\n",
       "3     3.0\n",
       "4     3.0\n",
       "5     3.0\n",
       "6     2.0\n",
       "7     2.0\n",
       "8     2.0\n",
       "9     1.0\n",
       "10    1.0\n",
       "11    1.0\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.transform(lambda x: x.rank(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(x):\n",
    "    return (x - x.mean()) / x.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    -1.161895\n",
       "1    -1.161895\n",
       "2    -1.161895\n",
       "3    -0.387298\n",
       "4    -0.387298\n",
       "5    -0.387298\n",
       "6     0.387298\n",
       "7     0.387298\n",
       "8     0.387298\n",
       "9     1.161895\n",
       "10    1.161895\n",
       "11    1.161895\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0    -1.161895\n",
       "1    -1.161895\n",
       "2    -1.161895\n",
       "3    -0.387298\n",
       "4    -0.387298\n",
       "5    -0.387298\n",
       "6     0.387298\n",
       "7     0.387298\n",
       "8     0.387298\n",
       "9     1.161895\n",
       "10    1.161895\n",
       "11    1.161895\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.transform(normalize)\n",
    "g.apply(normalize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4.5\n",
       "1     5.5\n",
       "2     6.5\n",
       "3     4.5\n",
       "4     5.5\n",
       "5     6.5\n",
       "6     4.5\n",
       "7     5.5\n",
       "8     6.5\n",
       "9     4.5\n",
       "10    5.5\n",
       "11    6.5\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0    -1.161895\n",
       "1    -1.161895\n",
       "2    -1.161895\n",
       "3    -0.387298\n",
       "4    -0.387298\n",
       "5    -0.387298\n",
       "6     0.387298\n",
       "7     0.387298\n",
       "8     0.387298\n",
       "9     1.161895\n",
       "10    1.161895\n",
       "11    1.161895\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.transform('mean')\n",
    "normalized = (df['value'] - g.transform('mean')) / g.transform('std')\n",
    "normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouped Time Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>time</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-20 00:00:00</td>\n",
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       "      <td>2017-05-20 00:01:00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-20 00:02:00</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-20 00:03:00</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-20 00:04:00</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-05-20 00:05:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-05-20 00:06:00</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-05-20 00:07:00</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-05-20 00:08:00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-05-20 00:09:00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-05-20 00:10:00</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-05-20 00:11:00</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-05-20 00:12:00</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017-05-20 00:13:00</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-05-20 00:14:00</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time  value\n",
       "0  2017-05-20 00:00:00      0\n",
       "1  2017-05-20 00:01:00      1\n",
       "2  2017-05-20 00:02:00      2\n",
       "3  2017-05-20 00:03:00      3\n",
       "4  2017-05-20 00:04:00      4\n",
       "5  2017-05-20 00:05:00      5\n",
       "6  2017-05-20 00:06:00      6\n",
       "7  2017-05-20 00:07:00      7\n",
       "8  2017-05-20 00:08:00      8\n",
       "9  2017-05-20 00:09:00      9\n",
       "10 2017-05-20 00:10:00     10\n",
       "11 2017-05-20 00:11:00     11\n",
       "12 2017-05-20 00:12:00     12\n",
       "13 2017-05-20 00:13:00     13\n",
       "14 2017-05-20 00:14:00     14"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = 15\n",
    "times = pd.date_range('2017-05-20 00:00', freq='1min', periods=N)\n",
    "df = pd.DataFrame({'time': times,\n",
    "                   'value': np.arange(N)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>value</th>\n",
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       "      <th>2017-05-20 00:00:00</th>\n",
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       "      <th>2017-05-20 00:05:00</th>\n",
       "      <td>5</td>\n",
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       "      <th>2017-05-20 00:10:00</th>\n",
       "      <td>5</td>\n",
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      "text/plain": [
       "                     value\n",
       "time                      \n",
       "2017-05-20 00:00:00      5\n",
       "2017-05-20 00:05:00      5\n",
       "2017-05-20 00:10:00      5"
      ]
     },
     "execution_count": 41,
     "metadata": {},
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   ],
   "source": [
    "df.set_index('time').resample('5min').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2017-05-20 00:00:00</td>\n",
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       "      <th>1</th>\n",
       "      <td>2017-05-20 00:00:00</td>\n",
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       "      <th>2</th>\n",
       "      <td>2017-05-20 00:00:00</td>\n",
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       "      <td>2017-05-20 00:01:00</td>\n",
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       "      <th>6</th>\n",
       "      <td>2017-05-20 00:02:00</td>\n",
       "      <td>a</td>\n",
       "      <td>6.0</td>\n",
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      "text/plain": [
       "                 time key  value\n",
       "0 2017-05-20 00:00:00   a    0.0\n",
       "1 2017-05-20 00:00:00   b    1.0\n",
       "2 2017-05-20 00:00:00   c    2.0\n",
       "3 2017-05-20 00:01:00   a    3.0\n",
       "4 2017-05-20 00:01:00   b    4.0\n",
       "5 2017-05-20 00:01:00   c    5.0\n",
       "6 2017-05-20 00:02:00   a    6.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'time': times.repeat(3),\n",
    "                    'key': np.tile(['a', 'b', 'c'], N),\n",
    "                    'value': np.arange(N * 3.)})\n",
    "df2[:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'pandas' has no attribute 'TimeGrouper'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-43-252dcf6297f9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtime_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTimeGrouper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'5min'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m    256\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0m_SparseArray\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 258\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"module 'pandas' has no attribute '{name}'\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    259\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    260\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'pandas' has no attribute 'TimeGrouper'"
     ]
    }
   ],
   "source": [
    "time_key = pd.TimeGrouper('5min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'time_key' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-44-c07fd522cfe4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m resampled = (df2.set_index('time')\n\u001b[1;32m----> 2\u001b[1;33m              \u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'key'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtime_key\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m              .sum())\n\u001b[0;32m      4\u001b[0m \u001b[0mresampled\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mresampled\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'time_key' is not defined"
     ]
    }
   ],
   "source": [
    "resampled = (df2.set_index('time')\n",
    "             .groupby(['key', time_key])\n",
    "             .sum())\n",
    "resampled\n",
    "resampled.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Techniques for Method Chaining"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "df = load_data()\n",
    "df2 = df[df['col2'] < 0]\n",
    "df2['col1_demeaned'] = df2['col1'] - df2['col1'].mean()\n",
    "result = df2.groupby('key').col1_demeaned.std()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "# Usual non-functional way\n",
    "df2 = df.copy()\n",
    "df2['k'] = v\n",
    "\n",
    "# Functional assign way\n",
    "df2 = df.assign(k=v)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "result = (df2.assign(col1_demeaned=df2.col1 - df2.col2.mean())\n",
    "          .groupby('key')\n",
    "          .col1_demeaned.std())\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "df = load_data()\n",
    "df2 = df[df['col2'] < 0]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "df = (load_data()\n",
    "      [lambda x: x['col2'] < 0])\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "result = (load_data()\n",
    "          [lambda x: x.col2 < 0]\n",
    "          .assign(col1_demeaned=lambda x: x.col1 - x.col1.mean())\n",
    "          .groupby('key')\n",
    "          .col1_demeaned.std())\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The pipe Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "a = f(df, arg1=v1)\n",
    "b = g(a, v2, arg3=v3)\n",
    "c = h(b, arg4=v4)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "result = (df.pipe(f, arg1=v1)\n",
    "          .pipe(g, v2, arg3=v3)\n",
    "          .pipe(h, arg4=v4))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "g = df.groupby(['key1', 'key2'])\n",
    "df['col1'] = df['col1'] - g.transform('mean')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "def group_demean(df, by, cols):\n",
    "    result = df.copy()\n",
    "    g = df.groupby(by)\n",
    "    for c in cols:\n",
    "        result[c] = df[c] - g[c].transform('mean')\n",
    "    return result\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "result = (df[df.col1 < 0]\n",
    "          .pipe(group_demean, ['key1', 'key2'], ['col1']))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = PREVIOUS_MAX_ROWS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  }
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