05-15-seriesdeepdive-code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REMOVESTART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.max_rows=10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REMOVEEND"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "url = 'https://github.com/mattharrison/datasets/raw/master/data/' \\\n",
    "      'vehicles.csv.zip'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matt/envs/menv/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3418: DtypeWarning: Columns (68,70,71,72,73,74,76,79) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(url)\n",
    "city_mpg = df.city08\n",
    "highway_mpg = df.highway08"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        19\n",
       "1         9\n",
       "2        23\n",
       "3        10\n",
       "4        17\n",
       "         ..\n",
       "41139    19\n",
       "41140    20\n",
       "41141    18\n",
       "41142    18\n",
       "41143    16\n",
       "Name: city08, Length: 41144, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        25\n",
       "1        14\n",
       "2        33\n",
       "3        12\n",
       "4        23\n",
       "         ..\n",
       "41139    26\n",
       "41140    28\n",
       "41141    24\n",
       "41142    24\n",
       "41143    21\n",
       "Name: highway08, Length: 41144, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highway_mpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "419"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dir(city_mpg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 + 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(2).__add__(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        22.0\n",
       "1        11.5\n",
       "2        28.0\n",
       "3        11.0\n",
       "4        20.0\n",
       "         ... \n",
       "41139    22.5\n",
       "41140    24.0\n",
       "41141    21.0\n",
       "41142    21.0\n",
       "41143    18.5\n",
       "Length: 41144, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(city_mpg + highway_mpg)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    10\n",
       "2    20\n",
       "2    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([10, 20, 30], index=[1,2,2])\n",
    "s2 = pd.Series([35, 44, 53], index=[2,2,4], name='s2')\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    35\n",
       "2    44\n",
       "4    53\n",
       "Name: s2, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     NaN\n",
       "2    55.0\n",
       "2    64.0\n",
       "2    65.0\n",
       "2    74.0\n",
       "4     NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 + s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     NaN\n",
       "2    55.0\n",
       "2    64.0\n",
       "2    65.0\n",
       "2    74.0\n",
       "4     NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 + s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     NaN\n",
       "2    55.0\n",
       "2    64.0\n",
       "2    65.0\n",
       "2    74.0\n",
       "4     NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.add(s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    10.0\n",
       "2    55.0\n",
       "2    64.0\n",
       "2    65.0\n",
       "2    74.0\n",
       "4    53.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.add(s2, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        22.0\n",
       "1        11.5\n",
       "2        28.0\n",
       "3        11.0\n",
       "4        20.0\n",
       "         ... \n",
       "41139    22.5\n",
       "41140    24.0\n",
       "41141    21.0\n",
       "41142    21.0\n",
       "41143    18.5\n",
       "Length: 41144, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((city_mpg +\n",
    "  highway_mpg)\n",
    "  / 2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        22.0\n",
       "1        11.5\n",
       "2        28.0\n",
       "3        11.0\n",
       "4        20.0\n",
       "         ... \n",
       "41139    22.5\n",
       "41140    24.0\n",
       "41141    21.0\n",
       "41142    21.0\n",
       "41143    18.5\n",
       "Length: 41144, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(city_mpg\n",
    "  .add(highway_mpg)\n",
    "  .div(2)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.369045304297103"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.is_monotonic_increasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.quantile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.quantile(.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1    13.0\n",
       "0.5    17.0\n",
       "0.9    24.0\n",
       "Name: city08, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.quantile([.1, .5, .9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10272"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(city_mpg\n",
    " .gt(20)\n",
    " .sum()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.965973167412017"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(city_mpg\n",
    " .gt(20)\n",
    " .mul(100)\n",
    " .mean() \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.369045304297103"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.agg('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def second_to_last(s):\n",
    "    return s.iloc[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean               18.369045\n",
       "var                62.503036\n",
       "max               150.000000\n",
       "second_to_last     18.000000\n",
       "Name: city08, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.agg(['mean', np.var, max, second_to_last])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        19\n",
       "1         9\n",
       "2        23\n",
       "3        10\n",
       "4        17\n",
       "         ..\n",
       "41139    19\n",
       "41140    20\n",
       "41141    18\n",
       "41142    18\n",
       "41143    16\n",
       "Name: city08, Length: 41144, dtype: Int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.convert_dtypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        19\n",
       "1         9\n",
       "2        23\n",
       "3        10\n",
       "4        17\n",
       "         ..\n",
       "41139    19\n",
       "41140    20\n",
       "41141    18\n",
       "41142    18\n",
       "41143    16\n",
       "Name: city08, Length: 41144, dtype: Int16"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.astype('Int16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot safely cast non-equivalent int64 to int8",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/arrays/integer.py\u001b[0m in \u001b[0;36msafe_cast\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"safe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Cannot cast array data from dtype('int64') to dtype('int8') according to the rule 'safe'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-15ba00a8d273>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcity_mpg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Int8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5813\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5814\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5815\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5816\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m     def convert(\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    328\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mignore_failures\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    590\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 592\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array_safe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_coerce_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1309\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1310\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1311\u001b[0m         \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m   1255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1257\u001b[0;31m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1259\u001b[0m     \u001b[0;31m# in pandas we don't store numpy str dtypes, so convert to object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m   1103\u001b[0m     \u001b[0;31m# dispatch on extension dtype if needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExtensionDtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1105\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstruct_array_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1107\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/arrays/integer.py\u001b[0m in \u001b[0;36m_from_sequence\u001b[0;34m(cls, scalars, dtype, copy)\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDtype\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m     ) -> IntegerArray:\n\u001b[0;32m--> 323\u001b[0;31m         \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoerce_to_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    324\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mIntegerArray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/arrays/integer.py\u001b[0m in \u001b[0;36mcoerce_to_array\u001b[0;34m(values, dtype, mask, copy)\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msafe_cast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msafe_cast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/arrays/integer.py\u001b[0m in \u001b[0;36msafe_cast\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mcasted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m         raise TypeError(\n\u001b[0m\u001b[1;32m    134\u001b[0m             \u001b[0;34mf\"cannot safely cast non-equivalent {values.dtype} to {np.dtype(dtype)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m         ) from err\n",
      "\u001b[0;31mTypeError\u001b[0m: cannot safely cast non-equivalent int64 to int8"
     ]
    }
   ],
   "source": [
    "city_mpg.astype('Int8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iinfo(min=-9223372036854775808, max=9223372036854775807, dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.iinfo('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iinfo(min=0, max=255, dtype=uint8)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.iinfo('uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "finfo(resolution=0.001, min=-6.55040e+04, max=6.55040e+04, dtype=float16)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.finfo('float16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "finfo(resolution=1e-15, min=-1.7976931348623157e+308, max=1.7976931348623157e+308, dtype=float64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.finfo('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "329152"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.nbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123432"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.astype('Int16').nbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "329152"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make = df.make\n",
    "make.nbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "329280"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2606395"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95888"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(make\n",
    " .astype('category')\n",
    " .memory_usage(deep=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        19\n",
       "1         9\n",
       "2        23\n",
       "3        10\n",
       "4        17\n",
       "         ..\n",
       "41139    19\n",
       "41140    20\n",
       "41141    18\n",
       "41142    18\n",
       "41143    16\n",
       "Name: city08, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        19\n",
       "1         9\n",
       "2        23\n",
       "3        10\n",
       "4        17\n",
       "         ..\n",
       "41139    19\n",
       "41140    20\n",
       "41141    18\n",
       "41142    18\n",
       "41143    16\n",
       "Name: city08, Length: 41144, dtype: category\n",
       "Categories (105, int64): [6, 7, 8, 9, ..., 137, 138, 140, 150]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        19\n",
       "1         9\n",
       "2        23\n",
       "3        10\n",
       "4        17\n",
       "         ..\n",
       "41139    19\n",
       "41140    20\n",
       "41141    18\n",
       "41142    18\n",
       "41143    16\n",
       "Name: city08, Length: 41144, dtype: category\n",
       "Categories (105, int64): [6 < 7 < 8 < 9 ... 137 < 138 < 140 < 150]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = pd.Series(sorted(set(city_mpg)))\n",
    "city_type = pd.CategoricalDtype(categories=values,\n",
    "    ordered=True)\n",
    "city_mpg.astype(city_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>city08</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41139</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41140</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41141</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41142</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41143</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       city08\n",
       "0          19\n",
       "1           9\n",
       "2          23\n",
       "3          10\n",
       "4          17\n",
       "...       ...\n",
       "41139      19\n",
       "41140      20\n",
       "41141      18\n",
       "41142      18\n",
       "41143      16\n",
       "\n",
       "[41144 rows x 1 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gt20(val):\n",
    "    return val > 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.48 ms ± 49.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "city_mpg.apply(gt20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76.7 µs ± 931 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "city_mpg.gt(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "make = df.make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Alfa Romeo\n",
       "1           Ferrari\n",
       "2             Dodge\n",
       "3             Dodge\n",
       "4            Subaru\n",
       "            ...    \n",
       "41139        Subaru\n",
       "41140        Subaru\n",
       "41141        Subaru\n",
       "41142        Subaru\n",
       "41143        Subaru\n",
       "Name: make, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chevrolet                      4003\n",
       "Ford                           3371\n",
       "Dodge                          2583\n",
       "GMC                            2494\n",
       "Toyota                         2071\n",
       "                               ... \n",
       "Volga Associated Automobile       1\n",
       "Panos                             1\n",
       "Mahindra                          1\n",
       "Excalibur Autos                   1\n",
       "London Coach Co Inc               1\n",
       "Name: make, Length: 136, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "top5 = make.value_counts().index[:5]\n",
    "def generalize_top5(val):\n",
    "    if val in top5:\n",
    "        return val\n",
    "    return 'Other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Other\n",
       "1        Other\n",
       "2        Dodge\n",
       "3        Dodge\n",
       "4        Other\n",
       "         ...  \n",
       "41139    Other\n",
       "41140    Other\n",
       "41141    Other\n",
       "41142    Other\n",
       "41143    Other\n",
       "Name: make, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.apply(generalize_top5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Other\n",
       "1        Other\n",
       "2        Dodge\n",
       "3        Dodge\n",
       "4        Other\n",
       "         ...  \n",
       "41139    Other\n",
       "41140    Other\n",
       "41141    Other\n",
       "41142    Other\n",
       "41143    Other\n",
       "Name: make, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.where(make.isin(top5), other='Other')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 ms ± 76.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "make.apply(generalize_top5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.65 ms ± 35.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "make.where(make.isin(top5), 'Other')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Other\n",
       "1        Other\n",
       "2        Dodge\n",
       "3        Dodge\n",
       "4        Other\n",
       "         ...  \n",
       "41139    Other\n",
       "41140    Other\n",
       "41141    Other\n",
       "41142    Other\n",
       "41143    Other\n",
       "Name: make, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.mask(~make.isin(top5), other='Other')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "vc = make.value_counts()\n",
    "top5 = vc.index[:5]\n",
    "top10 = vc.index[:10]\n",
    "def generalize(val):\n",
    "    if val in top5:\n",
    "        return val\n",
    "    elif val in top10:\n",
    "        return 'Top10'\n",
    "    else:\n",
    "        return 'Other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Other\n",
       "1        Other\n",
       "2        Dodge\n",
       "3        Dodge\n",
       "4        Other\n",
       "         ...  \n",
       "41139    Other\n",
       "41140    Other\n",
       "41141    Other\n",
       "41142    Other\n",
       "41143    Other\n",
       "Name: make, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.apply(generalize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Other\n",
       "1        Other\n",
       "2        Dodge\n",
       "3        Dodge\n",
       "4        Other\n",
       "         ...  \n",
       "41139    Other\n",
       "41140    Other\n",
       "41141    Other\n",
       "41142    Other\n",
       "41143    Other\n",
       "Name: make, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(make\n",
    "  .where(make.isin(top5), 'Top10')\n",
    "  .where(make.isin(top10), 'Other')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Other', 'Other', 'Dodge', ..., 'Other', 'Other', 'Other'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.select([make.isin(top5), make.isin(top10)],\n",
    "   [make, 'Top10'], 'Other')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Other\n",
       "1        Other\n",
       "2        Dodge\n",
       "3        Dodge\n",
       "4        Other\n",
       "         ...  \n",
       "41139    Other\n",
       "41140    Other\n",
       "41141    Other\n",
       "41142    Other\n",
       "41143    Other\n",
       "Length: 41144, dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(np.select([make.isin(top5), make.isin(top10)],\n",
    "   [make, 'Top10'], 'Other'), index=make.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyl = df.cylinders\n",
    "(cyl\n",
    " .isna()\n",
    " .sum()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7138     Nissan\n",
       "7139     Toyota\n",
       "8143     Toyota\n",
       "8144       Ford\n",
       "8146       Ford\n",
       "          ...  \n",
       "34563     Tesla\n",
       "34564     Tesla\n",
       "34565     Tesla\n",
       "34566     Tesla\n",
       "34567     Tesla\n",
       "Name: make, Length: 206, dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = cyl.isna()\n",
    "make.loc[missing]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7138    NaN\n",
       "7139    NaN\n",
       "8143    NaN\n",
       "8144    NaN\n",
       "8146    NaN\n",
       "         ..\n",
       "34563   NaN\n",
       "34564   NaN\n",
       "34565   NaN\n",
       "34566   NaN\n",
       "34567   NaN\n",
       "Name: cylinders, Length: 206, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyl[cyl.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7136    6.0\n",
       "7137    6.0\n",
       "7138    0.0\n",
       "7139    0.0\n",
       "7140    6.0\n",
       "7141    6.0\n",
       "Name: cylinders, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyl.fillna(0).loc[7136:7141]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    32.0\n",
       "1    40.0\n",
       "2     NaN\n",
       "3    42.0\n",
       "4    39.0\n",
       "5    32.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.Series([32, 40, None, 42, 39, 32])\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    32.0\n",
       "1    40.0\n",
       "2    41.0\n",
       "3    42.0\n",
       "4    39.0\n",
       "5    32.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      19\n",
       "1       9\n",
       "2      23\n",
       "3      10\n",
       "4      17\n",
       "       ..\n",
       "442    15\n",
       "443    15\n",
       "444    15\n",
       "445    15\n",
       "446    31\n",
       "Name: city08, Length: 447, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.loc[:446]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      19.0\n",
       "1      11.0\n",
       "2      23.0\n",
       "3      11.0\n",
       "4      17.0\n",
       "       ... \n",
       "442    15.0\n",
       "443    15.0\n",
       "444    15.0\n",
       "445    15.0\n",
       "446    27.0\n",
       "Name: city08, Length: 447, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(city_mpg\n",
    "    .loc[:446]\n",
    "    .clip(lower=city_mpg.quantile(.05),\n",
    "          upper=city_mpg.quantile(.95))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7901       6\n",
       "34557      6\n",
       "37161      6\n",
       "21060      6\n",
       "35887      6\n",
       "        ... \n",
       "34563    138\n",
       "34564    140\n",
       "32599    150\n",
       "31256    150\n",
       "33423    150\n",
       "Name: city08, Length: 41144, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        22.0\n",
       "1        11.5\n",
       "2        28.0\n",
       "3        11.0\n",
       "4        20.0\n",
       "         ... \n",
       "41139    22.5\n",
       "41140    24.0\n",
       "41141    21.0\n",
       "41142    21.0\n",
       "41143    18.5\n",
       "Length: 41144, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(city_mpg.sort_values() + highway_mpg) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        19\n",
       "1         9\n",
       "2        23\n",
       "3        10\n",
       "4        17\n",
       "         ..\n",
       "41139    19\n",
       "41140    20\n",
       "41141    18\n",
       "41142    18\n",
       "41143    16\n",
       "Name: city08, Length: 41144, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.sort_values().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         19\n",
       "1          9\n",
       "2         23\n",
       "3         10\n",
       "4         17\n",
       "        ... \n",
       "34364    127\n",
       "34409    114\n",
       "34564    140\n",
       "34565    115\n",
       "34566    104\n",
       "Name: city08, Length: 105, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        27060.5\n",
       "1          235.5\n",
       "2        35830.0\n",
       "3          607.5\n",
       "4        19484.0\n",
       "          ...   \n",
       "41139    27060.5\n",
       "41140    29719.5\n",
       "41141    23528.0\n",
       "41142    23528.0\n",
       "41143    15479.0\n",
       "Name: city08, Length: 41144, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.rank()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        25555.0\n",
       "1          136.0\n",
       "2        35119.0\n",
       "3          336.0\n",
       "4        17467.0\n",
       "          ...   \n",
       "41139    25555.0\n",
       "41140    28567.0\n",
       "41141    21502.0\n",
       "41142    21502.0\n",
       "41143    13492.0\n",
       "Name: city08, Length: 41144, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.rank(method='min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        14.0\n",
       "1         4.0\n",
       "2        18.0\n",
       "3         5.0\n",
       "4        12.0\n",
       "         ... \n",
       "41139    14.0\n",
       "41140    15.0\n",
       "41141    13.0\n",
       "41142    13.0\n",
       "41143    11.0\n",
       "Name: city08, Length: 41144, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_mpg.rank(method='dense')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Alfa Romeo\n",
       "1           Ferrari\n",
       "2             Dodge\n",
       "3             Dodge\n",
       "4               スバル\n",
       "            ...    \n",
       "41139           スバル\n",
       "41140           スバル\n",
       "41141           スバル\n",
       "41142           スバル\n",
       "41143           スバル\n",
       "Name: make, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.replace('Subaru', 'スバル')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Alfa Romeo\n",
       "1        ri-other-Fer\n",
       "2               Dodge\n",
       "3               Dodge\n",
       "4              Subaru\n",
       "             ...     \n",
       "41139          Subaru\n",
       "41140          Subaru\n",
       "41141          Subaru\n",
       "41142          Subaru\n",
       "41143          Subaru\n",
       "Name: make, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.replace(r'(Fer)ra(r.*)',\n",
    "   value=r'\\2-other-\\1', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        (5.856, 20.4]\n",
       "1        (5.856, 20.4]\n",
       "2         (20.4, 34.8]\n",
       "3        (5.856, 20.4]\n",
       "4        (5.856, 20.4]\n",
       "             ...      \n",
       "41139    (5.856, 20.4]\n",
       "41140    (5.856, 20.4]\n",
       "41141    (5.856, 20.4]\n",
       "41142    (5.856, 20.4]\n",
       "41143    (5.856, 20.4]\n",
       "Name: city08, Length: 41144, dtype: category\n",
       "Categories (10, interval[float64, right]): [(5.856, 20.4] < (20.4, 34.8] < (34.8, 49.2] < (49.2, 63.6] ... (92.4, 106.8] < (106.8, 121.2] < (121.2, 135.6] < (135.6, 150.0]]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(city_mpg, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        (10, 20]\n",
       "1         (0, 10]\n",
       "2        (20, 40]\n",
       "3         (0, 10]\n",
       "4        (10, 20]\n",
       "           ...   \n",
       "41139    (10, 20]\n",
       "41140    (10, 20]\n",
       "41141    (10, 20]\n",
       "41142    (10, 20]\n",
       "41143    (10, 20]\n",
       "Name: city08, Length: 41144, dtype: category\n",
       "Categories (5, interval[int64, right]): [(0, 10] < (10, 20] < (20, 40] < (40, 70] < (70, 150]]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(city_mpg, [0, 10, 20, 40, 70, 150])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         (18.0, 20.0]\n",
       "1        (5.999, 13.0]\n",
       "2         (21.0, 24.0]\n",
       "3        (5.999, 13.0]\n",
       "4         (16.0, 17.0]\n",
       "             ...      \n",
       "41139     (18.0, 20.0]\n",
       "41140     (18.0, 20.0]\n",
       "41141     (17.0, 18.0]\n",
       "41142     (17.0, 18.0]\n",
       "41143     (15.0, 16.0]\n",
       "Name: city08, Length: 41144, dtype: category\n",
       "Categories (10, interval[float64, right]): [(5.999, 13.0] < (13.0, 14.0] < (14.0, 15.0] < (15.0, 16.0] ... (18.0, 20.0] < (20.0, 21.0] < (21.0, 24.0] < (24.0, 150.0]]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(city_mpg, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        7\n",
       "1        1\n",
       "2        9\n",
       "3        1\n",
       "4        5\n",
       "        ..\n",
       "41139    7\n",
       "41140    7\n",
       "41141    6\n",
       "41142    6\n",
       "41143    4\n",
       "Name: city08, Length: 41144, dtype: category\n",
       "Categories (10, int64): [1 < 2 < 3 < 4 ... 7 < 8 < 9 < 10]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(city_mpg, 10, labels=list(range(1,11)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alfa Romeo    19\n",
       "Ferrari        9\n",
       "Dodge         23\n",
       "Dodge         10\n",
       "Subaru        17\n",
       "              ..\n",
       "Subaru        19\n",
       "Subaru        20\n",
       "Subaru        18\n",
       "Subaru        18\n",
       "Subaru        16\n",
       "Name: city08, Length: 41144, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2 = city_mpg.rename(make.to_dict())\n",
    "city2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Alfa Romeo', 'Ferrari', 'Dodge', 'Dodge', 'Subaru', 'Subaru', 'Subaru',\n",
       "       'Toyota', 'Toyota', 'Toyota',\n",
       "       ...\n",
       "       'Saab', 'Saturn', 'Saturn', 'Saturn', 'Saturn', 'Subaru', 'Subaru',\n",
       "       'Subaru', 'Subaru', 'Subaru'],\n",
       "      dtype='object', length=41144)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alfa Romeo    19\n",
       "Ferrari        9\n",
       "Dodge         23\n",
       "Dodge         10\n",
       "Subaru        17\n",
       "              ..\n",
       "Subaru        19\n",
       "Subaru        20\n",
       "Subaru        18\n",
       "Subaru        18\n",
       "Subaru        16\n",
       "Name: city08, Length: 41144, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2 = city_mpg.rename(make)\n",
    "city2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alfa Romeo    19\n",
       "Ferrari        9\n",
       "Dodge         23\n",
       "Dodge         10\n",
       "Subaru        17\n",
       "              ..\n",
       "Subaru        19\n",
       "Subaru        20\n",
       "Subaru        18\n",
       "Subaru        18\n",
       "Subaru        16\n",
       "Name: citympg, Length: 41144, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.rename('citympg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "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>index</th>\n",
       "      <th>city08</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alfa Romeo</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ferrari</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dodge</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dodge</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Subaru</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41139</th>\n",
       "      <td>Subaru</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41140</th>\n",
       "      <td>Subaru</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41141</th>\n",
       "      <td>Subaru</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41142</th>\n",
       "      <td>Subaru</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41143</th>\n",
       "      <td>Subaru</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            index  city08\n",
       "0      Alfa Romeo      19\n",
       "1         Ferrari       9\n",
       "2           Dodge      23\n",
       "3           Dodge      10\n",
       "4          Subaru      17\n",
       "...           ...     ...\n",
       "41139      Subaru      19\n",
       "41140      Subaru      20\n",
       "41141      Subaru      18\n",
       "41142      Subaru      18\n",
       "41143      Subaru      16\n",
       "\n",
       "[41144 rows x 2 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        19\n",
       "1         9\n",
       "2        23\n",
       "3        10\n",
       "4        17\n",
       "         ..\n",
       "41139    19\n",
       "41140    20\n",
       "41141    18\n",
       "41142    18\n",
       "41143    16\n",
       "Name: city08, Length: 41144, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subaru    17\n",
       "Subaru    21\n",
       "Subaru    22\n",
       "Subaru    19\n",
       "Subaru    20\n",
       "          ..\n",
       "Subaru    19\n",
       "Subaru    20\n",
       "Subaru    18\n",
       "Subaru    18\n",
       "Subaru    16\n",
       "Name: city08, Length: 885, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.loc['Subaru']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.loc['Fisker']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fisker    20\n",
       "Name: city08, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.loc[['Fisker']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ferrari         9\n",
       "Ferrari        12\n",
       "Ferrari        11\n",
       "Ferrari        10\n",
       "Ferrari        11\n",
       "               ..\n",
       "Lamborghini     6\n",
       "Lamborghini     8\n",
       "Lamborghini     8\n",
       "Lamborghini     8\n",
       "Lamborghini     8\n",
       "Name: city08, Length: 357, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.loc[['Ferrari', 'Lamborghini']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"Cannot get left slice bound for non-unique label: 'Ferrari'\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-98-ca8b99f7d7f7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcity2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ferrari'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'Lamborghini'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1140\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1141\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1142\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_slice_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1143\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1144\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getbool_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_slice_axis\u001b[0;34m(self, slice_obj, axis)\u001b[0m\n\u001b[1;32m   1174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1176\u001b[0;31m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslice_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1178\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_indexer\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   5684\u001b[0m         \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5685\u001b[0m         \"\"\"\n\u001b[0;32m-> 5686\u001b[0;31m         \u001b[0mstart_slice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5688\u001b[0m         \u001b[0;31m# return a slice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mslice_locs\u001b[0;34m(self, start, end, step, kind)\u001b[0m\n\u001b[1;32m   5886\u001b[0m         \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5887\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5888\u001b[0;31m             \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_slice_bound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"left\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5889\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5890\u001b[0m             \u001b[0mstart_slice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[0;34m(self, label, side, kind)\u001b[0m\n\u001b[1;32m   5818\u001b[0m                 )\n\u001b[1;32m   5819\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5820\u001b[0;31m                 raise KeyError(\n\u001b[0m\u001b[1;32m   5821\u001b[0m                     \u001b[0;34mf\"Cannot get {side} slice bound for non-unique \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5822\u001b[0m                     \u001b[0;34mf\"label: {repr(original_label)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"Cannot get left slice bound for non-unique label: 'Ferrari'\""
     ]
    }
   ],
   "source": [
    "city2.loc['Ferrari':'Lamborghini']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ferrari        10\n",
       "Ferrari        13\n",
       "Ferrari        13\n",
       "Ferrari         9\n",
       "Ferrari        10\n",
       "               ..\n",
       "Lamborghini    12\n",
       "Lamborghini     9\n",
       "Lamborghini     8\n",
       "Lamborghini    13\n",
       "Lamborghini     8\n",
       "Name: city08, Length: 11210, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.sort_index().loc['Ferrari':'Lamborghini']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Federal Coach    15\n",
       "Federal Coach    13\n",
       "Federal Coach    13\n",
       "Federal Coach    14\n",
       "Federal Coach    13\n",
       "                 ..\n",
       "Isuzu            15\n",
       "Isuzu            15\n",
       "Isuzu            15\n",
       "Isuzu            27\n",
       "Isuzu            18\n",
       "Name: city08, Length: 9040, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.sort_index().loc[\"F\":\"J\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dodge    23\n",
       "Dodge    10\n",
       "Dodge    12\n",
       "Dodge    11\n",
       "Dodge    11\n",
       "         ..\n",
       "Dodge    18\n",
       "Dodge    17\n",
       "Dodge    14\n",
       "Dodge    14\n",
       "Dodge    11\n",
       "Name: city08, Length: 2583, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.Index(['Dodge'])\n",
    "city2.loc[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dodge    23\n",
       "Dodge    10\n",
       "Dodge    12\n",
       "Dodge    11\n",
       "Dodge    11\n",
       "         ..\n",
       "Dodge    18\n",
       "Dodge    17\n",
       "Dodge    14\n",
       "Dodge    14\n",
       "Dodge    11\n",
       "Name: city08, Length: 5166, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.Index(['Dodge', 'Dodge'])\n",
    "city2.loc[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alfa Romeo    False\n",
       "Ferrari       False\n",
       "Dodge         False\n",
       "Dodge         False\n",
       "Subaru        False\n",
       "              ...  \n",
       "Subaru        False\n",
       "Subaru        False\n",
       "Subaru        False\n",
       "Subaru        False\n",
       "Subaru        False\n",
       "Name: city08, Length: 41144, dtype: bool"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = city2 > 50\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nissan     81\n",
       "Toyota     81\n",
       "Toyota     81\n",
       "Ford       74\n",
       "Nissan     84\n",
       "         ... \n",
       "Tesla     140\n",
       "Tesla     115\n",
       "Tesla     104\n",
       "Tesla      98\n",
       "Toyota     55\n",
       "Name: city08, Length: 236, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.loc[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Melon      4.389\n",
       "Carrots    3.069\n",
       "dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost = pd.Series([1.00, 2.25, 3.99, .99, 2.79], \n",
    "    index=['Gum', 'Cookie', 'Melon', 'Roll', 'Carrots'])\n",
    "inflation = 1.10\n",
    "(cost\n",
    "    .mul(inflation)\n",
    "    .loc[lambda s_: s_ > 3]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Melon    4.389\n",
       "dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost = pd.Series([1.00, 2.25, 3.99, .99, 2.79], \n",
    "   index=['Gum', 'Cookie', 'Melon', 'Roll', 'Carrots'])\n",
    "inflation = 1.10\n",
    "mask = cost > 3\n",
    "(cost\n",
    "    .mul(inflation)\n",
    "    .loc[mask]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gt3(s):\n",
    "    return s > 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "gt3 = lambda s: s > 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alfa Romeo    19\n",
       "Ferrari        9\n",
       "Subaru        16\n",
       "Name: city08, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.iloc[[0,1,-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alfa Romeo    19\n",
       "Ferrari        9\n",
       "Dodge         23\n",
       "Dodge         10\n",
       "Subaru        17\n",
       "Name: city08, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.iloc[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Saturn    21\n",
       "Saturn    24\n",
       "Saturn    21\n",
       "Subaru    19\n",
       "Subaru    20\n",
       "Subaru    18\n",
       "Subaru    18\n",
       "Subaru    16\n",
       "Name: city08, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.iloc[-8:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "iLocation based boolean indexing cannot use an indexable as a mask",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-114-59298d41640c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcity2\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcity2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1552\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1553\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getbool_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_key\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1398\u001b[0m                         \u001b[0;34m\"is not available\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1399\u001b[0m                     )\n\u001b[0;32m-> 1400\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m   1401\u001b[0m                     \u001b[0;34m\"iLocation based boolean indexing cannot use \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1402\u001b[0m                     \u001b[0;34m\"an indexable as a mask\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: iLocation based boolean indexing cannot use an indexable as a mask"
     ]
    }
   ],
   "source": [
    "mask = city2 > 50\n",
    "city2.iloc[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nissan     81\n",
       "Toyota     81\n",
       "Toyota     81\n",
       "Ford       74\n",
       "Nissan     84\n",
       "         ... \n",
       "Tesla     140\n",
       "Tesla     115\n",
       "Tesla     104\n",
       "Tesla      98\n",
       "Toyota     55\n",
       "Name: city08, Length: 236, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.iloc[mask.to_numpy()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nissan     81\n",
       "Toyota     81\n",
       "Toyota     81\n",
       "Ford       74\n",
       "Nissan     84\n",
       "         ... \n",
       "Tesla     140\n",
       "Tesla     115\n",
       "Tesla     104\n",
       "Tesla      98\n",
       "Toyota     55\n",
       "Name: city08, Length: 236, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.iloc[list(mask)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alfa Romeo    19\n",
       "Ferrari        9\n",
       "Dodge         23\n",
       "Name: city08, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subaru    18\n",
       "Subaru    18\n",
       "Subaru    16\n",
       "Name: city08, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Volvo         16\n",
       "Mitsubishi    19\n",
       "Buick         27\n",
       "Jeep          15\n",
       "Land Rover    13\n",
       "Saab          17\n",
       "Name: city08, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2.sample(6, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reindex from a duplicate axis",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-121-7de9bb7e417f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcity2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ford'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Subaru'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mfilter\u001b[0;34m(self, items, like, regex, axis)\u001b[0m\n\u001b[1;32m   4980\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mitems\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4981\u001b[0m             \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4982\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mitems\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4983\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mlike\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4984\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, index, **kwargs)\u001b[0m\n\u001b[1;32m   4578\u001b[0m     )\n\u001b[1;32m   4579\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4580\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4582\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mdeprecate_nonkeyword_arguments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mversion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallowed_args\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"self\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"labels\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   4816\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4817\u001b[0m         \u001b[0;31m# perform the reindex on the axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4818\u001b[0;31m         return self._reindex_axes(\n\u001b[0m\u001b[1;32m   4819\u001b[0m             \u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4820\u001b[0m         ).__finalize__(self, method=\"reindex\")\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_reindex_axes\u001b[0;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[1;32m   4837\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4838\u001b[0m             \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4839\u001b[0;31m             obj = obj._reindex_with_indexers(\n\u001b[0m\u001b[1;32m   4840\u001b[0m                 \u001b[0;34m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnew_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4841\u001b[0m                 \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_reindex_with_indexers\u001b[0;34m(self, reindexers, fill_value, copy, allow_dups)\u001b[0m\n\u001b[1;32m   4881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4882\u001b[0m             \u001b[0;31m# TODO: speed up on homogeneous DataFrame objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4883\u001b[0;31m             new_data = new_data.reindex_indexer(\n\u001b[0m\u001b[1;32m   4884\u001b[0m                 \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4885\u001b[0m                 \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mreindex_indexer\u001b[0;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate, only_slice)\u001b[0m\n\u001b[1;32m    668\u001b[0m         \u001b[0;31m# some axes don't allow reindexing with dups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 670\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_can_reindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_validate_can_reindex\u001b[0;34m(self, indexer)\u001b[0m\n\u001b[1;32m   3783\u001b[0m         \u001b[0;31m# trying to reindex on an axis with duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3784\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_index_as_unique\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3785\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cannot reindex from a duplicate axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3787\u001b[0m     def reindex(\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reindex from a duplicate axis"
     ]
    }
   ],
   "source": [
    "city2.filter(items=['Ford', 'Subaru'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "city2.filter(like='rd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "city2.filter(regex='(Ford)|(Subaru)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "city2.reindex(['Missing', 'Ford'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "city_mpg.reindex([0,0, 10, 20, 2_000_000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "s1 = pd.Series([10,20,30], index=['a', 'b', 'c'])\n",
    "s2 = pd.Series([15,25,35], index=['b', 'c', 'd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "s2.reindex(s1.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Inspect the index."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Sort the index."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Set the index to monotonically increasing integers starting from 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Set the index to monotonically increasing integers starting from 0, then convert these to the string version. Save this a ``s2``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Using ``s2``, pull out the first 5 entries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Using ``s2``, pull out the last 5 entries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Using ``s2``, pull out one hundred entries starting at index position 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Using ``s2``, create a series with values with index entries ``'20'``, ``'10'``, and ``'2'``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "make.astype('string')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "make.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "'Ford'.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "make.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "'Alfa Romeo'.find('A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "make.str.find('A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "make.str.extract(r'([^a-z A-Z])')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "(make\n",
    "  .str.extract(r'([^a-z A-Z])', expand=False)\n",
    "  .value_counts()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0-10\n",
       "1    11-15\n",
       "2    11-15\n",
       "3    61-65\n",
       "4    46-50\n",
       "dtype: object"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.Series(['0-10', '11-15', '11-15', '61-65', '46-50'])\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     [0, 10]\n",
       "1    [11, 15]\n",
       "2    [11, 15]\n",
       "3    [61, 65]\n",
       "4    [46, 50]\n",
       "dtype: object"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age.str.split('-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1    11\n",
       "2    11\n",
       "3    61\n",
       "4    46\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(age\n",
    "  .str.split('-', expand=True)\n",
    "  .iloc[:,0]\n",
    "  .astype(int)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    15\n",
       "2    15\n",
       "3    65\n",
       "4    50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(age\n",
    "  .str.slice(-2)\n",
    "  .astype(int)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    15\n",
       "2    15\n",
       "3    65\n",
       "4    50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(age\n",
    "  .str[-2:]\n",
    "  .astype(int)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1    13.0\n",
       "2    13.0\n",
       "3    63.0\n",
       "4    48.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(age\n",
    "  .str.split('-', expand=True)\n",
    "  .astype(int)\n",
    "  .mean(axis='columns')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "def between(row):\n",
    "    return random.randint(*row.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2\n",
       "1    15\n",
       "2    15\n",
       "3    62\n",
       "4    50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(age\n",
    "  .str.split('-', expand=True)\n",
    "  .astype(int)\n",
    "  .apply(between, axis='columns')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Ålfa Romeo\n",
       "1           Ferrari\n",
       "2             Dodge\n",
       "3             Dodge\n",
       "4            Subaru\n",
       "            ...    \n",
       "41139        Subaru\n",
       "41140        Subaru\n",
       "41141        Subaru\n",
       "41142        Subaru\n",
       "41143        Subaru\n",
       "Name: make, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.str.replace('A', 'Å')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Alfa Romeo\n",
       "1           Ferrari\n",
       "2             Dodge\n",
       "3             Dodge\n",
       "4            Subaru\n",
       "            ...    \n",
       "41139        Subaru\n",
       "41140        Subaru\n",
       "41141        Subaru\n",
       "41142        Subaru\n",
       "41143        Subaru\n",
       "Name: make, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.replace('A', 'Å')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Ålfa Romeo\n",
       "1           Ferrari\n",
       "2             Dodge\n",
       "3             Dodge\n",
       "4            Subaru\n",
       "            ...    \n",
       "41139        Subaru\n",
       "41140        Subaru\n",
       "41141        Subaru\n",
       "41142        Subaru\n",
       "41143        Subaru\n",
       "Name: make, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.replace({'Audi': 'Åudi', 'Acura': 'Åcura',\n",
    "    'Ashton Martin': 'Åshton Martin',\n",
    "    'Alfa Romeo': 'Ålfa Romeo'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Ålfa Romeo\n",
       "1           Ferrari\n",
       "2             Dodge\n",
       "3             Dodge\n",
       "4            Subaru\n",
       "            ...    \n",
       "41139        Subaru\n",
       "41140        Subaru\n",
       "41141        Subaru\n",
       "41142        Subaru\n",
       "41143        Subaru\n",
       "Name: make, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.replace('A', 'Å', regex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Using a string column, lowercase the values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Using a string column, slice out the first character."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Using a string column, slice out the last three characters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Using a string column, create a series extracting the numeric values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Using a string column, create a series extracting the non-ASCII values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Using a string column, create a dataframe with the dummy columns for every character in the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "col = pd.Series(['2015-03-08 08:00:00+00:00',\n",
    "    '2015-03-08 08:30:00+00:00',\n",
    "    '2015-03-08 09:00:00+00:00',\n",
    "    '2015-03-08 09:30:00+00:00',\n",
    "    '2015-11-01 06:30:00+00:00',\n",
    "    '2015-11-01 07:00:00+00:00',\n",
    "    '2015-11-01 07:30:00+00:00',\n",
    "    '2015-11-01 08:00:00+00:00',\n",
    "    '2015-11-01 08:30:00+00:00',\n",
    "    '2015-11-01 08:00:00+00:00',\n",
    "    '2015-11-01 08:30:00+00:00',\n",
    "    '2015-11-01 09:00:00+00:00',\n",
    "    '2015-11-01 09:30:00+00:00',\n",
    "    '2015-11-01 10:00:00+00:00'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015-03-08 08:00:00+00:00\n",
       "1    2015-03-08 08:30:00+00:00\n",
       "2    2015-03-08 09:00:00+00:00\n",
       "3    2015-03-08 09:30:00+00:00\n",
       "4    2015-11-01 06:30:00+00:00\n",
       "                ...           \n",
       "9    2015-11-01 08:00:00+00:00\n",
       "10   2015-11-01 08:30:00+00:00\n",
       "11   2015-11-01 09:00:00+00:00\n",
       "12   2015-11-01 09:30:00+00:00\n",
       "13   2015-11-01 10:00:00+00:00\n",
       "Length: 14, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utc_s = pd.to_datetime(col, utc=True)\n",
    "utc_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015-03-08 01:00:00-07:00\n",
       "1    2015-03-08 01:30:00-07:00\n",
       "2    2015-03-08 03:00:00-06:00\n",
       "3    2015-03-08 03:30:00-06:00\n",
       "4    2015-11-01 00:30:00-06:00\n",
       "                ...           \n",
       "9    2015-11-01 01:00:00-07:00\n",
       "10   2015-11-01 01:30:00-07:00\n",
       "11   2015-11-01 02:00:00-07:00\n",
       "12   2015-11-01 02:30:00-07:00\n",
       "13   2015-11-01 03:00:00-07:00\n",
       "Length: 14, dtype: datetime64[ns, America/Denver]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utc_s.dt.tz_convert('America/Denver')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "s = pd.Series(['2015-03-08 01:00:00-07:00',\n",
    " '2015-03-08 01:30:00-07:00',\n",
    " '2015-03-08 03:00:00-06:00',\n",
    " '2015-03-08 03:30:00-06:00',\n",
    " '2015-11-01 00:30:00-06:00',\n",
    " '2015-11-01 01:00:00-06:00',\n",
    " '2015-11-01 01:30:00-06:00',\n",
    " '2015-11-01 01:00:00-07:00',\n",
    " '2015-11-01 01:30:00-07:00',\n",
    " '2015-11-01 01:00:00-07:00',\n",
    " '2015-11-01 01:30:00-07:00',\n",
    " '2015-11-01 02:00:00-07:00',\n",
    " '2015-11-01 02:30:00-07:00',\n",
    " '2015-11-01 03:00:00-07:00'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015-03-08 01:00:00-07:00\n",
       "1    2015-03-08 01:30:00-07:00\n",
       "2    2015-03-08 03:00:00-06:00\n",
       "3    2015-03-08 03:30:00-06:00\n",
       "4    2015-11-01 00:30:00-06:00\n",
       "                ...           \n",
       "9    2015-11-01 01:00:00-07:00\n",
       "10   2015-11-01 01:30:00-07:00\n",
       "11   2015-11-01 02:00:00-07:00\n",
       "12   2015-11-01 02:30:00-07:00\n",
       "13   2015-11-01 03:00:00-07:00\n",
       "Length: 14, dtype: datetime64[ns, America/Denver]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(s, utc=True).dt.tz_convert('America/Denver')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "time = pd.Series(['2015-03-08 01:00:00',\n",
    " '2015-03-08 01:30:00',\n",
    " '2015-03-08 02:00:00',\n",
    " '2015-03-08 02:30:00',\n",
    " '2015-03-08 03:00:00',\n",
    " '2015-03-08 02:00:00',\n",
    " '2015-03-08 02:30:00',\n",
    " '2015-03-08 03:00:00',\n",
    " '2015-03-08 03:30:00',\n",
    " '2015-11-01 00:30:00',\n",
    " '2015-11-01 01:00:00',\n",
    " '2015-11-01 01:30:00',\n",
    " '2015-11-01 02:00:00',\n",
    " '2015-11-01 02:30:00',\n",
    " '2015-11-01 01:00:00',\n",
    " '2015-11-01 01:30:00',\n",
    " '2015-11-01 02:00:00',\n",
    " '2015-11-01 02:30:00',\n",
    " '2015-11-01 03:00:00'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "offset = pd.Series([-7, -7, -7, -7, -7, -6, -6,\n",
    "  -6, -6, -6, -6, -6, -6, -6, -7, -7, -7, -7, -7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015-03-07 18:00:07-07:00\n",
       "1    2015-03-07 18:30:07-07:00\n",
       "2    2015-03-07 19:00:07-07:00\n",
       "3    2015-03-07 19:30:07-07:00\n",
       "4    2015-03-07 20:00:07-07:00\n",
       "                ...           \n",
       "14   2015-10-31 19:00:07-06:00\n",
       "15   2015-10-31 19:30:07-06:00\n",
       "16   2015-10-31 20:00:07-06:00\n",
       "17   2015-10-31 20:30:07-06:00\n",
       "18   2015-10-31 21:00:07-06:00\n",
       "Length: 19, dtype: datetime64[ns, America/Denver]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.to_datetime(time)\n",
    "    .groupby(offset)\n",
    "    .transform(lambda s: s.dt.tz_localize(s.name)\n",
    "                          .dt.tz_convert('America/Denver'))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "offset = offset.replace({-7:'-07:00', -6:'-06:00'})\n",
    "local = (pd.to_datetime(time)\n",
    "    .groupby(offset)\n",
    "    .transform(lambda s: s.dt.tz_localize(s.name)\n",
    "                          .dt.tz_convert('America/Denver'))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015-03-08 01:00:00-07:00\n",
       "1    2015-03-08 01:30:00-07:00\n",
       "2    2015-03-08 03:00:00-06:00\n",
       "3    2015-03-08 03:30:00-06:00\n",
       "4    2015-03-08 04:00:00-06:00\n",
       "                ...           \n",
       "14   2015-11-01 01:00:00-07:00\n",
       "15   2015-11-01 01:30:00-07:00\n",
       "16   2015-11-01 02:00:00-07:00\n",
       "17   2015-11-01 02:30:00-07:00\n",
       "18   2015-11-01 03:00:00-07:00\n",
       "Length: 19, dtype: datetime64[ns, America/Denver]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015-03-08 08:00:00+00:00\n",
       "1    2015-03-08 08:30:00+00:00\n",
       "2    2015-03-08 09:00:00+00:00\n",
       "3    2015-03-08 09:30:00+00:00\n",
       "4    2015-03-08 10:00:00+00:00\n",
       "                ...           \n",
       "14   2015-11-01 08:00:00+00:00\n",
       "15   2015-11-01 08:30:00+00:00\n",
       "16   2015-11-01 09:00:00+00:00\n",
       "17   2015-11-01 09:30:00+00:00\n",
       "18   2015-11-01 10:00:00+00:00\n",
       "Length: 19, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local.dt.tz_convert('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1425801600\n",
       "1     1425803400\n",
       "2     1425805200\n",
       "3     1425807000\n",
       "4     1425808800\n",
       "         ...    \n",
       "14    1446364800\n",
       "15    1446366600\n",
       "16    1446368400\n",
       "17    1446370200\n",
       "18    1446372000\n",
       "Length: 19, dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "secs = local.view(int).floordiv(1e9).astype(int)\n",
    "secs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015-03-08 08:00:00+00:00\n",
       "1    2015-03-08 08:30:00+00:00\n",
       "2    2015-03-08 09:00:00+00:00\n",
       "3    2015-03-08 09:30:00+00:00\n",
       "4    2015-03-08 10:00:00+00:00\n",
       "                ...           \n",
       "14   2015-11-01 08:00:00+00:00\n",
       "15   2015-11-01 08:30:00+00:00\n",
       "16   2015-11-01 09:00:00+00:00\n",
       "17   2015-11-01 09:30:00+00:00\n",
       "18   2015-11-01 10:00:00+00:00\n",
       "Length: 19, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.to_datetime(secs, unit='s')\n",
    "  .dt.tz_localize('UTC'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "url = 'https://github.com/mattharrison/datasets'+\\\n",
    "    '/raw/master/data/alta-noaa-1980-2019.csv'\n",
    "alta_df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1980-01-01\n",
       "1       1980-01-02\n",
       "2       1980-01-03\n",
       "3       1980-01-04\n",
       "4       1980-01-05\n",
       "           ...    \n",
       "14155   2019-09-03\n",
       "14156   2019-09-04\n",
       "14157   2019-09-05\n",
       "14158   2019-09-06\n",
       "14159   2019-09-07\n",
       "Name: DATE, Length: 14160, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.to_datetime(alta_df.DATE)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           Martes\n",
       "1        Miércoles\n",
       "2           Jueves\n",
       "3          Viernes\n",
       "4           Sábado\n",
       "           ...    \n",
       "14155       Martes\n",
       "14156    Miércoles\n",
       "14157       Jueves\n",
       "14158      Viernes\n",
       "14159       Sábado\n",
       "Name: DATE, Length: 14160, dtype: object"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates.dt.day_name('es_ES')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "14155    False\n",
       "14156    False\n",
       "14157    False\n",
       "14158    False\n",
       "14159    False\n",
       "Name: DATE, Length: 14160, dtype: bool"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates.dt.is_month_end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        01/01/80\n",
       "1        02/01/80\n",
       "2        03/01/80\n",
       "3        04/01/80\n",
       "4        05/01/80\n",
       "           ...   \n",
       "14155    03/09/19\n",
       "14156    04/09/19\n",
       "14157    05/09/19\n",
       "14158    06/09/19\n",
       "14159    07/09/19\n",
       "Name: DATE, Length: 14160, dtype: object"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates.dt.strftime('%d/%m/%y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Convert a column with date information to a date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Convert a date column into UTC dates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Convert a date column into local dates with a timezone."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Convert a date column into epoch values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Convert an epoch number into UTC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "url = 'https://github.com/mattharrison/datasets'+\\\n",
    "    '/raw/master/data/alta-noaa-1980-2019.csv'\n",
    "alta_df = pd.read_csv(url)\n",
    "dates = pd.to_datetime(alta_df.DATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "snow = (alta_df\n",
    "   .SNOW\n",
    "   .rename(dates)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-01-01    2.0\n",
       "1980-01-02    3.0\n",
       "1980-01-03    1.0\n",
       "1980-01-04    0.0\n",
       "1980-01-05    0.0\n",
       "             ... \n",
       "2019-09-03    0.0\n",
       "2019-09-04    0.0\n",
       "2019-09-05    0.0\n",
       "2019-09-06    0.0\n",
       "2019-09-07    0.0\n",
       "Name: SNOW, Length: 14160, dtype: float64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snow.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1985-07-30   NaN\n",
       "1985-09-12   NaN\n",
       "1985-09-19   NaN\n",
       "1986-02-07   NaN\n",
       "1986-06-26   NaN\n",
       "              ..\n",
       "2017-04-26   NaN\n",
       "2017-09-20   NaN\n",
       "2017-10-02   NaN\n",
       "2017-12-23   NaN\n",
       "2018-12-03   NaN\n",
       "Name: SNOW, Length: 365, dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snow[snow.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1985-09-01    0.0\n",
       "1985-09-02    0.0\n",
       "1985-09-03    0.0\n",
       "1985-09-04    0.0\n",
       "1985-09-05    0.0\n",
       "             ... \n",
       "1985-09-16    0.0\n",
       "1985-09-17    0.0\n",
       "1985-09-18    0.0\n",
       "1985-09-19    NaN\n",
       "1985-09-20    0.0\n",
       "Name: SNOW, Length: 20, dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snow.loc['1985-09':'1985-09-20']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1985-09-01    0.0\n",
       "1985-09-02    0.0\n",
       "1985-09-03    0.0\n",
       "1985-09-04    0.0\n",
       "1985-09-05    0.0\n",
       "             ... \n",
       "1985-09-16    0.0\n",
       "1985-09-17    0.0\n",
       "1985-09-18    0.0\n",
       "1985-09-19    0.0\n",
       "1985-09-20    0.0\n",
       "Name: SNOW, Length: 20, dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "   .loc['1985-09':'1985-09-20']\n",
    "   .fillna(0)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1987-12-30    6.0\n",
       "1987-12-31    5.0\n",
       "1988-01-01    NaN\n",
       "1988-01-02    0.0\n",
       "1988-01-03    0.0\n",
       "             ... \n",
       "1988-01-06    6.0\n",
       "1988-01-07    4.0\n",
       "1988-01-08    9.0\n",
       "1988-01-09    5.0\n",
       "1988-01-10    2.0\n",
       "Name: SNOW, Length: 12, dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snow.loc['1987-12-30':'1988-01-10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1987-12-30    6.0\n",
       "1987-12-31    5.0\n",
       "1988-01-01    5.0\n",
       "1988-01-02    0.0\n",
       "1988-01-03    0.0\n",
       "             ... \n",
       "1988-01-06    6.0\n",
       "1988-01-07    4.0\n",
       "1988-01-08    9.0\n",
       "1988-01-09    5.0\n",
       "1988-01-10    2.0\n",
       "Name: SNOW, Length: 12, dtype: float64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "   .loc['1987-12-30':'1988-01-10']\n",
    "   .ffill()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1987-12-30    6.0\n",
       "1987-12-31    5.0\n",
       "1988-01-01    0.0\n",
       "1988-01-02    0.0\n",
       "1988-01-03    0.0\n",
       "             ... \n",
       "1988-01-06    6.0\n",
       "1988-01-07    4.0\n",
       "1988-01-08    9.0\n",
       "1988-01-09    5.0\n",
       "1988-01-10    2.0\n",
       "Name: SNOW, Length: 12, dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "    .loc['1987-12-30':'1988-01-10']\n",
    "    .bfill()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1987-12-30    6.0\n",
       "1987-12-31    5.0\n",
       "1988-01-01    2.5\n",
       "1988-01-02    0.0\n",
       "1988-01-03    0.0\n",
       "             ... \n",
       "1988-01-06    6.0\n",
       "1988-01-07    4.0\n",
       "1988-01-08    9.0\n",
       "1988-01-09    5.0\n",
       "1988-01-10    2.0\n",
       "Name: SNOW, Length: 12, dtype: float64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "    .loc['1987-12-30':'1988-01-10']\n",
    "    .interpolate()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-01-01    2.0\n",
       "1980-01-02    3.0\n",
       "1980-01-03    1.0\n",
       "1980-01-04    0.0\n",
       "1980-01-05    0.0\n",
       "             ... \n",
       "2019-09-03    0.0\n",
       "2019-09-04    0.0\n",
       "2019-09-05    0.0\n",
       "2019-09-06    0.0\n",
       "2019-09-07    0.0\n",
       "Name: SNOW, Length: 14160, dtype: float64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winter = (snow.index.quarter == 1) | (snow.index.quarter== 4)\n",
    "(snow\n",
    "    .where(~(winter & snow.isna()), snow.interpolate())\n",
    "    .where(~(~winter & snow.isna()), 0)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1985-09-19    0.0\n",
       "1988-01-01    2.5\n",
       "Name: SNOW, dtype: float64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "    .where(~(winter & snow.isna()), snow.interpolate())\n",
    "    .where(~(~winter & snow.isna()), 0)\n",
    "    .loc[['1985-09-19','1988-01-01']]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1987-12-30    6.0\n",
       "1987-12-31    5.0\n",
       "1988-01-02    0.0\n",
       "1988-01-03    0.0\n",
       "1988-01-05    2.0\n",
       "1988-01-06    6.0\n",
       "1988-01-07    4.0\n",
       "1988-01-08    9.0\n",
       "1988-01-09    5.0\n",
       "1988-01-10    2.0\n",
       "Name: SNOW, dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "    .loc['1987-12-30':'1988-01-10']\n",
    "    .dropna()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-01-01    NaN\n",
       "1980-01-02    2.0\n",
       "1980-01-03    3.0\n",
       "1980-01-04    1.0\n",
       "1980-01-05    0.0\n",
       "             ... \n",
       "2019-09-03    0.0\n",
       "2019-09-04    0.0\n",
       "2019-09-05    0.0\n",
       "2019-09-06    0.0\n",
       "2019-09-07    0.0\n",
       "Name: SNOW, Length: 14160, dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snow.shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-01-01    3.0\n",
       "1980-01-02    1.0\n",
       "1980-01-03    0.0\n",
       "1980-01-04    0.0\n",
       "1980-01-05    1.0\n",
       "             ... \n",
       "2019-09-03    0.0\n",
       "2019-09-04    0.0\n",
       "2019-09-05    0.0\n",
       "2019-09-06    0.0\n",
       "2019-09-07    NaN\n",
       "Name: SNOW, Length: 14160, dtype: float64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snow.shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-01-01    NaN\n",
       "1980-01-02    NaN\n",
       "1980-01-03    NaN\n",
       "1980-01-04    NaN\n",
       "1980-01-05    1.2\n",
       "             ... \n",
       "2019-09-03    0.0\n",
       "2019-09-04    0.0\n",
       "2019-09-05    0.0\n",
       "2019-09-06    0.0\n",
       "2019-09-07    0.0\n",
       "Name: SNOW, Length: 14160, dtype: float64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "  .add(snow.shift(1))\n",
    "  .add(snow.shift(2))\n",
    "  .add(snow.shift(3))\n",
    "  .add(snow.shift(4))\n",
    "  .div(5)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-01-01    NaN\n",
       "1980-01-02    NaN\n",
       "1980-01-03    NaN\n",
       "1980-01-04    NaN\n",
       "1980-01-05    1.2\n",
       "             ... \n",
       "2019-09-03    0.0\n",
       "2019-09-04    0.0\n",
       "2019-09-05    0.0\n",
       "2019-09-06    0.0\n",
       "2019-09-07    0.0\n",
       "Name: SNOW, Length: 14160, dtype: float64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "  .rolling(5)\n",
    "  .mean()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-01-31    20.0\n",
       "1980-02-29    25.0\n",
       "1980-03-31    16.0\n",
       "1980-04-30    10.0\n",
       "1980-05-31     9.0\n",
       "              ... \n",
       "2019-05-31     5.1\n",
       "2019-06-30     0.0\n",
       "2019-07-31     0.0\n",
       "2019-08-31     0.0\n",
       "2019-09-30     0.0\n",
       "Freq: M, Name: SNOW, Length: 477, dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "    .resample('M')\n",
    "    .max()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-01-31    20.0\n",
       "1980-03-31    25.0\n",
       "1980-05-31    10.0\n",
       "1980-07-31     1.0\n",
       "1980-09-30     0.0\n",
       "              ... \n",
       "2019-01-31    19.0\n",
       "2019-03-31    20.7\n",
       "2019-05-31    18.0\n",
       "2019-07-31     0.0\n",
       "2019-09-30     0.0\n",
       "Freq: 2M, Name: SNOW, Length: 239, dtype: float64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "    .resample('2M')\n",
    "    .max()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-05-31    25.0\n",
       "1981-05-31    26.0\n",
       "1982-05-31    34.0\n",
       "1983-05-31    38.0\n",
       "1984-05-31    25.0\n",
       "              ... \n",
       "2016-05-31    15.0\n",
       "2017-05-31    26.0\n",
       "2018-05-31    21.8\n",
       "2019-05-31    20.7\n",
       "2020-05-31     0.0\n",
       "Freq: A-MAY, Name: SNOW, Length: 41, dtype: float64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "    .resample('A-MAY')\n",
    "    .max()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-01-01    0.527009\n",
       "1980-01-02    0.790514\n",
       "1980-01-03    0.263505\n",
       "1980-01-04    0.000000\n",
       "1980-01-05    0.000000\n",
       "                ...   \n",
       "2019-09-03    0.000000\n",
       "2019-09-04    0.000000\n",
       "2019-09-05    0.000000\n",
       "2019-09-06    0.000000\n",
       "2019-09-07    0.000000\n",
       "Name: SNOW, Length: 14160, dtype: float64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "   .div(snow\n",
    "          .resample('Q')\n",
    "          .transform('sum'))\n",
    "   .mul(100)\n",
    "   .fillna(0)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-31     2.153969\n",
       "2016-11-30     9.772637\n",
       "2016-12-31    15.715995\n",
       "2017-01-31    25.468688\n",
       "2017-02-28    21.041085\n",
       "2017-03-31     9.274033\n",
       "2017-04-30    14.738732\n",
       "2017-05-31     1.834862\n",
       "Freq: M, Name: SNOW, dtype: float64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "season2017 = snow.loc['2016-10':'2017-05']\n",
    "(season2017\n",
    "    .resample('M')\n",
    "    .sum()\n",
    "    .div(season2017\n",
    "           .sum())\n",
    "    .mul(100)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "def season(idx):\n",
    "    year = idx.year\n",
    "    month = idx.month\n",
    "    return year.where((month < 10), year+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980    457.5\n",
       "1981    503.0\n",
       "1982    842.5\n",
       "1983    807.5\n",
       "1984    816.0\n",
       "        ...  \n",
       "2015    284.3\n",
       "2016    354.6\n",
       "2017    524.0\n",
       "2018    308.8\n",
       "2019    504.5\n",
       "Name: SNOW, Length: 40, dtype: float64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "  .groupby(season)\n",
    "  .sum()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-09-30    457.5\n",
       "1981-09-30    503.0\n",
       "1982-09-30    842.5\n",
       "1983-09-30    807.5\n",
       "1984-09-30    816.0\n",
       "              ...  \n",
       "2015-09-30    284.3\n",
       "2016-09-30    354.6\n",
       "2017-09-30    524.0\n",
       "2018-09-30    308.8\n",
       "2019-09-30    504.5\n",
       "Freq: A-SEP, Name: SNOW, Length: 40, dtype: float64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "  .resample('A-SEP')\n",
    "  .sum()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-01      0.0\n",
       "2016-10-02      0.0\n",
       "2016-10-03      4.9\n",
       "2016-10-04      4.9\n",
       "2016-10-05      5.5\n",
       "              ...  \n",
       "2017-09-26    524.0\n",
       "2017-09-27    524.0\n",
       "2017-09-28    524.0\n",
       "2017-09-29    524.0\n",
       "2017-09-30    524.0\n",
       "Name: SNOW, Length: 364, dtype: float64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "    .loc['2016-10':'2017-09']\n",
    "    .cumsum()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-01-01      2.0\n",
       "1980-01-02      5.0\n",
       "1980-01-03      6.0\n",
       "1980-01-04      6.0\n",
       "1980-01-05      6.0\n",
       "              ...  \n",
       "2019-09-03    504.5\n",
       "2019-09-04    504.5\n",
       "2019-09-05    504.5\n",
       "2019-09-06    504.5\n",
       "2019-09-07    504.5\n",
       "Name: SNOW, Length: 14160, dtype: float64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(snow\n",
    "    .resample('A-SEP')\n",
    "    .transform('cumsum')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Convert a column with date information to a date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Put the date information into the index for a numeric column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Calculate the average value of the column for each month."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Calculate the average value of the column for every 2 months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Calculate the percentage of the column out of the total for each month."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Calculate the average value of the column for a rolling window of size 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Using ``.loc`` pull out the first 3 months of a year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#. Using ``.loc`` pull out the last 4 months of a year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "url = 'https://github.com/mattharrison/datasets/raw/master/'\\\n",
    "    'data/alta-noaa-1980-2019.csv'\n",
    "alta_df = pd.read_csv(url)    # doctest: +SKIP\n",
    "dates = pd.to_datetime(alta_df.DATE)\n",
    "snow = (alta_df\n",
    "   .SNOW\n",
    "   .rename(dates)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-01-01    2.0\n",
       "1980-01-02    3.0\n",
       "1980-01-03    1.0\n",
       "1980-01-04    0.0\n",
       "1980-01-05    0.0\n",
       "             ... \n",
       "2019-09-03    0.0\n",
       "2019-09-04    0.0\n",
       "2019-09-05    0.0\n",
       "2019-09-06    0.0\n",
       "2019-09-07    0.0\n",
       "Name: SNOW, Length: 14160, dtype: float64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "snow.plot.hist()  # doctest: +SKIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Snowfall Histogram (in)'}, ylabel='Frequency'>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "snow[snow>0].plot.hist(bins=20, title='Snowfall Histogram (in)')  # doctest: +SKIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "snow.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(snow\n",
    "    [lambda s:(s.index.month == 1) & (s>0)]\n",
    "    .plot.box()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(snow\n",
    "    [lambda s:(s.index.month == 1) & (s>0)]\n",
    "    .plot.kde()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "snow.plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(snow\n",
    "   .iloc[-300:]\n",
    "   .plot.line()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(snow\n",
    " .resample('M')\n",
    " .mean()\n",
    " .plot.line()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(snow\n",
    " .resample('Q')\n",
    " .quantile([.5, .9, .99])\n",
    " .unstack()\n",
    " .iloc[-100:]\n",
    " .plot.line()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "October      2.153969\n",
       "November     9.772637\n",
       "December    15.715995\n",
       "January     25.468688\n",
       "February    21.041085\n",
       "March        9.274033\n",
       "April       14.738732\n",
       "May          1.834862\n",
       "Name: SNOW, dtype: float64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "season2017 = (snow.loc['2016-10':'2017-05'])\n",
    "(season2017\n",
    "  .resample('M')\n",
    "  .sum()\n",
    "  .div(season2017.sum())\n",
    "  .mul(100)\n",
    "  .rename(lambda idx: idx.month_name())\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'2017 Monthly Percent of Snowfall'}>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(season2017\n",
    "  .resample('M')\n",
    "  .sum()\n",
    "  .div(season2017.sum())\n",
    "  .mul(100)\n",
    "  .rename(lambda idx: idx.month_name())\n",
    "  .plot.bar(title='2017 Monthly Percent of Snowfall')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'2017 Monthly Percent of Snowfall'}>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(season2017\n",
    "  .resample('M')\n",
    "  .sum()\n",
    "  .div(season2017.sum())\n",
    "  .mul(100)\n",
    "  .rename(lambda idx: idx.month_name())\n",
    "  .plot.barh(title='2017 Monthly Percent of Snowfall')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matt/envs/menv/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3418: DtypeWarning: Columns (68,70,71,72,73,74,76,79) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "url = 'https://github.com/mattharrison/datasets/raw/master/data/'\\\n",
    "      'vehicles.csv.zip'\n",
    "df = pd.read_csv(url)    # doctest: +SKIP\n",
    "make = df.make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chevrolet                      4003\n",
       "Ford                           3371\n",
       "Dodge                          2583\n",
       "GMC                            2494\n",
       "Toyota                         2071\n",
       "                               ... \n",
       "Volga Associated Automobile       1\n",
       "Panos                             1\n",
       "Mahindra                          1\n",
       "Excalibur Autos                   1\n",
       "London Coach Co Inc               1\n",
       "Name: make, Length: 136, dtype: int64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(make\n",
    "    .value_counts()\n",
    "    .plot.bar()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top10 = make.value_counts().index[:10]\n",
    "(make\n",
    "    .where(make.isin(top10), 'Other')\n",
    "    .value_counts()\n",
    "    .plot.barh()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'2017 Monthly Percent of Snowfall'}, ylabel='SNOW'>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(season2017\n",
    "  .resample('M')\n",
    "  .sum()\n",
    "  .div(season2017.sum())\n",
    "  .mul(100)\n",
    "  .rename(lambda idx: idx.month_name())\n",
    "  .plot.pie(title='2017 Monthly Percent of Snowfall')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matt/envs/menv/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3418: DtypeWarning: Columns (68,70,71,72,73,74,76,79) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0        Alfa Romeo\n",
       "1           Ferrari\n",
       "2             Dodge\n",
       "3             Dodge\n",
       "4            Subaru\n",
       "            ...    \n",
       "41139        Subaru\n",
       "41140        Subaru\n",
       "41141        Subaru\n",
       "41142        Subaru\n",
       "41143        Subaru\n",
       "Name: make, Length: 41144, dtype: object"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "url = 'https://github.com/mattharrison/datasets/raw/master/' \\\n",
    "      'data/vehicles.csv.zip'\n",
    "df = pd.read_csv(url)  # doctest: +SKIP\n",
    "make = df.make\n",
    "make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chevrolet                      4003\n",
       "Ford                           3371\n",
       "Dodge                          2583\n",
       "GMC                            2494\n",
       "Toyota                         2071\n",
       "                               ... \n",
       "Volga Associated Automobile       1\n",
       "Panos                             1\n",
       "Mahindra                          1\n",
       "Excalibur Autos                   1\n",
       "London Coach Co Inc               1\n",
       "Name: make, Length: 136, dtype: int64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((41144,), 136)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.shape, make.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "cat_make = make.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2606395"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make.memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95888"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_make.memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: %%timeit is a cell magic, but the cell body is empty. Did you mean the line magic %timeit (single %)?\n"
     ]
    }
   ],
   "source": [
    "%%timeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "%%timeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Alfa Romeo\n",
       "1           Ferrari\n",
       "2             Dodge\n",
       "3             Dodge\n",
       "4            Subaru\n",
       "            ...    \n",
       "41139        Subaru\n",
       "41140        Subaru\n",
       "41141        Subaru\n",
       "41142        Subaru\n",
       "41143        Subaru\n",
       "Name: make, Length: 41144, dtype: category\n",
       "Categories (136, object): ['AM General' < 'ASC Incorporated' < 'Acura' < 'Alfa Romeo' ... 'Volvo' < 'Wallace Environmental' < 'Yugo' < 'smart']"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_type = pd.CategoricalDtype(\n",
    "    categories=sorted(make.unique()), ordered=True)\n",
    "ordered_make = make.astype(make_type)\n",
    "ordered_make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'smart'"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_make.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Categorical is not ordered for operation max\nyou can use .as_ordered() to change the Categorical to an ordered one\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-208-0dd04fc39a22>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcat_make\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mmax\u001b[0;34m(self, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m  10817\u001b[0m         )\n\u001b[1;32m  10818\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10819\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m  10820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10821\u001b[0m         \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"max\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mmax\u001b[0;34m(self, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m  10362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10363\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10364\u001b[0;31m         return self._stat_function(\n\u001b[0m\u001b[1;32m  10365\u001b[0m             \u001b[0;34m\"max\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnanops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10366\u001b[0m         )\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_stat_function\u001b[0;34m(self, name, func, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m  10352\u001b[0m                 \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10353\u001b[0m             )\n\u001b[0;32m> 10354\u001b[0;31m         return self._reduce(\n\u001b[0m\u001b[1;32m  10355\u001b[0m             \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10356\u001b[0m         )\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m   4381\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelegate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExtensionArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4382\u001b[0m             \u001b[0;31m# dispatch to ExtensionArray interface\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4383\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdelegate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4385\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/arrays/_mixins.py\u001b[0m in \u001b[0;36m_reduce\u001b[0;34m(self, name, skipna, **kwargs)\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0mmeth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    258\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"'{type(self).__name__}' does not implement reduction '{name}'\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    205\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/arrays/categorical.py\u001b[0m in \u001b[0;36mmax\u001b[0;34m(self, skipna, **kwargs)\u001b[0m\n\u001b[1;32m   2145\u001b[0m         \u001b[0mnv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_minmax_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"axis\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2146\u001b[0m         \u001b[0mnv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_max\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2147\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_for_ordered\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"max\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2149\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_codes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/envs/menv/lib/python3.8/site-packages/pandas/core/arrays/categorical.py\u001b[0m in \u001b[0;36mcheck_for_ordered\u001b[0;34m(self, op)\u001b[0m\n\u001b[1;32m   1643\u001b[0m         \u001b[0;34m\"\"\"assert that we are ordered\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1644\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mordered\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1645\u001b[0;31m             raise TypeError(\n\u001b[0m\u001b[1;32m   1646\u001b[0m                 \u001b[0;34mf\"Categorical is not ordered for operation {op}\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1647\u001b[0m                 \u001b[0;34m\"you can use .as_ordered() to change the \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Categorical is not ordered for operation max\nyou can use .as_ordered() to change the Categorical to an ordered one\n"
     ]
    }
   ],
   "source": [
    "cat_make.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20288    AM General\n",
       "20289    AM General\n",
       "369      AM General\n",
       "358      AM General\n",
       "19314    AM General\n",
       "            ...    \n",
       "31289         smart\n",
       "31290         smart\n",
       "29605         smart\n",
       "22974         smart\n",
       "26882         smart\n",
       "Name: make, Length: 41144, dtype: category\n",
       "Categories (136, object): ['AM General' < 'ASC Incorporated' < 'Acura' < 'Alfa Romeo' ... 'Volvo' < 'Wallace Environmental' < 'Yugo' < 'smart']"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_make.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        alfa romeo\n",
       "1           ferrari\n",
       "2             dodge\n",
       "3             dodge\n",
       "4            subaru\n",
       "            ...    \n",
       "41139        subaru\n",
       "41140        subaru\n",
       "41141        subaru\n",
       "41142        subaru\n",
       "41143        subaru\n",
       "Name: make, Length: 41144, dtype: category\n",
       "Categories (136, object): ['am general', 'asc incorporated', 'acura', 'alfa romeo', ..., 'volvo', 'wallace environmental', 'yugo', 'smart']"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_make.cat.rename_categories(\n",
    "   [c.lower() for c in cat_make.cat.categories])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        alfa romeo\n",
       "1           ferrari\n",
       "2             dodge\n",
       "3             dodge\n",
       "4            subaru\n",
       "            ...    \n",
       "41139        subaru\n",
       "41140        subaru\n",
       "41141        subaru\n",
       "41142        subaru\n",
       "41143        subaru\n",
       "Name: make, Length: 41144, dtype: category\n",
       "Categories (136, object): ['am general' < 'asc incorporated' < 'acura' < 'alfa romeo' ... 'volvo' < 'wallace environmental' < 'yugo' < 'smart']"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_make.cat.rename_categories(\n",
    "   {c:c.lower() for c in ordered_make.cat.categories})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Alfa Romeo\n",
       "1           Ferrari\n",
       "2             Dodge\n",
       "3             Dodge\n",
       "4            Subaru\n",
       "            ...    \n",
       "41139        Subaru\n",
       "41140        Subaru\n",
       "41141        Subaru\n",
       "41142        Subaru\n",
       "41143        Subaru\n",
       "Name: make, Length: 41144, dtype: category\n",
       "Categories (136, object): ['Acura' < 'Alfa Romeo' < 'AM General' < 'American Motors Corporation' ... 'Volvo' < 'VPG' < 'Wallace Environmental' < 'Yugo']"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_make.cat.reorder_categories( \n",
    "    sorted(cat_make.cat.categories, key=str.lower))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dodge                        17\n",
       "Oldsmobile                    8\n",
       "Ford                          8\n",
       "Buick                         7\n",
       "Chevrolet                     5\n",
       "                             ..\n",
       "Grumman Allied Industries     0\n",
       "Goldacre                      0\n",
       "Geo                           0\n",
       "Genesis                       0\n",
       "smart                         0\n",
       "Name: make, Length: 136, dtype: int64"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_make.iloc[:100].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make\n",
       "AM General                            NaN\n",
       "ASC Incorporated                      NaN\n",
       "Acura                                 NaN\n",
       "Alfa Romeo                     Alfa Romeo\n",
       "American Motors Corporation           NaN\n",
       "                                  ...    \n",
       "Volkswagen                     Volkswagen\n",
       "Volvo                               Volvo\n",
       "Wallace Environmental                 NaN\n",
       "Yugo                                  NaN\n",
       "smart                                 NaN\n",
       "Name: make, Length: 136, dtype: category\n",
       "Categories (136, object): ['AM General', 'ASC Incorporated', 'Acura', 'Alfa Romeo', ..., 'Volvo', 'Wallace Environmental', 'Yugo', 'smart']"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(cat_make\n",
    " .iloc[:100]\n",
    " .groupby(cat_make.iloc[:100])\n",
    " .first()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make\n",
       "Alfa Romeo          Alfa Romeo\n",
       "Audi                      Audi\n",
       "BMW                        BMW\n",
       "Buick                    Buick\n",
       "CX Automotive    CX Automotive\n",
       "                     ...      \n",
       "Rolls-Royce        Rolls-Royce\n",
       "Subaru                  Subaru\n",
       "Toyota                  Toyota\n",
       "Volkswagen          Volkswagen\n",
       "Volvo                    Volvo\n",
       "Name: make, Length: 25, dtype: object"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(make\n",
    " .iloc[:100]\n",
    " .groupby(make.iloc[:100])\n",
    " .first()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make\n",
       "Alfa Romeo      Alfa Romeo\n",
       "Ferrari            Ferrari\n",
       "Dodge                Dodge\n",
       "Subaru              Subaru\n",
       "Toyota              Toyota\n",
       "                  ...     \n",
       "Mazda                Mazda\n",
       "Oldsmobile      Oldsmobile\n",
       "Plymouth          Plymouth\n",
       "Pontiac            Pontiac\n",
       "Rolls-Royce    Rolls-Royce\n",
       "Name: make, Length: 25, dtype: category\n",
       "Categories (136, object): ['AM General', 'ASC Incorporated', 'Acura', 'Alfa Romeo', ..., 'Volvo', 'Wallace Environmental', 'Yugo', 'smart']"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(cat_make\n",
    " .iloc[:100]\n",
    " .groupby(cat_make.iloc[:100], observed=True)\n",
    " .first()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Alfa Romeo'"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_make.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Alfa Romeo\n",
       "Name: make, dtype: category\n",
       "Categories (136, object): ['AM General' < 'ASC Incorporated' < 'Acura' < 'Alfa Romeo' ... 'Volvo' < 'Wallace Environmental' < 'Yugo' < 'smart']"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_make.iloc[[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generalize_topn(ser, n=5, other='Other'):\n",
    "    topn = ser.value_counts().index[:n]\n",
    "    if isinstance(ser.dtype, pd.CategoricalDtype):\n",
    "        ser = ser.cat.set_categories(\n",
    "            topn.set_categories(list(topn)+[other]))\n",
    "    return ser.where(ser.isin(topn), other)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            NA\n",
       "1            NA\n",
       "2         Dodge\n",
       "3         Dodge\n",
       "4        Subaru\n",
       "          ...  \n",
       "41139    Subaru\n",
       "41140    Subaru\n",
       "41141    Subaru\n",
       "41142    Subaru\n",
       "41143    Subaru\n",
       "Name: make, Length: 41144, dtype: category\n",
       "Categories (21, object): ['Chevrolet', 'Ford', 'Dodge', 'GMC', ..., 'Volvo', 'Hyundai', 'Chrysler', 'NA']"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_make.pipe(generalize_topn, n=20, other='NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generalize_mapping(ser, mapping, default):\n",
    "    seen = None\n",
    "    res = ser.astype(str)\n",
    "    for old, new in mapping.items():\n",
    "         mask = ser.str.contains(old)\n",
    "         if seen is None:\n",
    "             seen = mask\n",
    "         else:\n",
    "             seen |= mask\n",
    "         res = res.where(~mask, new)\n",
    "    res = res.where(seen, default)\n",
    "    return res.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Other\n",
       "1        Other\n",
       "2           US\n",
       "3           US\n",
       "4        Other\n",
       "         ...  \n",
       "41139    Other\n",
       "41140    Other\n",
       "41141    Other\n",
       "41142    Other\n",
       "41143    Other\n",
       "Name: make, Length: 41144, dtype: category\n",
       "Categories (3, object): ['German', 'Other', 'US']"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generalize_mapping(cat_make, {'Ford': 'US', 'Tesla': 'US',\n",
    "    'Chevrolet': 'US', 'Dodge': 'US',\n",
    "    'Oldsmobile': 'US', 'Plymouth': 'US',\n",
    "    'BMW': 'German'}, 'Other')"
   ]
  }
 ],
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