ch05.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c427bf1e-6bc9-497d-a56c-63d161d982eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chapter 5: Getting Started with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2560b7c5-83bb-4123-9a4d-e61c7078339b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "172603d0-94e6-40f6-a72b-be7828e79df3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c63f7718-4b56-4bb8-80f9-05bdd6ea7ecf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3e98d4c-671c-4bf5-8a16-ad885f18ab91",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = pd.Series([4, 7, -5, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "63ebffda-fe16-403b-8a51-398ff98d15bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    7\n",
       "2   -5\n",
       "3    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f9785e51-cc8f-42e8-89d2-077e43acd3da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<NumpyExtensionArray>\n",
       "[4, 7, -5, 3]\n",
       "Length: 4, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c5154ee-a3db-41b1-8cf8-ab77aac2d731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "00e3d326-38c5-41ef-b93a-65ef7d738713",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj2 = pd.Series([4, 7, -5, 3], index=[\"d\", \"b\", \"a\", \"c\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e1f2ae8e-5a0f-4821-abed-30b443436cac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4\n",
       "b    7\n",
       "a   -5\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9bd25bee-1145-4f9e-b9cf-0e65c160750f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['d', 'b', 'a', 'c'], dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bff18d1a-6a44-4f80-a7dd-aaf88bd15417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2[\"a\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "089d4da7-3e1c-44ff-99dc-9e3f0b34eee2",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj2[\"d\"] = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6a5934fd-1ddc-40fb-99d2-54f20bd0f169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "a   -5\n",
       "d    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2[[\"c\", \"a\", \"d\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0cf3d936-89a0-4059-aacd-931bf992ed94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    6\n",
       "b    7\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2[obj2 > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a6c33566-da12-4e7f-a1f5-2f8c3ebbc041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    12\n",
       "b    14\n",
       "a   -10\n",
       "c     6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "981fec81-fce3-487f-a432-b1abbfb70510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d     403.428793\n",
       "b    1096.633158\n",
       "a       0.006738\n",
       "c      20.085537\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(obj2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f947928e-e2a4-4cd5-b582-173bfee5f264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"b\" in obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ce1deb07-91d7-4ca1-9ccb-15baa8ddce75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"e\" in obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "58121322-cdf1-4b7e-af8d-69617d2f1bba",
   "metadata": {},
   "outputs": [],
   "source": [
    "sdata = {\"Ohio\": 35000, \"Texas\": 71000, \"Oregon\": 16000, \"Utah\": 5000}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "95e1d8e0-4b06-45a8-85b9-f20367a36f63",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj3 = pd.Series(sdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1020691d-15fe-4d2a-b82c-14e31fb709ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio      35000\n",
       "Texas     71000\n",
       "Oregon    16000\n",
       "Utah       5000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "46bfc029-9da7-40c4-bf91-b9995bfcae71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e46b2eeb-500d-4c22-87a6-e63176b3fd6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "states = [\"California\", \"Ohio\", \"Oregon\", \"Texas\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "814137a0-1678-44c3-bd63-779ed051565b",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj4 = pd.Series(sdata, index=states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b854c074-5e19-4082-9c2a-d48749dcc6f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California        NaN\n",
       "Ohio          35000.0\n",
       "Oregon        16000.0\n",
       "Texas         71000.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4 # NaN (Not a Number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b37db2f2-3a90-45b1-b132-8f7e2bfd9496",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the terms “missing,” “NA,” or “null” interchangeably to refer to missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b42d8531-50d8-4daf-b2e9-6cff346772bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California     True\n",
       "Ohio          False\n",
       "Oregon        False\n",
       "Texas         False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(obj4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e4e1fc54-bb67-41fa-a173-c7bef4dae337",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    False\n",
       "Ohio           True\n",
       "Oregon         True\n",
       "Texas          True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.notna(obj4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7de28f94-dfdf-413d-9ffe-0b060b5d29a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California     True\n",
       "Ohio          False\n",
       "Oregon        False\n",
       "Texas         False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "164aa273-bafd-48d3-a19f-9fb125387551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio      35000\n",
       "Texas     71000\n",
       "Oregon    16000\n",
       "Utah       5000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "378cad82-879c-47e6-8704-6699bf739538",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California        NaN\n",
       "Ohio          35000.0\n",
       "Oregon        16000.0\n",
       "Texas         71000.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "34b155e4-eeda-477e-a412-c5fea54ecc78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California         NaN\n",
       "Ohio           70000.0\n",
       "Oregon         32000.0\n",
       "Texas         142000.0\n",
       "Utah               NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3 + obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "efaf31c7-33f1-4148-ab65-87f9b8b11890",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj4.name = \"population\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "06aac7f1-c8ca-4069-a052-8e226f7175f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj4.index.name = \"state\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0727fbe3-0d89-46eb-8f42-1dcc430a09c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "California        NaN\n",
       "Ohio          35000.0\n",
       "Oregon        16000.0\n",
       "Texas         71000.0\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ca241042-90be-43f1-a844-b0e6c64c2f64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    7\n",
       "2   -5\n",
       "3    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "76e9ae95-ab1c-41a1-aaa4-cc3e2c518cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj.index = [\"Bob\", \"Steve\", \"Jeff\", \"Ryan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "6ebc8fba-3209-45fa-bc0b-b990c97b7063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bob      4\n",
       "Steve    7\n",
       "Jeff    -5\n",
       "Ryan     3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "79414fb4-de4c-4011-a401-e0af58f29acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"state\": [\"Ohio\", \"Ohio\", \"Ohio\", \"Nevada\", \"Nevada\", \"Nevada\"],\n",
    "        \"year\": [2000, 2001, 2002, 2001, 2002, 2003],\n",
    "        \"pop\": [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "908567b5-4ba1-4304-8ed4-02eb927f5109",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7f322761-fe51-47cc-b21e-da4ad86955a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2003</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  pop\n",
       "0    Ohio  2000  1.5\n",
       "1    Ohio  2001  1.7\n",
       "2    Ohio  2002  3.6\n",
       "3  Nevada  2001  2.4\n",
       "4  Nevada  2002  2.9\n",
       "5  Nevada  2003  3.2"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b0e5dc8a-40ab-4014-b6d6-645843915bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  pop\n",
       "0    Ohio  2000  1.5\n",
       "1    Ohio  2001  1.7\n",
       "2    Ohio  2002  3.6\n",
       "3  Nevada  2001  2.4\n",
       "4  Nevada  2002  2.9"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5bed2b66-ff5c-4c27-923b-d6e80a97cdf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
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       "      <th>1</th>\n",
       "      <td>Ohio</td>\n",
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       "      <td>3.6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nevada</td>\n",
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       "      <td>2.4</td>\n",
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       "      <th>4</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>Nevada</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  pop\n",
       "1    Ohio  2001  1.7\n",
       "2    Ohio  2002  3.6\n",
       "3  Nevada  2001  2.4\n",
       "4  Nevada  2002  2.9\n",
       "5  Nevada  2003  3.2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
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    "frame.tail()"
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   "cell_type": "code",
   "execution_count": 39,
   "id": "fe5e25e9-50b6-4fc3-8a81-adaa8f960a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1.5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   state  pop\n",
       "0  2000    Ohio  1.5\n",
       "1  2001    Ohio  1.7\n",
       "2  2002    Ohio  3.6\n",
       "3  2001  Nevada  2.4\n",
       "4  2002  Nevada  2.9\n",
       "5  2003  Nevada  3.2"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data, columns=[\"year\", \"state\", \"pop\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a8c639a1-d969-4f27-b38c-25f5eb8afe18",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2 = pd.DataFrame(data, columns=[\"year\", \"state\", \"pop\", \"debt\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "99533d3f-83b2-4cb4-8552-d692fbc57b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   state  pop debt\n",
       "0  2000    Ohio  1.5  NaN\n",
       "1  2001    Ohio  1.7  NaN\n",
       "2  2002    Ohio  3.6  NaN\n",
       "3  2001  Nevada  2.4  NaN\n",
       "4  2002  Nevada  2.9  NaN\n",
       "5  2003  Nevada  3.2  NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b0a4cabd-567d-4088-9665-6c86b9872b33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'pop', 'debt'], dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9bf483fd-fcfa-4b4b-9067-68c2beafb197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Ohio\n",
       "1      Ohio\n",
       "2      Ohio\n",
       "3    Nevada\n",
       "4    Nevada\n",
       "5    Nevada\n",
       "Name: state, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2[\"state\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "375f4284-d25c-4d71-80b1-df4ebd1bf323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2000\n",
       "1    2001\n",
       "2    2002\n",
       "3    2001\n",
       "4    2002\n",
       "5    2003\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e03f4a64-7ed1-484b-902f-59e9244e7df1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# frame2[column] works for any column name, \n",
    "# but frame2.column works only when the column name is a valid Python variable name \n",
    "# and does not conflict with any of the method names in DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7cb1c45b-0224-49a3-aa26-2846958770d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year     2001\n",
       "state    Ohio\n",
       "pop       1.7\n",
       "debt      NaN\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9a2dfed9-9de9-446d-ab36-b63948861352",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year     2002\n",
       "state    Ohio\n",
       "pop       3.6\n",
       "debt      NaN\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "60212057-5f76-4827-a0f0-51919a63cb0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2[\"debt\"] = 16.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "22258129-99b0-4f15-adb6-378235a41760",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>2002</td>\n",
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       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.2</td>\n",
       "      <td>16.5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   year   state  pop  debt\n",
       "0  2000    Ohio  1.5  16.5\n",
       "1  2001    Ohio  1.7  16.5\n",
       "2  2002    Ohio  3.6  16.5\n",
       "3  2001  Nevada  2.4  16.5\n",
       "4  2002  Nevada  2.9  16.5\n",
       "5  2003  Nevada  3.2  16.5"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "96852ea5-0f58-47f2-b1b4-fe896493263b",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2[\"debt\"] = np.arange(6.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "825216df-9aff-4cec-91db-9e741009cae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   state  pop  debt\n",
       "0  2000    Ohio  1.5   0.0\n",
       "1  2001    Ohio  1.7   1.0\n",
       "2  2002    Ohio  3.6   2.0\n",
       "3  2001  Nevada  2.4   3.0\n",
       "4  2002  Nevada  2.9   4.0\n",
       "5  2003  Nevada  3.2   5.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8d2822ff-db28-4e2d-968d-01758952a50a",
   "metadata": {},
   "outputs": [],
   "source": [
    "val = pd.Series([-1.2, -1.5, -1.7], index=[2, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "46e5229c-25c6-4e98-9869-751faf74a64c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2   -1.2\n",
       "4   -1.5\n",
       "5   -1.7\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val"
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  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b7e3b85a-1ff5-4b40-b000-42360abe7d0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2[\"debt\"] = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "680435b7-80ec-4598-89c2-8d088338c6d6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Ohio</td>\n",
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       "      <td>2001</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
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       "      <td>-1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   year   state  pop  debt\n",
       "0  2000    Ohio  1.5   NaN\n",
       "1  2001    Ohio  1.7   NaN\n",
       "2  2002    Ohio  3.6  -1.2\n",
       "3  2001  Nevada  2.4   NaN\n",
       "4  2002  Nevada  2.9  -1.5\n",
       "5  2003  Nevada  3.2  -1.7"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "frame2"
   ]
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  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "deaf8370-19d3-402f-9321-933c08d2b7a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2[\"eastern\"] = frame2[\"state\"] == \"Ohio\""
   ]
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  {
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   "execution_count": 75,
   "id": "0f73f1d2-9570-4852-99ef-a967e1b5e1dc",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2002</td>\n",
       "      <td>Nevada</td>\n",
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       "      <td>-1.5</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
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       "      <td>-1.7</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   year   state  pop  debt  eastern\n",
       "0  2000    Ohio  1.5   NaN     True\n",
       "1  2001    Ohio  1.7   NaN     True\n",
       "2  2002    Ohio  3.6  -1.2     True\n",
       "3  2001  Nevada  2.4   NaN    False\n",
       "4  2002  Nevada  2.9  -1.5    False\n",
       "5  2003  Nevada  3.2  -1.7    False"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "018db777-dded-4894-bc75-3bf2f6c950bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# New columns cannot be created with the frame2.eastern dot attribute notation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f234f21c-a8c0-4d72-9d62-3271557b4ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "del frame2[\"eastern\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "55e9f8f8-4a9b-4914-ad69-9233a59f13c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'pop', 'debt'], dtype='object')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.columns"
   ]
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  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b8e8a454-5205-4b1b-b9b4-147e4081b45f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2003</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>3.2</td>\n",
       "      <td>-1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   state  pop  debt\n",
       "0  2000    Ohio  1.5   NaN\n",
       "1  2001    Ohio  1.7   NaN\n",
       "2  2002    Ohio  3.6  -1.2\n",
       "3  2001  Nevada  2.4   NaN\n",
       "4  2002  Nevada  2.9  -1.5\n",
       "5  2003  Nevada  3.2  -1.7"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "dd28ff4a-06a2-49c1-88b5-9c11408819bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The column can be explicitly copied with the Series’s copy method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "66975276-5f87-49ee-aec5-d15ea20f4aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "populations = {\"Ohio\": {2000: 1.5, 2001: 1.7, 2002: 3.6},\n",
    "               \"Nevada\": {2001: 2.4, 2002: 2.9}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ca85bd69-f7f3-49a9-966c-c84cbd0c6c38",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame3 = pd.DataFrame(populations)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64aaaca8-2cf0-42d9-bdd5-a85e55945267",
   "metadata": {},
   "outputs": [
    {
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       "      Ohio  Nevada\n",
       "2000   1.5     NaN\n",
       "2001   1.7     2.4\n",
       "2002   3.6     2.9"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   "outputs": [
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      "text/plain": [
       "        2000  2001  2002\n",
       "Ohio     1.5   1.7   3.6\n",
       "Nevada   NaN   2.4   2.9"
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   "execution_count": 15,
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   "outputs": [
    {
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       "      <th>2001</th>\n",
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       "      Ohio  Nevada\n",
       "2001   1.7     2.4\n",
       "2002   3.6     2.9\n",
       "2003   NaN     NaN"
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     "execution_count": 15,
     "metadata": {},
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   "source": [
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   "execution_count": 17,
   "id": "18010bc2-d969-405e-bb3e-7010da60d323",
   "metadata": {},
   "outputs": [],
   "source": [
    "pdata = {\"Ohio\": frame3[\"Ohio\"][:-1],\n",
    "         \"Nevada\": frame3[\"Nevada\"][:2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dc5e13e9-f552-45f1-8c1c-60b0ed92510a",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "      Ohio  Nevada\n",
       "2000   1.5     NaN\n",
       "2001   1.7     2.4"
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     "metadata": {},
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   "execution_count": 21,
   "id": "e84cf24e-6d82-4b03-9876-6e6d8709b107",
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   "source": [
    "frame3.index.name = \"year\""
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   "execution_count": 23,
   "id": "0d52c69c-5732-4cd6-9e9b-97e415ae6dc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame3.columns.name = \"state\""
   ]
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   "execution_count": 25,
   "id": "b0d3c1a0-f665-4cd8-b37c-6e5ab9479283",
   "metadata": {},
   "outputs": [
    {
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       "      <th>state</th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>1.7</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>3.6</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state  Ohio  Nevada\n",
       "year               \n",
       "2000    1.5     NaN\n",
       "2001    1.7     2.4\n",
       "2002    3.6     2.9"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6770e095-9198-4526-b497-ed8c58d4f1ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.5, nan],\n",
       "       [1.7, 2.4],\n",
       "       [3.6, 2.9]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "04d03a57-392b-44e6-93fb-e7851ac3f0a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2000, 'Ohio', 1.5, nan],\n",
       "       [2001, 'Ohio', 1.7, nan],\n",
       "       [2002, 'Ohio', 3.6, -1.2],\n",
       "       [2001, 'Nevada', 2.4, nan],\n",
       "       [2002, 'Nevada', 2.9, -1.5],\n",
       "       [2003, 'Nevada', 3.2, -1.7]], dtype=object)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "5145e30b-3fd4-408c-878b-9ba16e11cf29",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = pd.Series(np.arange(3), index=[\"a\", \"b\", \"c\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "c516619a-9f3b-4395-b017-764f53d9be47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "c    2\n",
       "dtype: int32"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ccd68064-cb15-449a-8ca8-b5228d8afa75",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = obj.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "07fc0ca1-d35e-4ff5-a774-b54fd7014166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c'], dtype='object')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "3b4741f1-19ca-4eeb-bf63-c47602d4210c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['b', 'c'], dtype='object')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "921b4d96-cf34-48cf-b908-8e2d643c8f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TypeError\n",
    "# index[1] = \"d\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "366cee2c-4432-4ad6-af8e-ebe96ce87238",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = pd.Index(np.arange(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4f7887b1-0bb9-44bd-8d4b-ea0256a4bace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([0, 1, 2], dtype='int32')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b3f4e589-187c-4c5f-b250-b890f45de63d",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj2 = pd.Series([1.5, -2.5, 0], index=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "e8a42f9d-c122-421b-94a8-002ded9ff2bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.5\n",
       "1   -2.5\n",
       "2    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "98f8bdd4-bd58-4b66-8c17-6cb1e7c0b016",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2.index is labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "adaca8bf-8234-4219-b985-e5c2a65be00d",
   "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>Ohio</th>\n",
       "      <th>Nevada</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>1.7</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>3.6</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Ohio  Nevada\n",
       "2000   1.5     NaN\n",
       "2001   1.7     2.4\n",
       "2002   3.6     2.9"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "cd81dfb1-2801-46a9-a015-c5ab3eef57f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Ohio', 'Nevada'], dtype='object')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "3dec9306-8303-4490-88e2-e9db9f5f5557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Ohio\" in frame3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "be6aa353-f246-455c-bfb2-1624bfb41908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2003 in frame3.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "e3ba8dc3-9016-4a8e-a10f-9a317d00968f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([2000, 2001, 2002], dtype='int64')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "c0107ca2-661a-4eb6-8dec-7b1db9c6bde9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['foo', 'foo', 'bar', 'bar'], dtype='object')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Index([\"foo\", \"foo\", \"bar\", \"bar\"]) # a pandas Index can contain duplicate labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd170ae1-1aee-4c91-9bb8-bc580a4fe24d",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = pd.Series([4.5, 7.2, -5.3, 3.6], index=[\"d\", \"b\", \"a\", \"c\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9456ad56-ffac-4174-8925-feab1c7dcb1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4.5\n",
       "b    7.2\n",
       "a   -5.3\n",
       "c    3.6\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a0d8bd6-d725-42a6-89b6-fbb27b1b56db",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj2 = obj.reindex([\"a\", \"b\", \"c\", \"d\", \"e\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "31786645-2e5f-4175-8905-93889880a180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -5.3\n",
       "b    7.2\n",
       "c    3.6\n",
       "d    4.5\n",
       "e    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fe585b08-8531-43c3-bd6a-cc5b364c681d",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj3 = pd.Series([\"blue\", \"purple\", \"yellow\"], index=[0, 2, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "04becff5-3b07-4710-a8d0-4fe52b131cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "2    purple\n",
       "4    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a9e42e52-3528-49e9-90bf-7a52e5513c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1      blue\n",
       "2    purple\n",
       "3    purple\n",
       "4    yellow\n",
       "5    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3.reindex(np.arange(6), method=\"ffill\") # forward-fills the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b3e3f5d8-8052-4395-99be-b83419bc4b60",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = pd.DataFrame(np.arange(9).reshape((3, 3)),\n",
    "                     index=[\"a\", \"c\", \"d\"],\n",
    "                     columns=[\"Ohio\", \"Texas\", \"California\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0e9f2ef6-dfa1-4707-98d5-710cd8158ce4",
   "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>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a     0      1           2\n",
       "c     3      4           5\n",
       "d     6      7           8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame"
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  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cf8c07e2-295b-46db-bfc9-0f577b28d74d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# When passed only a sequence, it reindexes the rows in the result.\n",
    "frame2 = frame.reindex(index=[\"a\", \"b\", \"c\", \"d\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "659242a4-31ca-4756-97ea-95ebfc3fc638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a   0.0    1.0         2.0\n",
       "b   NaN    NaN         NaN\n",
       "c   3.0    4.0         5.0\n",
       "d   6.0    7.0         8.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "aec9b42f-664a-4b60-943b-ac9b197c66b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The columns can be reindexed with the columns keyword.\n",
    "states = [\"Texas\", \"Utah\", \"California\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8c82cd53-7202-42d5-b16a-1a66aea321d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Texas  Utah  California\n",
       "a      1   NaN           2\n",
       "c      4   NaN           5\n",
       "d      7   NaN           8"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.reindex(columns=states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "763c39c0-afe3-4692-8ccd-235d09463f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Texas</th>\n",
       "      <th>Utah</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Texas  Utah  California\n",
       "a      1   NaN           2\n",
       "c      4   NaN           5\n",
       "d      7   NaN           8"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.reindex(states, axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6c983c64-5c00-46d7-a829-a9bf833b5592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>California</th>\n",
       "      <th>Texas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   California  Texas\n",
       "a           2      1\n",
       "d           8      7\n",
       "c           5      4"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.loc[[\"a\", \"d\", \"c\"], [\"California\", \"Texas\"]] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2a8c0cfb-4e7e-43c3-8bce-93a082cee40c",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = pd.Series(np.arange(5.), index=[\"a\", \"b\", \"c\", \"d\", \"e\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fcdf3f15-af61-489f-b489-840d074263a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    3.0\n",
       "e    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d2a544f7-ac98-4e5e-83c8-75c7fa90d455",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_obj = obj.drop(\"c\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1badeb18-5079-4ac7-bcae-51960d3fcf88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "d    3.0\n",
       "e    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "730c27c2-4017-4067-9a30-41a4df8fcc07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "e    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.drop([\"d\", \"c\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0695146c-8312-4a9e-9720-fb259969ed14",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(np.arange(16).reshape((4, 4)),\n",
    "                    index=[\"Ohio\", \"Colorado\", \"Utah\", \"New York\"],\n",
    "                    columns=[\"one\", \"two\", \"three\", \"four\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6a3c2012-4ca0-496d-8742-7833f39a2bf1",
   "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",
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8a07d958-f6bf-434d-892f-f5f0d240662c",
   "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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Utah        8    9     10    11\n",
       "New York   12   13     14    15"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calling drop with a sequence of labels will drop values from the row labels (axis 0).\n",
    "data.drop(index=[\"Colorado\", \"Ohio\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "19b4099b-1508-454d-ba65-52e906719800",
   "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>one</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  three  four\n",
       "Ohio        0      2     3\n",
       "Colorado    4      6     7\n",
       "Utah        8     10    11\n",
       "New York   12     14    15"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns=[\"two\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a55c02b4-e4d9-4717-b6ec-98a79b19c0eb",
   "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",
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       "\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>one</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  three  four\n",
       "Ohio        0      2     3\n",
       "Colorado    4      6     7\n",
       "Utah        8     10    11\n",
       "New York   12     14    15"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(\"two\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8f6daaca-5c79-4d9c-8674-333ff6aa5800",
   "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>one</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  three\n",
       "Ohio        0      2\n",
       "Colorado    4      6\n",
       "Utah        8     10\n",
       "New York   12     14"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop([\"two\", \"four\"], axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2e7167fb-17a4-4ce5-8e77-038d2fb454d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = pd.Series(np.arange(4.), index=[\"a\", \"b\", \"c\", \"d\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "95863e6d-47bc-4308-9517-f7ecc8b0bc9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7a4a0b7a-3314-46af-b0bc-df136b9df300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[\"b\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "18b7a02b-99e5-4626-b07b-1c4f47da5c36",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\TEMP\\ipykernel_14056\\2469632899.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  obj[1]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4e7b086c-b46b-4841-9321-ee20758533bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    2.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e3d8c0a2-189b-49c0-afd8-aa7a1c5849cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.0\n",
       "a    0.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[[\"b\", \"a\", \"d\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "90002fd4-5804-4b83-8c45-14fca749b2f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\TEMP\\ipykernel_14056\\2982346117.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  obj[[1, 3]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b    1.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[[1, 3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "190c2db4-6868-4f96-9a87-fad51722f4b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[obj < 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "30a3a0e1-4dde-42d9-8ba1-f1bb30a271bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.0\n",
       "a    0.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.loc[[\"b\", \"a\", \"d\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "550a2173-473e-4ec5-868f-410e21a4f3ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj1 = pd.Series([1, 2, 3], index=[2, 0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "00ecc646-00e5-44ba-85f3-ec6529659559",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj2 = pd.Series([1, 2, 3], index=[\"a\", \"b\", \"c\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "fbc995aa-aa11-4865-afc6-60721643d100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1\n",
       "0    2\n",
       "1    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "fa96dbf7-ab0d-4f9a-996a-57068827d947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "5e6d86b9-0c3a-46af-b238-444a0d0e852a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    3\n",
       "2    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj1[[0, 1, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3031fd15-673d-4064-89be-6738df1c1943",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\TEMP\\ipykernel_14056\\2599987575.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  obj2[[0, 1, 2]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2[[0, 1, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1e5d0071-95a5-4522-af14-f0aa78e70644",
   "metadata": {},
   "outputs": [],
   "source": [
    "# When using loc, the expression obj.loc[[0, 1, 2]] will fail when the index does not contain integers.\n",
    "# obj2.loc[[0, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "861e6329-8aa4-4eb3-aec5-04aa711ac58f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1\n",
       "0    2\n",
       "1    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj1.iloc[[0, 1, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "46fd47f4-75c5-498e-855c-100226872c54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2.iloc[[0, 1, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "7ae50ed1-6e3c-4653-93a1-7d81ba4fa89d",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj2.loc[\"b\":\"c\"] = 5 # the endpoint is inclusive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "981eb96d-a470-43ce-8e23-1e3b014316c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    5\n",
       "c    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "obj2"
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   "execution_count": 92,
   "id": "eeb58b77-35e8-4352-8e87-ba4897795159",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(np.arange(16).reshape((4, 4)),\n",
    "                    index=[\"Ohio\", \"Colorado\", \"Utah\", \"New York\"],\n",
    "                    columns=[\"one\", \"two\", \"three\", \"four\"])"
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   "cell_type": "code",
   "execution_count": 94,
   "id": "7d749d92-cd36-4d6d-aa86-9b6159154c54",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Utah         9\n",
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       "Ohio       True   True   True   True\n",
       "Colorado   True  False  False  False\n",
       "Utah      False  False  False  False\n",
       "New York  False  False  False  False"
      ]
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     "execution_count": 104,
     "metadata": {},
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    "data < 5"
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   "outputs": [],
   "source": [
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   "execution_count": 108,
   "id": "6e93c99c-3db3-4775-ac35-4825b8d42a77",
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   "outputs": [
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    "ser = pd.Series(np.arange(3.))"
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   "id": "95d268e3-4cd1-4503-b3ba-1fcf15c0552e",
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   "id": "a9152387-98d4-4a28-ac72-8ce9dfe0a1b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ser2 = pd.Series(np.arange(3.), index=[\"a\", \"b\", \"c\"])"
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   "id": "4f8ae646-971e-4113-ae18-8210b1775a1f",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\TEMP\\ipykernel_9632\\811950851.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  ser2[-1]\n"
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    "data.loc[:, \"one\"] = 1"
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    "data.iloc[2] = 5"
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    "data.loc[data[\"four\"] > 5] = 3"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\TEMP\\ipykernel_9632\\2628965532.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data.loc[data.three == 5][\"three\"] = 6\n"
     ]
    }
   ],
   "source": [
    "# The special SettingWithCopyWarning warns you that you are trying to modify a temporary value\n",
    "# (the nonempty result of data.loc[data.three == 5]) instead of the original DataFrame data.\n",
    "data.loc[data.three == 5][\"three\"] = 6"
   ]
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      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        1    0      0     0\n",
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       "New York    3    3      3     3"
      ]
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     "execution_count": 164,
     "metadata": {},
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   "source": [
    "data # data was unmodified"
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  {
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   "execution_count": 166,
   "id": "09193f20-00dc-4324-9532-172732ef7d61",
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    {
     "data": {
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      "text/plain": [
       "      one  two  three  four\n",
       "Utah    5    5      5     5"
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    "data.loc[data.three == 5]"
   ]
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  {
   "cell_type": "code",
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   "id": "37450ee8-c342-4576-b01e-7b318e3d48d0",
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   "source": [
    "# The fix is to rewrite the chained assignment to use a single loc operation.\n",
    "data.loc[data.three == 5, \"three\"] = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "10b7a9c9-1d55-49f5-a231-4f4b0aa9b49a",
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   "outputs": [
    {
     "data": {
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       "      <th>Utah</th>\n",
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       "          one  two  three  four\n",
       "Ohio        1    0      0     0\n",
       "Colorado    3    3      3     3\n",
       "Utah        5    5      6     5\n",
       "New York    3    3      3     3"
      ]
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     "execution_count": 170,
     "metadata": {},
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    "data"
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  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "90c40976-735f-4a33-aab6-42899cf0c9d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series([7.3, -2.5, 3.4, 1.5], index=[\"a\", \"c\", \"d\", \"e\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "5d2b45d0-15b4-483a-a0ff-121d7104682e",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = pd.Series([-2.1, 3.6, -1.5, 4, 3.1],\n",
    "               index = [\"a\", \"c\", \"e\", \"f\", \"g\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "22a60f5d-b71b-47b7-afec-0a3b2e4b1189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    7.3\n",
       "c   -2.5\n",
       "d    3.4\n",
       "e    1.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "fd57642e-a91c-4502-8e7f-be31eceacf43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -2.1\n",
       "c    3.6\n",
       "e   -1.5\n",
       "f    4.0\n",
       "g    3.1\n",
       "dtype: float64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "bde93fb5-84f9-42dd-8323-a20a1f33fef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5.2\n",
       "c    1.1\n",
       "d    NaN\n",
       "e    0.0\n",
       "f    NaN\n",
       "g    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The internal data alignment introduces missing values in the label locations that don’t overlap. \n",
    "# Missing values will then propagate in further arithmetic computations.\n",
    "s1 + s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "74a59b10-25a8-4531-ba81-ac87046bd3bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(np.arange(9.).reshape((3, 3)), columns=list(\"bcd\"),\n",
    "                   index=[\"Ohio\", \"Texas\", \"Colorado\"])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "888b7e24-9cf7-412a-9ca9-d94a3d08ed29",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(np.arange(12.).reshape((4, 3)), columns=list(\"bde\"),\n",
    "                   index=[\"Utah\", \"Ohio\", \"Texas\", \"Oregon\"])"
   ]
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   "execution_count": 192,
   "id": "072cfc6e-2559-4382-89e5-aa685ca3fd2f",
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   "execution_count": 194,
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       "          b     d     e\n",
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   "execution_count": 196,
   "id": "381e0b8d-fe71-4eab-a8f7-5c6b4b1f434b",
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   "outputs": [
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       "            b   c     d   e\n",
       "Colorado  NaN NaN   NaN NaN\n",
       "Ohio      3.0 NaN   6.0 NaN\n",
       "Oregon    NaN NaN   NaN NaN\n",
       "Texas     9.0 NaN  12.0 NaN\n",
       "Utah      NaN NaN   NaN NaN"
      ]
     },
     "execution_count": 196,
     "metadata": {},
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   ],
   "source": [
    "df1 + df2"
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  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "30a00744-f12a-4281-9c79-afbd3f6b79b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({\"A\": [1, 2]})"
   ]
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  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "15e81a5a-9daa-40f5-b315-c6f5f628a677",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({\"B\": [3, 4]})"
   ]
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   "cell_type": "code",
   "execution_count": 202,
   "id": "b167c68b-7086-484e-9f6b-44b2ec6fcee2",
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   "outputs": [
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   "execution_count": 206,
   "id": "2dbe6c5d-560e-47c6-9155-3f04b9dbb1fd",
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   "metadata": {},
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   "source": [
    "df1 = pd.DataFrame(np.arange(12.).reshape((3, 4)),\n",
    "                   columns=list(\"abcd\"))"
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  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "6f3a0f20-0387-4137-a575-e1f0777867b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(np.arange(20.).reshape((4, 5)),\n",
    "                   columns=list(\"abcde\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "0ce79beb-ac55-4be8-b162-d3720299fb46",
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   "outputs": [
    {
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       "     a    b     c     d\n",
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   "execution_count": 214,
   "id": "3fd10a42-6854-40d4-b6d3-b2168f59d6c6",
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   "outputs": [
    {
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       "      a     b     c     d     e\n",
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       "3  15.0  16.0  17.0  18.0  19.0"
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     "execution_count": 214,
     "metadata": {},
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   "source": [
    "df2"
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  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "077dc689-b316-4fd7-9d9a-0b8340073ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.loc[1, \"b\"] = np.nan # NaN (Not a Number)"
   ]
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  {
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   "execution_count": 218,
   "id": "3f3a00e6-d13a-4791-9f53-f0f1ee0a47f9",
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       "     a    b     c     d\n",
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       "       a         b         c         d\n",
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   "execution_count": 234,
   "id": "ed8a27e8-65b4-4fd4-9839-8bb548f0541d",
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    {
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       "     a    b     c     d  e\n",
       "0  0.0  1.0   2.0   3.0  0\n",
       "1  4.0  5.0   6.0   7.0  0\n",
       "2  8.0  9.0  10.0  11.0  0"
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     "execution_count": 234,
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   "source": [
    "df1.reindex(columns=df2.columns, fill_value=0)"
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  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "dfa72a5a-7d8a-4a89-b5bc-ead2a419d6fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.arange(12.).reshape((3, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "b8427e74-6bad-4a58-8660-8613b78adf18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  2.,  3.],\n",
       "       [ 4.,  5.,  6.,  7.],\n",
       "       [ 8.,  9., 10., 11.]])"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "31c03c4e-8d79-4ebb-a95f-aba8b66fcf53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 2., 3.])"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "308f246a-584a-4c03-bf22-5e49976a2ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [4., 4., 4., 4.],\n",
       "       [8., 8., 8., 8.]])"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr - arr[0] # broadcasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "11a670ba-11a4-40e6-97b4-09674f32c861",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = pd.DataFrame(np.arange(12.).reshape((4, 3)),\n",
    "                     columns=list(\"bde\"),\n",
    "                     index=[\"Utah\", \"Ohio\", \"Texas\", \"Oregon\"])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "014ec01d-9353-4b07-9efd-c51f526ce6dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "series = frame.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "12e41165-6ece-4f49-8033-8a3f2329fbb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Utah</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9.0</td>\n",
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      "text/plain": [
       "          b     d     e\n",
       "Utah    0.0   1.0   2.0\n",
       "Ohio    3.0   4.0   5.0\n",
       "Texas   6.0   7.0   8.0\n",
       "Oregon  9.0  10.0  11.0"
      ]
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     "execution_count": 260,
     "metadata": {},
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   ],
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    "frame"
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  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "976b0417-93bb-47a6-9cbd-5c6086deeeca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    0.0\n",
       "d    1.0\n",
       "e    2.0\n",
       "Name: Utah, dtype: float64"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "aaf5e8e9-6f32-4222-a782-d48320ecd690",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Utah</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
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       "          b    d    e\n",
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       "Texas   6.0  6.0  6.0\n",
       "Oregon  9.0  9.0  9.0"
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     "execution_count": 264,
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   "execution_count": 268,
   "id": "bb24e54f-ae1c-4ded-b189-a6b7edac88c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "series2 = pd.Series(np.arange(3), index=[\"b\", \"e\", \"f\"])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "83b47b67-741e-4a17-86dc-112b47d55851",
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    {
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       "b    0\n",
       "e    1\n",
       "f    2\n",
       "dtype: int32"
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     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "series2"
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   "cell_type": "code",
   "execution_count": 272,
   "id": "ff61aefd-0f55-4da9-896e-053a2d40fd44",
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       "      <th>e</th>\n",
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       "      <th>Utah</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9.0</td>\n",
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       "          b   d     e   f\n",
       "Utah    0.0 NaN   3.0 NaN\n",
       "Ohio    3.0 NaN   6.0 NaN\n",
       "Texas   6.0 NaN   9.0 NaN\n",
       "Oregon  9.0 NaN  12.0 NaN"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
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    "frame + series2"
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  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "a2abdaed-b299-4062-a2f6-863e6706af15",
   "metadata": {},
   "outputs": [],
   "source": [
    "series3 = frame[\"d\"]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "9156ac8e-1d07-4050-8a8a-8117a8a5555e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>b</th>\n",
       "      <th>d</th>\n",
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       "      <th>Utah</th>\n",
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       "      <th>Texas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
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       "          b     d     e\n",
       "Utah    0.0   1.0   2.0\n",
       "Ohio    3.0   4.0   5.0\n",
       "Texas   6.0   7.0   8.0\n",
       "Oregon  9.0  10.0  11.0"
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     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
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    "frame"
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   "cell_type": "code",
   "execution_count": 278,
   "id": "64619c9d-ea0b-4ee2-90dc-ac732461dfd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utah       1.0\n",
       "Ohio       4.0\n",
       "Texas      7.0\n",
       "Oregon    10.0\n",
       "Name: d, dtype: float64"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series3"
   ]
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  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "dc9ae610-3258-408e-8925-d1b8fa79039b",
   "metadata": {},
   "outputs": [
    {
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       "          b    d    e\n",
       "Utah   -1.0  0.0  1.0\n",
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     "metadata": {},
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   "source": [
    "# Match on the DataFrame’s row index (axis=\"index\") and broadcast across the columns.\n",
    "frame.sub(series3, axis=\"index\") "
   ]
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   "cell_type": "code",
   "execution_count": 284,
   "id": "e209770f-ae8a-4a76-841d-3b97c421d590",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = pd.DataFrame(np.random.standard_normal((4, 3)),\n",
    "                     columns=list(\"bde\"),\n",
    "                     index=[\"Utah\", \"Ohio\", \"Texas\", \"Oregon\"])"
   ]
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   "cell_type": "code",
   "execution_count": 286,
   "id": "3a88f12e-2a86-4f06-821d-35f6fab08bbc",
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       "      <td>0.188967</td>\n",
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       "      <th>Ohio</th>\n",
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       "      <td>-0.441588</td>\n",
       "      <td>0.364817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>0.174507</td>\n",
       "      <td>0.048997</td>\n",
       "      <td>0.143391</td>\n",
       "    </tr>\n",
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       "               b         d         e\n",
       "Utah   -1.135852  0.415381  0.188967\n",
       "Ohio    0.013946 -0.441588  0.364817\n",
       "Texas   0.174507  0.048997  0.143391\n",
       "Oregon  0.375481  1.175486 -0.301975"
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     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
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   "cell_type": "code",
   "execution_count": 288,
   "id": "bec8d52c-9b33-4f76-8c78-f5f301df701d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Utah</th>\n",
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       "      <td>0.188967</td>\n",
       "    </tr>\n",
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       "      <th>Ohio</th>\n",
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       "      <td>0.364817</td>\n",
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       "      <th>Texas</th>\n",
       "      <td>0.174507</td>\n",
       "      <td>0.048997</td>\n",
       "      <td>0.143391</td>\n",
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       "      <th>Oregon</th>\n",
       "      <td>0.375481</td>\n",
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       "               b         d         e\n",
       "Utah    1.135852  0.415381  0.188967\n",
       "Ohio    0.013946  0.441588  0.364817\n",
       "Texas   0.174507  0.048997  0.143391\n",
       "Oregon  0.375481  1.175486  0.301975"
      ]
     },
     "execution_count": 288,
     "metadata": {},
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   ],
   "source": [
    "np.abs(frame) # element-wise array methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "944cb264-4d17-4a9b-b3b1-f2da1e7182e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f1(x):\n",
    "    return x.max() - x.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "fcc8a428-087c-4078-a788-80a2318a6781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.511333\n",
       "d    1.617074\n",
       "e    0.666793\n",
       "dtype: float64"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.apply(f1)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "bca009c2-4be6-49ef-919d-c3685c3b4be0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utah      1.551233\n",
       "Ohio      0.806405\n",
       "Texas     0.125510\n",
       "Oregon    1.477461\n",
       "dtype: float64"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.apply(f1, axis=\"columns\") # apply across the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "b5f2275b-30b2-46a5-b191-f03b1b4461e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f2(x):\n",
    "    return pd.Series([x.min(), x.max()], index=[\"min\", \"max\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "aade397a-681d-4dab-a9f1-d4a2d2d9f80b",
   "metadata": {},
   "outputs": [
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     "data": {
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       "      <th>e</th>\n",
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       "      <th>min</th>\n",
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       "            b         d         e\n",
       "min -1.135852 -0.441588 -0.301975\n",
       "max  0.375481  1.175486  0.364817"
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   "cell_type": "code",
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   "id": "2bcf8ff9-2079-4610-aaf5-8a924ad78233",
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   "outputs": [],
   "source": [
    "def my_format(x):\n",
    "    return f\"{x:.2f}\""
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  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "91b888cf-5e14-4c5c-b2f5-b2b0f3552786",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\TEMP\\ipykernel_9632\\1462544007.py:1: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  frame.applymap(my_format)\n"
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-1.14</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>0.38</td>\n",
       "      <td>1.18</td>\n",
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       "            b      d      e\n",
       "Utah    -1.14   0.42   0.19\n",
       "Ohio     0.01  -0.44   0.36\n",
       "Texas    0.17   0.05   0.14\n",
       "Oregon   0.38   1.18  -0.30"
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       "Utah       0.19\n",
       "Ohio       0.36\n",
       "Texas      0.14\n",
       "Oregon    -0.30\n",
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       "      <td>0.36</td>\n",
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       "    <tr>\n",
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       "      <td>0.05</td>\n",
       "      <td>0.14</td>\n",
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       "    <tr>\n",
       "      <th>Oregon</th>\n",
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       "            b      d      e\n",
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   "execution_count": 312,
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   "execution_count": 314,
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    "frame = pd.DataFrame(np.arange(8).reshape((2, 4)),\n",
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    "obj = pd.Series([4, 7, -3, 2])"
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   "execution_count": 334,
   "id": "ba6796cf-01ce-4d00-b270-4488ab3f71d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 334,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 336,
   "id": "81568570-0aa4-4f74-87fe-745822cb7672",
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   "outputs": [],
   "source": [
    "obj = pd.Series([4, np.nan, 7, np.nan, -3, 2])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "796e413f-b620-488e-8599-1c2b74d18445",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "4   -3.0\n",
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     "execution_count": 340,
     "metadata": {},
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   "execution_count": 342,
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     "execution_count": 342,
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   "execution_count": 346,
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       "   b  a\n",
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       "3  2  1\n",
       "0  4  0\n",
       "1  7  1"
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     },
     "execution_count": 348,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 350,
   "id": "b46c7669-73c1-4b3c-b416-1b228fc1e3e8",
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   "outputs": [
    {
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   "execution_count": 352,
   "id": "1eb8c4f7-a947-4cd7-bf3c-a5fd32401d39",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = pd.Series([7, -5, 7, 4, 2, 0, 4])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "4e635d81-8ca2-490f-963b-3f56d2f19ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7\n",
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       "2    7\n",
       "3    4\n",
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       "dtype: int64"
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     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
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    "obj"
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  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "bf2d4b3e-3559-4f45-b8e2-f05b9a12736e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.5\n",
       "1    1.0\n",
       "2    6.5\n",
       "3    4.5\n",
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       "5    2.0\n",
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     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "# By default, rank breaks ties by assigning each group the mean rank.\n",
    "obj.rank()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "ac70307b-bdc6-4ddf-8f05-e2e54f3cf2dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.0\n",
       "1    1.0\n",
       "2    7.0\n",
       "3    4.0\n",
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       "5    2.0\n",
       "6    5.0\n",
       "dtype: float64"
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     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "# Ranks can also be assigned according to the order in which they’re observed in the data.\n",
    "obj.rank(method=\"first\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "d7eba236-5876-4435-b579-5f81fe029686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.5\n",
       "1    7.0\n",
       "2    1.5\n",
       "3    3.5\n",
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       "5    6.0\n",
       "6    3.5\n",
       "dtype: float64"
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     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
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   "cell_type": "code",
   "execution_count": 370,
   "id": "129c00eb-bb4f-4613-aa99-1cb9fa57e6dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = pd.DataFrame({\"b\": [4.3, 7, -3, 2], \"a\": [0, 1, 0, 1],\n",
    "                      \"c\": [-2, 5, 8, -2.5]})"
   ]
  },
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   "cell_type": "code",
   "execution_count": 372,
   "id": "b99f69f7-e888-4402-89e3-0111f50bdde2",
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       "     b  a    c\n",
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       "     b    a    c\n",
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     "metadata": {},
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   "execution_count": 376,
   "id": "96562587-8697-4556-827e-9a80593f1ac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = pd.Series(np.arange(5), index=[\"a\", \"a\", \"b\", \"b\", \"c\"])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "60c52312-56a0-49af-925a-310bbe5c82f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "a    1\n",
       "b    2\n",
       "b    3\n",
       "c    4\n",
       "dtype: int32"
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     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "obj"
   ]
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  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "b3482828-957a-4d0c-aa9c-c09fcfa95a72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
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     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "obj.index.is_unique"
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  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "f3c89ced-eec6-46b6-a164-02ba3ce55194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "a    1\n",
       "dtype: int32"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[\"a\"] # return a Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "ace1118c-3701-445a-80cb-26c4c150ff8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
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     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "obj[\"c\"] # return a scalar value"
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   "cell_type": "code",
   "execution_count": 392,
   "id": "8ae4e111-51df-4d16-9759-03bfbc3630cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.standard_normal((5, 3)),\n",
    "                  index=[\"a\", \"a\", \"b\", \"b\", \"c\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "e638579c-8d57-4be5-8eaa-0b32ece65b78",
   "metadata": {},
   "outputs": [
    {
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       "          0         1         2\n",
       "a  1.465892 -1.242291  0.619307\n",
       "a -0.585602  1.079481 -0.172835\n",
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       "b  1.835334 -0.842012  0.199897\n",
       "c  1.192978  1.005752 -0.493626"
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   "execution_count": 400,
   "id": "9ae9de91-cb84-4618-8621-d0e2bd26f43e",
   "metadata": {},
   "outputs": [
    {
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       "          0         1         2\n",
       "b  0.222561  0.790924  1.695566\n",
       "b  1.835334 -0.842012  0.199897"
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   "cell_type": "code",
   "execution_count": 402,
   "id": "4dce0acb-4b90-4713-8ef2-99ec7bdc4feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0    1.192978\n",
       "1    1.005752\n",
       "2   -0.493626\n",
       "Name: c, dtype: float64"
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     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
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    "df.loc[\"c\"] # return a Series"
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   "cell_type": "code",
   "execution_count": 7,
   "id": "62e99bb6-119c-4eb1-98d1-5ed8c1a13032",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[1.4, np.nan], [7.1, -4.5],\n",
    "                   [np.nan, np.nan], [0.75, -1.3]],\n",
    "                  index=[\"a\", \"b\", \"c\", \"d\"],\n",
    "                  columns=[\"one\", \"two\"])"
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   "cell_type": "code",
   "execution_count": 9,
   "id": "48b71400-3bb7-453d-a4ad-bb0f4d172850",
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   "outputs": [
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>7.10</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.75</td>\n",
       "      <td>-1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    one  two\n",
       "a  1.40  NaN\n",
       "b  7.10 -4.5\n",
       "c   NaN  NaN\n",
       "d  0.75 -1.3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cf64933e-e36e-4dee-b27b-467a84d6fe0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    9.25\n",
       "two   -5.80\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c71544e7-d237-4d06-9a17-f72dc7d15edc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.40\n",
       "b    2.60\n",
       "c    0.00\n",
       "d   -0.55\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# When an entire row or column contains all NA values, the sum is 0.\n",
    "df.sum(axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1012b209-fe81-44b8-bf50-e4a8cb6f0b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one   NaN\n",
       "two   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum(axis=\"index\", skipna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5fc15cf0-7ae2-4996-8a52-6f2f93957f30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     NaN\n",
       "b    2.60\n",
       "c     NaN\n",
       "d   -0.55\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum(axis=\"columns\", skipna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c732823a-4f4d-41dc-aebc-fc65210088ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.400\n",
       "b    1.300\n",
       "c      NaN\n",
       "d   -0.275\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4019d25d-c905-4ce9-ac78-0efaf98f7e1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    b\n",
       "two    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "01477b11-ddf8-4f28-b9ee-45403cf5f36d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>8.50</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>9.25</td>\n",
       "      <td>-5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    one  two\n",
       "a  1.40  NaN\n",
       "b  8.50 -4.5\n",
       "c   NaN  NaN\n",
       "d  9.25 -5.8"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a76dfb74-83c5-4165-8c93-1cbafbc1bc7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.083333</td>\n",
       "      <td>-2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.493685</td>\n",
       "      <td>2.262742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>-4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.075000</td>\n",
       "      <td>-3.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>-2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.250000</td>\n",
       "      <td>-2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>-1.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            one       two\n",
       "count  3.000000  2.000000\n",
       "mean   3.083333 -2.900000\n",
       "std    3.493685  2.262742\n",
       "min    0.750000 -4.500000\n",
       "25%    1.075000 -3.700000\n",
       "50%    1.400000 -2.900000\n",
       "75%    4.250000 -2.100000\n",
       "max    7.100000 -1.300000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9e69938b-5ddc-40b1-aaa5-dbfcb69db866",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = pd.Series([\"a\", \"a\", \"b\", \"c\"] * 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "65552123-615e-4687-b6b8-60a304141e15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     a\n",
       "1     a\n",
       "2     b\n",
       "3     c\n",
       "4     a\n",
       "5     a\n",
       "6     b\n",
       "7     c\n",
       "8     a\n",
       "9     a\n",
       "10    b\n",
       "11    c\n",
       "12    a\n",
       "13    a\n",
       "14    b\n",
       "15    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5cd712a2-b5c6-4be5-bb1b-853b4593ebba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     16\n",
       "unique     3\n",
       "top        a\n",
       "freq       8\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b1a3995d-3140-45a9-bdbc-b358d73bfa97",
   "metadata": {},
   "outputs": [],
   "source": [
    "price = pd.read_pickle(\"examples/yahoo_price.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "14b61a61-5dd9-4721-9fbd-9f478d4bc42b",
   "metadata": {},
   "outputs": [],
   "source": [
    "volume = pd.read_pickle(\"examples/yahoo_volume.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c2cd1127-b872-4359-84e3-d74d5f8076b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = price.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ebfb5a04-004a-4455-97d5-52847776493e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>AAPL</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
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       "    <tr>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2016-10-17</th>\n",
       "      <td>-0.000680</td>\n",
       "      <td>0.001837</td>\n",
       "      <td>0.002072</td>\n",
       "      <td>-0.003483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-18</th>\n",
       "      <td>-0.000681</td>\n",
       "      <td>0.019616</td>\n",
       "      <td>-0.026168</td>\n",
       "      <td>0.007690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-19</th>\n",
       "      <td>-0.002979</td>\n",
       "      <td>0.007846</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>-0.002255</td>\n",
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       "    <tr>\n",
       "      <th>2016-10-20</th>\n",
       "      <td>-0.000512</td>\n",
       "      <td>-0.005652</td>\n",
       "      <td>0.001719</td>\n",
       "      <td>-0.004867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-21</th>\n",
       "      <td>-0.003930</td>\n",
       "      <td>0.003011</td>\n",
       "      <td>-0.012474</td>\n",
       "      <td>0.042096</td>\n",
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       "                AAPL      GOOG       IBM      MSFT\n",
       "Date                                              \n",
       "2016-10-17 -0.000680  0.001837  0.002072 -0.003483\n",
       "2016-10-18 -0.000681  0.019616 -0.026168  0.007690\n",
       "2016-10-19 -0.002979  0.007846  0.003583 -0.002255\n",
       "2016-10-20 -0.000512 -0.005652  0.001719 -0.004867\n",
       "2016-10-21 -0.003930  0.003011 -0.012474  0.042096"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.tail()"
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  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "02f952a6-7ed3-4555-8091-2573f2e2c80d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
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    "returns[\"MSFT\"].corr(returns[\"IBM\"])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e5112a59-f460-40e5-a60b-dee2c3a6ae77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns[\"MSFT\"].cov(returns[\"IBM\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7978c6bf-ace3-4d95-a662-32ce44b24b1f",
   "metadata": {},
   "outputs": [
    {
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       "          AAPL      GOOG       IBM      MSFT\n",
       "AAPL  1.000000  0.407919  0.386817  0.389695\n",
       "GOOG  0.407919  1.000000  0.405099  0.465919\n",
       "IBM   0.386817  0.405099  1.000000  0.499764\n",
       "MSFT  0.389695  0.465919  0.499764  1.000000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
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    }
   ],
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    "returns.corr()"
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   "cell_type": "code",
   "execution_count": 53,
   "id": "a0706369-9d07-4124-b9dd-12536da185f8",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          AAPL      GOOG       IBM      MSFT\n",
       "AAPL  0.000277  0.000107  0.000078  0.000095\n",
       "GOOG  0.000107  0.000251  0.000078  0.000108\n",
       "IBM   0.000078  0.000078  0.000146  0.000089\n",
       "MSFT  0.000095  0.000108  0.000089  0.000215"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1f233a28-12db-4cf2-b38e-14fc51cbaf62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    0.386817\n",
       "GOOG    0.405099\n",
       "IBM     1.000000\n",
       "MSFT    0.499764\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.corrwith(returns[\"IBM\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4cf9b1f0-b649-4a6a-983f-91ad3b317e82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL   -0.075565\n",
       "GOOG   -0.007067\n",
       "IBM    -0.204849\n",
       "MSFT   -0.092950\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.corrwith(volume)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4fcd6cb1-187e-4a2c-9b02-8c9761a66793",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = pd.Series([\"c\", \"a\", \"d\", \"a\", \"a\", \"b\", \"b\", \"c\", \"c\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "815f823c-0e10-42a1-b813-ba974ca22b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    c\n",
       "1    a\n",
       "2    d\n",
       "3    a\n",
       "4    a\n",
       "5    b\n",
       "6    b\n",
       "7    c\n",
       "8    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "81b63c3d-7f28-49ab-9080-852531041d93",
   "metadata": {},
   "outputs": [],
   "source": [
    "uniques = obj.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8cdd2979-a3a4-43aa-8cf2-bee145878a20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['c', 'a', 'd', 'b'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b218ad1d-7c63-4937-b08c-00d0a7649580",
   "metadata": {},
   "outputs": [],
   "source": [
    "uniques.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "97798f65-4672-4f2d-b980-f40ea24d8749",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 'b', 'c', 'd'], dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "dda281cb-cdd5-4f0e-a5f9-4da7513fc009",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "a    3\n",
       "b    2\n",
       "d    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a028180e-c540-4185-baef-9a8186363079",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\TEMP\\ipykernel_1708\\164454357.py:1: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  pd.value_counts(obj.to_numpy(), sort=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "a    3\n",
       "d    1\n",
       "b    2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(obj.to_numpy(), sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "fc5fbd8d-0bf6-4523-91af-7a74af04313d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    c\n",
       "1    a\n",
       "2    d\n",
       "3    a\n",
       "4    a\n",
       "5    b\n",
       "6    b\n",
       "7    c\n",
       "8    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ddfce62a-1841-442a-be01-8c0bde2bc158",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = obj.isin([\"b\", \"c\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0c1889b2-e98b-41b8-80ea-cd98f3d830f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5     True\n",
       "6     True\n",
       "7     True\n",
       "8     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "cf887c9f-ccea-43d4-86f8-82f19eeff6e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    c\n",
       "5    b\n",
       "6    b\n",
       "7    c\n",
       "8    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3fc81d79-1301-4cd5-bc13-7f30396559c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "to_match = pd.Series([\"c\", \"a\", \"b\", \"b\", \"c\", \"a\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "11d1a1e6-8d79-4a2f-9821-58072478ec9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_vals = pd.Series([\"c\", \"b\", \"a\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "bab479fa-f2af-4ef0-b562-83ab60602d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "indices = pd.Index(unique_vals).get_indexer(to_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "9343be42-c0ba-4c54-b6a6-0cc8462cc436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 1, 0, 2], dtype=int64)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices"
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  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "421c406c-3a4f-4847-b7c0-c36b0cdf3e33",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame({\"Qu1\": [1, 3, 4, 3, 4],\n",
    "                     \"Qu2\": [2, 3, 1, 2, 3],\n",
    "                     \"Qu3\": [1, 5, 2, 4, 4]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f62f885c-2256-46f5-96e1-556c98455da5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>Qu1</th>\n",
       "      <th>Qu2</th>\n",
       "      <th>Qu3</th>\n",
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      "text/plain": [
       "   Qu1  Qu2  Qu3\n",
       "0    1    2    1\n",
       "1    3    3    5\n",
       "2    4    1    2\n",
       "3    3    2    4\n",
       "4    4    3    4"
      ]
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     "execution_count": 95,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 97,
   "id": "178a8210-0acd-4c00-94c5-951ebfb687f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Qu1\n",
       "1    1\n",
       "3    2\n",
       "4    2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Qu1\"].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "10c3864f-9ec6-41bd-87b5-477bb80d0ce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\TEMP\\ipykernel_1708\\2451317050.py:1: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  result = data.apply(pd.value_counts).fillna(0)\n"
     ]
    }
   ],
   "source": [
    "result = data.apply(pd.value_counts).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c88c01f4-7802-4b9d-a79a-f83b4f9f8ca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Qu1</th>\n",
       "      <th>Qu2</th>\n",
       "      <th>Qu3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   Qu1  Qu2  Qu3\n",
       "1  1.0  1.0  1.0\n",
       "2  0.0  2.0  1.0\n",
       "3  2.0  2.0  0.0\n",
       "4  2.0  0.0  2.0\n",
       "5  0.0  0.0  1.0"
      ]
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     "execution_count": 103,
     "metadata": {},
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    "# The row labels in the result are the distinct values occurring in all of the columns. \n",
    "# The values are the respective counts of these values in each column.\n",
    "result"
   ]
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  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1db2f531-4875-47ea-a486-2e14dc53111f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame({\"a\": [1, 1, 1, 2, 2], \"b\": [0, 0, 1, 0, 0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "baac28ed-9afd-4369-8217-375afb3873c9",
   "metadata": {},
   "outputs": [
    {
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       "   a  b\n",
       "0  1  0\n",
       "1  1  0\n",
       "2  1  1\n",
       "3  2  0\n",
       "4  2  0"
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     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "66dd60f9-9b3d-4768-9eb5-542f6e19c87f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a  b\n",
       "1  0    2\n",
       "2  0    2\n",
       "1  1    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.value_counts() # hierarchical index"
   ]
  }
 ],
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