ch5_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ch5. getting started with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(12345)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "PREVIOUS_MAX_ROWS = pd.options.display.max_rows\n",
    "pd.options.display.max_rows = 20\n",
    "np.set_printoptions(precision=4, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## intro to pandas data structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    4\n1    8\n2   -5\n3    2\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "obj = pd.Series([4, 8, -5, 2])\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([ 4,  8, -5,  2])"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "# the value attribute\n",
    "obj.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "RangeIndex(start=0, stop=4, step=1)"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "# the index attribute\n",
    "obj.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a     3\nb    -2\nc     6\nd    88\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "obj2 = pd.Series([3, -2, 6, 88], index=['a', 'b', 'c', 'd'])\n",
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['a', 'b', 'c', 'd'], dtype='object')"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "obj2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([ 3, -2,  6, 88])"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "obj2.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "'1.0.2'"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "-2"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "# get one value by its index\n",
    "obj2['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "b    -2\nd    88\na     3\nc     6\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "# get multiple values by their index as a list\n",
    "obj2[['b', 'd', 'a', 'c']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "c     6\nd    88\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "# filter by boolean array\n",
    "obj2[obj2 > 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a      6\nb     -4\nc     12\nd    176\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "obj2 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    2.008554e+01\nb    1.353353e-01\nc    4.034288e+02\nd    1.651636e+38\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "np.exp(obj2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "True"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "# series as a dict\n",
    "'c' in obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "False"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "'k' in obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Ohio     344\nTexas    873\nUtah     486\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "# create Series from dict\n",
    "sdata = {'Ohio': 344, 'Texas': 873, 'Utah': 486}\n",
    "obj3 = pd.Series(sdata)\n",
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Cal       NaN\nOhio    344.0\nNY        NaN\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "# override index\n",
    "states = ['Cal', 'Ohio', 'NY']\n",
    "obj4 = pd.Series(sdata, index=states)\n",
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Cal      True\nOhio    False\nNY       True\ndtype: bool"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "pd.isnull(obj4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "2"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "pd.isnull(obj4).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "1"
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "pd.notnull(obj4).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Cal      True\nOhio    False\nNY       True\ndtype: bool"
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "obj4.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "obj.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    4\n1    8\n2   -5\n3    2\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Cal       NaN\nOhio    344.0\nNY        NaN\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Cal        NaN\nNY         NaN\nOhio     688.0\nTexas      NaN\nUtah       NaN\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "obj3 + obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj4.name = 'population'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj4.index.name = 'state'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "state\nCal       NaN\nOhio    344.0\nNY        NaN\nName: population, dtype: float64"
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    4\n1    8\n2   -5\n3    2\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "b    4\ns    8\nj   -5\nr    2\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "source": [
    "obj.index = ['b', 's', 'j', 'r']\n",
    "obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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]}\n",
    "frame = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "    state  year  pop\n0    Ohio  2000  1.5\n1    Ohio  2001  1.7\n2    Ohio  2002  3.6\n3  Nevada  2001  2.4\n4  Nevada  2002  2.9\n5  Nevada  2003  3.2",
      "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>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>"
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "    state  year  pop\n0    Ohio  2000  1.5\n1    Ohio  2001  1.7\n2    Ohio  2002  3.6\n3  Nevada  2001  2.4\n4  Nevada  2002  2.9",
      "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>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>"
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "source": [
    "frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   year   state  pop\n0  2000    Ohio  1.5\n1  2001    Ohio  1.7\n2  2002    Ohio  3.6\n3  2001  Nevada  2.4\n4  2002  Nevada  2.9\n5  2003  Nevada  3.2",
      "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>year</th>\n      <th>state</th>\n      <th>pop</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    </tr>\n    <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      <td>2.9</td>\n    </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>"
     },
     "metadata": {},
     "execution_count": 36
    }
   ],
   "source": [
    "pd.DataFrame(data, columns=['year', 'state', 'pop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       year   state  pop debt\none    2000    Ohio  1.5  NaN\ntwo    2001    Ohio  1.7  NaN\nthree  2002    Ohio  3.6  NaN\nfour   2001  Nevada  2.4  NaN\nfive   2002  Nevada  2.9  NaN\nsix    2003  Nevada  3.2  NaN",
      "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>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>debt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>one</th>\n      <td>2000</td>\n      <td>Ohio</td>\n      <td>1.5</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>2001</td>\n      <td>Ohio</td>\n      <td>1.7</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>three</th>\n      <td>2002</td>\n      <td>Ohio</td>\n      <td>3.6</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>four</th>\n      <td>2001</td>\n      <td>Nevada</td>\n      <td>2.4</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>five</th>\n      <td>2002</td>\n      <td>Nevada</td>\n      <td>2.9</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>six</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>"
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "frame2 = pd.DataFrame(\n",
    "    data, \n",
    "    columns=['year', 'state', 'pop', 'debt'], \n",
    "    index=['one', 'two', 'three', 'four', 'five', 'six']\n",
    ")\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['year', 'state', 'pop', 'debt'], dtype='object')"
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "frame2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "one        Ohio\ntwo        Ohio\nthree      Ohio\nfour     Nevada\nfive     Nevada\nsix      Nevada\nName: state, dtype: object"
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "frame2['state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "pandas.core.series.Series"
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "source": [
    "type(frame2['state'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "one      2000\ntwo      2001\nthree    2002\nfour     2001\nfive     2002\nsix      2003\nName: year, dtype: int64"
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "source": [
    "frame2.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "two        Ohio\nthree      Ohio\nfour     Nevada\nName: state, dtype: object"
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "source": [
    "frame2['state']['two':'four']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "'state'"
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "frame2['state'].name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['one', 'two', 'three', 'four', 'five', 'six'], dtype='object')"
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "source": [
    "frame2['state'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "year       2001\nstate    Nevada\npop         2.4\ndebt        NaN\nName: four, dtype: object"
     },
     "metadata": {},
     "execution_count": 45
    }
   ],
   "source": [
    "frame2.loc['four']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       year   state  pop  debt\none    2000    Ohio  1.5  16.5\ntwo    2001    Ohio  1.7  16.5\nthree  2002    Ohio  3.6  16.5\nfour   2001  Nevada  2.4  16.5\nfive   2002  Nevada  2.9  16.5\nsix    2003  Nevada  3.2  16.5",
      "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>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>debt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>one</th>\n      <td>2000</td>\n      <td>Ohio</td>\n      <td>1.5</td>\n      <td>16.5</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>2001</td>\n      <td>Ohio</td>\n      <td>1.7</td>\n      <td>16.5</td>\n    </tr>\n    <tr>\n      <th>three</th>\n      <td>2002</td>\n      <td>Ohio</td>\n      <td>3.6</td>\n      <td>16.5</td>\n    </tr>\n    <tr>\n      <th>four</th>\n      <td>2001</td>\n      <td>Nevada</td>\n      <td>2.4</td>\n      <td>16.5</td>\n    </tr>\n    <tr>\n      <th>five</th>\n      <td>2002</td>\n      <td>Nevada</td>\n      <td>2.9</td>\n      <td>16.5</td>\n    </tr>\n    <tr>\n      <th>six</th>\n      <td>2003</td>\n      <td>Nevada</td>\n      <td>3.2</td>\n      <td>16.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "frame2['debt'] = 16.5\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       year   state  pop  debt\none    2000    Ohio  1.5   0.0\ntwo    2001    Ohio  1.7   1.0\nthree  2002    Ohio  3.6   2.0\nfour   2001  Nevada  2.4   3.0\nfive   2002  Nevada  2.9   4.0\nsix    2003  Nevada  3.2   5.0",
      "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>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>debt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>one</th>\n      <td>2000</td>\n      <td>Ohio</td>\n      <td>1.5</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>2001</td>\n      <td>Ohio</td>\n      <td>1.7</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>three</th>\n      <td>2002</td>\n      <td>Ohio</td>\n      <td>3.6</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>four</th>\n      <td>2001</td>\n      <td>Nevada</td>\n      <td>2.4</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>five</th>\n      <td>2002</td>\n      <td>Nevada</td>\n      <td>2.9</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>six</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>"
     },
     "metadata": {},
     "execution_count": 47
    }
   ],
   "source": [
    "frame2['debt'] = np.arange(6.0)\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "two     -1.2\nfour    -1.5\nseven   -1.7\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 48
    }
   ],
   "source": [
    "val = pd.Series(\n",
    "    [-1.2, -1.5, -1.7], \n",
    "    index=['two', 'four', 'seven']\n",
    "    )\n",
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       year   state  pop  debt\none    2000    Ohio  1.5   NaN\ntwo    2001    Ohio  1.7  -1.2\nthree  2002    Ohio  3.6   NaN\nfour   2001  Nevada  2.4  -1.5\nfive   2002  Nevada  2.9   NaN\nsix    2003  Nevada  3.2   NaN",
      "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>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>debt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>one</th>\n      <td>2000</td>\n      <td>Ohio</td>\n      <td>1.5</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>2001</td>\n      <td>Ohio</td>\n      <td>1.7</td>\n      <td>-1.2</td>\n    </tr>\n    <tr>\n      <th>three</th>\n      <td>2002</td>\n      <td>Ohio</td>\n      <td>3.6</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>four</th>\n      <td>2001</td>\n      <td>Nevada</td>\n      <td>2.4</td>\n      <td>-1.5</td>\n    </tr>\n    <tr>\n      <th>five</th>\n      <td>2002</td>\n      <td>Nevada</td>\n      <td>2.9</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>six</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>"
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "source": [
    "frame2['debt'] = val\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       year   state  pop  debt  eastern\none    2000    Ohio  1.5   NaN     True\ntwo    2001    Ohio  1.7  -1.2     True\nthree  2002    Ohio  3.6   NaN     True\nfour   2001  Nevada  2.4  -1.5    False\nfive   2002  Nevada  2.9   NaN    False\nsix    2003  Nevada  3.2   NaN    False",
      "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>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>debt</th>\n      <th>eastern</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>one</th>\n      <td>2000</td>\n      <td>Ohio</td>\n      <td>1.5</td>\n      <td>NaN</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>2001</td>\n      <td>Ohio</td>\n      <td>1.7</td>\n      <td>-1.2</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>three</th>\n      <td>2002</td>\n      <td>Ohio</td>\n      <td>3.6</td>\n      <td>NaN</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>four</th>\n      <td>2001</td>\n      <td>Nevada</td>\n      <td>2.4</td>\n      <td>-1.5</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>five</th>\n      <td>2002</td>\n      <td>Nevada</td>\n      <td>2.9</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>six</th>\n      <td>2003</td>\n      <td>Nevada</td>\n      <td>3.2</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 50
    }
   ],
   "source": [
    "frame2['eastern'] = frame2['state'] == 'Ohio'\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       year   state  pop  debt\none    2000    Ohio  1.5   NaN\ntwo    2001    Ohio  1.7  -1.2\nthree  2002    Ohio  3.6   NaN\nfour   2001  Nevada  2.4  -1.5\nfive   2002  Nevada  2.9   NaN\nsix    2003  Nevada  3.2   NaN",
      "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>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>debt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>one</th>\n      <td>2000</td>\n      <td>Ohio</td>\n      <td>1.5</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>two</th>\n      <td>2001</td>\n      <td>Ohio</td>\n      <td>1.7</td>\n      <td>-1.2</td>\n    </tr>\n    <tr>\n      <th>three</th>\n      <td>2002</td>\n      <td>Ohio</td>\n      <td>3.6</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>four</th>\n      <td>2001</td>\n      <td>Nevada</td>\n      <td>2.4</td>\n      <td>-1.5</td>\n    </tr>\n    <tr>\n      <th>five</th>\n      <td>2002</td>\n      <td>Nevada</td>\n      <td>2.9</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>six</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>"
     },
     "metadata": {},
     "execution_count": 51
    }
   ],
   "source": [
    "del frame2['eastern']\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['year', 'state', 'pop', 'debt'], dtype='object')"
     },
     "metadata": {},
     "execution_count": 52
    }
   ],
   "source": [
    "frame2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop = {\n",
    "    'Nevada' : {2001: 2.4, 2002: 2.9}, \n",
    "    'Ohio'   : {2000: 1.5, 2001: 1.7, 2002: 3.6}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      Nevada  Ohio\n2001     2.4   1.7\n2002     2.9   3.6\n2000     NaN   1.5",
      "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>Nevada</th>\n      <th>Ohio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2001</th>\n      <td>2.4</td>\n      <td>1.7</td>\n    </tr>\n    <tr>\n      <th>2002</th>\n      <td>2.9</td>\n      <td>3.6</td>\n    </tr>\n    <tr>\n      <th>2000</th>\n      <td>NaN</td>\n      <td>1.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 54
    }
   ],
   "source": [
    "frame3 = pd.DataFrame(pop)\n",
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "        2001  2002  2000\nNevada   2.4   2.9   NaN\nOhio     1.7   3.6   1.5",
      "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>2001</th>\n      <th>2002</th>\n      <th>2000</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Nevada</th>\n      <td>2.4</td>\n      <td>2.9</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Ohio</th>\n      <td>1.7</td>\n      <td>3.6</td>\n      <td>1.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 55
    }
   ],
   "source": [
    "frame3.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      Nevada  Ohio\n2001     2.4   1.7\n2002     2.9   3.6\n2003     NaN   NaN",
      "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>Nevada</th>\n      <th>Ohio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2001</th>\n      <td>2.4</td>\n      <td>1.7</td>\n    </tr>\n    <tr>\n      <th>2002</th>\n      <td>2.9</td>\n      <td>3.6</td>\n    </tr>\n    <tr>\n      <th>2003</th>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 56
    }
   ],
   "source": [
    "pd.DataFrame(pop, index=[2001, 2002, 2003])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      Ohio  Nevada\n2001   1.7     2.4\n2002   3.6     2.9",
      "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>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>"
     },
     "metadata": {},
     "execution_count": 57
    }
   ],
   "source": [
    "pdata = {\n",
    "    'Ohio'   : frame3['Ohio'][:-1],\n",
    "    'Nevada' : frame3['Nevada'][:2]\n",
    "}\n",
    "pd.DataFrame(pdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "state  Nevada  Ohio\nyear               \n2001      2.4   1.7\n2002      2.9   3.6\n2000      NaN   1.5",
      "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>state</th>\n      <th>Nevada</th>\n      <th>Ohio</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>2001</th>\n      <td>2.4</td>\n      <td>1.7</td>\n    </tr>\n    <tr>\n      <th>2002</th>\n      <td>2.9</td>\n      <td>3.6</td>\n    </tr>\n    <tr>\n      <th>2000</th>\n      <td>NaN</td>\n      <td>1.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 58
    }
   ],
   "source": [
    "frame3.index.name = 'year'\n",
    "frame3.columns.name = 'state'\n",
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[2.4, 1.7],\n       [2.9, 3.6],\n       [nan, 1.5]])"
     },
     "metadata": {},
     "execution_count": 59
    }
   ],
   "source": [
    "frame3.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[[1, 2],\n        [2, 1]],\n\n       [[3, 4],\n        [4, 3]],\n\n       [[5, 6],\n        [6, 5]]])"
     },
     "metadata": {},
     "execution_count": 60
    }
   ],
   "source": [
    "three_d_arr = np.array([\n",
    "    [[1, 2], [2, 1]], \n",
    "    [[3, 4], [4, 3]],\n",
    "    [[5, 6], [6, 5]]\n",
    "])\n",
    "three_d_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[1, 2],\n       [2, 1]])"
     },
     "metadata": {},
     "execution_count": 61
    }
   ],
   "source": [
    "three_d_arr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "state  Nevada  Ohio          3d_array\nyear                                 \n2001      2.4   1.7  [[1, 2], [2, 1]]\n2002      2.9   3.6  [[3, 4], [4, 3]]\n2000      NaN   1.5  [[5, 6], [6, 5]]",
      "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>state</th>\n      <th>Nevada</th>\n      <th>Ohio</th>\n      <th>3d_array</th>\n    </tr>\n    <tr>\n      <th>year</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2001</th>\n      <td>2.4</td>\n      <td>1.7</td>\n      <td>[[1, 2], [2, 1]]</td>\n    </tr>\n    <tr>\n      <th>2002</th>\n      <td>2.9</td>\n      <td>3.6</td>\n      <td>[[3, 4], [4, 3]]</td>\n    </tr>\n    <tr>\n      <th>2000</th>\n      <td>NaN</td>\n      <td>1.5</td>\n      <td>[[5, 6], [6, 5]]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 62
    }
   ],
   "source": [
    "frame3['3d_array'] = [three_d_arr[0], three_d_arr[1], three_d_arr[2]]\n",
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[2.4, 1.7, array([[1, 2],\n       [2, 1]])],\n       [2.9, 3.6, array([[3, 4],\n       [4, 3]])],\n       [nan, 1.5, array([[5, 6],\n       [6, 5]])]], dtype=object)"
     },
     "metadata": {},
     "execution_count": 63
    }
   ],
   "source": [
    "frame3.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "year\n2001    [[1, 2], [2, 1]]\n2002    [[3, 4], [4, 3]]\n2000    [[5, 6], [6, 5]]\nName: 3d_array, dtype: object"
     },
     "metadata": {},
     "execution_count": 64
    }
   ],
   "source": [
    "frame3['3d_array']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[2000, 'Ohio', 1.5, nan],\n       [2001, 'Ohio', 1.7, -1.2],\n       [2002, 'Ohio', 3.6, nan],\n       [2001, 'Nevada', 2.4, -1.5],\n       [2002, 'Nevada', 2.9, nan],\n       [2003, 'Nevada', 3.2, nan]], dtype=object)"
     },
     "metadata": {},
     "execution_count": 65
    }
   ],
   "source": [
    "frame2.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## index objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['a', 'b', 'c'], dtype='object')"
     },
     "metadata": {},
     "execution_count": 66
    }
   ],
   "source": [
    "obj = pd.Series(range(3), index=['a', 'b', 'c'])\n",
    "index = obj.index\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['b', 'c'], dtype='object')"
     },
     "metadata": {},
     "execution_count": 67
    }
   ],
   "source": [
    "index[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Int64Index([0, 1, 2], dtype='int64')"
     },
     "metadata": {},
     "execution_count": 68
    }
   ],
   "source": [
    "labels = pd.Index(np.arange(3))\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    1.5\n1   -2.5\n2    0.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 69
    }
   ],
   "source": [
    "obj2 = pd.Series([1.5, -2.5, 0], index=labels)\n",
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "True"
     },
     "metadata": {},
     "execution_count": 70
    }
   ],
   "source": [
    "obj2.index is labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "state  Nevada  Ohio          3d_array\nyear                                 \n2001      2.4   1.7  [[1, 2], [2, 1]]\n2002      2.9   3.6  [[3, 4], [4, 3]]\n2000      NaN   1.5  [[5, 6], [6, 5]]",
      "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>state</th>\n      <th>Nevada</th>\n      <th>Ohio</th>\n      <th>3d_array</th>\n    </tr>\n    <tr>\n      <th>year</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2001</th>\n      <td>2.4</td>\n      <td>1.7</td>\n      <td>[[1, 2], [2, 1]]</td>\n    </tr>\n    <tr>\n      <th>2002</th>\n      <td>2.9</td>\n      <td>3.6</td>\n      <td>[[3, 4], [4, 3]]</td>\n    </tr>\n    <tr>\n      <th>2000</th>\n      <td>NaN</td>\n      <td>1.5</td>\n      <td>[[5, 6], [6, 5]]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 71
    }
   ],
   "source": [
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['Nevada', 'Ohio', '3d_array'], dtype='object', name='state')"
     },
     "metadata": {},
     "execution_count": 72
    }
   ],
   "source": [
    "frame3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "False"
     },
     "metadata": {},
     "execution_count": 73
    }
   ],
   "source": [
    "'Ohin' in frame3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "False"
     },
     "metadata": {},
     "execution_count": 74
    }
   ],
   "source": [
    "2003 in frame3.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "state  Nevada  Ohio\nyear               \n2001      2.4   1.7\n2002      2.9   3.6\n2000      NaN   1.5",
      "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>state</th>\n      <th>Nevada</th>\n      <th>Ohio</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>2001</th>\n      <td>2.4</td>\n      <td>1.7</td>\n    </tr>\n    <tr>\n      <th>2002</th>\n      <td>2.9</td>\n      <td>3.6</td>\n    </tr>\n    <tr>\n      <th>2000</th>\n      <td>NaN</td>\n      <td>1.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 75
    }
   ],
   "source": [
    "frame3.drop(['3d_array'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['foo', 'foo', 'bar', 'bar'], dtype='object')"
     },
     "metadata": {},
     "execution_count": 76
    }
   ],
   "source": [
    "dup_labels = pd.Index(['foo', 'foo', 'bar', 'bar'])\n",
    "dup_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 essential functionality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### reindexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "d    4.5\nb    7.2\na   -5.3\nc    3.6\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 77
    }
   ],
   "source": [
    "obj = pd.Series(\n",
    "    [4.5, 7.2, -5.3, 3.6], \n",
    "    index=['d', 'b','a', 'c']\n",
    ")\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a   -5.3\nb    7.2\nc    3.6\nd    4.5\ne    NaN\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 78
    }
   ],
   "source": [
    "obj2 = obj.reindex(['a', 'b', 'c', 'd', 'e'])\n",
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0      blue\n2    purple\n4    yellow\ndtype: object"
     },
     "metadata": {},
     "execution_count": 79
    }
   ],
   "source": [
    "obj3 = pd.Series(['blue', 'purple', 'yellow'], index=[0, 2, 4])\n",
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0      blue\n1      blue\n2    purple\n3    purple\n4    yellow\n5    yellow\ndtype: object"
     },
     "metadata": {},
     "execution_count": 80
    }
   ],
   "source": [
    "obj3.reindex(range(6), method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   Ohio  Texas  California\na     0      1           2\nc     3      4           5\nd     6      7           8",
      "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>"
     },
     "metadata": {},
     "execution_count": 81
    }
   ],
   "source": [
    "frame = pd.DataFrame(np.arange(9).reshape((3, 3)),\n",
    "                     index=['a', 'c', 'd'],\n",
    "                     columns=['Ohio', 'Texas', 'California'])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   Ohio  Texas  California\na   0.0    1.0         2.0\nb   NaN    NaN         NaN\nc   3.0    4.0         5.0\nd   6.0    7.0         8.0",
      "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>0.0</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>"
     },
     "metadata": {},
     "execution_count": 82
    }
   ],
   "source": [
    "frame2 = frame.reindex(['a', 'b', 'c', 'd'])\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   Texas  Utah  California\na      1   NaN           2\nc      4   NaN           5\nd      7   NaN           8",
      "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>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>"
     },
     "metadata": {},
     "execution_count": 83
    }
   ],
   "source": [
    "states = ['Texas', 'Utah', 'California']\n",
    "frame.reindex(columns=states)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### drop entires from an axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    0.0\nb    1.0\nc    2.0\nd    3.0\ne    4.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 84
    }
   ],
   "source": [
    "obj = pd.Series(np.arange(5.), index=['a', 'b', 'c', 'd', 'e'])\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    0.0\nb    1.0\nd    3.0\ne    4.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 85
    }
   ],
   "source": [
    "new_obj = obj.drop('c')\n",
    "new_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "b    1.0\nc    2.0\ne    4.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 86
    }
   ],
   "source": [
    "obj.drop(['d', 'a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    0.0\nb    1.0\nc    2.0\nd    3.0\ne    4.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 87
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          one  two  three  four\nOhio        0    1      2     3\nColorado    4    5      6     7\nUtah        8    9     10    11\nNew York   12   13     14    15",
      "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>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>"
     },
     "metadata": {},
     "execution_count": 88
    }
   ],
   "source": [
    "\n",
    "data = pd.DataFrame(np.arange(16).reshape((4, 4)),\n",
    "                    index=['Ohio', 'Colorado', 'Utah', 'New York'],\n",
    "                    columns=['one', 'two', 'three', 'four'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          one  two  three  four\nUtah        8    9     10    11\nNew York   12   13     14    15",
      "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>"
     },
     "metadata": {},
     "execution_count": 89
    }
   ],
   "source": [
    "data.drop(['Ohio', 'Colorado'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          one  two  three  four\nOhio        0    1      2     3\nColorado    4    5      6     7\nUtah        8    9     10    11\nNew York   12   13     14    15",
      "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>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>"
     },
     "metadata": {},
     "execution_count": 90
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          one  three  four\nOhio        0      2     3\nColorado    4      6     7\nUtah        8     10    11\nNew York   12     14    15",
      "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>"
     },
     "metadata": {},
     "execution_count": 91
    }
   ],
   "source": [
    "data.drop('two', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          one  two  three  four\nOhio        0    1      2     3\nColorado    4    5      6     7\nUtah        8    9     10    11\nNew York   12   13     14    15",
      "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>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>"
     },
     "metadata": {},
     "execution_count": 92
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          one  three\nOhio        0      2\nColorado    4      6\nUtah        8     10\nNew York   12     14",
      "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>"
     },
     "metadata": {},
     "execution_count": 93
    }
   ],
   "source": [
    "data.drop(['two', 'four'], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          one  two  three  four\nOhio        0    1      2     3\nColorado    4    5      6     7\nUtah        8    9     10    11\nNew York   12   13     14    15",
      "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>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>"
     },
     "metadata": {},
     "execution_count": 94
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj.drop('c', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    0.0\nb    1.0\nd    3.0\ne    4.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 96
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['one', 'four'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          two  three\nOhio        1      2\nColorado    5      6\nUtah        9     10\nNew York   13     14",
      "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>two</th>\n      <th>three</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Ohio</th>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Colorado</th>\n      <td>5</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>Utah</th>\n      <td>9</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>New York</th>\n      <td>13</td>\n      <td>14</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 98
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### indexing, selection, filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    0.0\nb    1.0\nc    2.0\nd    3.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 99
    }
   ],
   "source": [
    "obj_sr = pd.Series(\n",
    "    np.arange(4.), \n",
    "    index=['a', 'b', 'c', 'd'])\n",
    "obj_sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "1.0"
     },
     "metadata": {},
     "execution_count": 100
    }
   ],
   "source": [
    "obj_sr['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "1.0"
     },
     "metadata": {},
     "execution_count": 101
    }
   ],
   "source": [
    "obj_sr[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "c    2.0\nd    3.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 102
    }
   ],
   "source": [
    "obj_sr[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "b    1.0\na    0.0\nd    3.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 103
    }
   ],
   "source": [
    "obj_sr[['b', 'a', 'd']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "b    1.0\na    0.0\nd    3.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 104
    }
   ],
   "source": [
    "obj_sr[['b', 'a', 'd']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "b    1.0\ne    4.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 105
    }
   ],
   "source": [
    "obj[[1, 3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    0.0\nb    1.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 106
    }
   ],
   "source": [
    "obj[obj < 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "b    1.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 107
    }
   ],
   "source": [
    "obj['b' : 'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "KeyError",
     "evalue": "'Passing list-likes to .loc or [] with any missing labels is no longer supported, see https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-108-d210a1143d58>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    908\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 910\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    956\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    957\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 958\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    959\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    960\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1766\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1767\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1768\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1769\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1770\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1952\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot index with multidimensional key\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1953\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1954\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1955\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1956\u001b[0m             \u001b[0;31m# nested tuple slicing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1593\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1594\u001b[0m             \u001b[0;31m# A collection of keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1595\u001b[0;31m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1596\u001b[0m             return self.obj._reindex_with_indexers(\n\u001b[1;32m   1597\u001b[0m                 \u001b[0;34m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1551\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1552\u001b[0m         self._validate_read_indexer(\n\u001b[0;32m-> 1553\u001b[0;31m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1554\u001b[0m         )\n\u001b[1;32m   1555\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1653\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1654\u001b[0m                 raise KeyError(\n\u001b[0;32m-> 1655\u001b[0;31m                     \u001b[0;34m\"Passing list-likes to .loc or [] with any missing labels \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1656\u001b[0m                     \u001b[0;34m\"is no longer supported, see \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1657\u001b[0m                     \u001b[0;34m\"https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\"\u001b[0m  \u001b[0;31m# noqa:E501\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Passing list-likes to .loc or [] with any missing labels is no longer supported, see https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike'"
     ]
    }
   ],
   "source": [
    "obj[['b', 'c']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    0.0\nb    5.0\nd    3.0\ne    4.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 109
    }
   ],
   "source": [
    "obj['b':'c'] = 5\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      one  two  three  four\nOhio    0    1      2     3\nColo    4    5      6     7\nUtah    8    9     10    11\nNY     12   13     14    15",
      "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>Ohio</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Colo</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>NY</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>"
     },
     "metadata": {},
     "execution_count": 110
    }
   ],
   "source": [
    "data = pd.DataFrame(\n",
    "    np.arange(16).reshape((4,4)), \n",
    "    index=['Ohio', 'Colo', 'Utah', 'NY'],\n",
    "    columns=['one', 'two', 'three', 'four']\n",
    ")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Ohio     1\nColo     5\nUtah     9\nNY      13\nName: two, dtype: int64"
     },
     "metadata": {},
     "execution_count": 111
    }
   ],
   "source": [
    "data['two']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      three  one\nOhio      2    0\nColo      6    4\nUtah     10    8\nNY       14   12",
      "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>three</th>\n      <th>one</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Ohio</th>\n      <td>2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Colo</th>\n      <td>6</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>Utah</th>\n      <td>10</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>NY</th>\n      <td>14</td>\n      <td>12</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 112
    }
   ],
   "source": [
    "data[['three', 'one']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      one  two  three  four\nOhio    0    1      2     3\nColo    4    5      6     7",
      "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>Ohio</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Colo</th>\n      <td>4</td>\n      <td>5</td>\n      <td>6</td>\n      <td>7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 113
    }
   ],
   "source": [
    "data[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      one  two  three  four\nColo    4    5      6     7\nUtah    8    9     10    11\nNY     12   13     14    15",
      "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>Colo</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>NY</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>"
     },
     "metadata": {},
     "execution_count": 114
    }
   ],
   "source": [
    "# apply a boolean mask on the df\n",
    "# only cells marked as 'true' will be selected\n",
    "data[data['three'] > 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "        one    two  three   four\nOhio   True   True   True   True\nColo   True  False  False  False\nUtah  False  False  False  False\nNY    False  False  False  False",
      "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>Ohio</th>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>Colo</th>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>Utah</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>NY</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 115
    }
   ],
   "source": [
    "# create a boolean mask\n",
    "data < 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      one  two  three  four\nOhio  999  999    999   999\nColo  999    5      6     7\nUtah    8    9     10    11\nNY     12   13     14    15",
      "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>Ohio</th>\n      <td>999</td>\n      <td>999</td>\n      <td>999</td>\n      <td>999</td>\n    </tr>\n    <tr>\n      <th>Colo</th>\n      <td>999</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>NY</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>"
     },
     "metadata": {},
     "execution_count": 116
    }
   ],
   "source": [
    "# apply the booleam mask to the df\n",
    "# value will be assigned only to cells masked as 'true'\n",
    "data[data < 5] = 999\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## selection with loc and iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      one  two  three  four\nOhio    0    1      2     3\nColo    4    5      6     7\nUtah    8    9     10    11\nNY     12   13     14    15",
      "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>Ohio</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>Colo</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>NY</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>"
     },
     "metadata": {},
     "execution_count": 117
    }
   ],
   "source": [
    "data = pd.DataFrame(\n",
    "    np.arange(16).reshape((4,4)), \n",
    "    index=['Ohio', 'Colo', 'Utah', 'NY'],\n",
    "    columns=['one', 'two', 'three', 'four']\n",
    ")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "two     5\nfour    7\nName: Colo, dtype: int64"
     },
     "metadata": {},
     "execution_count": 118
    }
   ],
   "source": [
    "data.loc['Colo', ['two', 'four']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "two      5\nthree    6\nfour     7\nName: Colo, dtype: int64"
     },
     "metadata": {},
     "execution_count": 119
    }
   ],
   "source": [
    "data.loc['Colo', 'two':'four']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "four    11\none      8\ntwo      9\nName: Utah, dtype: int64"
     },
     "metadata": {},
     "execution_count": 120
    }
   ],
   "source": [
    "data.iloc[2, [3, 0, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "one       8\ntwo       9\nthree    10\nfour     11\nName: Utah, dtype: int64"
     },
     "metadata": {},
     "execution_count": 121
    }
   ],
   "source": [
    "data.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      one  two  three\nColo    4    5      6\nUtah    8    9     10\nNY     12   13     14",
      "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Colo</th>\n      <td>4</td>\n      <td>5</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>Utah</th>\n      <td>8</td>\n      <td>9</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>NY</th>\n      <td>12</td>\n      <td>13</td>\n      <td>14</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 122
    }
   ],
   "source": [
    "data.iloc[:, :3][data['three'] > 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "14"
     },
     "metadata": {},
     "execution_count": 123
    }
   ],
   "source": [
    "data.at['NY' , 'three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "2"
     },
     "metadata": {},
     "execution_count": 124
    }
   ],
   "source": [
    "data.iat[0 , 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## integer indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    0.0\n1    1.0\n2    2.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 125
    }
   ],
   "source": [
    "ser = pd.Series(np.arange(3.))\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    0.0\nb    1.0\nc    2.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 126
    }
   ],
   "source": [
    "ser2 = pd.Series(np.arange(3.), index=['a', 'b', 'c'])\n",
    "ser2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "2.0"
     },
     "metadata": {},
     "execution_count": 127
    }
   ],
   "source": [
    "ser2[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    0.0\n1    1.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 128
    }
   ],
   "source": [
    "ser[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    0.0\n1    1.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 129
    }
   ],
   "source": [
    "ser.loc[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    0.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 130
    }
   ],
   "source": [
    "ser.iloc[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## arithmetic and data alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series(\n",
    "    [7.3, -2.5, 3.4, 1.5],\n",
    "    index=['a', 'c', 'd', 'e'])\n",
    "\n",
    "s2 = pd.Series(\n",
    "    [-2.1, 3.6, -1.5, 4, 3.1],\n",
    "    index=['a', 'c', 'e', 'f', 'g'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    7.3\nc   -2.5\nd    3.4\ne    1.5\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 132
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a   -2.1\nc    3.6\ne   -1.5\nf    4.0\ng    3.1\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 133
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    5.2\nc    1.1\nd    NaN\ne    0.0\nf    NaN\ng    NaN\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 134
    }
   ],
   "source": [
    "s1 + s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(\n",
    "    np.arange(9.).reshape((3, 3)), \n",
    "    columns=list('bcd'),\n",
    "    index=['Ohio', 'Texas', 'Colorado'])\n",
    "\n",
    "df2 = pd.DataFrame(\n",
    "    np.arange(12.).reshape((4, 3)), \n",
    "    columns=list('bde'),\n",
    "    index=['Utah', 'Ohio', 'Texas', 'Oregon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            b    c    d\nOhio      0.0  1.0  2.0\nTexas     3.0  4.0  5.0\nColorado  6.0  7.0  8.0",
      "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>b</th>\n      <th>c</th>\n      <th>d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Ohio</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>Texas</th>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>Colorado</th>\n      <td>6.0</td>\n      <td>7.0</td>\n      <td>8.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 136
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          b     d     e\nUtah    0.0   1.0   2.0\nOhio    3.0   4.0   5.0\nTexas   6.0   7.0   8.0\nOregon  9.0  10.0  11.0",
      "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>b</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Utah</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n    </tr>\n    <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    </tr>\n    <tr>\n      <th>Oregon</th>\n      <td>9.0</td>\n      <td>10.0</td>\n      <td>11.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 137
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            b   c     d   e\nColorado  NaN NaN   NaN NaN\nOhio      3.0 NaN   6.0 NaN\nOregon    NaN NaN   NaN NaN\nTexas     9.0 NaN  12.0 NaN\nUtah      NaN NaN   NaN NaN",
      "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>b</th>\n      <th>c</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Colorado</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Ohio</th>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>6.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Oregon</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Texas</th>\n      <td>9.0</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Utah</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 138
    }
   ],
   "source": [
    "df1 + df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'A': [1, 2]})\n",
    "df2 = pd.DataFrame({'B': [3, 4]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   A\n0  1\n1  2",
      "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>A</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 140
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   B\n0  3\n1  4",
      "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>B</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 141
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "    A   B\n0 NaN NaN\n1 NaN NaN",
      "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>A</th>\n      <th>B</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 142
    }
   ],
   "source": [
    "df1 - df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## arithmetic methods with fill values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(\n",
    "    np.arange(12.).reshape((3, 4)),\n",
    "    columns=list('abcd'))\n",
    "\n",
    "df2 = pd.DataFrame(\n",
    "    np.arange(20.).reshape((4, 5)),\n",
    "    columns=list('abcde'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     a    b     c     d\n0  0.0  1.0   2.0   3.0\n1  4.0  5.0   6.0   7.0\n2  8.0  9.0  10.0  11.0",
      "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>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>6.0</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8.0</td>\n      <td>9.0</td>\n      <td>10.0</td>\n      <td>11.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 144
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      a     b     c     d     e\n0   0.0   1.0   2.0   3.0   4.0\n1   5.0   6.0   7.0   8.0   9.0\n2  10.0  11.0  12.0  13.0  14.0\n3  15.0  16.0  17.0  18.0  19.0",
      "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>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5.0</td>\n      <td>6.0</td>\n      <td>7.0</td>\n      <td>8.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10.0</td>\n      <td>11.0</td>\n      <td>12.0</td>\n      <td>13.0</td>\n      <td>14.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15.0</td>\n      <td>16.0</td>\n      <td>17.0</td>\n      <td>18.0</td>\n      <td>19.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 145
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.loc[1, 'b'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     a    b     c     d\n0  0.0  1.0   2.0   3.0\n1  4.0  5.0   6.0   7.0\n2  8.0  9.0  10.0  11.0",
      "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>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>6.0</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8.0</td>\n      <td>9.0</td>\n      <td>10.0</td>\n      <td>11.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 147
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      a     b     c     d     e\n0   0.0   1.0   2.0   3.0   4.0\n1   5.0   NaN   7.0   8.0   9.0\n2  10.0  11.0  12.0  13.0  14.0\n3  15.0  16.0  17.0  18.0  19.0",
      "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>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>7.0</td>\n      <td>8.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10.0</td>\n      <td>11.0</td>\n      <td>12.0</td>\n      <td>13.0</td>\n      <td>14.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15.0</td>\n      <td>16.0</td>\n      <td>17.0</td>\n      <td>18.0</td>\n      <td>19.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 148
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      a     b     c     d   e\n0   0.0   2.0   4.0   6.0 NaN\n1   9.0   NaN  13.0  15.0 NaN\n2  18.0  20.0  22.0  24.0 NaN\n3   NaN   NaN   NaN   NaN NaN",
      "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>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>6.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>9.0</td>\n      <td>NaN</td>\n      <td>13.0</td>\n      <td>15.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>18.0</td>\n      <td>20.0</td>\n      <td>22.0</td>\n      <td>24.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 149
    }
   ],
   "source": [
    "df1 + df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      a     b     c     d     e\n0   0.0   2.0   4.0   6.0   4.0\n1   9.0   5.0  13.0  15.0   9.0\n2  18.0  20.0  22.0  24.0  14.0\n3  15.0  16.0  17.0  18.0  19.0",
      "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>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>6.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>9.0</td>\n      <td>5.0</td>\n      <td>13.0</td>\n      <td>15.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>18.0</td>\n      <td>20.0</td>\n      <td>22.0</td>\n      <td>24.0</td>\n      <td>14.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15.0</td>\n      <td>16.0</td>\n      <td>17.0</td>\n      <td>18.0</td>\n      <td>19.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 150
    }
   ],
   "source": [
    "df1.add(df2, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       a         b         c         d\n0    inf  1.000000  0.500000  0.333333\n1  0.250  0.200000  0.166667  0.142857\n2  0.125  0.111111  0.100000  0.090909",
      "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>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>inf</td>\n      <td>1.000000</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.250</td>\n      <td>0.200000</td>\n      <td>0.166667</td>\n      <td>0.142857</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.125</td>\n      <td>0.111111</td>\n      <td>0.100000</td>\n      <td>0.090909</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 151
    }
   ],
   "source": [
    "1 / df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       a         b         c         d\n0    inf  1.000000  0.500000  0.333333\n1  0.250  0.200000  0.166667  0.142857\n2  0.125  0.111111  0.100000  0.090909",
      "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>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>inf</td>\n      <td>1.000000</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.250</td>\n      <td>0.200000</td>\n      <td>0.166667</td>\n      <td>0.142857</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.125</td>\n      <td>0.111111</td>\n      <td>0.100000</td>\n      <td>0.090909</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 152
    }
   ],
   "source": [
    "df1.rdiv(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     a    b     c     d\n0  0.0  1.0   2.0   3.0\n1  4.0  5.0   6.0   7.0\n2  8.0  9.0  10.0  11.0",
      "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>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>6.0</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8.0</td>\n      <td>9.0</td>\n      <td>10.0</td>\n      <td>11.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 153
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     a    b     c     d  e\n0  0.0  1.0   2.0   3.0  0\n1  4.0  5.0   6.0   7.0  0\n2  8.0  9.0  10.0  11.0  0",
      "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>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>6.0</td>\n      <td>7.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8.0</td>\n      <td>9.0</td>\n      <td>10.0</td>\n      <td>11.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 154
    }
   ],
   "source": [
    "df1.reindex(columns=df2.columns, fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## operations btw dataframe and series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[ 0.,  1.,  2.,  3.],\n       [ 4.,  5.,  6.,  7.],\n       [ 8.,  9., 10., 11.]])"
     },
     "metadata": {},
     "execution_count": 155
    }
   ],
   "source": [
    "arr = np.arange(12.).reshape((3,4))\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([0., 1., 2., 3.])"
     },
     "metadata": {},
     "execution_count": 156
    }
   ],
   "source": [
    "arr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[0., 0., 0., 0.],\n       [4., 4., 4., 4.],\n       [8., 8., 8., 8.]])"
     },
     "metadata": {},
     "execution_count": 157
    }
   ],
   "source": [
    "arr - arr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = pd.DataFrame(\n",
    "    np.arange(12.).reshape((4, 3)),\n",
    "    columns=list('bde'),\n",
    "    index=['Utah', 'Ohio', 'Texas', 'Oregon'])\n",
    "series = frame.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          b     d     e\nUtah    0.0   1.0   2.0\nOhio    3.0   4.0   5.0\nTexas   6.0   7.0   8.0\nOregon  9.0  10.0  11.0",
      "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>b</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Utah</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n    </tr>\n    <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    </tr>\n    <tr>\n      <th>Oregon</th>\n      <td>9.0</td>\n      <td>10.0</td>\n      <td>11.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 159
    }
   ],
   "source": [
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "b    0.0\nd    1.0\ne    2.0\nName: Utah, dtype: float64"
     },
     "metadata": {},
     "execution_count": 160
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "'Utah'"
     },
     "metadata": {},
     "execution_count": 161
    }
   ],
   "source": [
    "series.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['b', 'd', 'e'], dtype='object')"
     },
     "metadata": {},
     "execution_count": 162
    }
   ],
   "source": [
    "series.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          b    d    e\nUtah    0.0  0.0  0.0\nOhio    3.0  3.0  3.0\nTexas   6.0  6.0  6.0\nOregon  9.0  9.0  9.0",
      "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>b</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Utah</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <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    </tr>\n    <tr>\n      <th>Oregon</th>\n      <td>9.0</td>\n      <td>9.0</td>\n      <td>9.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 163
    }
   ],
   "source": [
    "frame - series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "b    0\ne    1\nf    2\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 164
    }
   ],
   "source": [
    "series2 = pd.Series(range(3), index=['b', 'e', 'f'])\n",
    "series2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          b   d     e   f\nUtah    0.0 NaN   3.0 NaN\nOhio    3.0 NaN   6.0 NaN\nTexas   6.0 NaN   9.0 NaN\nOregon  9.0 NaN  12.0 NaN",
      "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>b</th>\n      <th>d</th>\n      <th>e</th>\n      <th>f</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Utah</th>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Ohio</th>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>6.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Texas</th>\n      <td>6.0</td>\n      <td>NaN</td>\n      <td>9.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Oregon</th>\n      <td>9.0</td>\n      <td>NaN</td>\n      <td>12.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 165
    }
   ],
   "source": [
    "frame + series2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Utah       1.0\nOhio       4.0\nTexas      7.0\nOregon    10.0\nName: d, dtype: float64"
     },
     "metadata": {},
     "execution_count": 166
    }
   ],
   "source": [
    "series3 = frame['d']\n",
    "series3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          b    d    e\nUtah   -1.0  0.0  1.0\nOhio   -1.0  0.0  1.0\nTexas  -1.0  0.0  1.0\nOregon -1.0  0.0  1.0",
      "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>b</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Utah</th>\n      <td>-1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Ohio</th>\n      <td>-1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Texas</th>\n      <td>-1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Oregon</th>\n      <td>-1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 167
    }
   ],
   "source": [
    "frame.sub(series3, axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## function application and mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "               b         d         e\nUtah   -0.204708  0.478943 -0.519439\nOhio   -0.555730  1.965781  1.393406\nTexas   0.092908  0.281746  0.769023\nOregon  1.246435  1.007189 -1.296221",
      "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>b</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Utah</th>\n      <td>-0.204708</td>\n      <td>0.478943</td>\n      <td>-0.519439</td>\n    </tr>\n    <tr>\n      <th>Ohio</th>\n      <td>-0.555730</td>\n      <td>1.965781</td>\n      <td>1.393406</td>\n    </tr>\n    <tr>\n      <th>Texas</th>\n      <td>0.092908</td>\n      <td>0.281746</td>\n      <td>0.769023</td>\n    </tr>\n    <tr>\n      <th>Oregon</th>\n      <td>1.246435</td>\n      <td>1.007189</td>\n      <td>-1.296221</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 168
    }
   ],
   "source": [
    "frame = pd.DataFrame(\n",
    "    np.random.randn(4, 3), columns=list('bde'),\n",
    "    index=['Utah', 'Ohio', 'Texas', 'Oregon'])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "               b         d         e\nUtah    0.204708  0.478943  0.519439\nOhio    0.555730  1.965781  1.393406\nTexas   0.092908  0.281746  0.769023\nOregon  1.246435  1.007189  1.296221",
      "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>b</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Utah</th>\n      <td>0.204708</td>\n      <td>0.478943</td>\n      <td>0.519439</td>\n    </tr>\n    <tr>\n      <th>Ohio</th>\n      <td>0.555730</td>\n      <td>1.965781</td>\n      <td>1.393406</td>\n    </tr>\n    <tr>\n      <th>Texas</th>\n      <td>0.092908</td>\n      <td>0.281746</td>\n      <td>0.769023</td>\n    </tr>\n    <tr>\n      <th>Oregon</th>\n      <td>1.246435</td>\n      <td>1.007189</td>\n      <td>1.296221</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 169
    }
   ],
   "source": [
    "np.abs(frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "b    1.802165\nd    1.684034\ne    2.689627\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 170
    }
   ],
   "source": [
    "f = lambda x: x.max() - x.min()\n",
    "frame.apply(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Utah      0.998382\nOhio      2.521511\nTexas     0.676115\nOregon    2.542656\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 171
    }
   ],
   "source": [
    "frame.apply(f, axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Utah      0.998382\nOhio      2.521511\nTexas     0.676115\nOregon    2.542656\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 172
    }
   ],
   "source": [
    "frame.apply(f, axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### the function passed to **_apply()_** can return a Seriers with multiple values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            b         d         e\nmin -0.555730  0.281746 -1.296221\nmax  1.246435  1.965781  1.393406",
      "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>b</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>min</th>\n      <td>-0.555730</td>\n      <td>0.281746</td>\n      <td>-1.296221</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.246435</td>\n      <td>1.965781</td>\n      <td>1.393406</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 173
    }
   ],
   "source": [
    "def f(x):\n",
    "    return pd.Series(\n",
    "        [x.min(), x.max()],\n",
    "        index=['min', 'max'])\n",
    "frame.apply(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            b     d      e\nUtah    -0.20  0.48  -0.52\nOhio    -0.56  1.97   1.39\nTexas    0.09  0.28   0.77\nOregon   1.25  1.01  -1.30",
      "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>b</th>\n      <th>d</th>\n      <th>e</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Utah</th>\n      <td>-0.20</td>\n      <td>0.48</td>\n      <td>-0.52</td>\n    </tr>\n    <tr>\n      <th>Ohio</th>\n      <td>-0.56</td>\n      <td>1.97</td>\n      <td>1.39</td>\n    </tr>\n    <tr>\n      <th>Texas</th>\n      <td>0.09</td>\n      <td>0.28</td>\n      <td>0.77</td>\n    </tr>\n    <tr>\n      <th>Oregon</th>\n      <td>1.25</td>\n      <td>1.01</td>\n      <td>-1.30</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 174
    }
   ],
   "source": [
    "f_format = lambda x: '%.2f' % x\n",
    "frame.applymap(f_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Utah      -0.52\nOhio       1.39\nTexas      0.77\nOregon    -1.30\nName: e, dtype: object"
     },
     "metadata": {},
     "execution_count": 175
    }
   ],
   "source": [
    "frame['e'].map(f_format)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## sorting and ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "d    0\na    1\nb    2\nc    3\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 176
    }
   ],
   "source": [
    "obj = pd.Series(range(4), index=['d', 'a', 'b', 'c'])\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    1\nb    2\nc    3\nd    0\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 177
    }
   ],
   "source": [
    "obj.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   d  a  b  c\nZ  0  1  2  3\nA  4  5  6  7",
      "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>d</th>\n      <th>a</th>\n      <th>b</th>\n      <th>c</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Z</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>4</td>\n      <td>5</td>\n      <td>6</td>\n      <td>7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 178
    }
   ],
   "source": [
    "frame = pd.DataFrame(\n",
    "    np.arange(8).reshape((2, 4)),\n",
    "    index=list('ZA'),\n",
    "    columns=['d', 'a', 'b', 'c'])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   d  a  b  c\nA  4  5  6  7\nZ  0  1  2  3",
      "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>d</th>\n      <th>a</th>\n      <th>b</th>\n      <th>c</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>A</th>\n      <td>4</td>\n      <td>5</td>\n      <td>6</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>Z</th>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 179
    }
   ],
   "source": [
    "frame.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   a  b  c  d\nZ  1  2  3  0\nA  5  6  7  4",
      "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>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Z</th>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>5</td>\n      <td>6</td>\n      <td>7</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 180
    }
   ],
   "source": [
    "frame.sort_index(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   d  c  b  a\nZ  0  3  2  1\nA  4  7  6  5",
      "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>d</th>\n      <th>c</th>\n      <th>b</th>\n      <th>a</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Z</th>\n      <td>0</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>A</th>\n      <td>4</td>\n      <td>7</td>\n      <td>6</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 181
    }
   ],
   "source": [
    "frame.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    4\n1    7\n2   -3\n3    2\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 182
    }
   ],
   "source": [
    "obj = pd.Series([4, 7, -3, 2])\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "2   -3\n3    2\n0    4\n1    7\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 183
    }
   ],
   "source": [
    "obj.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    4.0\n1    NaN\n2    7.0\n3    NaN\n4   -3.0\n5    2.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 184
    }
   ],
   "source": [
    "obj = pd.Series([4, np.nan, 7, np.nan, -3, 2])\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "4   -3.0\n5    2.0\n0    4.0\n2    7.0\n1    NaN\n3    NaN\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 185
    }
   ],
   "source": [
    "obj.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   b  a\n0  4  0\n1  7  1\n2 -3  0\n3  2  1",
      "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>b</th>\n      <th>a</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>7</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 186
    }
   ],
   "source": [
    "frame = pd.DataFrame({\n",
    "    'b': [4, 7, -3, 2], \n",
    "    'a': [0, 1, 0, 1]})\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   b  a\n2 -3  0\n3  2  1\n0  4  0\n1  7  1",
      "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>b</th>\n      <th>a</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>-3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>7</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 187
    }
   ],
   "source": [
    "frame.sort_values(by='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   b  a\n2 -3  0\n0  4  0\n3  2  1\n1  7  1",
      "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>b</th>\n      <th>a</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>-3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>7</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 188
    }
   ],
   "source": [
    "frame.sort_values(by=['a', 'b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    7\n1   -5\n2    7\n3    4\n4    2\n5    0\n6    4\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 189
    }
   ],
   "source": [
    "obj = pd.Series([7, -5, 7, 4, 2, 0, 4])\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    6.5\n1    1.0\n2    6.5\n3    4.5\n4    3.0\n5    2.0\n6    4.5\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 190
    }
   ],
   "source": [
    "obj.rank()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    6.0\n1    1.0\n2    7.0\n3    4.0\n4    3.0\n5    2.0\n6    5.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 191
    }
   ],
   "source": [
    "obj.rank(method='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    2.0\n1    7.0\n2    2.0\n3    4.0\n4    5.0\n5    6.0\n6    4.0\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 192
    }
   ],
   "source": [
    "obj.rank(ascending=False, method='max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     b  a    c\n0  4.3  0 -2.0\n1  7.0  1  5.0\n2 -3.0  0  8.0\n3  2.0  1 -2.5",
      "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>b</th>\n      <th>a</th>\n      <th>c</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4.3</td>\n      <td>0</td>\n      <td>-2.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>7.0</td>\n      <td>1</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-3.0</td>\n      <td>0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2.0</td>\n      <td>1</td>\n      <td>-2.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 193
    }
   ],
   "source": [
    "frame = pd.DataFrame({\n",
    "    'b': [4.3, 7, -3, 2], 'a': [0, 1, 0, 1],\n",
    "    'c': [-2, 5, 8, -2.5]})\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     b    a    c\n0  3.0  2.0  1.0\n1  3.0  1.0  2.0\n2  1.0  2.0  3.0\n3  3.0  2.0  1.0",
      "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>b</th>\n      <th>a</th>\n      <th>c</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 194
    }
   ],
   "source": [
    "frame.rank(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## axis indexes with duplicate labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    0\na    1\nb    2\nb    3\nc    4\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 195
    }
   ],
   "source": [
    "obj = pd.Series(range(5), index=['a', 'a', 'b', 'b', 'c'])\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "False"
     },
     "metadata": {},
     "execution_count": 196
    }
   ],
   "source": [
    "obj.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    0\na    1\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 197
    }
   ],
   "source": [
    "obj['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "4"
     },
     "metadata": {},
     "execution_count": 198
    }
   ],
   "source": [
    "obj['c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          0         1         2\na  0.274992  0.228913  1.352917\na  0.886429 -2.001637 -0.371843\nb  1.669025 -0.438570 -0.539741\nb  0.476985  3.248944 -1.021228",
      "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>0</th>\n      <th>1</th>\n      <th>2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td>0.274992</td>\n      <td>0.228913</td>\n      <td>1.352917</td>\n    </tr>\n    <tr>\n      <th>a</th>\n      <td>0.886429</td>\n      <td>-2.001637</td>\n      <td>-0.371843</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td>1.669025</td>\n      <td>-0.438570</td>\n      <td>-0.539741</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td>0.476985</td>\n      <td>3.248944</td>\n      <td>-1.021228</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 199
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(4, 3), index=['a', 'a', 'b', 'b'])\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          0         1         2\nb  1.669025 -0.438570 -0.539741\nb  0.476985  3.248944 -1.021228",
      "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>0</th>\n      <th>1</th>\n      <th>2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>b</th>\n      <td>1.669025</td>\n      <td>-0.438570</td>\n      <td>-0.539741</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td>0.476985</td>\n      <td>3.248944</td>\n      <td>-1.021228</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 200
    }
   ],
   "source": [
    "df.loc['b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## summarizing and computing descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "    one  two\na  1.40  NaN\nb  7.10 -4.5\nc   NaN  NaN\nd  0.75 -1.3",
      "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    </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>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>"
     },
     "metadata": {},
     "execution_count": 201
    }
   ],
   "source": [
    "df = pd.DataFrame([[1.4, np.nan], \n",
    "                   [7.1, -4.5],\n",
    "                   [np.nan, np.nan], \n",
    "                   [0.75, -1.3]],\n",
    "                  index=['a', 'b', 'c', 'd'],\n",
    "                  columns=['one', 'two'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "one    9.25\ntwo   -5.80\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 202
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    1.40\nb    2.60\nc    0.00\nd   -0.55\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 203
    }
   ],
   "source": [
    "df.sum(axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    1.40\nb    2.60\nc    0.00\nd   -0.55\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 204
    }
   ],
   "source": [
    "df.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "one    9.25\ntwo   -5.80\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 205
    }
   ],
   "source": [
    "df.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a      NaN\nb    1.300\nc      NaN\nd   -0.275\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 206
    }
   ],
   "source": [
    "df.mean(axis='columns', skipna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "one    b\ntwo    d\ndtype: object"
     },
     "metadata": {},
     "execution_count": 207
    }
   ],
   "source": [
    "df.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "    one  two\na  1.40  NaN\nb  8.50 -4.5\nc   NaN  NaN\nd  9.25 -5.8",
      "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    </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>"
     },
     "metadata": {},
     "execution_count": 208
    }
   ],
   "source": [
    "df.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            one       two\ncount  3.000000  2.000000\nmean   3.083333 -2.900000\nstd    3.493685  2.262742\nmin    0.750000 -4.500000\n25%    1.075000 -3.700000\n50%    1.400000 -2.900000\n75%    4.250000 -2.100000\nmax    7.100000 -1.300000",
      "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    </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>"
     },
     "metadata": {},
     "execution_count": 212
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0     a\n1     a\n2     b\n3     c\n4     a\n5     a\n6     b\n7     c\n8     a\n9     a\n10    b\n11    c\n12    a\n13    a\n14    b\n15    c\ndtype: object"
     },
     "metadata": {},
     "execution_count": 214
    }
   ],
   "source": [
    "obj = pd.Series(['a', 'a', 'b', 'c'] * 4)\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "count     16\nunique     3\ntop        a\nfreq       8\ndtype: object"
     },
     "metadata": {},
     "execution_count": 215
    }
   ],
   "source": [
    "obj.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## correlation and covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "price = pd.read_pickle('examples/yahoo_price.pkl')\n",
    "volume = pd.read_pickle('examples/yahoo_volume.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "pandas.core.frame.DataFrame"
     },
     "metadata": {},
     "execution_count": 219
    }
   ],
   "source": [
    "type(price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                AAPL      GOOG       IBM      MSFT\nDate                                              \n2016-10-17 -0.000680  0.001837  0.002072 -0.003483\n2016-10-18 -0.000681  0.019616 -0.026168  0.007690\n2016-10-19 -0.002979  0.007846  0.003583 -0.002255\n2016-10-20 -0.000512 -0.005652  0.001719 -0.004867\n2016-10-21 -0.003930  0.003011 -0.012474  0.042096",
      "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>AAPL</th>\n      <th>GOOG</th>\n      <th>IBM</th>\n      <th>MSFT</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <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    </tr>\n    <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    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 221
    }
   ],
   "source": [
    "returns = price.pct_change()\n",
    "returns.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.49976361144151144"
     },
     "metadata": {},
     "execution_count": 222
    }
   ],
   "source": [
    "returns['MSFT'].corr(returns['IBM'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "8.870655479703546e-05"
     },
     "metadata": {},
     "execution_count": 223
    }
   ],
   "source": [
    "returns['MSFT'].cov(returns['IBM'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.49976361144151144"
     },
     "metadata": {},
     "execution_count": 224
    }
   ],
   "source": [
    "returns.MSFT.corr(returns.IBM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          AAPL      GOOG       IBM      MSFT\nAAPL  1.000000  0.407919  0.386817  0.389695\nGOOG  0.407919  1.000000  0.405099  0.465919\nIBM   0.386817  0.405099  1.000000  0.499764\nMSFT  0.389695  0.465919  0.499764  1.000000",
      "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>AAPL</th>\n      <th>GOOG</th>\n      <th>IBM</th>\n      <th>MSFT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>AAPL</th>\n      <td>1.000000</td>\n      <td>0.407919</td>\n      <td>0.386817</td>\n      <td>0.389695</td>\n    </tr>\n    <tr>\n      <th>GOOG</th>\n      <td>0.407919</td>\n      <td>1.000000</td>\n      <td>0.405099</td>\n      <td>0.465919</td>\n    </tr>\n    <tr>\n      <th>IBM</th>\n      <td>0.386817</td>\n      <td>0.405099</td>\n      <td>1.000000</td>\n      <td>0.499764</td>\n    </tr>\n    <tr>\n      <th>MSFT</th>\n      <td>0.389695</td>\n      <td>0.465919</td>\n      <td>0.499764</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 225
    }
   ],
   "source": [
    "returns.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          AAPL      GOOG       IBM      MSFT\nAAPL  0.000277  0.000107  0.000078  0.000095\nGOOG  0.000107  0.000251  0.000078  0.000108\nIBM   0.000078  0.000078  0.000146  0.000089\nMSFT  0.000095  0.000108  0.000089  0.000215",
      "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>AAPL</th>\n      <th>GOOG</th>\n      <th>IBM</th>\n      <th>MSFT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>AAPL</th>\n      <td>0.000277</td>\n      <td>0.000107</td>\n      <td>0.000078</td>\n      <td>0.000095</td>\n    </tr>\n    <tr>\n      <th>GOOG</th>\n      <td>0.000107</td>\n      <td>0.000251</td>\n      <td>0.000078</td>\n      <td>0.000108</td>\n    </tr>\n    <tr>\n      <th>IBM</th>\n      <td>0.000078</td>\n      <td>0.000078</td>\n      <td>0.000146</td>\n      <td>0.000089</td>\n    </tr>\n    <tr>\n      <th>MSFT</th>\n      <td>0.000095</td>\n      <td>0.000108</td>\n      <td>0.000089</td>\n      <td>0.000215</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 226
    }
   ],
   "source": [
    "returns.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "AAPL    0.386817\nGOOG    0.405099\nIBM     1.000000\nMSFT    0.499764\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 227
    }
   ],
   "source": [
    "returns.corrwith(returns.IBM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "AAPL   -0.075565\nGOOG   -0.007067\nIBM    -0.204849\nMSFT   -0.092950\ndtype: float64"
     },
     "metadata": {},
     "execution_count": 228
    }
   ],
   "source": [
    "returns.corrwith(volume)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    c\n1    a\n2    d\n3    a\n4    a\n5    b\n6    b\n7    c\n8    c\ndtype: object"
     },
     "metadata": {},
     "execution_count": 230
    }
   ],
   "source": [
    "obj = pd.Series(['c', 'a', 'd', 'a', 'a', 'b', 'b', 'c', 'c'])\n",
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array(['c', 'a', 'd', 'b'], dtype=object)"
     },
     "metadata": {},
     "execution_count": 231
    }
   ],
   "source": [
    "obj.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "a    3\nc    3\nb    2\nd    1\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 232
    }
   ],
   "source": [
    "obj.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "c    3\na    3\nd    1\nb    2\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 233
    }
   ],
   "source": [
    "pd.value_counts(obj.values, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0     True\n1    False\n2    False\n3    False\n4    False\n5     True\n6     True\n7     True\n8     True\ndtype: bool"
     },
     "metadata": {},
     "execution_count": 234
    }
   ],
   "source": [
    "mask = obj.isin(['b', 'c'])\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    c\n5    b\n6    b\n7    c\n8    c\ndtype: object"
     },
     "metadata": {},
     "execution_count": 235
    }
   ],
   "source": [
    "obj[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    c\n1    a\n2    b\n3    b\n4    c\n5    a\ndtype: object"
     },
     "metadata": {},
     "execution_count": 236
    }
   ],
   "source": [
    "to_match = pd.Series(['c', 'a', 'b', 'b', 'c', 'a'])\n",
    "to_match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    c\n1    b\n2    a\ndtype: object"
     },
     "metadata": {},
     "execution_count": 237
    }
   ],
   "source": [
    "unique_vals = pd.Series(['c', 'b', 'a'])\n",
    "unique_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([0, 2, 1, 1, 0, 2])"
     },
     "metadata": {},
     "execution_count": 238
    }
   ],
   "source": [
    "pd.Index(unique_vals).get_indexer(to_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Index(['c', 'b', 'a'], dtype='object')"
     },
     "metadata": {},
     "execution_count": 239
    }
   ],
   "source": [
    "pd.Index(unique_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   Qu1  Qu2  Qu3\n0    1    2    1\n1    3    3    5\n2    4    1    2\n3    3    2    4\n4    4    3    4",
      "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>Qu1</th>\n      <th>Qu2</th>\n      <th>Qu3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>3</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>2</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 240
    }
   ],
   "source": [
    "data = pd.DataFrame({'Qu1': [1, 3, 4, 3, 4],\n",
    "                     'Qu2': [2, 3, 1, 2, 3],\n",
    "                     'Qu3': [1, 5, 2, 4, 4]})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   Qu1  Qu2  Qu3\n1  1.0  1.0  1.0\n2  0.0  2.0  1.0\n3  2.0  2.0  0.0\n4  2.0  0.0  2.0\n5  0.0  0.0  1.0",
      "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>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      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 242
    }
   ],
   "source": [
    "results = data.apply(pd.value_counts).fillna(0)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.7.5-final"
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  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
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