Chuong3_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.2.0'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Đối tượng Series và cách tạo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series là mảng 1 chiều gồm dữ liệu được đánh chỉ số\n",
    "\n",
    "Cú pháp hàm tạo rút gọn: pd.Series(data=None, index=None)\n",
    "\n",
    "với:\n",
    "- data là đối tượng chứa dữ liệu của Series (có kiểu array-like, Iterable, dict, hay scalar value)\n",
    "- index là chuỗi giá trị có cùng độ dài với data (có kiểu array-like hay Index (1d))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nó có thể được tạo từ 1 danh sách:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.25\n",
       "1    0.50\n",
       "2    0.75\n",
       "3    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.75, 1.0])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series bao gồm cả chuỗi giá trị và chuỗi chỉ số mà chúng ta có thể truy cập bằng các thuộc tính values và index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25, 0.5 , 0.75, 1.  ])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.50\n",
       "2    0.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data can be a list or NumPy array, index defaults to an integer sequence \n",
    "pd.Series([2,4,6])\n",
    "pd.Series(np.arange(2,7,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100    5\n",
       "200    5\n",
       "300    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data can be a scalar, which is repeated to fill the specified index \n",
    "pd.Series(5,index=[100,200,300])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    a\n",
       "1    b\n",
       "3    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data can be a dictionary, in which index defaults to the dictionary keys\n",
    "pd.Series({2:'a',1:'b',3:'c'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    c\n",
       "2    a\n",
       "dtype: object"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index can be explicitly set if a different result is preferred \n",
    "pd.Series({2:'a', 1:'b', 3:'c'}, index=[3,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Series như là mảng NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chỉ số của Series không cần phải là số nguyên mà có thể chứa giá trị có kiểu bất kỳ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "c    0.75\n",
       "d    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use strings as indices\n",
    "data = pd.Series([0.25, 0.5, 0.75, 1.0], index=['a', 'b', 'c', 'd']) \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.25\n",
       "5    0.50\n",
       "3    0.75\n",
       "7    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use noncontiguous or nonsequential indices\n",
    "data = pd.Series([0.25, 0.5, 0.75, 1.0], index=[2, 5, 3, 7]) \n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Series như là từ điển đặc biệt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "• Từ điển là cấu trúc ánh xạ các khoá bất kỳ sang các giá trị bất kỳ\n",
    "\n",
    "• Series là cấu trúc ánh xạ các khoá định kiểu sang các giá trị định kiểu\n",
    "\n",
    "• Thông tin kiểu của Series làm nó thực thi hiệu quả hơn nhiều so với Từ điển trong một số tác vụ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Có thể tạo Series từ Từ điển"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    38332521\n",
       "Texas         26448193\n",
       "New York      19651127\n",
       "Florida       19552860\n",
       "dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_dict = {'California': 38332521, 'Texas': 26448193, 'New York': 19651127, 'Florida': 19552860}\n",
    "population = pd.Series(population_dict)\n",
    "population\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Truy nhập giá trị thông qua khoá tương tự như Từ điển"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38332521"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population['California']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Khác với Từ điển, Series hỗ trợ các thao tác mảng như “cắt lát” (slicing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    38332521\n",
       "Texas         26448193\n",
       "New York      19651127\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population['California':'New York']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame giống với mảng NumPy 2 chiều có tên cột và chỉ số hàng\n",
    "\n",
    "Có thể xem DataFrame như chuỗi gồm các Series chia sẻ cùng chỉ số"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Texas         695662\n",
       "New York      141297\n",
       "dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # First, construct a Series listing the area of 3 states\n",
    "area_dict = {'California': 423967, 'Texas': 695662, 'New York': 141297} \n",
    "area = pd.Series(area_dict)\n",
    "area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    38332521\n",
       "Texas         26448193\n",
       "New York      19651127\n",
       "dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Second, construct a Series listing the population of 3 states\n",
    "population_dict = {'California': 38332521, 'Texas': 26448193, 'New York': 19651127}\n",
    "population = pd.Series(population_dict)\n",
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area\n",
       "California    38332521  423967\n",
       "Texas         26448193  695662\n",
       "New York      19651127  141297"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Final, use a dictionary to create a DataFrame object containing this information\n",
    "states = pd.DataFrame({'population': population, 'area': area}) \n",
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['California', 'Texas', 'New York'], dtype='object')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['population', 'area'], dtype='object')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Texas         695662\n",
       "New York      141297\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population\n",
       "California    38332521\n",
       "Texas         26448193\n",
       "New York      19651127"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a single column DataFrame can be constructed from a single Series\n",
    "pd.DataFrame(population, columns=['population'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   a  b\n",
       "0  0  0\n",
       "1  1  2\n",
       "2  2  4"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Any list of dictionaries can be made into a DataFrame. \n",
    "data = [{'a': i, 'b': 2 * i} for i in range(3)]\n",
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foo</th>\n",
       "      <th>bar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.631567</td>\n",
       "      <td>0.314247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.390522</td>\n",
       "      <td>0.914862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.643343</td>\n",
       "      <td>0.169972</td>\n",
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       "  </tbody>\n",
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       "        foo       bar\n",
       "a  0.631567  0.314247\n",
       "b  0.390522  0.914862\n",
       "c  0.643343  0.169972"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a DataFrame with any specified column and index names from a 2D NumPy array\n",
    "pd.DataFrame(np.random.rand(3, 2), columns=['foo', 'bar'],index=['a', 'b', 'c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
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       "        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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a  b    c\n",
       "0  1.0  2  NaN\n",
       "1  NaN  3  4.0"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if some keys in the dictionary are missing, Pandas will fill them in with NaN values\n",
    "pd.DataFrame([{'a':1,'b':2},{'b':3,'c':4}])\n",
    "# pd.DataFrame([{'a':1,'b':2,'c':np.NaN },{'a':np.NaN,'b':3,'c':4}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2, 5, 7, 11], dtype='int64')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# construct an Index from a list of integers\n",
    "ind = pd.Index([2, 5, 7, 11])\n",
    "ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2, 3, 5, 7, 11], dtype='int64')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can use standard Python indexing notation to retrieve values or slices\n",
    "ind[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can use standard Python indexing notation to retrieve values or slices\n",
    "ind[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2, 5, 11], dtype='int64')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 (5,) 1 int64\n"
     ]
    }
   ],
   "source": [
    "print(ind.size, ind.shape, ind.ndim, ind.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Index does not support mutable operations",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-906a9fa1424c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mind\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   4258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4259\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\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[0mvalue\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-> 4260\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Index does not support mutable operations\"\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   4261\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4262\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\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;31mTypeError\u001b[0m: Index does not support mutable operations"
     ]
    }
   ],
   "source": [
    "ind[1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 5, 7], dtype='int64')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indA = pd.Index([1, 3, 5, 7, 9]) \n",
    "indB = pd.Index([2, 3, 5, 7, 11]) \n",
    "indA & indB # intersection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1, 2, 3, 5, 7, 9, 11], dtype='int64')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indA | indB # union"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1, 2, 9, 11], dtype='int64')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indA ^ indB # symmetric difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "c    0.75\n",
       "d    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.Series([0.25, 0.5, 0.75, 1.0],index=['a', 'b', 'c', 'd'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series object provides a mapping from a collection of keys to a collection of values\n",
    "data['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use dictionary-like Python expressions and methods to examine the keys/indices and values\n",
    "'a' in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd'], dtype='object')"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('a', 0.25), ('b', 0.5), ('c', 0.75), ('d', 1.0)]"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "c    0.75\n",
       "d    1.00\n",
       "e    1.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extend a Series by assigning to a new index value\n",
    "data['e'] = 1.25\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "c    0.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slicing by explicit index \n",
    "data['a':'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "dtype: float64"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slicing by implicit integer index \n",
    "data[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    0.50\n",
       "c    0.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# masking\n",
    "data[(data > 0.3) & (data < 0.8)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "e    1.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fancy indexing \n",
    "data[['a', 'e']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "e    1.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# so sánh hàm tạo với fancy indexing\n",
    "data1 = pd.Series(data, index=['a','e'])\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "e    1.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# so sánh masking với fancy indexing\n",
    "data2 = data[(data.index=='a') | (data.index=='e')]\n",
    "data2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Các thuộc tính định vị trí (indexer): loc & iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "3    b\n",
       "5    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series(['a', 'b', 'c'], index=[1, 3, 5])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "3    b\n",
       "dtype: object"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    b\n",
       "5    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>California</td>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Texas</td>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New York</td>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area       pop\n",
       "California  423967  38332521\n",
       "Texas       695662  26448193\n",
       "New York    141297  19651127"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area = pd.Series({'California': 423967, 'Texas': 695662, 'New York': 141297})\n",
    "pop = pd.Series({'California': 38332521, 'Texas': 26448193,'New York': 19651127})\n",
    "data = pd.DataFrame({'area':area, 'pop':pop})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Texas         695662\n",
       "New York      141297\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# individual Series of the DataFrame can be accessed via dictionary-style indexing of the column name\n",
    "data['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Texas         695662\n",
       "New York      141297\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use attribute-style access with column names that are strings\n",
    "data.area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.pop of               area       pop\n",
       "California  423967  38332521\n",
       "Texas       695662  26448193\n",
       "New York    141297  19651127>"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>California</td>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Texas</td>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New York</td>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area       pop\n",
       "California  423967  38332521\n",
       "Texas       695662  26448193\n",
       "New York    141297  19651127"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>California</td>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "      <td>90.413926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Texas</td>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New York</td>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area       pop     density\n",
       "California  423967  38332521   90.413926\n",
       "Texas       695662  26448193   38.018740\n",
       "New York    141297  19651127  139.076746"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this dictionary-style syntax can also be used to modify the object, in this case to add a new column\n",
    "data['density'] = data['pop'] / data['area']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.23967000e+05, 3.83325210e+07, 9.04139261e+01],\n",
       "       [6.95662000e+05, 2.64481930e+07, 3.80187404e+01],\n",
       "       [1.41297000e+05, 1.96511270e+07, 1.39076746e+02]])"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.23967000e+05, 3.83325210e+07, 9.04139261e+01],\n",
       "       [6.95662000e+05, 2.64481930e+07, 3.80187404e+01],\n",
       "       [1.41297000e+05, 1.96511270e+07, 1.39076746e+02]])"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can examine the raw underlying data array using the values attribute\n",
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>California</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>area</td>\n",
       "      <td>4.239670e+05</td>\n",
       "      <td>6.956620e+05</td>\n",
       "      <td>1.412970e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pop</td>\n",
       "      <td>3.833252e+07</td>\n",
       "      <td>2.644819e+07</td>\n",
       "      <td>1.965113e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>density</td>\n",
       "      <td>9.041393e+01</td>\n",
       "      <td>3.801874e+01</td>\n",
       "      <td>1.390767e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           California         Texas      New York\n",
       "area     4.239670e+05  6.956620e+05  1.412970e+05\n",
       "pop      3.833252e+07  2.644819e+07  1.965113e+07\n",
       "density  9.041393e+01  3.801874e+01  1.390767e+02"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transpose the full DataFrame to swap rows and columns\n",
    "data.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.23967000e+05, 3.83325210e+07, 9.04139261e+01])"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Texas         695662\n",
       "New York      141297\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>area</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>California</td>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Texas</td>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area       pop\n",
       "California  423967  38332521\n",
       "Texas       695662  26448193"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the iloc indexer, we can index the underlying array as if it is a simple NumPy array\n",
    "data.iloc[:2, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>California</td>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Texas</td>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area       pop\n",
       "California  423967  38332521\n",
       "Texas       695662  26448193"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:'Texas', :'pop']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>New York</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               pop     density\n",
       "New York  19651127  139.076746"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in the loc indexer we can combine masking and fancy indexing\n",
    "data.loc[data.density > 100, ['pop', 'density']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>California</td>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Texas</td>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New York</td>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area       pop     density\n",
       "California  423967  38332521   90.000000\n",
       "Texas       695662  26448193   38.018740\n",
       "New York    141297  19651127  139.076746"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modifying values is done in the similar way with NumPy\n",
    "data.iloc[0, 2] = 90\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Texas</td>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New York</td>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            area       pop     density\n",
       "Texas     695662  26448193   38.018740\n",
       "New York  141297  19651127  139.076746"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slicing refers to rows\n",
    "data['Texas':'New York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Texas</td>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New York</td>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            area       pop     density\n",
       "Texas     695662  26448193   38.018740\n",
       "New York  141297  19651127  139.076746"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Texas</td>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New York</td>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            area       pop     density\n",
       "Texas     695662  26448193   38.018740\n",
       "New York  141297  19651127  139.076746"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Such slices can also refer to rows by number rather than by index\n",
    "data[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>New York</td>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            area       pop     density\n",
       "New York  141297  19651127  139.076746"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# direct masking operations are also interpreted row-wise rather than column-wise\n",
    "data[data.density > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    3\n",
       "2    7\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# defining a simple Series\n",
    "rng = np.random.RandomState(42)\n",
    "ser = pd.Series(rng.randint(0, 10, 4))\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   A  B  C  D\n",
       "0  6  9  2  6\n",
       "1  7  4  3  7\n",
       "2  7  2  5  4"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# defining a simple DataFrame\n",
    "df = pd.DataFrame(rng.randint(0, 10, (3, 4)), columns=['A','B','C', 'D'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     403.428793\n",
       "1      20.085537\n",
       "2    1096.633158\n",
       "3      54.598150\n",
       "dtype: float64"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.707107</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>-7.071068e-01</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.707107</td>\n",
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       "      <td>1.224647e-16</td>\n",
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      "text/plain": [
       "          A             B         C             D\n",
       "0 -1.000000  7.071068e-01  1.000000 -1.000000e+00\n",
       "1 -0.707107  1.224647e-16  0.707107 -7.071068e-01\n",
       "2 -0.707107  1.000000e+00 -0.707107  1.224647e-16"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sin(df * np.pi / 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "# suppose we are combining two different data sources: area & population\n",
    "area = pd.Series({'Alaska': 1723337, 'Texas': 695662, 'California': 423967}, name='area')\n",
    "population = pd.Series({'California': 38332521, 'Texas': 26448193, 'New York': 19651127}, name='population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alaska              NaN\n",
       "California    90.413926\n",
       "New York            NaN\n",
       "Texas         38.018740\n",
       "dtype: float64"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# then compute the population density\n",
    "population / area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    5.0\n",
       "2    9.0\n",
       "3    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# any missing values are filled in with NaN by default for any of Python’s built-in arithmetic expressions\n",
    "A = pd.Series([2, 4, 6], index=[0, 1, 2]) \n",
    "B = pd.Series([1, 3, 5], index=[1, 2, 3]) \n",
    "A+B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "1    5.0\n",
       "2    9.0\n",
       "3    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can modify the fill value using appropriate object methods in place of the operators\n",
    "A.add(B, fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Index alignment in DataFrame\n",
    "\n",
    "A similar type of alignment takes place for both columns and indices when you are performing operations on DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
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       "   A   B\n",
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     "execution_count": 273,
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   "source": [
    "A = pd.DataFrame(rng.randint(0, 20, (2, 2)), columns=list('AB'))\n",
    "A"
   ]
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  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   B  A  C\n",
       "0  4  0  9\n",
       "1  5  8  0\n",
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     "metadata": {},
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   "source": [
    "B = pd.DataFrame(rng.randint(0, 10, (3, 3)), columns=list('BAC'))\n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      A     B   C\n",
       "0   1.0  15.0 NaN\n",
       "1  13.0   6.0 NaN\n",
       "2   NaN   NaN NaN"
      ]
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     "execution_count": 275,
     "metadata": {},
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   "source": [
    "A + B"
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  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      A     B     C\n",
       "0   1.0  15.0  13.5\n",
       "1  13.0   6.0   4.5\n",
       "2   6.5  13.5  10.5"
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     "execution_count": 276,
     "metadata": {},
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   "source": [
    "# we’ll fill with the mean of all values in A (which we compute by first stacking the rows of A)\n",
    "fill = A.stack().mean()\n",
    "A.add(B, fill_value=fill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 8, 2, 4],\n",
       "       [2, 6, 4, 8],\n",
       "       [6, 1, 3, 8]])"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# find the difference of a two-dimensional array and one of its rows\n",
    "A = rng.randint(10, size=(3, 4)) \n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0,  0],\n",
       "       [-1, -2,  2,  4],\n",
       "       [ 3, -7,  1,  4]])"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A - A[0] # subtraction is applied row-wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
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       "   Q  R  S  T\n",
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     "execution_count": 279,
     "metadata": {},
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   "source": [
    "# In Pandas, the convention similarly operates row-wise by default\n",
    "df = pd.DataFrame(A, columns=list('QRST')) \n",
    "df - df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
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       "   Q  R  S  T\n",
       "0 -5  0 -6 -4\n",
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     "execution_count": 280,
     "metadata": {},
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   ],
   "source": [
    "# If you would instead like to operate column-wise, you can use the object methods mentioned earlier, while specifying the axis keyword\n",
    "df.subtract(df['R'], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Q  R  S  T\n",
       "0  3  8  2  4\n",
       "1  2  6  4  8\n",
       "2  6  1  3  8"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that these DataFrame/Series operations, like the operations discussed before, will automatically align indices between the two elements\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Q    3\n",
       "S    2\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "halfrow = df.iloc[0, ::2]\n",
    "halfrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Q   R    S   T\n",
       "0  0.0 NaN  0.0 NaN\n",
       "1 -1.0 NaN  2.0 NaN\n",
       "2  3.0 NaN  1.0 NaN"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df - halfrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = [('California', 2000), ('California', 2010),\n",
    "                    ('New York', 2000), ('New York', 2010),\n",
    "                    ('Texas', 2000), ('Texas', 2010)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "populations = [33871648, 37253956,\n",
    "                          18976457, 19378102,\n",
    "                          20851820, 25145561]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(California, 2000)    33871648\n",
       "(California, 2010)    37253956\n",
       "(New York, 2000)      18976457\n",
       "(New York, 2010)      19378102\n",
       "(Texas, 2000)         20851820\n",
       "(Texas, 2010)         25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop = pd.Series(populations, index=index)\n",
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(California, 2010)    37253956\n",
       "(New York, 2010)      19378102\n",
       "(Texas, 2010)         25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop[[i for i in pop.index if i[1] == 2010]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('California', 2000),\n",
       "            ('California', 2010),\n",
       "            (  'New York', 2000),\n",
       "            (  'New York', 2010),\n",
       "            (     'Texas', 2000),\n",
       "            (     'Texas', 2010)],\n",
       "           )"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can create a multi-index from the tuples as follows\n",
    "index = pd.MultiIndex.from_tuples(index)\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "New York    2000    18976457\n",
       "            2010    19378102\n",
       "Texas       2000    20851820\n",
       "            2010    25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop = pop.reindex(index) \n",
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    37253956\n",
       "New York      19378102\n",
       "Texas         25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop[:, 2010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>California</td>\n",
       "      <td>33871648</td>\n",
       "      <td>37253956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New York</td>\n",
       "      <td>18976457</td>\n",
       "      <td>19378102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Texas</td>\n",
       "      <td>20851820</td>\n",
       "      <td>25145561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                2000      2010\n",
       "California  33871648  37253956\n",
       "New York    18976457  19378102\n",
       "Texas       20851820  25145561"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df = pop.unstack() \n",
    "pop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "New York    2000    18976457\n",
       "            2010    19378102\n",
       "Texas       2000    20851820\n",
       "            2010    25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "New York    2000    18976457\n",
       "            2010    19378102\n",
       "Texas       2000    20851820\n",
       "            2010    25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .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></th>\n",
       "      <th>total</th>\n",
       "      <th>under18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">California</td>\n",
       "      <td>2000</td>\n",
       "      <td>33871648</td>\n",
       "      <td>9267089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010</td>\n",
       "      <td>37253956</td>\n",
       "      <td>9284094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">New York</td>\n",
       "      <td>2000</td>\n",
       "      <td>18976457</td>\n",
       "      <td>4687374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010</td>\n",
       "      <td>19378102</td>\n",
       "      <td>4318033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">Texas</td>\n",
       "      <td>2000</td>\n",
       "      <td>20851820</td>\n",
       "      <td>5906301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010</td>\n",
       "      <td>25145561</td>\n",
       "      <td>6879014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    total  under18\n",
       "California 2000  33871648  9267089\n",
       "           2010  37253956  9284094\n",
       "New York   2000  18976457  4687374\n",
       "           2010  19378102  4318033\n",
       "Texas      2000  20851820  5906301\n",
       "           2010  25145561  6879014"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df = pd.DataFrame({'total': pop,'under18': [9267089, 9284094,4687374, 4318033,5906301, 6879014]})\n",
    "pop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    0.273594\n",
       "            2010    0.249211\n",
       "New York    2000    0.247010\n",
       "            2010    0.222831\n",
       "Texas       2000    0.283251\n",
       "            2010    0.273568\n",
       "dtype: float64"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute the fraction of people under 18 by year\n",
    "f_u18 = pop_df['under18'] / pop_df['total']\n",
    "f_u18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2000</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>California</td>\n",
       "      <td>0.273594</td>\n",
       "      <td>0.249211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>New York</td>\n",
       "      <td>0.247010</td>\n",
       "      <td>0.222831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Texas</td>\n",
       "      <td>0.283251</td>\n",
       "      <td>0.273568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                2000      2010\n",
       "California  0.273594  0.249211\n",
       "New York    0.247010  0.222831\n",
       "Texas       0.283251  0.273568"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_u18.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    0.273594\n",
       "            2010    0.249211\n",
       "New York    2000    0.247010\n",
       "            2010    0.222831\n",
       "Texas       2000    0.283251\n",
       "            2010    0.273568\n",
       "dtype: float64"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_u18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">a</td>\n",
       "      <td>1</td>\n",
       "      <td>0.551737</td>\n",
       "      <td>0.869688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.269804</td>\n",
       "      <td>0.166352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">b</td>\n",
       "      <td>1</td>\n",
       "      <td>0.913341</td>\n",
       "      <td>0.992192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.926699</td>\n",
       "      <td>0.171090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        data1     data2\n",
       "a 1  0.551737  0.869688\n",
       "  2  0.269804  0.166352\n",
       "b 1  0.913341  0.992192\n",
       "  2  0.926699  0.171090"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(4, 2),\n",
    "index=[['a', 'a', 'b', 'b'], [1, 2, 1, 2]],columns=['data1', 'data2'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "Texas       2000    20851820\n",
       "            2010    25145561\n",
       "New York    2000    18976457\n",
       "            2010    19378102\n",
       "dtype: int64"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {('California', 2000): 33871648,\n",
    "                    ('California', 2010): 37253956,\n",
    "                    ('Texas', 2000): 20851820,\n",
    "                    ('Texas', 2010): 25145561,\n",
    "                    ('New York', 2000): 18976457,\n",
    "                    ('New York', 2010): 19378102}\n",
    "pd.Series(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('a', 1),\n",
       "            ('a', 2),\n",
       "            ('b', 1),\n",
       "            ('b', 2)],\n",
       "           )"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'], [1, 2, 1, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pd.MultiIndex) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('a', 1),\n",
       "            ('a', 2),\n",
       "            ('b', 1),\n",
       "            ('b', 2)],\n",
       "           )"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.MultiIndex.from_arrays([['a', 'a', 'b', 'b'], [1, 2, 1, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('a', 1),\n",
       "            ('a', 2),\n",
       "            ('b', 1),\n",
       "            ('b', 2)],\n",
       "           )"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1), ('b', 2)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('a', 1),\n",
       "            ('a', 2),\n",
       "            ('b', 1),\n",
       "            ('b', 2)],\n",
       "           )"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.MultiIndex.from_product([['a', 'b'], [1, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ipykernel_launcher:1: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MultiIndex([('a', 1),\n",
       "            ('a', 2),\n",
       "            ('b', 1),\n",
       "            ('b', 2)],\n",
       "           )"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.MultiIndex(levels=[['a', 'b'], [1, 2]], labels=[[0, 0, 1, 1], [0, 1, 0, 1]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MultiIndex for columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2013</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>41.0</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>39.1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>29.0</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2014</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>35.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>38.7</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.3</td>\n",
       "      <td>40.0</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject      Bob       Guido         Sue      \n",
       "type          HR  Temp    HR  Temp    HR  Temp\n",
       "year visit                                    \n",
       "2013 1      47.0  37.0  54.0  36.5  41.0  35.9\n",
       "     2      41.0  39.1  32.0  35.7  29.0  35.9\n",
       "2014 1      47.0  35.1  27.0  37.3  27.0  37.0\n",
       "     2      16.0  38.7  36.0  37.3  40.0  36.3"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hierarchical indices and columns\n",
    "index = pd.MultiIndex.from_product([[2013, 2014], [1, 2]],names=['year', 'visit'])\n",
    "columns = pd.MultiIndex.from_product([['Bob', 'Guido', 'Sue'], ['HR', 'Temp']],names=['subject', 'type'])\n",
    "# mock some data\n",
    "data = np.round(np.random.randn(4, 6), 1) \n",
    "data[:, ::2] *= 10\n",
    "data += 37\n",
    "# create the DataFrame\n",
    "health_data = pd.DataFrame(data, index=index, columns=columns) \n",
    "health_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2013</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>35.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2014</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type          HR  Temp\n",
       "year visit            \n",
       "2013 1      54.0  36.5\n",
       "     2      32.0  35.7\n",
       "2014 1      27.0  37.3\n",
       "     2      36.0  37.3"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index the top-level column by the person’s name and get a full Data Frame containing just that person’s information\n",
    "health_data['Guido']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "New York    2000    18976457\n",
       "            2010    19378102\n",
       "Texas       2000    20851820\n",
       "            2010    25145561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33871648"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can access single elements by indexing with multiple terms\n",
    "pop['California', 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000    33871648\n",
       "2010    37253956\n",
       "dtype: int64"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MultiIndex also supports partial indexing, or indexing just one of the levels in the index\n",
    "pop['California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2000    33871648\n",
       "            2010    37253956\n",
       "New York    2000    18976457\n",
       "            2010    19378102\n",
       "dtype: int64"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Partial slicing is available as well, as long as the MultiIndex is sorted\n",
    "pop.loc['California':'New York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    33871648\n",
       "New York      18976457\n",
       "Texas         20851820\n",
       "dtype: int64"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With sorted indices, we can perform partial indexing on lower levels by passing an empty slice in the first index\n",
    "pop[:, 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2013</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>41.0</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>39.1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>29.0</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2014</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>35.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>38.7</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.3</td>\n",
       "      <td>40.0</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject      Bob       Guido         Sue      \n",
       "type          HR  Temp    HR  Temp    HR  Temp\n",
       "year visit                                    \n",
       "2013 1      47.0  37.0  54.0  36.5  41.0  35.9\n",
       "     2      41.0  39.1  32.0  35.7  29.0  35.9\n",
       "2014 1      47.0  35.1  27.0  37.3  27.0  37.0\n",
       "     2      16.0  38.7  36.0  37.3  40.0  36.3"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  visit\n",
       "2013  1        54.0\n",
       "      2        32.0\n",
       "2014  1        27.0\n",
       "      2        36.0\n",
       "Name: (Guido, HR), dtype: float64"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can recover Guido’s heart rate data\n",
    "health_data['Guido', 'HR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>visit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2013</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject      Bob      \n",
       "type          HR  Temp\n",
       "year visit            \n",
       "2013 1      47.0  37.0\n",
       "     2      41.0  39.1"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_data.iloc[:2, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  visit\n",
       "2013  1        47.0\n",
       "      2        41.0\n",
       "2014  1        47.0\n",
       "      2        16.0\n",
       "Name: (Bob, HR), dtype: float64"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_data.loc[:, ('Bob', 'HR')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "  <thead>\n",
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       "      <th>subject</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Bob</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Guido</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Sue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
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       "      <th>Temp</th>\n",
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       "    <tr>\n",
       "      <th>year</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2013</td>\n",
       "      <td>44.0</td>\n",
       "      <td>38.05</td>\n",
       "      <td>43.0</td>\n",
       "      <td>36.1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014</td>\n",
       "      <td>31.5</td>\n",
       "      <td>36.90</td>\n",
       "      <td>31.5</td>\n",
       "      <td>37.3</td>\n",
       "      <td>33.5</td>\n",
       "      <td>36.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subject   Bob        Guido         Sue       \n",
       "type       HR   Temp    HR  Temp    HR   Temp\n",
       "year                                         \n",
       "2013     44.0  38.05  43.0  36.1  35.0  35.90\n",
       "2014     31.5  36.90  31.5  37.3  33.5  36.65"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average out the measurements in the two visits each year\n",
    "data_mean = health_data.mean(level='year') \n",
    "data_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>type</th>\n",
       "      <th>HR</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2013</td>\n",
       "      <td>40.666667</td>\n",
       "      <td>36.683333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014</td>\n",
       "      <td>32.166667</td>\n",
       "      <td>36.950000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type         HR       Temp\n",
       "year                      \n",
       "2013  40.666667  36.683333\n",
       "2014  32.166667  36.950000"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By further making use of the axis keyword, we can take the mean among levels on the columns as well\n",
    "data_mean.mean(axis=1, level='type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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