Chapter5_6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = Series([4,7,-5,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    7\n",
       "2   -5\n",
       "3    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  7, -5,  3])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj2 = Series([4,7,-5,3],index=['d','b','a','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4\n",
       "b    7\n",
       "a   -5\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['d', 'b', 'a', 'c'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -5\n",
       "b    7\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2[['a','b','c']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4\n",
       "b    7\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2[obj2>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d     8\n",
       "b    14\n",
       "a   -10\n",
       "c     6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d      54.598150\n",
       "b    1096.633158\n",
       "a       0.006738\n",
       "c      20.085537\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(obj2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'b' in obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'e' in obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = {'ohio':35000,'texas':71000,'oregon':16000,'uthah':5000}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj3 = Series(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ohio      35000\n",
       "texas     71000\n",
       "oregon    16000\n",
       "uthah      5000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = ['california','ohio','oregon','texas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj4 = Series(adata,index=states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "california        NaN\n",
       "ohio          35000.0\n",
       "oregon        16000.0\n",
       "texas         71000.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "california     True\n",
       "ohio          False\n",
       "oregon        False\n",
       "texas         False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(obj4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "california    False\n",
       "ohio           True\n",
       "oregon         True\n",
       "texas          True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.notnull(obj4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "california     True\n",
       "ohio          False\n",
       "oregon        False\n",
       "texas         False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ohio      35000\n",
       "texas     71000\n",
       "oregon    16000\n",
       "uthah      5000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "california        NaN\n",
       "ohio          35000.0\n",
       "oregon        16000.0\n",
       "texas         71000.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "california         NaN\n",
       "ohio           70000.0\n",
       "oregon         32000.0\n",
       "texas         142000.0\n",
       "uthah              NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3+obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj4.name ='population'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj4.index.name = 'state'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "california        NaN\n",
       "ohio          35000.0\n",
       "oregon        16000.0\n",
       "texas         71000.0\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    7\n",
       "2   -5\n",
       "3    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj.index = ['bob','steve','jeff','ryan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bob      4\n",
       "steve    7\n",
       "jeff    -5\n",
       "ryan     3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'state':['ohio','ohio','ohio','nevada','nevada','nevada'],\n",
    "       'year':[2000,2001,2002,2001,2002,2003],\n",
    "       'pop':[1.5,1.7,3.6,2.4,2.9,3.2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame= DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ohio</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ohio</td>\n",
       "      <td>2002</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nevada</td>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nevada</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nevada</td>\n",
       "      <td>2003</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  pop\n",
       "0    ohio  2000  1.5\n",
       "1    ohio  2001  1.7\n",
       "2    ohio  2002  3.6\n",
       "3  nevada  2001  2.4\n",
       "4  nevada  2002  2.9\n",
       "5  nevada  2003  3.2"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ohio</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ohio</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ohio</td>\n",
       "      <td>2002</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nevada</td>\n",
       "      <td>2001</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nevada</td>\n",
       "      <td>2002</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  pop\n",
       "0    ohio  2000  1.5\n",
       "1    ohio  2001  1.7\n",
       "2    ohio  2002  3.6\n",
       "3  nevada  2001  2.4\n",
       "4  nevada  2002  2.9"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>ohio</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>ohio</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>ohio</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>nevada</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>nevada</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2003</td>\n",
       "      <td>nevada</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   state  pop\n",
       "0  2000    ohio  1.5\n",
       "1  2001    ohio  1.7\n",
       "2  2002    ohio  3.6\n",
       "3  2001  nevada  2.4\n",
       "4  2002  nevada  2.9\n",
       "5  2003  nevada  3.2"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(data,columns=['year','state','pop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2 = DataFrame(data,columns=['year','state','pop','debt'],\n",
    "                     index = ['one','two','three','four','five','six'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       year   state  pop debt\n",
       "one    2000    ohio  1.5  NaN\n",
       "two    2001    ohio  1.7  NaN\n",
       "three  2002    ohio  3.6  NaN\n",
       "four   2001  nevada  2.4  NaN\n",
       "five   2002  nevada  2.9  NaN\n",
       "six    2003  nevada  3.2  NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one        ohio\n",
       "two        ohio\n",
       "three      ohio\n",
       "four     nevada\n",
       "five     nevada\n",
       "six      nevada\n",
       "Name: state, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2['state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      2000\n",
       "two      2001\n",
       "three    2002\n",
       "four     2001\n",
       "five     2002\n",
       "six      2003\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year     2002\n",
       "state    ohio\n",
       "pop       3.6\n",
       "debt      NaN\n",
       "Name: three, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.loc['three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2['debt'] = 16.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    ohio  1.5  16.5\n",
       "two    2001    ohio  1.7  16.5\n",
       "three  2002    ohio  3.6  16.5\n",
       "four   2001  nevada  2.4  16.5\n",
       "five   2002  nevada  2.9  16.5\n",
       "six    2003  nevada  3.2  16.5"
      ]
     },
     "execution_count": 47,
     "metadata": {},
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    "frame2"
   ]
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  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2['debt'] = np.arange(6.)"
   ]
  },
  {
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   "execution_count": 49,
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      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    ohio  1.5   0.0\n",
       "two    2001    ohio  1.7   1.0\n",
       "three  2002    ohio  3.6   2.0\n",
       "four   2001  nevada  2.4   3.0\n",
       "five   2002  nevada  2.9   4.0\n",
       "six    2003  nevada  3.2   5.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "frame2"
   ]
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  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "val = Series([-1.2,-1.5,-1.7],index=['two','four','five'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2['debt'] = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    ohio  1.5   NaN\n",
       "two    2001    ohio  1.7  -1.2\n",
       "three  2002    ohio  3.6   NaN\n",
       "four   2001  nevada  2.4  -1.5\n",
       "five   2002  nevada  2.9  -1.7\n",
       "six    2003  nevada  3.2   NaN"
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     "execution_count": 52,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2['eastern'] = frame2.state =='ohio'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "       year   state  pop  debt  eastern\n",
       "one    2000    ohio  1.5   NaN     True\n",
       "two    2001    ohio  1.7  -1.2     True\n",
       "three  2002    ohio  3.6   NaN     True\n",
       "four   2001  nevada  2.4  -1.5    False\n",
       "five   2002  nevada  2.9  -1.7    False\n",
       "six    2003  nevada  3.2   NaN    False"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "del frame2['eastern']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    ohio  1.5   NaN\n",
       "two    2001    ohio  1.7  -1.2\n",
       "three  2002    ohio  3.6   NaN\n",
       "four   2001  nevada  2.4  -1.5\n",
       "five   2002  nevada  2.9  -1.7\n",
       "six    2003  nevada  3.2   NaN"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop = {'nevada':{2001:2.4,2002:2.9},\n",
    "      'ohio':{2000:1.5,2001:1.7,2002:3.6}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame3= DataFrame(pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nevada</th>\n",
       "      <th>ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      nevada  ohio\n",
       "2000     NaN   1.5\n",
       "2001     2.4   1.7\n",
       "2002     2.9   3.6"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>2001</th>\n",
       "      <th>2002</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>nevada</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ohio</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        2000  2001  2002\n",
       "nevada   NaN   2.4   2.9\n",
       "ohio     1.5   1.7   3.6"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'astype'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-61-cf359609f7d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2002\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2003\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    346\u001b[0m                                  dtype=dtype, copy=copy)\n\u001b[1;32m    347\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\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--> 348\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    349\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_init_dict\u001b[0;34m(self, data, index, columns, dtype)\u001b[0m\n\u001b[1;32m    457\u001b[0m             \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 459\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_arrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_init_ndarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_arrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[1;32m   7357\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7358\u001b[0m     \u001b[0;31m# don't force copy because getting jammed in an ndarray anyway\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7359\u001b[0;31m     \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_homogenize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\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   7360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7361\u001b[0m     \u001b[0;31m# from BlockManager perspective\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_homogenize\u001b[0;34m(data, index, dtype)\u001b[0m\n\u001b[1;32m   7659\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7660\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0moindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7661\u001b[0;31m                     \u001b[0moindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'O'\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   7662\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7663\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mDatetimeIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'astype'"
     ]
    }
   ],
   "source": [
    "DataFrame(pop,index=[2001,2002,2003])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdata = {'ohio':frame3['ohio'][:-1],\n",
    "        'nevada':frame3['nevada'][:2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataFrame(pdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame3.index.name = 'year';frame3.columns.name = 'state'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>state</th>\n",
       "      <th>nevada</th>\n",
       "      <th>ohio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state  nevada  ohio\n",
       "year               \n",
       "2000      NaN   1.5\n",
       "2001      2.4   1.7\n",
       "2002      2.9   3.6"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[nan, 1.5],\n",
       "       [2.4, 1.7],\n",
       "       [2.9, 3.6]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2000, 'ohio', 1.5, nan],\n",
       "       [2001, 'ohio', 1.7, -1.2],\n",
       "       [2002, 'ohio', 3.6, nan],\n",
       "       [2001, 'nevada', 2.4, -1.5],\n",
       "       [2002, 'nevada', 2.9, -1.7],\n",
       "       [2003, 'nevada', 3.2, nan]], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = Series(range(3),index=['a','b','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "index= obj.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c'], dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['b', 'c'], dtype='object')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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-70-a452e55ce13b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'd'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/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   2063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\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-> 2065\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   2066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2067\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": [
    "index[1] = 'd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = pd.Index(np.arange(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2], dtype='int64')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj2= Series([1.5,-2.5,0],index = labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.5\n",
       "1   -2.5\n",
       "2    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2.index is labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>state</th>\n",
       "      <th>nevada</th>\n",
       "      <th>ohio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state  nevada  ohio\n",
       "year               \n",
       "2000      NaN   1.5\n",
       "2001      2.4   1.7\n",
       "2002      2.9   3.6"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['nevada', 'ohio'], dtype='object', name='state')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'ohio' in frame3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2003 in frame3.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "dup_labels = pd.Index(['foo','foo','bar','bar'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['foo', 'foo', 'bar', 'bar'], dtype='object')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = Series([4.5,7.5,-5.3,3.6],index=['d','b','a','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4.5\n",
       "b    7.5\n",
       "a   -5.3\n",
       "c    3.6\n",
       "dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj2 = obj.reindex(list('abcde'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -5.3\n",
       "b    7.5\n",
       "c    3.6\n",
       "d    4.5\n",
       "e    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj3 = Series(['blue','purple','yellow'],index=[0,2,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "2    purple\n",
       "4    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1      blue\n",
       "2    purple\n",
       "3    purple\n",
       "4    yellow\n",
       "5    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3.reindex(range(6),method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame=  DataFrame(np.arange(9).reshape((3,3)),\n",
    "                    index=['a','b','c'],\n",
    "                    columns=['ohio','texas','california'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ohio</th>\n",
       "      <th>texas</th>\n",
       "      <th>california</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ohio  texas  california\n",
       "a     0      1           2\n",
       "b     3      4           5\n",
       "c     6      7           8"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame2 = frame.reindex(['a','b','c','d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ohio</th>\n",
       "      <th>texas</th>\n",
       "      <th>california</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ohio  texas  california\n",
       "a   0.0    1.0         2.0\n",
       "b   3.0    4.0         5.0\n",
       "c   6.0    7.0         8.0\n",
       "d   NaN    NaN         NaN"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = ['texas','utah','california']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>texas</th>\n",
       "      <th>utah</th>\n",
       "      <th>california</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   texas  utah  california\n",
       "a      1   NaN           2\n",
       "b      4   NaN           5\n",
       "c      7   NaN           8"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.reindex(columns = states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "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>texas</th>\n",
       "      <th>utah</th>\n",
       "      <th>california</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   texas  utah  california\n",
       "a    1.0   NaN         2.0\n",
       "b    4.0   NaN         5.0\n",
       "c    7.0   NaN         8.0\n",
       "d    NaN   NaN         NaN"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.loc[['a','b','c','d'],states]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = Series(np.arange(5.),index=['a','b','c','d','e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    3.0\n",
       "e    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_obj = obj.drop('c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "d    3.0\n",
       "e    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "e    4.0\n",
       "dtype: float64"
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     "execution_count": 100,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "data= DataFrame(np.arange(16.).reshape((4,4)),index=['ohio','colorado','utah','new york'],\n",
    "                  columns=['one','two','three','four'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
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       "      <th>utah</th>\n",
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       "           one   two  three  four\n",
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    "data"
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   "execution_count": 103,
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   "outputs": [
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    "data.drop(['colorado','ohio'])"
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   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
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       "           one  three  four\n",
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       "utah       8.0   10.0  11.0\n",
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     "metadata": {},
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    "data.drop('two',axis=1)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>colorado</th>\n",
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       "      <th>utah</th>\n",
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       "      <th>new york</th>\n",
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       "      <td>14.0</td>\n",
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       "           one  three\n",
       "ohio       0.0    2.0\n",
       "colorado   4.0    6.0\n",
       "utah       8.0   10.0\n",
       "new york  12.0   14.0"
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     "execution_count": 105,
     "metadata": {},
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   "source": [
    "data.drop(['two','four'],axis='columns')"
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  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj.drop('c',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "d    3.0\n",
       "e    4.0\n",
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     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
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    "obj"
   ]
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  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = Series(np.arange(4.),index=['a','b','c','d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
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     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
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    "obj"
   ]
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  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "obj[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    2.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.0\n",
       "a    0.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[['b','a','d']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[[1,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[obj<2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.0\n",
       "c    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj['b':'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj['b':'c']=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    5.0\n",
       "c    5.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "data= DataFrame(np.arange(16.).reshape((4,4)),index=['ohio','colorado','utah','new york'],\n",
    "                  columns=['one','two','three','four'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
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       "           one   two  three  four\n",
       "ohio       0.0   1.0    2.0   3.0\n",
       "colorado   4.0   5.0    6.0   7.0\n",
       "utah       8.0   9.0   10.0  11.0\n",
       "new york  12.0  13.0   14.0  15.0"
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   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
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       "ohio         1.0\n",
       "colorado     5.0\n",
       "utah         9.0\n",
       "new york    13.0\n",
       "Name: two, dtype: float64"
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     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
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    "data['two']"
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   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
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   "metadata": {},
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     "execution_count": 125,
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       "           one   two  three  four\n",
       "ohio       0.0   0.0    0.0   0.0\n",
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       "utah       8.0   9.0   10.0  11.0\n",
       "new york  12.0  13.0   14.0  15.0"
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   "execution_count": 128,
   "metadata": {},
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    {
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       "two      5.0\n",
       "three    6.0\n",
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      ]
     },
     "execution_count": 128,
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    "data.loc['colorado',['two','three']]"
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   "execution_count": 129,
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    {
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       "one      8.0\n",
       "two      9.0\n",
       "Name: utah, dtype: float64"
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    "data.iloc[2,[3,0,1]]"
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   "execution_count": 130,
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    {
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      "text/plain": [
       "one       8.0\n",
       "two       9.0\n",
       "three    10.0\n",
       "four     11.0\n",
       "Name: utah, dtype: float64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
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   "source": [
    "data.iloc[2]"
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   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
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       "      <th>utah</th>\n",
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       "          four  one  two\n",
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   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ohio        0.0\n",
       "colorado    5.0\n",
       "utah        9.0\n",
       "Name: two, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.loc[:'utah','two']"
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   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
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      "text/html": [
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       "      <td>14.0</td>\n",
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       "           one   two  three\n",
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    "data.iloc[:,:3][data.three>5]"
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  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser  = Series(np.arange(3.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "1    1.0\n",
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       "dtype: float64"
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     "execution_count": 135,
     "metadata": {},
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    "ser"
   ]
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  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser2 =Series(np.arange(3.),index=['a','b','c'])"
   ]
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   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "b    1.0\n",
       "c    2.0\n",
       "dtype: float64"
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     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
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    "ser2"
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  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
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     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "ser2[-1]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "dtype: float64"
      ]
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     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "ser[:1]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    1.0\n",
       "dtype: float64"
      ]
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     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.loc[:1]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "dtype: float64"
      ]
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     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "ser.iloc[:1]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = Series([7.2,-2.5,3.4,1.5],index=['a','c','d','e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = Series([-2.1,3.6,3.4,4,1.5],index=['a','c','e','f','g'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    7.2\n",
       "c   -2.5\n",
       "d    3.4\n",
       "e    1.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "s1"
   ]
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  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -2.1\n",
       "c    3.6\n",
       "e    3.4\n",
       "f    4.0\n",
       "g    1.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
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  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5.1\n",
       "c    1.1\n",
       "d    NaN\n",
       "e    4.9\n",
       "f    NaN\n",
       "g    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1+s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = DataFrame(np.arange(9.).reshape((3,3)),columns=list('bcd'),index=['ohio','texas','oregon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>texas</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>oregon</th>\n",
       "      <td>6.0</td>\n",
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      "text/plain": [
       "          b    c    d\n",
       "ohio    0.0  1.0  2.0\n",
       "texas   3.0  4.0  5.0\n",
       "oregon  6.0  7.0  8.0"
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   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = DataFrame(np.arange(12.).reshape((4,3)),columns=list('bcd'),index=['utah','ohio','texas','oregon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>texas</th>\n",
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       "      <th>oregon</th>\n",
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      "text/plain": [
       "          b     c     d\n",
       "utah    0.0   1.0   2.0\n",
       "ohio    3.0   4.0   5.0\n",
       "texas   6.0   7.0   8.0\n",
       "oregon  9.0  10.0  11.0"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>15.0</td>\n",
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       "           b     c     d\n",
       "ohio     3.0   5.0   7.0\n",
       "oregon  15.0  17.0  19.0\n",
       "texas    9.0  11.0  13.0\n",
       "utah     NaN   NaN   NaN"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1+df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = DataFrame(np.arange(12.).reshape((3,4)),columns=list('abcd'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = DataFrame(np.arange(20.).reshape((4,5)),columns=list('abcde'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.loc[1,'b'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
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    "df2"
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   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
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       "      a     b     c     d   e\n",
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       "2  18.0  20.0  22.0  24.0 NaN\n",
       "3   NaN   NaN   NaN   NaN NaN"
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     "execution_count": 157,
     "metadata": {},
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   "source": [
    "df1 + df2"
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  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
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       "      a     b     c     d     e\n",
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     "execution_count": 158,
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   "source": [
    "df1.add(df2,fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
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    "1/df1"
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  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
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       "          a         b         c         d\n",
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     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1.rdiv(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.arange(12.).reshape((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  2.,  3.],\n",
       "       [ 4.,  5.,  6.,  7.],\n",
       "       [ 8.,  9., 10., 11.]])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 2., 3.])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [4., 4., 4., 4.],\n",
       "       [8., 8., 8., 8.]])"
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     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "arr - arr[0]"
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  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = DataFrame(np.arange(12.).reshape((4,3)),columns=list('bcd'),index=['utah','ohio','texas','oregon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "series = frame.iloc[0]"
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   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
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       "b    0.0\n",
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       "Name: utah, dtype: float64"
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    "series"
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    "series2 = Series(range(3),index=[list('bef')])"
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   "execution_count": 171,
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    {
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   "execution_count": 172,
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    "series3 = frame['d']"
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   "execution_count": 173,
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    "series2"
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       "          b    c    d\n",
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   "execution_count": 176,
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    "frame = DataFrame(np.random.randn(4,3),columns=list('bde'),index=['utah','ohio','texas','oregon'])"
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   "cell_type": "code",
   "execution_count": 177,
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    "frame"
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   "execution_count": 178,
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       "               b         d         e\n",
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       "texas   0.540064  1.202785  2.293819\n",
       "oregon  1.185636  0.294228  1.232250"
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     "execution_count": 178,
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    "np.abs(frame)"
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   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = lambda x:x.max() - x.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
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       "b    1.152034\n",
       "d    1.117610\n",
       "e    3.526069\n",
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     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "frame.apply(f)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "utah      0.812678\n",
       "ohio      1.642227\n",
       "texas     3.496604\n",
       "oregon    0.938022\n",
       "dtype: float64"
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     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
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    "frame.apply(f,axis=1)"
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   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return Series([x.min(),x.max()],index=['min','max'])"
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  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
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    "format1 = lambda x:'%.2f' %x"
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   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ohio</th>\n",
       "      <td>1.51</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texas</th>\n",
       "      <td>0.54</td>\n",
       "      <td>1.20</td>\n",
       "      <td>-2.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oregon</th>\n",
       "      <td>1.19</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           b     d      e\n",
       "utah    0.36  0.09  -0.46\n",
       "ohio    1.51  0.99  -0.13\n",
       "texas   0.54  1.20  -2.29\n",
       "oregon  1.19  0.29   1.23"
      ]
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     "execution_count": 185,
     "metadata": {},
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   "source": [
    "frame.applymap(format1)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "utah      -0.46\n",
       "ohio      -0.13\n",
       "texas     -2.29\n",
       "oregon     1.23\n",
       "Name: e, dtype: object"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
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  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = Series(range(4),index=['d','a','b','c'])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    0\n",
       "dtype: int64"
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     "execution_count": 188,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = DataFrame(np.arange(8).reshape((2,4)),\n",
    "                    index=['three','one'],\n",
    "                    columns=['d','a','b','c'])"
   ]
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  {
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   "execution_count": 190,
   "metadata": {},
   "outputs": [
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     "data": {
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    "obj = Series([4,7,-3,2])"
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   "execution_count": 195,
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   "outputs": [
    {
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   "execution_count": 196,
   "metadata": {},
   "outputs": [],
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    "obj = Series([4,np.nan,7,np.nan,-3,2])"
   ]
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   "execution_count": 197,
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     "execution_count": 197,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
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    "frame = DataFrame({'b':[4,7,-3,2],'a':[0,1,0,1]})"
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   "execution_count": 199,
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   "metadata": {},
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    "obj= Series([7,-5,7,4,2,0,4])"
   ]
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   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
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       "dtype: float64"
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     "execution_count": 203,
     "metadata": {},
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    "obj.rank()"
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   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.0\n",
       "1    1.0\n",
       "2    7.0\n",
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       "5    2.0\n",
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       "dtype: float64"
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     "execution_count": 204,
     "metadata": {},
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    "obj.rank(method='first')"
   ]
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   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "1    7.0\n",
       "2    2.0\n",
       "3    4.0\n",
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       "dtype: float64"
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     "execution_count": 205,
     "metadata": {},
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    "obj.rank(ascending=False,method='max')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = DataFrame({'b':[4.3,7,-3,2],'a':[0,1,0,1],'c':[-2,5,8,-2.5]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
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   "outputs": [
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       "      <td>1</td>\n",
       "      <td>-2.5</td>\n",
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       "     b  a    c\n",
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       "1  7.0  1  5.0\n",
       "2 -3.0  0  8.0\n",
       "3  2.0  1 -2.5"
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     "execution_count": 207,
     "metadata": {},
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    "frame"
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   "execution_count": 208,
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       "     b    a    c\n",
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       "3  3.0  2.0  1.0"
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   "execution_count": 209,
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   "source": [
    "obj = Series(range(5),index=['a','a','b','b','c'])"
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   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "a    1\n",
       "b    2\n",
       "b    3\n",
       "c    4\n",
       "dtype: int64"
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     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
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   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
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     "execution_count": 211,
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   "execution_count": 212,
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      "text/plain": [
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       "a    1\n",
       "dtype: int64"
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     "execution_count": 212,
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    "obj['a']"
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   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
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     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "obj['c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = DataFrame(np.random.randn(4,3),index=['a','a','b','b'])"
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  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
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       "      <td>0.034879</td>\n",
       "      <td>-1.777984</td>\n",
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       "      <th>b</th>\n",
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       "          0         1         2\n",
       "a -0.428994 -0.063039  1.673011\n",
       "a  2.013948 -0.935172 -0.148307\n",
       "b -1.507722  0.034879 -1.777984\n",
       "b -0.302180 -0.223479 -1.440601"
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    "df"
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   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
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       "          0         1         2\n",
       "b -1.507722  0.034879 -1.777984\n",
       "b -0.302180 -0.223479 -1.440601"
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     "execution_count": 216,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = DataFrame([[1.4,np.nan],[7.1,-4.5],[np.nan,np.nan],[0.75,-1.3]],\n",
    "                 index=['a','b','c','d'],\n",
    "                 columns=['one','two'])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>b</th>\n",
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       "      <th>d</th>\n",
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      "text/plain": [
       "    one  two\n",
       "a  1.40  NaN\n",
       "b  7.10 -4.5\n",
       "c   NaN  NaN\n",
       "d  0.75 -1.3"
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     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    9.25\n",
       "two   -5.80\n",
       "dtype: float64"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
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    "df.sum()"
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  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.40\n",
       "b    2.60\n",
       "c    0.00\n",
       "d   -0.55\n",
       "dtype: float64"
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     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
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    "df.sum(axis=1)"
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  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a      NaN\n",
       "b    1.300\n",
       "c      NaN\n",
       "d   -0.275\n",
       "dtype: float64"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
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    "df.mean(axis=1,skipna=False)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    b\n",
       "two    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>b</th>\n",
       "      <td>8.50</td>\n",
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       "      <th>d</th>\n",
       "      <td>9.25</td>\n",
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       "    one  two\n",
       "a  1.40  NaN\n",
       "b  8.50 -4.5\n",
       "c   NaN  NaN\n",
       "d  9.25 -5.8"
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     "execution_count": 223,
     "metadata": {},
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   "source": [
    "df.cumsum()"
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  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.083333</td>\n",
       "      <td>-2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.493685</td>\n",
       "      <td>2.262742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>-4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.075000</td>\n",
       "      <td>-3.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>-2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.250000</td>\n",
       "      <td>-2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>-1.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            one       two\n",
       "count  3.000000  2.000000\n",
       "mean   3.083333 -2.900000\n",
       "std    3.493685  2.262742\n",
       "min    0.750000 -4.500000\n",
       "25%    1.075000 -3.700000\n",
       "50%    1.400000 -2.900000\n",
       "75%    4.250000 -2.100000\n",
       "max    7.100000 -1.300000"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = Series(['a','a','b','c']*4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     16\n",
       "unique     3\n",
       "top        a\n",
       "freq       8\n",
       "dtype: object"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj  = Series(['a','c','d','a','a','b','b','c','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "uniques = obj.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 'c', 'd', 'b'], dtype=object)"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "a    3\n",
       "b    2\n",
       "d    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3\n",
       "c    3\n",
       "b    2\n",
       "d    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(obj.values,sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    c\n",
       "2    d\n",
       "3    a\n",
       "4    a\n",
       "5    b\n",
       "6    b\n",
       "7    c\n",
       "8    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask =obj.isin(['b','c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    c\n",
       "5    b\n",
       "6    b\n",
       "7    c\n",
       "8    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_match= Series(['c','a','b','b','c','a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "uniques = Series(['c','b','a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 1, 0, 2])"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Index(uniques).get_indexer(to_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "data =DataFrame({'qu1':[1,3,4,3,4],\n",
    "                   'qu2':[2,3,1,2,3],\n",
    "                   'qu3':[1,5,2,4,4]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = data.apply(pd.value_counts).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "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>qu1</th>\n",
       "      <th>qu2</th>\n",
       "      <th>qu3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   qu1  qu2  qu3\n",
       "1  1.0  1.0  1.0\n",
       "2  0.0  2.0  1.0\n",
       "3  2.0  2.0  0.0\n",
       "4  2.0  0.0  2.0\n",
       "5  0.0  0.0  1.0"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('save_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "query =\"\"\"\n",
    "create table test\n",
    "(a varchar(20),b varchar(20),\n",
    "c real, d integer\n",
    ");\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "con =sqlite3.connect('mydata.sqlite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlite3.Cursor at 0x11c208340>"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.execute(query)"
   ]
  }
 ],
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   "display_name": "Python 3",
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   "codemirror_mode": {
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