pandas/03.14 Pandas For Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Introduction \n",
    "\n",
    "Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.\n",
    "\n",
    "You can say pandas is extremely powerful version of Excel\n",
    "\n",
    "In this section we are going to talk about \n",
    "\n",
    "* Introduction To pandas \n",
    "* Seies \n",
    "* DataFrames \n",
    "* Missing Data\n",
    "* Merging , Joining , And Concatenating \n",
    "* Operations \n",
    "* Data Input and Output "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fristly we are going to talk about Series DataType .\n",
    "\n",
    "A Series is very similar to numpy array , it is built on top of NumPy Array..\n",
    "But Series can have axis labels , meaning it can be indexed by labels instead of just number location \n",
    "\n",
    "Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets import numpy and pandas \n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can convert a list , numpy array , or dict to Series\n",
    "\n",
    "labels = ['Shivendra','Ragavendra','Narendra']\n",
    "my_list= [21,25,30]\n",
    "arr=np.array([10,20,30])\n",
    "d={'Shivendra':21,'Raghavendra':25,'Narendra':30}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    21\n",
       "1    25\n",
       "2    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using List \n",
    "pd.Series(data=my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Shivendra     21\n",
       "Ragavendra    25\n",
       "Narendra      30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series (data=my_list,index=labels )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Shivendra     21\n",
       "Ragavendra    25\n",
       "Narendra      30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(my_list,labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "dtype: int32"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NumPy Array\n",
    "pd.Series(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Shivendra     10\n",
       "Ragavendra    20\n",
       "Narendra      30\n",
       "dtype: int32"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series (data=arr,index=labels )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Shivendra      21\n",
       "Raghavendra    25\n",
       "Narendra       30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dictonary\n",
    "pd.Series (d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data In A Series \n",
    "\n",
    "A Pandas Series can hold a variety of Objects "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Shivendra\n",
       "1    Ragavendra\n",
       "2      Narendra\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series (data=labels )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using an Index\n",
    "\n",
    "The key to using a Series is understanding its index. Pandas makes use of these index names or numbers by allowing for fast look ups of information (works like a hash table or dictionary).\n",
    "\n",
    "Let's see some examples of how to grab information from a Series. Let us create two sereis, ser1 and ser2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser1= pd.Series ([1,2,3,4], index =['Chennai','Bihar','West Bengal','Rajasthan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chennai        1\n",
       "Bihar          2\n",
       "West Bengal    3\n",
       "Rajasthan      4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser2=pd.Series ([1,2,5,4],index=['Chennai','Bihar','Assam','Rajasthan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chennai      1\n",
       "Bihar        2\n",
       "Assam        5\n",
       "Rajasthan    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1['Chennai']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Assam          NaN\n",
       "Bihar          4.0\n",
       "Chennai        2.0\n",
       "Rajasthan      8.0\n",
       "West Bengal    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Operations are then also done based off of index:\n",
    "ser1+ser2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrames\n",
    "Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the data, rows, and columns.\n",
    "\n",
    "DataFrames are the workhorse of pandas and are directly inspired by the R programming language. We can think of a DataFrame as a bunch of Series objects put together to share the same index. Let's use pandas to explore this topic!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.random import randn\n",
    "np.random.seed(101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame (randn(5,5),index='Chennai Bihar UtterPredesh Delhi Mumbai'.split(),columns ='SRM NIT_PATNA BHU IIT_DELHI IIT_Bombay'.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection and Indexing\n",
    "\n",
    "Let's learn the various methods to grab data from a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chennai         2.706850\n",
       "Bihar          -0.319318\n",
       "UtterPredesh    0.528813\n",
       "Delhi           0.955057\n",
       "Mumbai          0.302665\n",
       "Name: SRM, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SRM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>SRM</th>\n",
       "      <th>BHU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.907969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>-0.319318</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>0.188695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>1.978757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>-1.706086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SRM       BHU\n",
       "Chennai       2.706850  0.907969\n",
       "Bihar        -0.319318  0.605965\n",
       "UtterPredesh  0.528813  0.188695\n",
       "Delhi         0.955057  1.978757\n",
       "Mumbai        0.302665 -1.706086"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can pass a list of columns names \n",
    "df[['SRM' , 'BHU']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chennai         2.706850\n",
       "Bihar          -0.319318\n",
       "UtterPredesh    0.528813\n",
       "Delhi           0.955057\n",
       "Mumbai          0.302665\n",
       "Name: SRM, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.SRM # SQL syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataframe  Columns are just Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['SRM'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new columns \n",
    "df['UPES']=df['SRM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Harshita']=df['SRM'] + df['BHU']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "      <th>UPES</th>\n",
       "      <th>Harshita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "      <td>2.706850</td>\n",
       "      <td>3.614819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>0.286647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>0.717509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "      <td>0.955057</td>\n",
       "      <td>2.933814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "      <td>0.302665</td>\n",
       "      <td>-1.403420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay      UPES  \\\n",
       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118  2.706850   \n",
       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122 -0.319318   \n",
       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237  0.528813   \n",
       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509  0.955057   \n",
       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841  0.302665   \n",
       "\n",
       "              Harshita  \n",
       "Chennai       3.614819  \n",
       "Bihar         0.286647  \n",
       "UtterPredesh  0.717509  \n",
       "Delhi         2.933814  \n",
       "Mumbai       -1.403420  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('UPES',axis=1) # Axis = 1 for column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "      <th>UPES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "      <td>2.706850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>-0.319318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.528813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "      <td>0.955057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "      <td>0.302665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay      UPES\n",
       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118  2.706850\n",
       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122 -0.319318\n",
       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237  0.528813\n",
       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509  0.955057\n",
       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841  0.302665"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # But again it will be appeared we need to use inplace to remove it parmanently"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('UPES',axis=1,inplace =True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Harshita',axis=1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('Delhi',axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SRM           2.706850\n",
       "NIT_PATNA     0.628133\n",
       "BHU           0.907969\n",
       "IIT_DELHI     0.503826\n",
       "IIT_Bombay    0.651118\n",
       "Name: Chennai, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Chennai']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SRM          -0.319318\n",
       "NIT_PATNA    -0.848077\n",
       "BHU           0.605965\n",
       "IIT_DELHI    -2.018168\n",
       "IIT_Bombay    0.740122\n",
       "Name: Bihar, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can select based on indexing \n",
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.8480769834036315"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Bihar','NIT_PATNA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>-0.848077</td>\n",
       "      <td>-2.018168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        NIT_PATNA  IIT_DELHI\n",
       "Bihar   -0.848077  -2.018168\n",
       "Mumbai   1.693723  -1.159119"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[['Bihar','Mumbai'],['NIT_PATNA','IIT_DELHI']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                SRM  NIT_PATNA    BHU  IIT_DELHI  IIT_Bombay\n",
       "Chennai        True       True   True       True        True\n",
       "Bihar         False      False   True      False        True\n",
       "UtterPredesh   True      False   True      False       False\n",
       "Delhi          True       True   True       True        True\n",
       "Mumbai         True       True  False      False       False"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df>0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.740122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
       "Bihar              NaN        NaN  0.605965        NaN    0.740122\n",
       "UtterPredesh  0.528813        NaN  0.188695        NaN         NaN\n",
       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
       "Mumbai        0.302665   1.693723       NaN        NaN         NaN"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df [df['SRM']>0] # It will not print Bihar Cz Bihar is having negetive number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chennai         0.907969\n",
       "UtterPredesh    0.188695\n",
       "Delhi           1.978757\n",
       "Mumbai         -1.706086\n",
       "Name: BHU, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['SRM']>0]['BHU'] # It will not print Bihar data since it is having negetive number "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chennai         2.706850\n",
       "Bihar          -0.319318\n",
       "UtterPredesh    0.528813\n",
       "Delhi           0.955057\n",
       "Name: SRM, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['BHU']>0]['SRM'] # It will not print mumbai's data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   BHU  IIT_DELHI\n",
       "Chennai       0.907969   0.503826\n",
       "UtterPredesh  0.188695  -0.758872\n",
       "Delhi         1.978757   2.605967\n",
       "Mumbai       -1.706086  -1.159119"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['SRM']>0][['BHU','IIT_DELHI']]"
   ]
  },
  {
   "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",
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       "\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>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
       "Chennai  2.706850   0.628133  0.907969   0.503826    0.651118\n",
       "Delhi    0.955057   0.190794  1.978757   2.605967    0.683509"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['SRM']>0.955)& df['BHU']>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More Index Details\n",
    "\n",
    "Let's discuss some more features of indexing, including resetting the index or setting it something else. We'll also talk about index hierarchy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118\n",
       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509\n",
       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>index</th>\n",
       "      <th>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UtterPredesh</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          index       SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
       "0       Chennai  2.706850   0.628133  0.907969   0.503826    0.651118\n",
       "1         Bihar -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
       "2  UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
       "3         Delhi  0.955057   0.190794  1.978757   2.605967    0.683509\n",
       "4        Mumbai  0.302665   1.693723 -1.706086  -1.159119   -0.134841"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "newind ='Tamil_Nadu BIHAR UP Delhi Maharastra'.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['States']=newind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "      <th>States</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "      <td>Tamil_Nadu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>BIHAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>UP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "      <td>Maharastra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay      States\n",
       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118  Tamil_Nadu\n",
       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122       BIHAR\n",
       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237          UP\n",
       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509       Delhi\n",
       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841  Maharastra"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\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>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>States</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tamil_Nadu</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIHAR</th>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UP</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maharastra</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
       "States                                                          \n",
       "Tamil_Nadu  2.706850   0.628133  0.907969   0.503826    0.651118\n",
       "BIHAR      -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
       "UP          0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
       "Delhi       0.955057   0.190794  1.978757   2.605967    0.683509\n",
       "Maharastra  0.302665   1.693723 -1.706086  -1.159119   -0.134841"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('States')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "      <th>States</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chennai</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "      <td>Tamil_Nadu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>BIHAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UtterPredesh</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>UP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "      <td>Maharastra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay      States\n",
       "Chennai       2.706850   0.628133  0.907969   0.503826    0.651118  Tamil_Nadu\n",
       "Bihar        -0.319318  -0.848077  0.605965  -2.018168    0.740122       BIHAR\n",
       "UtterPredesh  0.528813  -0.589001  0.188695  -0.758872   -0.933237          UP\n",
       "Delhi         0.955057   0.190794  1.978757   2.605967    0.683509       Delhi\n",
       "Mumbai        0.302665   1.693723 -1.706086  -1.159119   -0.134841  Maharastra"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('States',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>SRM</th>\n",
       "      <th>NIT_PATNA</th>\n",
       "      <th>BHU</th>\n",
       "      <th>IIT_DELHI</th>\n",
       "      <th>IIT_Bombay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>States</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tamil_Nadu</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>0.651118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIHAR</th>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UP</th>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>0.955057</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maharastra</th>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.693723</td>\n",
       "      <td>-1.706086</td>\n",
       "      <td>-1.159119</td>\n",
       "      <td>-0.134841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SRM  NIT_PATNA       BHU  IIT_DELHI  IIT_Bombay\n",
       "States                                                          \n",
       "Tamil_Nadu  2.706850   0.628133  0.907969   0.503826    0.651118\n",
       "BIHAR      -0.319318  -0.848077  0.605965  -2.018168    0.740122\n",
       "UP          0.528813  -0.589001  0.188695  -0.758872   -0.933237\n",
       "Delhi       0.955057   0.190794  1.978757   2.605967    0.683509\n",
       "Maharastra  0.302665   1.693723 -1.706086  -1.159119   -0.134841"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Index and Index Hierarchy\n",
    "\n",
    "Let us go over how to work with Multi-Index, first we'll create a quick example of what a Multi-Indexed DataFrame would look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# index Levels \n",
    "outside =['Big_data','Big_data','Big_data','AI','AI','AI']\n",
    "inside =[1,2,3,1,2,3]\n",
    "hier_index=list(zip(outside,inside))\n",
    "hier_index=pd.MultiIndex.from_tuples(hier_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('Big_data', 1),\n",
       "            ('Big_data', 2),\n",
       "            ('Big_data', 3),\n",
       "            (      'AI', 1),\n",
       "            (      'AI', 2),\n",
       "            (      'AI', 3)],\n",
       "           )"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hier_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(np.random.rand (6,2),index=hier_index,columns=['Core','volunteers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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></th>\n",
       "      <th>Core</th>\n",
       "      <th>volunteers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Big_data</th>\n",
       "      <th>1</th>\n",
       "      <td>0.701371</td>\n",
       "      <td>0.487635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.680678</td>\n",
       "      <td>0.521548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.043397</td>\n",
       "      <td>0.223937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">AI</th>\n",
       "      <th>1</th>\n",
       "      <td>0.575205</td>\n",
       "      <td>0.120434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.500117</td>\n",
       "      <td>0.138010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.052808</td>\n",
       "      <td>0.178277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Core  volunteers\n",
       "Big_data 1  0.701371    0.487635\n",
       "         2  0.680678    0.521548\n",
       "         3  0.043397    0.223937\n",
       "AI       1  0.575205    0.120434\n",
       "         2  0.500117    0.138010\n",
       "         3  0.052808    0.178277"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's show how to index this! For index hierarchy we use df.loc[], if this was on the columns axis, you would just use normal bracket notation df[]. Calling one level of the index returns the sub-dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Core</th>\n",
       "      <th>volunteers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.701371</td>\n",
       "      <td>0.487635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.680678</td>\n",
       "      <td>0.521548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.043397</td>\n",
       "      <td>0.223937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Core  volunteers\n",
       "1  0.701371    0.487635\n",
       "2  0.680678    0.521548\n",
       "3  0.043397    0.223937"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Big_data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Core          0.701371\n",
       "volunteers    0.487635\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Big_data'].loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FrozenList([None, None])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index.names=['Domain','S.NO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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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></th>\n",
       "      <th>Core</th>\n",
       "      <th>volunteers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domain</th>\n",
       "      <th>S.NO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Big_data</th>\n",
       "      <th>1</th>\n",
       "      <td>0.701371</td>\n",
       "      <td>0.487635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.680678</td>\n",
       "      <td>0.521548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.043397</td>\n",
       "      <td>0.223937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">AI</th>\n",
       "      <th>1</th>\n",
       "      <td>0.575205</td>\n",
       "      <td>0.120434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.500117</td>\n",
       "      <td>0.138010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.052808</td>\n",
       "      <td>0.178277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Core  volunteers\n",
       "Domain   S.NO                      \n",
       "Big_data 1     0.701371    0.487635\n",
       "         2     0.680678    0.521548\n",
       "         3     0.043397    0.223937\n",
       "AI       1     0.575205    0.120434\n",
       "         2     0.500117    0.138010\n",
       "         3     0.052808    0.178277"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Core</th>\n",
       "      <th>volunteers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S.NO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.701371</td>\n",
       "      <td>0.487635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.680678</td>\n",
       "      <td>0.521548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.043397</td>\n",
       "      <td>0.223937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Core  volunteers\n",
       "S.NO                      \n",
       "1     0.701371    0.487635\n",
       "2     0.680678    0.521548\n",
       "3     0.043397    0.223937"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.xs('Big_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data = {\n",
    "    'day': ['1/1/2017','1/2/2017','1/3/2017','1/4/2017','1/5/2017','1/6/2017'],\n",
    "    'temperature': [32,35,28,24,32,31],\n",
    "    'windspeed': [6,7,2,7,4,2],\n",
    "    'event': ['Rain', 'Sunny', 'Snow','Snow','Rain', 'Sunny']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(weather_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 4)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape # rows, columns = df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail() # df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'>Columns</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['day', 'temperature', 'windspeed', 'event'], dtype='object')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1/1/2017\n",
       "1    1/2/2017\n",
       "2    1/3/2017\n",
       "3    1/4/2017\n",
       "4    1/5/2017\n",
       "5    1/6/2017\n",
       "Name: day, dtype: object"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['day']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['day'])"
   ]
  },
  {
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature\n",
       "0  1/1/2017           32\n",
       "1  1/2/2017           35\n",
       "2  1/3/2017           28\n",
       "3  1/4/2017           24\n",
       "4  1/5/2017           32\n",
       "5  1/6/2017           31"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['day','temperature']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'>Operations On DataFrame</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['temperature'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "1  1/2/2017           35          7  Sunny"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['temperature']>32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1/2/2017\n",
       "Name: day, dtype: object"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['day'][df['temperature'] == df['temperature'].max()] # Kinda doing SQL in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8297084310253524"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['temperature'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sunny'"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['event'].max() # But mean() won't work since data type is string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.333333</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.829708</td>\n",
       "      <td>2.338090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.750000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temperature  windspeed\n",
       "count     6.000000   6.000000\n",
       "mean     30.333333   4.666667\n",
       "std       3.829708   2.338090\n",
       "min      24.000000   2.000000\n",
       "25%      28.750000   2.500000\n",
       "50%      31.500000   5.000000\n",
       "75%      32.000000   6.750000\n",
       "max      35.000000   7.000000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Data\n",
    "\n",
    "Let's show a few convenient methods to deal with Missing Data in pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'A':[1,2,np.nan],\n",
    "                  'B':[5,np.nan,np.nan],\n",
    "                  'C':[1,2,3]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B  C\n",
       "0  1.0  5.0  1\n",
       "1  2.0  NaN  2\n",
       "2  NaN  NaN  3"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B  C\n",
       "0  1.0  5.0  1"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C\n",
       "0  1\n",
       "1  2\n",
       "2  3"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B  C\n",
       "0  1.0  5.0  1\n",
       "1  2.0  NaN  2"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(thresh=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>shivendra</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shivendra</td>\n",
       "      <td>shivendra</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A          B  C\n",
       "0          1          5  1\n",
       "1          2  shivendra  2\n",
       "2  shivendra  shivendra  3"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(value='shivendra')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    1.5\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A'].fillna(value=df['A'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Groupby\n",
    "\n",
    "The groupby method allows you to group rows of data together and call aggregate functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# Create dataframe\n",
    "data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],\n",
    "       'Person':['Shivendra','Abhishek','Sowjanya','Manish','Mini','Satya'],\n",
    "       'Sales':[200,120,340,124,243,350]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Person</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>Shivendra</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>Abhishek</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>Sowjanya</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>Manish</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FB</td>\n",
       "      <td>Mini</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FB</td>\n",
       "      <td>Satya</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company     Person  Sales\n",
       "0    GOOG  Shivendra    200\n",
       "1    GOOG   Abhishek    120\n",
       "2    MSFT   Sowjanya    340\n",
       "3    MSFT     Manish    124\n",
       "4      FB       Mini    243\n",
       "5      FB      Satya    350"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now you can use the .groupby() method to group rows together based off of a column name. For instance let's group based off of Company. This will create a DataFrameGroupBy object:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001F12523CB08>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Company')"
   ]
  },
  {
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>296.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sales\n",
       "Company       \n",
       "FB       296.5\n",
       "GOOG     160.0\n",
       "MSFT     232.0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#You can save this object as a new variable:\n",
    "by_comp = df.groupby(\"Company\")\n",
    "#And then call aggregate methods off the object:\n",
    "by_comp.mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>296.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sales\n",
       "Company       \n",
       "FB       296.5\n",
       "GOOG     160.0\n",
       "MSFT     232.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Company').mean()"
   ]
  },
  {
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>75.660426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>56.568542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>152.735065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sales\n",
       "Company            \n",
       "FB        75.660426\n",
       "GOOG      56.568542\n",
       "MSFT     152.735065"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#More examples of aggregate methods:\n",
    "by_comp.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>Person</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>Satya</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>Shivendra</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>Sowjanya</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Person  Sales\n",
       "Company                  \n",
       "FB           Satya    350\n",
       "GOOG     Shivendra    200\n",
       "MSFT      Sowjanya    340"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_comp.max()"
   ]
  },
  {
   "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",
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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>Person</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>Mini</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>Abhishek</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>Manish</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Person  Sales\n",
       "Company                 \n",
       "FB           Mini    243\n",
       "GOOG     Abhishek    120\n",
       "MSFT       Manish    124"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_comp.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>2.0</td>\n",
       "      <td>296.5</td>\n",
       "      <td>75.660426</td>\n",
       "      <td>243.0</td>\n",
       "      <td>269.75</td>\n",
       "      <td>296.5</td>\n",
       "      <td>323.25</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>2.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>56.568542</td>\n",
       "      <td>120.0</td>\n",
       "      <td>140.00</td>\n",
       "      <td>160.0</td>\n",
       "      <td>180.00</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>2.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>152.735065</td>\n",
       "      <td>124.0</td>\n",
       "      <td>178.00</td>\n",
       "      <td>232.0</td>\n",
       "      <td>286.00</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sales                                                        \n",
       "        count   mean         std    min     25%    50%     75%    max\n",
       "Company                                                              \n",
       "FB        2.0  296.5   75.660426  243.0  269.75  296.5  323.25  350.0\n",
       "GOOG      2.0  160.0   56.568542  120.0  140.00  160.0  180.00  200.0\n",
       "MSFT      2.0  232.0  152.735065  124.0  178.00  232.0  286.00  340.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_comp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Company</th>\n",
       "      <th>FB</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Sales</th>\n",
       "      <th>count</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>296.500000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>75.660426</td>\n",
       "      <td>56.568542</td>\n",
       "      <td>152.735065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>243.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>124.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>269.750000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>178.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>296.500000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>323.250000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>286.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>340.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Company              FB        GOOG        MSFT\n",
       "Sales count    2.000000    2.000000    2.000000\n",
       "      mean   296.500000  160.000000  232.000000\n",
       "      std     75.660426   56.568542  152.735065\n",
       "      min    243.000000  120.000000  124.000000\n",
       "      25%    269.750000  140.000000  178.000000\n",
       "      50%    296.500000  160.000000  232.000000\n",
       "      75%    323.250000  180.000000  286.000000\n",
       "      max    350.000000  200.000000  340.000000"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_comp.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales  count      2.000000\n",
       "       mean     160.000000\n",
       "       std       56.568542\n",
       "       min      120.000000\n",
       "       25%      140.000000\n",
       "       50%      160.000000\n",
       "       75%      180.000000\n",
       "       max      200.000000\n",
       "Name: GOOG, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_comp.describe().transpose()['GOOG']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merging, Joining, and Concatenating\n",
    "\n",
    "There are 3 main ways of combining DataFrames together: Merging, Joining and Concatenating. In this we will discuss these 3 methods with examples.\n",
    "\n",
    "____"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                        'B': ['B0', 'B1', 'B2', 'B3'],\n",
    "                        'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                        'D': ['D0', 'D1', 'D2', 'D3']},\n",
    "                        index=[0, 1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
    "                        'B': ['B4', 'B5', 'B6', 'B7'],\n",
    "                        'C': ['C4', 'C5', 'C6', 'C7'],\n",
    "                        'D': ['D4', 'D5', 'D6', 'D7']},\n",
    "                         index=[4, 5, 6, 7]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],\n",
    "                        'B': ['B8', 'B9', 'B10', 'B11'],\n",
    "                        'C': ['C8', 'C9', 'C10', 'C11'],\n",
    "                        'D': ['D8', 'D9', 'D10', 'D11']},\n",
    "                        index=[8, 9, 10, 11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    A   B   C   D\n",
       "0  A0  B0  C0  D0\n",
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       "2  A2  B2  C2  D2\n",
       "3  A3  B3  C3  D3"
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     },
     "execution_count": 101,
     "metadata": {},
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   "source": [
    "df1"
   ]
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  {
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   "execution_count": 102,
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       "      <td>C5</td>\n",
       "      <td>D5</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
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       "    A   B   C   D\n",
       "4  A4  B4  C4  D4\n",
       "5  A5  B5  C5  D5\n",
       "6  A6  B6  C6  D6\n",
       "7  A7  B7  C7  D7"
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     "execution_count": 102,
     "metadata": {},
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   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
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       "      A    B    C    D\n",
       "8    A8   B8   C8   D8\n",
       "9    A9   B9   C9   D9\n",
       "10  A10  B10  C10  D10\n",
       "11  A11  B11  C11  D11"
      ]
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     "execution_count": 103,
     "metadata": {},
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   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenation\n",
    "\n",
    "Concatenation basically glues together DataFrames. Keep in mind that dimensions should match along the axis you are concatenating on. You can use **pd.concat** and pass in a list of DataFrames to concatenate together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
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       "      <td>A8</td>\n",
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       "      <td>C8</td>\n",
       "      <td>D8</td>\n",
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       "      <td>A9</td>\n",
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       "      <th>10</th>\n",
       "      <td>A10</td>\n",
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       "      <td>C10</td>\n",
       "      <td>D10</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A11</td>\n",
       "      <td>B11</td>\n",
       "      <td>C11</td>\n",
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      "text/plain": [
       "      A    B    C    D\n",
       "0    A0   B0   C0   D0\n",
       "1    A1   B1   C1   D1\n",
       "2    A2   B2   C2   D2\n",
       "3    A3   B3   C3   D3\n",
       "4    A4   B4   C4   D4\n",
       "5    A5   B5   C5   D5\n",
       "6    A6   B6   C6   D6\n",
       "7    A7   B7   C7   D7\n",
       "8    A8   B8   C8   D8\n",
       "9    A9   B9   C9   D9\n",
       "10  A10  B10  C10  D10\n",
       "11  A11  B11  C11  D11"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2,df3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>B9</td>\n",
       "      <td>C9</td>\n",
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       "      <th>10</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A10</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>A11</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "      A    B    C    D    A    B    C    D    A    B    C    D\n",
       "0    A0   B0   C0   D0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "1    A1   B1   C1   D1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "2    A2   B2   C2   D2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "3    A3   B3   C3   D3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "4   NaN  NaN  NaN  NaN   A4   B4   C4   D4  NaN  NaN  NaN  NaN\n",
       "5   NaN  NaN  NaN  NaN   A5   B5   C5   D5  NaN  NaN  NaN  NaN\n",
       "6   NaN  NaN  NaN  NaN   A6   B6   C6   D6  NaN  NaN  NaN  NaN\n",
       "7   NaN  NaN  NaN  NaN   A7   B7   C7   D7  NaN  NaN  NaN  NaN\n",
       "8   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A8   B8   C8   D8\n",
       "9   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A9   B9   C9   D9\n",
       "10  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A10  B10  C10  D10\n",
       "11  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A11  B11  C11  D11"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2,df3],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Operations\n",
    "\n",
    "There are lots of operations with pandas that will be really useful to you, but don't fall into any distinct category. Let's show them here in this lecture:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>444</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>555</td>\n",
       "      <td>def</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2 col3\n",
       "0     1   444  abc\n",
       "1     2   555  def\n",
       "2     3   666  ghi\n",
       "3     4   444  xyz"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'col1':[1,2,3,4],'col2':[444,555,666,444],'col3':['abc','def','ghi','xyz']})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([444, 555, 666], dtype=int64)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col2'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "444    2\n",
       "555    1\n",
       "666    1\n",
       "Name: col2, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select from DataFrame using criteria from multiple columns\n",
    "newdf = df[(df['col1']>2) & (df['col2']==444)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2 col3\n",
       "3     4   444  xyz"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying Functions\n",
    "def times2(x):\n",
    "    return x*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    6\n",
       "3    8\n",
       "Name: col1, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col1'].apply(times2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    3\n",
       "2    3\n",
       "3    3\n",
       "Name: col3, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col3'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col1'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Permanently Removing a Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['col1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>444</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>555</td>\n",
       "      <td>def</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col2 col3\n",
       "0   444  abc\n",
       "1   555  def\n",
       "2   666  ghi\n",
       "3   444  xyz"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['col2', 'col3'], dtype='object')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get columns and index names \n",
    "df.columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>444</td>\n",
       "      <td>abc</td>\n",
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       "      <th>1</th>\n",
       "      <td>555</td>\n",
       "      <td>def</td>\n",
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       "      <th>2</th>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
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       "      <th>3</th>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
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       "</table>\n",
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      "text/plain": [
       "   col2 col3\n",
       "0   444  abc\n",
       "1   555  def\n",
       "2   666  ghi\n",
       "3   444  xyz"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>444</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>555</td>\n",
       "      <td>def</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col2 col3\n",
       "0   444  abc\n",
       "3   444  xyz\n",
       "1   555  def\n",
       "2   666  ghi"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='col2') #inplace=False by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    col2   col3\n",
       "0  False  False\n",
       "1  False  False\n",
       "2  False  False\n",
       "3  False  False"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check is there any null value or not \n",
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>444</td>\n",
       "      <td>abc</td>\n",
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       "      <td>555</td>\n",
       "      <td>def</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   col2 col3\n",
       "0   444  abc\n",
       "1   555  def\n",
       "2   666  ghi\n",
       "3   444  xyz"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Drop rows with NaN Values\n",
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>555.0</td>\n",
       "      <td>def</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>444.0</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1   col2 col3\n",
       "0   1.0    NaN  abc\n",
       "1   2.0  555.0  def\n",
       "2   3.0  666.0  ghi\n",
       "3   NaN  444.0  xyz"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'col1':[1,2,3,np.nan],\n",
    "                   'col2':[np.nan,555,666,444],\n",
    "                   'col3':['abc','def','ghi','xyz']})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>FILL</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>555</td>\n",
       "      <td>def</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FILL</td>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2 col3\n",
       "0     1  FILL  abc\n",
       "1     2   555  def\n",
       "2     3   666  ghi\n",
       "3  FILL   444  xyz"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna('FILL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'A':['foo','foo','foo','bar','bar','bar'],\n",
    "     'B':['one','one','two','two','one','one'],\n",
    "       'C':['x','y','x','y','x','y'],\n",
    "       'D':[1,3,2,5,4,1]}\n",
    "\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>x</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>y</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>x</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>y</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>x</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>y</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B  C  D\n",
       "0  foo  one  x  1\n",
       "1  foo  one  y  3\n",
       "2  foo  two  x  2\n",
       "3  bar  two  y  5\n",
       "4  bar  one  x  4\n",
       "5  bar  one  y  1"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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