panda-basics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['w','x','y','z']\n",
    "list = [10,20,30,40]\n",
    "array = np.array([10,20,30,40])\n",
    "dict = {'w':10, 'y':20, 'x':30, 'z':40}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "3    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(data=list) #creating panda series from a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "w    10\n",
       "x    20\n",
       "y    30\n",
       "z    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(data=list, index=labels) #creating panda series with custom index values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "3    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(array) #creating panda series from a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "w    10\n",
       "x    20\n",
       "y    30\n",
       "z    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(array, labels) #creating panda series with custom index values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "w    10\n",
       "y    20\n",
       "x    30\n",
       "z    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series =  pd.Series(dict) #creating panda series from a python dictionary\n",
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series['w'] #indexing within panda series using custom index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series[0] #indexing within panda series using original index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import randn\n",
    "np.random.seed(1)  #ensure the random values generated are same for every execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Score1</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Score3</th>\n",
       "      <th>Score4</th>\n",
       "      <th>Score5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.310117</td>\n",
       "      <td>-2.434838</td>\n",
       "      <td>1.038825</td>\n",
       "      <td>2.186980</td>\n",
       "      <td>0.441364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.100155</td>\n",
       "      <td>-0.136445</td>\n",
       "      <td>-0.119054</td>\n",
       "      <td>0.017409</td>\n",
       "      <td>-1.122019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.517094</td>\n",
       "      <td>-0.997027</td>\n",
       "      <td>0.248799</td>\n",
       "      <td>-0.296641</td>\n",
       "      <td>0.495211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.174703</td>\n",
       "      <td>0.986335</td>\n",
       "      <td>0.213534</td>\n",
       "      <td>2.190700</td>\n",
       "      <td>-1.896361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.646917</td>\n",
       "      <td>0.901487</td>\n",
       "      <td>2.528326</td>\n",
       "      <td>-0.248635</td>\n",
       "      <td>0.043669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>-0.226314</td>\n",
       "      <td>1.331457</td>\n",
       "      <td>-0.287308</td>\n",
       "      <td>0.680070</td>\n",
       "      <td>-0.319802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>-1.272559</td>\n",
       "      <td>0.313548</td>\n",
       "      <td>0.503185</td>\n",
       "      <td>1.293226</td>\n",
       "      <td>-0.110447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>-0.617362</td>\n",
       "      <td>0.562761</td>\n",
       "      <td>0.240737</td>\n",
       "      <td>0.280665</td>\n",
       "      <td>-0.073113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1.160339</td>\n",
       "      <td>0.369493</td>\n",
       "      <td>1.904659</td>\n",
       "      <td>1.111057</td>\n",
       "      <td>0.659050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>-1.627438</td>\n",
       "      <td>0.602319</td>\n",
       "      <td>0.420282</td>\n",
       "      <td>0.810952</td>\n",
       "      <td>1.044442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Score1    Score2    Score3    Score4    Score5\n",
       "A -0.310117 -2.434838  1.038825  2.186980  0.441364\n",
       "B -0.100155 -0.136445 -0.119054  0.017409 -1.122019\n",
       "C -0.517094 -0.997027  0.248799 -0.296641  0.495211\n",
       "D -0.174703  0.986335  0.213534  2.190700 -1.896361\n",
       "E -0.646917  0.901487  2.528326 -0.248635  0.043669\n",
       "F -0.226314  1.331457 -0.287308  0.680070 -0.319802\n",
       "G -1.272559  0.313548  0.503185  1.293226 -0.110447\n",
       "H -0.617362  0.562761  0.240737  0.280665 -0.073113\n",
       "I  1.160339  0.369493  1.904659  1.111057  0.659050\n",
       "J -1.627438  0.602319  0.420282  0.810952  1.044442"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(randn(10,5), index = 'A B C D E F G H I J'.split(),\n",
    "                 columns= 'Score1 Score2 Score3 Score4 Score5'.split()) #create dataframe in pandas\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.310117\n",
       "B   -0.100155\n",
       "C   -0.517094\n",
       "D   -0.174703\n",
       "E   -0.646917\n",
       "F   -0.226314\n",
       "G   -1.272559\n",
       "H   -0.617362\n",
       "I    1.160339\n",
       "J   -1.627438\n",
       "Name: Score1, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Score1'] #indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score1</th>\n",
       "      <th>Score2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.310117</td>\n",
       "      <td>-2.434838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.100155</td>\n",
       "      <td>-0.136445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.517094</td>\n",
       "      <td>-0.997027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.174703</td>\n",
       "      <td>0.986335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.646917</td>\n",
       "      <td>0.901487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>-0.226314</td>\n",
       "      <td>1.331457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>-1.272559</td>\n",
       "      <td>0.313548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>-0.617362</td>\n",
       "      <td>0.562761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1.160339</td>\n",
       "      <td>0.369493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>-1.627438</td>\n",
       "      <td>0.602319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Score1    Score2\n",
       "A -0.310117 -2.434838\n",
       "B -0.100155 -0.136445\n",
       "C -0.517094 -0.997027\n",
       "D -0.174703  0.986335\n",
       "E -0.646917  0.901487\n",
       "F -0.226314  1.331457\n",
       "G -1.272559  0.313548\n",
       "H -0.617362  0.562761\n",
       "I  1.160339  0.369493\n",
       "J -1.627438  0.602319"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Score1', 'Score2']] #indexing multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Score1</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Score3</th>\n",
       "      <th>Score4</th>\n",
       "      <th>Score5</th>\n",
       "      <th>Score6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.310117</td>\n",
       "      <td>-2.434838</td>\n",
       "      <td>1.038825</td>\n",
       "      <td>2.186980</td>\n",
       "      <td>0.441364</td>\n",
       "      <td>-2.744955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.100155</td>\n",
       "      <td>-0.136445</td>\n",
       "      <td>-0.119054</td>\n",
       "      <td>0.017409</td>\n",
       "      <td>-1.122019</td>\n",
       "      <td>-0.236600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.517094</td>\n",
       "      <td>-0.997027</td>\n",
       "      <td>0.248799</td>\n",
       "      <td>-0.296641</td>\n",
       "      <td>0.495211</td>\n",
       "      <td>-1.514121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.174703</td>\n",
       "      <td>0.986335</td>\n",
       "      <td>0.213534</td>\n",
       "      <td>2.190700</td>\n",
       "      <td>-1.896361</td>\n",
       "      <td>0.811632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.646917</td>\n",
       "      <td>0.901487</td>\n",
       "      <td>2.528326</td>\n",
       "      <td>-0.248635</td>\n",
       "      <td>0.043669</td>\n",
       "      <td>0.254570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>-0.226314</td>\n",
       "      <td>1.331457</td>\n",
       "      <td>-0.287308</td>\n",
       "      <td>0.680070</td>\n",
       "      <td>-0.319802</td>\n",
       "      <td>1.105143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>-1.272559</td>\n",
       "      <td>0.313548</td>\n",
       "      <td>0.503185</td>\n",
       "      <td>1.293226</td>\n",
       "      <td>-0.110447</td>\n",
       "      <td>-0.959011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>-0.617362</td>\n",
       "      <td>0.562761</td>\n",
       "      <td>0.240737</td>\n",
       "      <td>0.280665</td>\n",
       "      <td>-0.073113</td>\n",
       "      <td>-0.054601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1.160339</td>\n",
       "      <td>0.369493</td>\n",
       "      <td>1.904659</td>\n",
       "      <td>1.111057</td>\n",
       "      <td>0.659050</td>\n",
       "      <td>1.529831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>-1.627438</td>\n",
       "      <td>0.602319</td>\n",
       "      <td>0.420282</td>\n",
       "      <td>0.810952</td>\n",
       "      <td>1.044442</td>\n",
       "      <td>-1.025119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Score1    Score2    Score3    Score4    Score5    Score6\n",
       "A -0.310117 -2.434838  1.038825  2.186980  0.441364 -2.744955\n",
       "B -0.100155 -0.136445 -0.119054  0.017409 -1.122019 -0.236600\n",
       "C -0.517094 -0.997027  0.248799 -0.296641  0.495211 -1.514121\n",
       "D -0.174703  0.986335  0.213534  2.190700 -1.896361  0.811632\n",
       "E -0.646917  0.901487  2.528326 -0.248635  0.043669  0.254570\n",
       "F -0.226314  1.331457 -0.287308  0.680070 -0.319802  1.105143\n",
       "G -1.272559  0.313548  0.503185  1.293226 -0.110447 -0.959011\n",
       "H -0.617362  0.562761  0.240737  0.280665 -0.073113 -0.054601\n",
       "I  1.160339  0.369493  1.904659  1.111057  0.659050  1.529831\n",
       "J -1.627438  0.602319  0.420282  0.810952  1.044442 -1.025119"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Score6'] = df['Score1'] + df['Score2']  #Adding columns to existing dataframe\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Score1</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Score3</th>\n",
       "      <th>Score4</th>\n",
       "      <th>Score5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.310117</td>\n",
       "      <td>-2.434838</td>\n",
       "      <td>1.038825</td>\n",
       "      <td>2.186980</td>\n",
       "      <td>0.441364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.100155</td>\n",
       "      <td>-0.136445</td>\n",
       "      <td>-0.119054</td>\n",
       "      <td>0.017409</td>\n",
       "      <td>-1.122019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.517094</td>\n",
       "      <td>-0.997027</td>\n",
       "      <td>0.248799</td>\n",
       "      <td>-0.296641</td>\n",
       "      <td>0.495211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.174703</td>\n",
       "      <td>0.986335</td>\n",
       "      <td>0.213534</td>\n",
       "      <td>2.190700</td>\n",
       "      <td>-1.896361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.646917</td>\n",
       "      <td>0.901487</td>\n",
       "      <td>2.528326</td>\n",
       "      <td>-0.248635</td>\n",
       "      <td>0.043669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>-0.226314</td>\n",
       "      <td>1.331457</td>\n",
       "      <td>-0.287308</td>\n",
       "      <td>0.680070</td>\n",
       "      <td>-0.319802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>-1.272559</td>\n",
       "      <td>0.313548</td>\n",
       "      <td>0.503185</td>\n",
       "      <td>1.293226</td>\n",
       "      <td>-0.110447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>-0.617362</td>\n",
       "      <td>0.562761</td>\n",
       "      <td>0.240737</td>\n",
       "      <td>0.280665</td>\n",
       "      <td>-0.073113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1.160339</td>\n",
       "      <td>0.369493</td>\n",
       "      <td>1.904659</td>\n",
       "      <td>1.111057</td>\n",
       "      <td>0.659050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>-1.627438</td>\n",
       "      <td>0.602319</td>\n",
       "      <td>0.420282</td>\n",
       "      <td>0.810952</td>\n",
       "      <td>1.044442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Score1    Score2    Score3    Score4    Score5\n",
       "A -0.310117 -2.434838  1.038825  2.186980  0.441364\n",
       "B -0.100155 -0.136445 -0.119054  0.017409 -1.122019\n",
       "C -0.517094 -0.997027  0.248799 -0.296641  0.495211\n",
       "D -0.174703  0.986335  0.213534  2.190700 -1.896361\n",
       "E -0.646917  0.901487  2.528326 -0.248635  0.043669\n",
       "F -0.226314  1.331457 -0.287308  0.680070 -0.319802\n",
       "G -1.272559  0.313548  0.503185  1.293226 -0.110447\n",
       "H -0.617362  0.562761  0.240737  0.280665 -0.073113\n",
       "I  1.160339  0.369493  1.904659  1.111057  0.659050\n",
       "J -1.627438  0.602319  0.420282  0.810952  1.044442"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('Score6', axis = 1) #deleting data from dataframe based on axis, axis=1 specifies columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Score6', axis = 1, inplace= True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Score1</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Score3</th>\n",
       "      <th>Score4</th>\n",
       "      <th>Score5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.517094</td>\n",
       "      <td>-0.997027</td>\n",
       "      <td>0.248799</td>\n",
       "      <td>-0.296641</td>\n",
       "      <td>0.495211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.174703</td>\n",
       "      <td>0.986335</td>\n",
       "      <td>0.213534</td>\n",
       "      <td>2.190700</td>\n",
       "      <td>-1.896361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.646917</td>\n",
       "      <td>0.901487</td>\n",
       "      <td>2.528326</td>\n",
       "      <td>-0.248635</td>\n",
       "      <td>0.043669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>-0.226314</td>\n",
       "      <td>1.331457</td>\n",
       "      <td>-0.287308</td>\n",
       "      <td>0.680070</td>\n",
       "      <td>-0.319802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>-1.272559</td>\n",
       "      <td>0.313548</td>\n",
       "      <td>0.503185</td>\n",
       "      <td>1.293226</td>\n",
       "      <td>-0.110447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>-0.617362</td>\n",
       "      <td>0.562761</td>\n",
       "      <td>0.240737</td>\n",
       "      <td>0.280665</td>\n",
       "      <td>-0.073113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1.160339</td>\n",
       "      <td>0.369493</td>\n",
       "      <td>1.904659</td>\n",
       "      <td>1.111057</td>\n",
       "      <td>0.659050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>-1.627438</td>\n",
       "      <td>0.602319</td>\n",
       "      <td>0.420282</td>\n",
       "      <td>0.810952</td>\n",
       "      <td>1.044442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Score1    Score2    Score3    Score4    Score5\n",
       "C -0.517094 -0.997027  0.248799 -0.296641  0.495211\n",
       "D -0.174703  0.986335  0.213534  2.190700 -1.896361\n",
       "E -0.646917  0.901487  2.528326 -0.248635  0.043669\n",
       "F -0.226314  1.331457 -0.287308  0.680070 -0.319802\n",
       "G -1.272559  0.313548  0.503185  1.293226 -0.110447\n",
       "H -0.617362  0.562761  0.240737  0.280665 -0.073113\n",
       "I  1.160339  0.369493  1.904659  1.111057  0.659050\n",
       "J -1.627438  0.602319  0.420282  0.810952  1.044442"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('B', axis = 0)  #axis=0 specifies rows "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Score1   -0.517094\n",
       "Score2   -0.997027\n",
       "Score3    0.248799\n",
       "Score4   -0.296641\n",
       "Score5    0.495211\n",
       "Name: C, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['C'] #Using custom index of rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Score1   -0.174703\n",
       "Score2    0.986335\n",
       "Score3    0.213534\n",
       "Score4    2.190700\n",
       "Score5   -1.896361\n",
       "Name: D, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2] #using in built index to locate rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2487991613877705"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['C', 'Score3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score1</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Score3</th>\n",
       "      <th>Score4</th>\n",
       "      <th>Score5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>C</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>D</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>F</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score1  Score2  Score3  Score4  Score5\n",
       "B   False   False   False   False   False\n",
       "C   False   False   False   False   False\n",
       "D   False    True   False    True   False\n",
       "E   False    True    True   False   False\n",
       "F   False    True   False    True   False\n",
       "G   False   False    True    True   False\n",
       "H   False    True   False   False   False\n",
       "I    True   False    True    True    True\n",
       "J   False    True   False    True    True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df > 0.5 #boolean values based on conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Score5</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>C</th>\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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       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.986335</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.190700</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.901487</td>\n",
       "      <td>2.528326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.331457</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.680070</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.503185</td>\n",
       "      <td>1.293226</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.562761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1.160339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.904659</td>\n",
       "      <td>1.111057</td>\n",
       "      <td>0.659050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.602319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.810952</td>\n",
       "      <td>1.044442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Score1    Score2    Score3    Score4    Score5\n",
       "B       NaN       NaN       NaN       NaN       NaN\n",
       "C       NaN       NaN       NaN       NaN       NaN\n",
       "D       NaN  0.986335       NaN  2.190700       NaN\n",
       "E       NaN  0.901487  2.528326       NaN       NaN\n",
       "F       NaN  1.331457       NaN  0.680070       NaN\n",
       "G       NaN       NaN  0.503185  1.293226       NaN\n",
       "H       NaN  0.562761       NaN       NaN       NaN\n",
       "I  1.160339       NaN  1.904659  1.111057  0.659050\n",
       "J       NaN  0.602319       NaN  0.810952  1.044442"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df>0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "D    0.213534\n",
       "E    2.528326\n",
       "F   -0.287308\n",
       "H    0.240737\n",
       "J    0.420282\n",
       "Name: Score3, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Score2']>0.5]['Score3'] #value of Score3 based on conditions on Score2 column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Score2</th>\n",
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       "      <th>Score4</th>\n",
       "      <th>Score5</th>\n",
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       "  <tbody>\n",
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       "      <th>B</th>\n",
       "      <td>-0.100155</td>\n",
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       "      <td>0.017409</td>\n",
       "      <td>-1.122019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.517094</td>\n",
       "      <td>-0.997027</td>\n",
       "      <td>0.248799</td>\n",
       "      <td>-0.296641</td>\n",
       "      <td>0.495211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.174703</td>\n",
       "      <td>0.986335</td>\n",
       "      <td>0.213534</td>\n",
       "      <td>2.190700</td>\n",
       "      <td>-1.896361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.646917</td>\n",
       "      <td>0.901487</td>\n",
       "      <td>2.528326</td>\n",
       "      <td>-0.248635</td>\n",
       "      <td>0.043669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>-0.226314</td>\n",
       "      <td>1.331457</td>\n",
       "      <td>-0.287308</td>\n",
       "      <td>0.680070</td>\n",
       "      <td>-0.319802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>-1.272559</td>\n",
       "      <td>0.313548</td>\n",
       "      <td>0.503185</td>\n",
       "      <td>1.293226</td>\n",
       "      <td>-0.110447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>-0.617362</td>\n",
       "      <td>0.562761</td>\n",
       "      <td>0.240737</td>\n",
       "      <td>0.280665</td>\n",
       "      <td>-0.073113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1.160339</td>\n",
       "      <td>0.369493</td>\n",
       "      <td>1.904659</td>\n",
       "      <td>1.111057</td>\n",
       "      <td>0.659050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>-1.627438</td>\n",
       "      <td>0.602319</td>\n",
       "      <td>0.420282</td>\n",
       "      <td>0.810952</td>\n",
       "      <td>1.044442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Score1    Score2    Score3    Score4    Score5\n",
       "B -0.100155 -0.136445 -0.119054  0.017409 -1.122019\n",
       "C -0.517094 -0.997027  0.248799 -0.296641  0.495211\n",
       "D -0.174703  0.986335  0.213534  2.190700 -1.896361\n",
       "E -0.646917  0.901487  2.528326 -0.248635  0.043669\n",
       "F -0.226314  1.331457 -0.287308  0.680070 -0.319802\n",
       "G -1.272559  0.313548  0.503185  1.293226 -0.110447\n",
       "H -0.617362  0.562761  0.240737  0.280665 -0.073113\n",
       "I  1.160339  0.369493  1.904659  1.111057  0.659050\n",
       "J -1.627438  0.602319  0.420282  0.810952  1.044442"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Score1</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Score3</th>\n",
       "      <th>Score4</th>\n",
       "      <th>Score5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B</td>\n",
       "      <td>-0.100155</td>\n",
       "      <td>-0.136445</td>\n",
       "      <td>-0.119054</td>\n",
       "      <td>0.017409</td>\n",
       "      <td>-1.122019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>-0.517094</td>\n",
       "      <td>-0.997027</td>\n",
       "      <td>0.248799</td>\n",
       "      <td>-0.296641</td>\n",
       "      <td>0.495211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D</td>\n",
       "      <td>-0.174703</td>\n",
       "      <td>0.986335</td>\n",
       "      <td>0.213534</td>\n",
       "      <td>2.190700</td>\n",
       "      <td>-1.896361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E</td>\n",
       "      <td>-0.646917</td>\n",
       "      <td>0.901487</td>\n",
       "      <td>2.528326</td>\n",
       "      <td>-0.248635</td>\n",
       "      <td>0.043669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>-0.226314</td>\n",
       "      <td>1.331457</td>\n",
       "      <td>-0.287308</td>\n",
       "      <td>0.680070</td>\n",
       "      <td>-0.319802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>G</td>\n",
       "      <td>-1.272559</td>\n",
       "      <td>0.313548</td>\n",
       "      <td>0.503185</td>\n",
       "      <td>1.293226</td>\n",
       "      <td>-0.110447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>H</td>\n",
       "      <td>-0.617362</td>\n",
       "      <td>0.562761</td>\n",
       "      <td>0.240737</td>\n",
       "      <td>0.280665</td>\n",
       "      <td>-0.073113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I</td>\n",
       "      <td>1.160339</td>\n",
       "      <td>0.369493</td>\n",
       "      <td>1.904659</td>\n",
       "      <td>1.111057</td>\n",
       "      <td>0.659050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>J</td>\n",
       "      <td>-1.627438</td>\n",
       "      <td>0.602319</td>\n",
       "      <td>0.420282</td>\n",
       "      <td>0.810952</td>\n",
       "      <td>1.044442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  index    Score1    Score2    Score3    Score4    Score5\n",
       "0     B -0.100155 -0.136445 -0.119054  0.017409 -1.122019\n",
       "1     C -0.517094 -0.997027  0.248799 -0.296641  0.495211\n",
       "2     D -0.174703  0.986335  0.213534  2.190700 -1.896361\n",
       "3     E -0.646917  0.901487  2.528326 -0.248635  0.043669\n",
       "4     F -0.226314  1.331457 -0.287308  0.680070 -0.319802\n",
       "5     G -1.272559  0.313548  0.503185  1.293226 -0.110447\n",
       "6     H -0.617362  0.562761  0.240737  0.280665 -0.073113\n",
       "7     I  1.160339  0.369493  1.904659  1.111057  0.659050\n",
       "8     J -1.627438  0.602319  0.420282  0.810952  1.044442"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index() #reseting index to system generated default values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_index = 'AB BC CD DE EF FG GH HI IJ'.split() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score1</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Score3</th>\n",
       "      <th>Score4</th>\n",
       "      <th>Score5</th>\n",
       "      <th>Score9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.100155</td>\n",
       "      <td>-0.136445</td>\n",
       "      <td>-0.119054</td>\n",
       "      <td>0.017409</td>\n",
       "      <td>-1.122019</td>\n",
       "      <td>AB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.517094</td>\n",
       "      <td>-0.997027</td>\n",
       "      <td>0.248799</td>\n",
       "      <td>-0.296641</td>\n",
       "      <td>0.495211</td>\n",
       "      <td>BC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.174703</td>\n",
       "      <td>0.986335</td>\n",
       "      <td>0.213534</td>\n",
       "      <td>2.190700</td>\n",
       "      <td>-1.896361</td>\n",
       "      <td>CD</td>\n",
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       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>-0.646917</td>\n",
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       "      <td>-0.248635</td>\n",
       "      <td>0.043669</td>\n",
       "      <td>DE</td>\n",
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       "    <tr>\n",
       "      <th>F</th>\n",
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       "      <td>1.331457</td>\n",
       "      <td>-0.287308</td>\n",
       "      <td>0.680070</td>\n",
       "      <td>-0.319802</td>\n",
       "      <td>EF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>-1.272559</td>\n",
       "      <td>0.313548</td>\n",
       "      <td>0.503185</td>\n",
       "      <td>1.293226</td>\n",
       "      <td>-0.110447</td>\n",
       "      <td>FG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>-0.617362</td>\n",
       "      <td>0.562761</td>\n",
       "      <td>0.240737</td>\n",
       "      <td>0.280665</td>\n",
       "      <td>-0.073113</td>\n",
       "      <td>GH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1.160339</td>\n",
       "      <td>0.369493</td>\n",
       "      <td>1.904659</td>\n",
       "      <td>1.111057</td>\n",
       "      <td>0.659050</td>\n",
       "      <td>HI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>-1.627438</td>\n",
       "      <td>0.602319</td>\n",
       "      <td>0.420282</td>\n",
       "      <td>0.810952</td>\n",
       "      <td>1.044442</td>\n",
       "      <td>IJ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Score1    Score2    Score3    Score4    Score5 Score9\n",
       "B -0.100155 -0.136445 -0.119054  0.017409 -1.122019     AB\n",
       "C -0.517094 -0.997027  0.248799 -0.296641  0.495211     BC\n",
       "D -0.174703  0.986335  0.213534  2.190700 -1.896361     CD\n",
       "E -0.646917  0.901487  2.528326 -0.248635  0.043669     DE\n",
       "F -0.226314  1.331457 -0.287308  0.680070 -0.319802     EF\n",
       "G -1.272559  0.313548  0.503185  1.293226 -0.110447     FG\n",
       "H -0.617362  0.562761  0.240737  0.280665 -0.073113     GH\n",
       "I  1.160339  0.369493  1.904659  1.111057  0.659050     HI\n",
       "J -1.627438  0.602319  0.420282  0.810952  1.044442     IJ"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Score9'] = new_index #adding series in exisitng dataframe\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Score1</th>\n",
       "      <th>Score2</th>\n",
       "      <th>Score3</th>\n",
       "      <th>Score4</th>\n",
       "      <th>Score5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score9</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>AB</th>\n",
       "      <td>-0.100155</td>\n",
       "      <td>-0.136445</td>\n",
       "      <td>-0.119054</td>\n",
       "      <td>0.017409</td>\n",
       "      <td>-1.122019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BC</th>\n",
       "      <td>-0.517094</td>\n",
       "      <td>-0.997027</td>\n",
       "      <td>0.248799</td>\n",
       "      <td>-0.296641</td>\n",
       "      <td>0.495211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD</th>\n",
       "      <td>-0.174703</td>\n",
       "      <td>0.986335</td>\n",
       "      <td>0.213534</td>\n",
       "      <td>2.190700</td>\n",
       "      <td>-1.896361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>-0.646917</td>\n",
       "      <td>0.901487</td>\n",
       "      <td>2.528326</td>\n",
       "      <td>-0.248635</td>\n",
       "      <td>0.043669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EF</th>\n",
       "      <td>-0.226314</td>\n",
       "      <td>1.331457</td>\n",
       "      <td>-0.287308</td>\n",
       "      <td>0.680070</td>\n",
       "      <td>-0.319802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FG</th>\n",
       "      <td>-1.272559</td>\n",
       "      <td>0.313548</td>\n",
       "      <td>0.503185</td>\n",
       "      <td>1.293226</td>\n",
       "      <td>-0.110447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GH</th>\n",
       "      <td>-0.617362</td>\n",
       "      <td>0.562761</td>\n",
       "      <td>0.240737</td>\n",
       "      <td>0.280665</td>\n",
       "      <td>-0.073113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HI</th>\n",
       "      <td>1.160339</td>\n",
       "      <td>0.369493</td>\n",
       "      <td>1.904659</td>\n",
       "      <td>1.111057</td>\n",
       "      <td>0.659050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IJ</th>\n",
       "      <td>-1.627438</td>\n",
       "      <td>0.602319</td>\n",
       "      <td>0.420282</td>\n",
       "      <td>0.810952</td>\n",
       "      <td>1.044442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Score1    Score2    Score3    Score4    Score5\n",
       "Score9                                                  \n",
       "AB     -0.100155 -0.136445 -0.119054  0.017409 -1.122019\n",
       "BC     -0.517094 -0.997027  0.248799 -0.296641  0.495211\n",
       "CD     -0.174703  0.986335  0.213534  2.190700 -1.896361\n",
       "DE     -0.646917  0.901487  2.528326 -0.248635  0.043669\n",
       "EF     -0.226314  1.331457 -0.287308  0.680070 -0.319802\n",
       "FG     -1.272559  0.313548  0.503185  1.293226 -0.110447\n",
       "GH     -0.617362  0.562761  0.240737  0.280665 -0.073113\n",
       "HI      1.160339  0.369493  1.904659  1.111057  0.659050\n",
       "IJ     -1.627438  0.602319  0.420282  0.810952  1.044442"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Score9', inplace=True) #modifying index wrt to a series\n",
    "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>Cricket</th>\n",
       "      <th>Baseball</th>\n",
       "      <th>Tennis</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cricket  Baseball  Tennis\n",
       "0      1.0       5.0       1\n",
       "1      2.0       NaN       2\n",
       "2      NaN       NaN       3\n",
       "3      4.0       5.0       4\n",
       "4      6.0       7.0       5\n",
       "5      7.0       2.0       6\n",
       "6      2.0       4.0       7\n",
       "7      NaN       5.0       8"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = pd.DataFrame({'Cricket':[1,2,np.nan,4,6,7,2,np.nan],\n",
    "                  'Baseball':[5,np.nan,np.nan,5,7,2,4,5],\n",
    "                  'Tennis':[1,2,3,4,5,6,7,8]})\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cricket  Baseball  Tennis\n",
       "0      1.0       5.0       1\n",
       "3      4.0       5.0       4\n",
       "4      6.0       7.0       5\n",
       "5      7.0       2.0       6\n",
       "6      2.0       4.0       7"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.dropna() #dropping rows/columns with nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Tennis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>8</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Tennis\n",
       "0       1\n",
       "1       2\n",
       "2       3\n",
       "3       4\n",
       "4       5\n",
       "5       6\n",
       "6       7\n",
       "7       8"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.dropna(axis= 1) #dropping columns with nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cricket  Baseball  Tennis\n",
       "0      1.0       5.0       1\n",
       "1      2.0       NaN       2\n",
       "3      4.0       5.0       4\n",
       "4      6.0       7.0       5\n",
       "5      7.0       2.0       6\n",
       "6      2.0       4.0       7\n",
       "7      NaN       5.0       8"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.dropna(thresh= 2) #dropping rows based on no of nan(threshold) values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7.0</td>\n",
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       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cricket  Baseball  Tennis\n",
       "0      1.0       5.0       1\n",
       "1      2.0       0.0       2\n",
       "2      0.0       0.0       3\n",
       "3      4.0       5.0       4\n",
       "4      6.0       7.0       5\n",
       "5      7.0       2.0       6\n",
       "6      2.0       4.0       7\n",
       "7      0.0       5.0       8"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.fillna(value=0) #filling nan with custom value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustID</th>\n",
       "      <th>CustName</th>\n",
       "      <th>Profitinlakhs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>UIPat</td>\n",
       "      <td>2005</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>DatRob</td>\n",
       "      <td>3245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>Goog</td>\n",
       "      <td>1245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1002</td>\n",
       "      <td>Chrysler</td>\n",
       "      <td>8765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1003</td>\n",
       "      <td>Ford</td>\n",
       "      <td>5463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1003</td>\n",
       "      <td>GM</td>\n",
       "      <td>3547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CustID  CustName  Profitinlakhs\n",
       "0   1001     UIPat           2005\n",
       "1   1001    DatRob           3245\n",
       "2   1002      Goog           1245\n",
       "3   1002  Chrysler           8765\n",
       "4   1003      Ford           5463\n",
       "5   1003        GM           3547"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat = {'CustID':['1001','1001','1002','1002','1003','1003'],\n",
    "       'CustName':['UIPat','DatRob','Goog','Chrysler','Ford','GM'],\n",
    "       'Profitinlakhs':[2005,3245,1245,8765,5463,3547]}\n",
    "dataframe = pd.DataFrame(dat)\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_byCustId = dataframe.groupby('CustID') #grouping by custId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Profitinlakhs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>2625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>5005</td>\n",
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       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>4505</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "        Profitinlakhs\n",
       "CustID               \n",
       "1001             2625\n",
       "1002             5005\n",
       "1003             4505"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_byCustId.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    <tr>\n",
       "      <th>CustID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>876.812409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>5317.442995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>1354.816593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Profitinlakhs\n",
       "CustID               \n",
       "1001       876.812409\n",
       "1002      5317.442995\n",
       "1003      1354.816593"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_byCustId.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustName</th>\n",
       "      <th>Profitinlakhs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>UIPat</td>\n",
       "      <td>3245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>Goog</td>\n",
       "      <td>8765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>GM</td>\n",
       "      <td>5463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustName  Profitinlakhs\n",
       "CustID                        \n",
       "1001      UIPat           3245\n",
       "1002       Goog           8765\n",
       "1003         GM           5463"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_byCustId.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustName</th>\n",
       "      <th>Profitinlakhs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CustName  Profitinlakhs\n",
       "CustID                         \n",
       "1001           2              2\n",
       "1002           2              2\n",
       "1003           2              2"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_byCustId.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Profitinlakhs</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>CustID</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>1001</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>876.812409</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2315.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>2935.0</td>\n",
       "      <td>3245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5005.0</td>\n",
       "      <td>5317.442995</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>3125.0</td>\n",
       "      <td>5005.0</td>\n",
       "      <td>6885.0</td>\n",
       "      <td>8765.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4505.0</td>\n",
       "      <td>1354.816593</td>\n",
       "      <td>3547.0</td>\n",
       "      <td>4026.0</td>\n",
       "      <td>4505.0</td>\n",
       "      <td>4984.0</td>\n",
       "      <td>5463.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Profitinlakhs                                                       \\\n",
       "               count    mean          std     min     25%     50%     75%   \n",
       "CustID                                                                      \n",
       "1001             2.0  2625.0   876.812409  2005.0  2315.0  2625.0  2935.0   \n",
       "1002             2.0  5005.0  5317.442995  1245.0  3125.0  5005.0  6885.0   \n",
       "1003             2.0  4505.0  1354.816593  3547.0  4026.0  4505.0  4984.0   \n",
       "\n",
       "                \n",
       "           max  \n",
       "CustID          \n",
       "1001    3245.0  \n",
       "1002    8765.0  \n",
       "1003    5463.0  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_byCustId.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustID</th>\n",
       "      <th>1001</th>\n",
       "      <th>1002</th>\n",
       "      <th>1003</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Profitinlakhs</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>2625.000000</td>\n",
       "      <td>5005.000000</td>\n",
       "      <td>4505.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>876.812409</td>\n",
       "      <td>5317.442995</td>\n",
       "      <td>1354.816593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2005.000000</td>\n",
       "      <td>1245.000000</td>\n",
       "      <td>3547.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2315.000000</td>\n",
       "      <td>3125.000000</td>\n",
       "      <td>4026.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2625.000000</td>\n",
       "      <td>5005.000000</td>\n",
       "      <td>4505.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2935.000000</td>\n",
       "      <td>6885.000000</td>\n",
       "      <td>4984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3245.000000</td>\n",
       "      <td>8765.000000</td>\n",
       "      <td>5463.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CustID                      1001         1002         1003\n",
       "Profitinlakhs count     2.000000     2.000000     2.000000\n",
       "              mean   2625.000000  5005.000000  4505.000000\n",
       "              std     876.812409  5317.442995  1354.816593\n",
       "              min    2005.000000  1245.000000  3547.000000\n",
       "              25%    2315.000000  3125.000000  4026.000000\n",
       "              50%    2625.000000  5005.000000  4505.000000\n",
       "              75%    2935.000000  6885.000000  4984.000000\n",
       "              max    3245.000000  8765.000000  5463.000000"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_byCustId.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "Table1 = pd.DataFrame({'CustID': ['1001', '1002', '1003', '1004'],\n",
    "                     'Q1': ['101', '102', '103', '104'],\n",
    "                     'Q2': ['201', '202', '203', '204']})\n",
    "   \n",
    "Table2 = pd.DataFrame({'CustID': ['1001', '1006', '1003', '1004'],\n",
    "                          'Q3': ['301', '302', '303', '304'],\n",
    "                          'Q4': ['401', '402', '403', '404']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustID</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
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       "      <td>201</td>\n",
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       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>103</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>104</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CustID   Q1   Q2\n",
       "0   1001  101  201\n",
       "1   1002  102  202\n",
       "2   1003  103  203\n",
       "3   1004  104  204"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Table1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>CustID</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>301</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1006</td>\n",
       "      <td>302</td>\n",
       "      <td>402</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
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       "      <td>403</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
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       "      <td>404</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "  CustID   Q3   Q4\n",
       "0   1001  301  401\n",
       "1   1006  302  402\n",
       "2   1003  303  403\n",
       "3   1004  304  404"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Table2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>CustID</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>101</td>\n",
       "      <td>201</td>\n",
       "      <td>301</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>102</td>\n",
       "      <td>202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1003</td>\n",
       "      <td>103</td>\n",
       "      <td>203</td>\n",
       "      <td>303</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1004</td>\n",
       "      <td>104</td>\n",
       "      <td>204</td>\n",
       "      <td>304</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>302</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CustID   Q1   Q2   Q3   Q4\n",
       "0   1001  101  201  301  401\n",
       "1   1002  102  202  NaN  NaN\n",
       "2   1003  103  203  303  403\n",
       "3   1004  104  204  304  404\n",
       "4   1006  NaN  NaN  302  402"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(Table1,Table2,how='outer',on='CustID') #merging two dataframes(merge is wrt to columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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",
       "        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>CustID</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2</th>\n",
       "      <th>Q3</th>\n",
       "      <th>Q4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001</td>\n",
       "      <td>101</td>\n",
       "      <td>201</td>\n",
       "      <td>301</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1003</td>\n",
       "      <td>103</td>\n",
       "      <td>203</td>\n",
       "      <td>303</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1004</td>\n",
       "      <td>104</td>\n",
       "      <td>204</td>\n",
       "      <td>304</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CustID   Q1   Q2   Q3   Q4\n",
       "0   1001  101  201  301  401\n",
       "1   1003  103  203  303  403\n",
       "2   1004  104  204  304  404"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(Table1,Table2,how='inner',on='CustID') #exact match to CustID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  CustID   Q1   Q2   Q3   Q4\n",
       "0   1001  101  201  301  401\n",
       "1   1002  102  202  NaN  NaN\n",
       "2   1003  103  203  303  403\n",
       "3   1004  104  204  304  404"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(Table1,Table2,how='left',on='CustID') #matches to Table1 wrt to custId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "   custID SaleType SalesCode\n",
       "0       1      big       121\n",
       "1       2    small       131\n",
       "2       3   medium       141\n",
       "3       4      big       151"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = pd.DataFrame({'custID':[1,2,3,4],'SaleType':['big','small','medium','big'],'SalesCode':['121','131','141','151']})\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>custID</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SalesCode</th>\n",
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       "  </thead>\n",
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       "      <td>121</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   custID SaleType SalesCode\n",
       "0       1      big       121"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['big', 'small', 'medium'], dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe['SaleType'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe['SaleType'].nunique()  #no of unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "big       2\n",
       "medium    1\n",
       "small     1\n",
       "Name: SaleType, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe['SaleType'].value_counts() #counts of selected categorical info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>custID</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SalesCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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      ],
      "text/plain": [
       "   custID SaleType SalesCode\n",
       "0       1      big       121\n",
       "3       4      big       151"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select from DataFrame using criteria from multiple columns\n",
    "newdataframe = dataframe[(dataframe['custID']!=3) & (dataframe['SaleType']=='big')]\n",
    "newdataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying functions\n",
    "def profit(a):\n",
    "    return a*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4\n",
       "1     8\n",
       "2    12\n",
       "3    16\n",
       "Name: custID, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe['custID'].apply(profit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    5\n",
       "2    6\n",
       "3    3\n",
       "Name: SaleType, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe['SaleType'].apply(len) #length of string in selected column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['custID', 'SaleType', 'SalesCode'], dtype='object')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.columns #name of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
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      "text/plain": [
       "   custID SaleType SalesCode\n",
       "0       1      big       121\n",
       "3       4      big       151\n",
       "2       3   medium       141\n",
       "1       2    small       131"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.sort_values('SaleType') #sorting selected column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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      "text/plain": [
       "   custID  SaleType  SalesCode\n",
       "0   False     False      False\n",
       "1   False     False      False\n",
       "2   False     False      False\n",
       "3   False     False      False"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.isnull()"
   ]
  }
 ],
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