Python_Finance_pandas_practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PANDAS PRACTICE\n",
    "\n",
    "#SERIES\n",
    "#import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining list, array and dictionary\n",
    "labels=['a','b','c']\n",
    "my_list=[10,20,30]\n",
    "arr=np.array([10,20,30])\n",
    "d={'a':10,'b':20,'c':100}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(my_list,labels)     #changing index to labels list"
   ]
  },
  {
   "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": [
    "pd.Series(arr)             #Making a series out of the numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int32"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(arr,labels)      #Making a series out of array with labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     10\n",
       "b     20\n",
       "c    100\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(d)              #Series out of a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(data=labels)   #Interchanging labels and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      <built-in function sum>\n",
       "1    <built-in function print>\n",
       "2      <built-in function len>\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([sum,print,len])   #Passing in built functions to a Series, might not be used but functionality is there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US    1\n",
       "FR    2\n",
       "GR    3\n",
       "EN    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1=pd.Series([1,2,3,4],index=['US','FR','GR','EN'])  #Creating Series with index\n",
    "ser1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "US    10\n",
       "IN    20\n",
       "GR    30\n",
       "JP    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2=pd.Series([10,20,30,40],index=['US','IN','GR','JP'])  #Creating new series\n",
    "ser2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EN     NaN\n",
       "FR     NaN\n",
       "GR    33.0\n",
       "IN     NaN\n",
       "JP     NaN\n",
       "US    11.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1+ser2     #Adding Series, common have value, which are not there got their values changed to NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DATAFRAMES\n",
    "#import libraries numpy and pd already imported above\n",
    "from numpy.random import randn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(101)  #Declaring seed to make results device & time independent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
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       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
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      "text/plain": [
       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118 -0.319318 -0.848077  0.605965\n",
       "C -2.018168  0.740122  0.528813 -0.589001\n",
       "D  0.188695 -0.758872 -0.933237  0.955057\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(randn(5,4),['A','B','C','D','E'],['W','X','Y','Z'])   #W,X,Y,Z are series and they share a common index A,B,C,D,E\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2.706850\n",
       "B    0.651118\n",
       "C   -2.018168\n",
       "D    0.188695\n",
       "E    0.190794\n",
       "Name: W, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['W']  #Grabbing W column from the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['W'])   #The type will be shown as a series, when we take a column from a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)        #Type of df object is dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2.706850\n",
       "B    0.651118\n",
       "C   -2.018168\n",
       "D    0.188695\n",
       "E    0.190794\n",
       "Name: W, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.W            #Another way to grab a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <th>W</th>\n",
       "      <th>Z</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.503826</td>\n",
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       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>-0.589001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>0.955057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>0.683509</td>\n",
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      "text/plain": [
       "          W         Z\n",
       "A  2.706850  0.503826\n",
       "B  0.651118  0.605965\n",
       "C -2.018168 -0.589001\n",
       "D  0.188695  0.955057\n",
       "E  0.190794  0.683509"
      ]
     },
     "execution_count": 28,
     "metadata": {},
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    }
   ],
   "source": [
    "df[['W','Z']]   #Displaying two columns out of df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>new</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>1.536102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-1.167395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>1.268936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "      <td>-1.692109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "      <td>4.584725</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "          W         X         Y         Z       new\n",
       "A  2.706850  0.628133  0.907969  0.503826  1.536102\n",
       "B  0.651118 -0.319318 -0.848077  0.605965 -1.167395\n",
       "C -2.018168  0.740122  0.528813 -0.589001  1.268936\n",
       "D  0.188695 -0.758872 -0.933237  0.955057 -1.692109\n",
       "E  0.190794  1.978757  2.605967  0.683509  4.584725"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['new']=df['X']+df['Y']  #Creating a new col by adding two other columns\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
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       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
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       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
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      "text/plain": [
       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118 -0.319318 -0.848077  0.605965\n",
       "C -2.018168  0.740122  0.528813 -0.589001\n",
       "D  0.188695 -0.758872 -0.933237  0.955057\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('new',axis=1)   #Dropping a column, axis 1 means column. Axis 0 means row which is by default but it will give error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>1.536102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-1.167395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>1.268936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "      <td>-1.692109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "      <td>4.584725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z       new\n",
       "A  2.706850  0.628133  0.907969  0.503826  1.536102\n",
       "B  0.651118 -0.319318 -0.848077  0.605965 -1.167395\n",
       "C -2.018168  0.740122  0.528813 -0.589001  1.268936\n",
       "D  0.188695 -0.758872 -0.933237  0.955057 -1.692109\n",
       "E  0.190794  1.978757  2.605967  0.683509  4.584725"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df #The original df will still retain the new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "          W         X         Y         Z\n",
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       "D  0.188695 -0.758872 -0.933237  0.955057\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.drop('new',axis=1,inplace=True)  #To make the change persist in df as well inplace=True\n",
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "          W         X         Y         Z\n",
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   "source": [
    "df.drop('E')    #Dropping E row. The axis is 0 by default so we don't have to explicitely specify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "          W         Z\n",
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     "execution_count": 42,
     "metadata": {},
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   "source": [
    "df[['W','Z']]   #Selecting Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W    2.706850\n",
       "X    0.628133\n",
       "Y    0.907969\n",
       "Z    0.503826\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selecting rows\n",
    "df.loc['A']    #The row is returned as a series with columns as labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W    2.706850\n",
       "X    0.628133\n",
       "Y    0.907969\n",
       "Z    0.503826\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]   #Another way to select row by numerical based index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5038257538223936"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.loc['A','Z'] #grabbing a specific value from dataframe by loc row and col labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5038257538223936"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0,3]    #grabbing a specific value from dataframe by iloc integer based index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "          Y         Z\n",
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   "source": [
    "df.loc[['A','B'],['Y','Z']]  #Grabbing a subset of array"
   ]
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  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.605965</td>\n",
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       "      <th>C</th>\n",
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       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118 -0.319318 -0.848077  0.605965\n",
       "C -2.018168  0.740122  0.528813 -0.589001\n",
       "D  0.188695 -0.758872 -0.933237  0.955057\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 50,
     "metadata": {},
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    }
   ],
   "source": [
    "df  #Initial Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "       W      X      Y      Z\n",
       "A   True   True   True   True\n",
       "B   True  False  False   True\n",
       "C  False   True   True  False\n",
       "D   True  False  False   True\n",
       "E   True   True   True   True"
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     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df > 0    #We get boolean value as output when we do a comparison operation on the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "booldf=df>0 #Getting the boolean values in booldf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.503826</td>\n",
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       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.955057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\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": [
       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118       NaN       NaN  0.605965\n",
       "C       NaN  0.740122  0.528813       NaN\n",
       "D  0.188695       NaN       NaN  0.955057\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[booldf]  #Prints where booldf is true for the df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.190794</td>\n",
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       "      <td>0.683509</td>\n",
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       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118       NaN       NaN  0.605965\n",
       "C       NaN  0.740122  0.528813       NaN\n",
       "D  0.188695       NaN       NaN  0.955057\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 56,
     "metadata": {},
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   ],
   "source": [
    "df[df>0]    #using the comparison directly with the df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A     True\n",
       "B     True\n",
       "C    False\n",
       "D     True\n",
       "E     True\n",
       "Name: W, dtype: bool"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df['W']>0   #Comparing a column returns a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118 -0.319318 -0.848077  0.605965\n",
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       "E  0.190794  1.978757  2.605967  0.683509"
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     "execution_count": 58,
     "metadata": {},
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   ],
   "source": [
    "df[df['W']>0]    #Filtering with condition, since for C row it is false it will not appear in the df output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
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       "          W         X         Y         Z\n",
       "C -2.018168  0.740122  0.528813 -0.589001"
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   "source": [
    "df[df['Z']<0]  #Row C is returned because that is the only place where Col Z is less than 0"
   ]
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  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultdf=df[df['W']>0]   #This will return a subset and store it "
   ]
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  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
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       "E  0.190794  1.978757  2.605967  0.683509"
      ]
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     "execution_count": 61,
     "metadata": {},
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   ],
   "source": [
    "resultdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.628133\n",
       "B   -0.319318\n",
       "D   -0.758872\n",
       "E    1.978757\n",
       "Name: X, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultdf['X']    #Accessing only X column from the new subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.628133\n",
       "B   -0.319318\n",
       "D   -0.758872\n",
       "E    1.978757\n",
       "Name: X, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['W']>0]['X']  #Combining the above steps into one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A     True\n",
       "B     True\n",
       "C    False\n",
       "D     True\n",
       "E     True\n",
       "Name: W, dtype: bool"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boolser=df['W']>0\n",
    "boolser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
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    "df[boolser]"
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  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
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       "          X         Y\n",
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     "metadata": {},
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    "df[boolser][['X','Y']]"
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  {
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   "execution_count": 76,
   "metadata": {},
   "outputs": [
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       "          X         Y\n",
       "A  0.628133  0.907969\n",
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     "execution_count": 76,
     "metadata": {},
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   ],
   "source": [
    "df[df['W']>0][['X','Y']]   #One liner same as above step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['W']>0) & (df['X']>0)] #and conditions note: don't use 'and' but use '&' instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>W</th>\n",
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       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
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      "text/plain": [
       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "C -2.018168  0.740122  0.528813 -0.589001\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['W']<0) | (df['X']>0)]  #Or condition note: don't use 'or' but use '|' instead because we are dealing with series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\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": [
       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118 -0.319318 -0.848077  0.605965\n",
       "C -2.018168  0.740122  0.528813 -0.589001\n",
       "D  0.188695 -0.758872 -0.933237  0.955057\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df   #initial df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.503826</td>\n",
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       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "  index         W         X         Y         Z\n",
       "0     A  2.706850  0.628133  0.907969  0.503826\n",
       "1     B  0.651118 -0.319318 -0.848077  0.605965\n",
       "2     C -2.018168  0.740122  0.528813 -0.589001\n",
       "3     D  0.188695 -0.758872 -0.933237  0.955057\n",
       "4     E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()   #Resetting index of df, notice that index is reset as a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "newind='CA NY CO CT NJ'.split()  #Splitting a string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CA', 'NY', 'CO', 'CT', 'NJ']"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['States']=newind   #Creating a new col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>CA</td>\n",
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       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "      <td>CT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "      <td>NJ</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z States\n",
       "A  2.706850  0.628133  0.907969  0.503826     CA\n",
       "B  0.651118 -0.319318 -0.848077  0.605965     NY\n",
       "C -2.018168  0.740122  0.528813 -0.589001     CO\n",
       "D  0.188695 -0.758872 -0.933237  0.955057     CT\n",
       "E  0.190794  1.978757  2.605967  0.683509     NJ"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>CA</th>\n",
       "      <td>2.706850</td>\n",
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       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
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       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>-2.018168</td>\n",
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       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\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": [
       "               W         X         Y         Z\n",
       "States                                        \n",
       "CA      2.706850  0.628133  0.907969  0.503826\n",
       "NY      0.651118 -0.319318 -0.848077  0.605965\n",
       "CO     -2.018168  0.740122  0.528813 -0.589001\n",
       "CT      0.188695 -0.758872 -0.933237  0.955057\n",
       "NJ      0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('States')   #df states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "      <td>CT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "      <td>NJ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z States\n",
       "A  2.706850  0.628133  0.907969  0.503826     CA\n",
       "B  0.651118 -0.319318 -0.848077  0.605965     NY\n",
       "C -2.018168  0.740122  0.528813 -0.589001     CO\n",
       "D  0.188695 -0.758872 -0.933237  0.955057     CT\n",
       "E  0.190794  1.978757  2.605967  0.683509     NJ"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df            #To persist use inplace=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('G1', 1), ('G1', 2), ('G1', 3), ('G2', 1), ('G2', 2), ('G2', 3)]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Index Levels\n",
    "outside=['G1','G1','G1','G2','G2','G2']\n",
    "inside=[1,2,3,1,2,3]\n",
    "hier_index=list(zip(outside,inside))\n",
    "hier_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['G1', 'G2'], [1, 2, 3]],\n",
       "           labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hier_index=pd.MultiIndex.from_tuples(hier_index)   #Creating a multi level index\n",
    "hier_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">G1</th>\n",
       "      <th>1</th>\n",
       "      <td>1.025984</td>\n",
       "      <td>-0.156598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.031579</td>\n",
       "      <td>0.649826</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.154846</td>\n",
       "      <td>-0.610259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">G2</th>\n",
       "      <th>1</th>\n",
       "      <td>-0.755325</td>\n",
       "      <td>-0.346419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.147027</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "             A         B\n",
       "G1 1  1.025984 -0.156598\n",
       "   2 -0.031579  0.649826\n",
       "   3  2.154846 -0.610259\n",
       "G2 1 -0.755325 -0.346419\n",
       "   2  0.147027 -0.479448\n",
       "   3  0.558769  1.024810"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(randn(6,2),hier_index,['A','B'])    #6 rows 2 col of rand normal, adding multilevel index and Col header A and B\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "          A         B\n",
       "1  1.025984 -0.156598\n",
       "2 -0.031579  0.649826\n",
       "3  2.154846 -0.610259"
      ]
     },
     "execution_count": 104,
     "metadata": {},
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   ],
   "source": [
    "df.loc['G1']   # Selecting G1 Rows from level 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.031579\n",
       "B    0.649826\n",
       "Name: 2, dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['G1'].loc[2]   #Selecting 2 row inside G1 level 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FrozenList([None, None])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.names     #Current name of these index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "      <th rowspan=\"3\" valign=\"top\">G1</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">G2</th>\n",
       "      <th>1</th>\n",
       "      <td>-0.755325</td>\n",
       "      <td>-0.346419</td>\n",
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      "text/plain": [
       "                       A         B\n",
       "Groups Numbers                    \n",
       "G1     1        1.025984 -0.156598\n",
       "       2       -0.031579  0.649826\n",
       "       3        2.154846 -0.610259\n",
       "G2     1       -0.755325 -0.346419\n",
       "       2        0.147027 -0.479448\n",
       "       3        0.558769  1.024810"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.names=['Groups','Numbers']   #Assigning name to index\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.47944803904109595"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['G2'].loc[2]['B'] #Grab value present at index G2, 2 and B Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                A         B\n",
       "Numbers                    \n",
       "1        1.025984 -0.156598\n",
       "2       -0.031579  0.649826\n",
       "3        2.154846 -0.610259"
      ]
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     "execution_count": 119,
     "metadata": {},
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   ],
   "source": [
    "df.xs('G1')  #Cross-section function, it can go inside a multi level index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th>Groups</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>G1</th>\n",
       "      <td>1.025984</td>\n",
       "      <td>-0.156598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G2</th>\n",
       "      <td>-0.755325</td>\n",
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      "text/plain": [
       "               A         B\n",
       "Groups                    \n",
       "G1      1.025984 -0.156598\n",
       "G2     -0.755325 -0.346419"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.xs(1,level='Numbers')   #Numbers Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "d={'A':[1,2,np.nan],'B':[5,np.nan,np.nan],'C':[9,0,9]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': [1, 2, nan], 'B': [5, nan, nan], 'C': [9, 0, 9]}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     A    B  C\n",
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "     A    B  C\n",
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    "df.dropna()"
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   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
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       "     A    B  C\n",
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   C\n",
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       "1  0\n",
       "2  9"
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     },
     "execution_count": 134,
     "metadata": {},
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   "source": [
    "df.dropna(axis=1)   #Dropping and and selecting col"
   ]
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  {
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   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
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       "     A    B  C\n",
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    "df.dropna(thresh=2)  #Setting a threshold of two nan values"
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   "metadata": {},
   "outputs": [
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       "      A     B  C\n",
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    "df.fillna(value='FILL')  #Filling some value for NAN"
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   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    NaN\n",
       "Name: A, dtype: float64"
      ]
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     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.A"
   ]
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  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    1.5\n",
       "Name: A, dtype: float64"
      ]
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     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.A.fillna(value=df['A'].mean())   #Mean of values in place of NA"
   ]
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  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Group By"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "data ={'Company':['Google','Google','Apple','Amazon','Netflix','Netflix'],\n",
    "      'Person':['Zee','Ysh','Ang','Shr','Gwn','Jon'],\n",
    "      'Sales':[200,120,340,124,243,350]}"
   ]
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  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Company': ['Google', 'Google', 'Apple', 'Amazon', 'Netflix', 'Netflix'],\n",
       " 'Person': ['Zee', 'Ysh', 'Ang', 'Shr', 'Gwn', 'Jon'],\n",
       " 'Sales': [200, 120, 340, 124, 243, 350]}"
      ]
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     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   Company Person  Sales\n",
       "0   Google    Zee    200\n",
       "1   Google    Ysh    120\n",
       "2    Apple    Ang    340\n",
       "3   Amazon    Shr    124\n",
       "4  Netflix    Gwn    243\n",
       "5  Netflix    Jon    350"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df=pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "byComp=df.groupby('Company')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "         Sales\n",
       "Company       \n",
       "Amazon   124.0\n",
       "Apple    340.0\n",
       "Google   160.0\n",
       "Netflix  296.5"
      ]
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     "execution_count": 155,
     "metadata": {},
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   ],
   "source": [
    "byComp.mean() #Mean by company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>593</td>\n",
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      "text/plain": [
       "         Sales\n",
       "Company       \n",
       "Amazon     124\n",
       "Apple      340\n",
       "Google     320\n",
       "Netflix    593"
      ]
     },
     "execution_count": 156,
     "metadata": {},
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   ],
   "source": [
    "byComp.sum() #Sum by company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "         Person  Sales\n",
       "Company               \n",
       "Amazon        1      1\n",
       "Apple         1      1\n",
       "Google        2      2\n",
       "Netflix       2      2"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byComp.count()  #Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>NaN</td>\n",
       "      <td>340.0</td>\n",
       "      <td>340.00</td>\n",
       "      <td>340.0</td>\n",
       "      <td>340.00</td>\n",
       "      <td>340.0</td>\n",
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       "      <th>Google</th>\n",
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       "      <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",
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       "      <th>Netflix</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",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sales                                                       \n",
       "        count   mean        std    min     25%    50%     75%    max\n",
       "Company                                                             \n",
       "Amazon    1.0  124.0        NaN  124.0  124.00  124.0  124.00  124.0\n",
       "Apple     1.0  340.0        NaN  340.0  340.00  340.0  340.00  340.0\n",
       "Google    2.0  160.0  56.568542  120.0  140.00  160.0  180.00  200.0\n",
       "Netflix   2.0  296.5  75.660426  243.0  269.75  296.5  323.25  350.0"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Company').describe()  #Summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>296.500000</td>\n",
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       "      <td>75.660426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>124.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>243.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>124.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>269.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>124.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>296.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>124.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>323.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>124.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>350.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Company      Amazon  Apple      Google     Netflix\n",
       "Sales count     1.0    1.0    2.000000    2.000000\n",
       "      mean    124.0  340.0  160.000000  296.500000\n",
       "      std       NaN    NaN   56.568542   75.660426\n",
       "      min     124.0  340.0  120.000000  243.000000\n",
       "      25%     124.0  340.0  140.000000  269.750000\n",
       "      50%     124.0  340.0  160.000000  296.500000\n",
       "      75%     124.0  340.0  180.000000  323.250000\n",
       "      max     124.0  340.0  200.000000  350.000000"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Company').describe().transpose()   #Transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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: Google, dtype: float64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Company').describe().transpose()['Google']   #Getting info for single company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.DataFrame({'A':['A0','A1'],'B':['B0','B1']},index=[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=pd.DataFrame({'A':['A2','A3'],'B':['B2','B3']},index=[2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=pd.DataFrame({'A':['A4','A5'],'B':['B4','B5']},index=[4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
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   "outputs": [
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   "execution_count": 174,
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       "    A   B\n",
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     "execution_count": 174,
     "metadata": {},
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    "pd.concat([df1,df2,df3])   #Concatonation"
   ]
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   "execution_count": 175,
   "metadata": {},
   "outputs": [
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       "     A    B    A    B    A    B\n",
       "0   A0   B0  NaN  NaN  NaN  NaN\n",
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      ]
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     "execution_count": 175,
     "metadata": {},
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   "source": [
    "pd.concat([df1,df2,df3],axis=1)#Column wise concotonation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "left=pd.DataFrame({'A':['A0','A1','A2'],'B':['B1','B2','B3'],'Key':['H0','H1','H2']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "right=pd.DataFrame({'C':['C0','C1','C2'],'D':['D1','D2','D3'],'Key':['H0','H1','H2']})"
   ]
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  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
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   "execution_count": 179,
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   "execution_count": 181,
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     "execution_count": 181,
     "metadata": {},
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   "source": [
    "pd.merge(left,right,how=\"inner\",on='Key')  #Merging both dataframes on Key col"
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  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "left=pd.DataFrame({'A':['A0','A1','A2'],'B':['B0','B1','B2']},index=['K0','K1','K2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "right=pd.DataFrame({'C':['C0','C2','C3'],'D':['D0','D1','D2']},index=['K0','K2','K3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
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      "text/plain": [
       "     A   B\n",
       "K0  A0  B0\n",
       "K1  A1  B1\n",
       "K2  A2  B2"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>C2</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K3</th>\n",
       "      <td>C3</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     C   D\n",
       "K0  C0  D0\n",
       "K2  C2  D1\n",
       "K3  C3  D2"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "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",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B    C    D\n",
       "K0  A0  B0   C0   D0\n",
       "K1  A1  B1  NaN  NaN\n",
       "K2  A2  B2   C2   D1"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left.join(right)   #Joining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C3</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B    C    D\n",
       "K0   A0   B0   C0   D0\n",
       "K1   A1   B1  NaN  NaN\n",
       "K2   A2   B2   C2   D1\n",
       "K3  NaN  NaN   C3   D2"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left.join(right,how=\"outer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pandas Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({'col1':[1,2,3,4],'col2':[444,555,666,444],'col3':['abc','def','ghi','xyz']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>0</th>\n",
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       "      <td>444</td>\n",
       "      <td>abc</td>\n",
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       "    <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": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([444, 555, 666], dtype=int64)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['col2'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col2'].nunique()   #Count of unique same as above operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "444    2\n",
       "555    1\n",
       "666    1\n",
       "Name: col2, dtype: int64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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",
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       "      <td>xyz</td>\n",
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      "text/plain": [
       "   col1  col2 col3\n",
       "2     3   666  ghi\n",
       "3     4   444  xyz"
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     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[df['col1']>2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
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      "text/plain": [
       "   col1  col2 col3\n",
       "3     4   444  xyz"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['col1']>2) & (df['col2']==444)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "def times2(x):   #Define a col to use later with df\n",
    "    return x*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    6\n",
       "3    8\n",
       "Name: col1, dtype: int64"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col1'].apply(times2)   #Custom functions can be applied using apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    3\n",
       "2    3\n",
       "3    3\n",
       "Name: col3, dtype: int64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col3'].apply(len)   #Built in functions call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     4\n",
       "2     9\n",
       "3    16\n",
       "Name: col1, dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col1'].apply(lambda x:x**2)  #Applying lambda to write custom function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  </thead>\n",
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       "    <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": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "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",
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       "  </thead>\n",
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       "      <th>1</th>\n",
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       "      <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": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('col1',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['col1', 'col2', 'col3'], dtype='object')"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns   #get list of columnnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   col1  col2 col3\n",
       "0     1   444  abc\n",
       "3     4   444  xyz\n",
       "1     2   555  def\n",
       "2     3   666  ghi"
      ]
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     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.sort_values('col2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    col1   col2   col3\n",
       "0  False  False  False\n",
       "1  False  False  False\n",
       "2  False  False  False\n",
       "3  False  False  False"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>555.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>444.0</td>\n",
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      ],
      "text/plain": [
       "col3    abc    def    ghi    xyz\n",
       "col1                            \n",
       "1     444.0    NaN    NaN    NaN\n",
       "2       NaN  555.0    NaN    NaN\n",
       "3       NaN    NaN  666.0    NaN\n",
       "4       NaN    NaN    NaN  444.0"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(values='col2',index='col1',columns=['col3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\ZEESHAN\\\\Python Study\\\\Python Finance\\\\Python-for-Finance-Repo-master\\\\02-NumPy'"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd  #Present working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('.\\\\..\\\\03- General Pandas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
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    "pd.read_csv(\"example.csv\")"
   ]
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   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
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    "df"
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    "df.to_csv('My_output')"
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       "   Unnamed: 0   a   b   c   d\n",
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    "pd.read_csv('My_output')"
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   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('My_output',index=False)"
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   ],
   "source": [
    "pd.read_excel('Excel_Sample.xlsx',sheet_name='Sheet1')   #Reading an excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('Newout.xlsx',sheet_name='my_sheet')  #Writing excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b   c   d\n",
       "0   0   1   2   3\n",
       "1   4   5   6   7\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('Newout.xlsx',sheet_name='my_sheet')  #Reading the newly written excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_html('https://www.fdic.gov/bank/individual/failed/banklist.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bank Name</th>\n",
       "      <th>City</th>\n",
       "      <th>ST</th>\n",
       "      <th>CERT</th>\n",
       "      <th>Acquiring Institution</th>\n",
       "      <th>Closing Date</th>\n",
       "      <th>Updated Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Enloe State Bank</td>\n",
       "      <td>Cooper</td>\n",
       "      <td>TX</td>\n",
       "      <td>10716</td>\n",
       "      <td>Legend Bank, N. A.</td>\n",
       "      <td>May 31, 2019</td>\n",
       "      <td>August 22, 2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Washington Federal Bank for Savings</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>30570</td>\n",
       "      <td>Royal Savings Bank</td>\n",
       "      <td>December 15, 2017</td>\n",
       "      <td>July 24, 2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Farmers and Merchants State Bank of Argonia</td>\n",
       "      <td>Argonia</td>\n",
       "      <td>KS</td>\n",
       "      <td>17719</td>\n",
       "      <td>Conway Bank</td>\n",
       "      <td>October 13, 2017</td>\n",
       "      <td>August 12, 2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fayette County Bank</td>\n",
       "      <td>Saint Elmo</td>\n",
       "      <td>IL</td>\n",
       "      <td>1802</td>\n",
       "      <td>United Fidelity Bank, fsb</td>\n",
       "      <td>May 26, 2017</td>\n",
       "      <td>January 29, 2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Guaranty Bank, (d/b/a BestBank in Georgia &amp; Mi...</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>WI</td>\n",
       "      <td>30003</td>\n",
       "      <td>First-Citizens Bank &amp; Trust Company</td>\n",
       "      <td>May 5, 2017</td>\n",
       "      <td>March 22, 2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Bank Name        City  ST   CERT  \\\n",
       "0                               The Enloe State Bank      Cooper  TX  10716   \n",
       "1                Washington Federal Bank for Savings     Chicago  IL  30570   \n",
       "2    The Farmers and Merchants State Bank of Argonia     Argonia  KS  17719   \n",
       "3                                Fayette County Bank  Saint Elmo  IL   1802   \n",
       "4  Guaranty Bank, (d/b/a BestBank in Georgia & Mi...   Milwaukee  WI  30003   \n",
       "\n",
       "                 Acquiring Institution       Closing Date      Updated Date  \n",
       "0                   Legend Bank, N. A.       May 31, 2019   August 22, 2019  \n",
       "1                   Royal Savings Bank  December 15, 2017     July 24, 2019  \n",
       "2                          Conway Bank   October 13, 2017   August 12, 2019  \n",
       "3            United Fidelity Bank, fsb       May 26, 2017  January 29, 2019  \n",
       "4  First-Citizens Bank & Trust Company        May 5, 2017    March 22, 2018  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine=create_engine('sqlite:///:memory:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_sql('my_table',engine)   #Writing df to sql as a table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "sqldf=pd.read_sql('my_table',con=engine)   #read table from sql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "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>index</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   a   b   c   d\n",
       "0      0   0   1   2   3\n",
       "1      1   4   5   6   7\n",
       "2      2   8   9  10  11\n",
       "3      3  12  13  14  15"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqldf    #displaying the dataframe extracted from sql table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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