How To Keep Columns In Pandas

A Data frame is a two-dimensional information structure, i.due east., data is aligned in a tabular fashion in rows and columns. We can perform basic operations on rows/columns like selecting, deleting, adding, and renaming. In this article, we are using nba.csv file.

Dealing with Columns

In social club to deal with columns, nosotros perform basic operations on columns similar selecting, deleting, calculation and renaming.

Column Selection:
In Order to select a column in Pandas DataFrame, nosotros tin can either access the columns by calling them past their columns proper noun.

import pandas as pd

data = { 'Name' :[ 'Jai' , 'Princi' , 'Gaurav' , 'Anuj' ],

'Historic period' :[ 27 , 24 , 22 , 32 ],

'Address' :[ 'Delhi' , 'Kanpur' , 'Allahabad' , 'Kannauj' ],

'Qualification' :[ 'Msc' , 'MA' , 'MCA' , 'Phd' ]}

df = pd.DataFrame(data)

print (df[[ 'Name' , 'Qualification' ]])

import pandas as pd

information = { 'Proper noun' : [ 'Jai' , 'Princi' , 'Gaurav' , 'Anuj' ],

'Tiptop' : [ 5.one , half-dozen.2 , 5.1 , 5.2 ],

'Qualification' : [ 'Msc' , 'MA' , 'Msc' , 'Msc' ]}

df = pd.DataFrame(data)

address = [ 'Delhi' , 'Bangalore' , 'Chennai' , 'Patna' ]

df[ 'Address' ] = address

print (df)

            import            pandas as pd          
            data                        =            pd.read_csv(            "nba.csv"            , index_col                        =            "Name"            )          
            information.drop([            "Team"            ,                        "Weight"            ], axis                        =            one            , inplace                        =            True            )          
            print            (data)          

Output:
As shown in the output images, the new output doesn't take the passed columns. Those values were dropped since axis was set equal to ane and the changes were made in the original data frame since inplace was True.

Data Frame earlier Dropping Columns-

Data Frame after Dropping Columns-

For more examples refer to Delete columns from DataFrame using Pandas.drop()

Dealing with Rows:

In order to deal with rows, we can perform basic operations on rows like selecting, deleting, calculation and renaming.

Row Pick:
Pandas provide a unique method to recollect rows from a Data frame.DataFrame.loc[] method is used to retrieve rows from Pandas DataFrame. Rows can likewise exist selected by passing integer location to an iloc[] function.

            import            pandas as pd          
            data                        =            pd.read_csv(            "nba.csv"            , index_col                        =            "Name"            )          
            first                        =            data.loc[            "Avery Bradley"            ]          
            second                        =            information.loc[            "R.J. Hunter"            ]          
            impress            (first,                        "\northward\n\due north"            , second)          

Output:
As shown in the output image, ii series were returned since there was only one parameter both of the times.

For more than examples refer to Pandas Extracting rows using .loc[]

Row Addition:
In Order to add a Row in Pandas DataFrame, we tin can concat the sometime dataframe with new one.

            import            pandas as pd          
            df                        =            pd.read_csv(            "nba.csv"            , index_col                        =            "Proper noun"            )          
            df.head(            10            )          
            new_row                        =            pd.DataFrame({            'Proper noun'            :            'Geeks'            ,                        'Squad'            :            'Boston'            ,                        'Number'            :            3            ,          
                        'Position'            :            'PG'            ,                        'Age'            :            33            ,                        'Acme'            :            '6-2'            ,          
                        'Weight'            :            189            ,                        'College'            :            'MIT'            ,                        'Salary'            :            99999            },          
                        index                        =            [            0            ])          
            df                        =            pd.concat([new_row, df]).reset_index(drop                        =            True            )          
            df.head(            5            )          

Output:

Data Frame before Calculation Row-

Data Frame afterward Adding Row-

For more examples refer to Add a row at tiptop in pandas DataFrame

Row Deletion:
In Order to delete a row in Pandas DataFrame, we tin can use the drib() method. Rows is deleted by dropping Rows past index label.

            import            pandas every bit pd          
            data                        =            pd.read_csv(            "nba.csv"            , index_col                        =            "Proper noun"            )          
            information.drop([            "Avery Bradley"            ,                        "John Holland"            ,                        "R.J. Hunter"            ,          
                        "R.J. Hunter"            ], inplace                        =            Truthful            )          
            information          

Output:
As shown in the output images, the new output doesn't have the passed values. Those values were dropped and the changes were made in the original data frame since inplace was True.

Data Frame before Dropping values-