Skip to main content

Return to Answer

Post Timeline

Active reading [<https://en.wikipedia.org/wiki/Pandas_%28software%29> <https://en.wiktionary.org/wiki/you#Pronoun>].
Source Link
Peter Mortensen
Peter Mortensen
  • 31.1k
  • 22
  • 111
  • 134

You can use pandasPandas. I

I create the DataFrame:

    import pandas as pd
    df = pd.DataFrame([[1, 2,5], [5,4, 5], [7,7, 8], [7,6,9]], 
                      index=['Jane', 'Peter','Alex','Ann'],
                      columns=['Test_1', 'Test_2', 'Test_3'])

           Test_1  Test_2  Test_3
    Jane        1       2       5
    Peter       5       4       5
    Alex        7       7       8
    Ann         7       6       9

To select 1one or more columns by name:

    df[['Test_1', 'Test_3']]

           Test_1  Test_3
    Jane        1       5
    Peter       5       5
    Alex        7       8
    Ann         7       9

    df.Test_2

And yoyou get column Test_2:

    Jane     2
    Peter    4
    Alex     7
    Ann      6

You can also select columns and rows from these rows using .loc(). This is called "slicing". Notice that I take from column Test_1to to Test_3:

    df.loc[:, 'Test_1':'Test_3']

            Test_1  Test_2  Test_3
     Jane        1       2       5
     Peter       5       4       5
     Alex        7       7       8
     Ann         7       6       9

    df.loc[['Peter', 'Ann'], ['Test_1', 'Test_3']]

           Test_1  Test_3
    Peter       5       5
    Ann         7       9

You can use pandas. I create the DataFrame:

    import pandas as pd
    df = pd.DataFrame([[1, 2,5], [5,4, 5], [7,7, 8], [7,6,9]], 
                      index=['Jane', 'Peter','Alex','Ann'],
                      columns=['Test_1', 'Test_2', 'Test_3'])

           Test_1  Test_2  Test_3
    Jane        1       2       5
    Peter       5       4       5
    Alex        7       7       8
    Ann         7       6       9

To select 1 or more columns by name:

    df[['Test_1','Test_3']]

           Test_1  Test_3
    Jane        1       5
    Peter       5       5
    Alex        7       8
    Ann         7       9

    df.Test_2

And yo get column Test_2

    Jane     2
    Peter    4
    Alex     7
    Ann      6

You can also select columns and rows from these rows using .loc(). This is called "slicing". Notice that I take from column Test_1to Test_3

    df.loc[:,'Test_1':'Test_3']

            Test_1  Test_2  Test_3
     Jane        1       2       5
     Peter       5       4       5
     Alex        7       7       8
     Ann         7       6       9

    df.loc[['Peter', 'Ann'],['Test_1','Test_3']]

           Test_1  Test_3
    Peter       5       5
    Ann         7       9

You can use Pandas.

I create the DataFrame:

import pandas as pd
df = pd.DataFrame([[1, 2,5], [5,4, 5], [7,7, 8], [7,6,9]],
                  index=['Jane', 'Peter','Alex','Ann'],
                  columns=['Test_1', 'Test_2', 'Test_3'])

       Test_1  Test_2  Test_3
Jane        1       2       5
Peter       5       4       5
Alex        7       7       8
Ann         7       6       9

To select one or more columns by name:

df[['Test_1', 'Test_3']]

       Test_1  Test_3
Jane        1       5
Peter       5       5
Alex        7       8
Ann         7       9

df.Test_2

And you get column Test_2:

Jane     2
Peter    4
Alex     7
Ann      6

You can also select columns and rows from these rows using .loc(). This is called "slicing". Notice that I take from column Test_1 to Test_3:

df.loc[:, 'Test_1':'Test_3']

       Test_1  Test_2  Test_3
Jane        1       2       5
Peter       5       4       5
Alex        7       7       8
Ann         7       6       9

df.loc[['Peter', 'Ann'], ['Test_1', 'Test_3']]

       Test_1  Test_3
Peter       5       5
Ann         7       9
Source Link
pink.slash
pink.slash
  • 2k
  • 18
  • 14

You can use pandas. I create the DataFrame:

    import pandas as pd
    df = pd.DataFrame([[1, 2,5], [5,4, 5], [7,7, 8], [7,6,9]], 
                      index=['Jane', 'Peter','Alex','Ann'],
                      columns=['Test_1', 'Test_2', 'Test_3'])

The DataFrame:

           Test_1  Test_2  Test_3
    Jane        1       2       5
    Peter       5       4       5
    Alex        7       7       8
    Ann         7       6       9

To select 1 or more columns by name:

    df[['Test_1','Test_3']]

           Test_1  Test_3
    Jane        1       5
    Peter       5       5
    Alex        7       8
    Ann         7       9

You can also use:

    df.Test_2

And yo get column Test_2

    Jane     2
    Peter    4
    Alex     7
    Ann      6

You can also select columns and rows from these rows using .loc(). This is called "slicing". Notice that I take from column Test_1to Test_3

    df.loc[:,'Test_1':'Test_3']

The "Slice" is:

            Test_1  Test_2  Test_3
     Jane        1       2       5
     Peter       5       4       5
     Alex        7       7       8
     Ann         7       6       9

And if you just want Peter and Ann from columns Test_1 and Test_3:

    df.loc[['Peter', 'Ann'],['Test_1','Test_3']]

You get:

           Test_1  Test_3
    Peter       5       5
    Ann         7       9