Select rows from a DataFrame based on values in a column in pandas

How to select rows from a DataFrame based on values in some column in pandas?
In SQL I would use:

    select * from table where colume_name = some_value.

I tried to look at pandas documentation but did not immediately find the answer.

To select rows whose column value equals a scalar, some_value, use ==:

    df.loc[df['column_name'] == some_value]

To select rows whose column value is in an iterable, some_values, use isin:

    df.loc[df['column_name'].isin(some_values)]

Combine multiple conditions with &:

    df.loc[(df['column_name'] == some_value) & df['other_column'].isin(some_values)]

To select rows whose column value does not equal some_value, use !=:

    df.loc[df['column_name'] != some_value]

isin returns a boolean Series, so to select rows whose value is not in some_values, negate the boolean Series using ~:

    df.loc[~df['column_name'].isin(some_values)]

For example,

    import pandas as pd
    import numpy as np
    df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
                       'B': 'one one two three two two one three'.split(),
                       'C': np.arange(8), 'D': np.arange(8) * 2})
    print(df)
    #      A      B  C   D
    # 0  foo    one  0   0
    # 1  bar    one  1   2
    # 2  foo    two  2   4
    # 3  bar  three  3   6
    # 4  foo    two  4   8
    # 5  bar    two  5  10
    # 6  foo    one  6  12
    # 7  foo  three  7  14

    print(df.loc[df['A'] == 'foo'])

yields

         A      B  C   D
    0  foo    one  0   0
    2  foo    two  2   4
    4  foo    two  4   8
    6  foo    one  6  12
    7  foo  three  7  14

If you have multiple values you want to include, put them in a list (or more generally, any iterable) and use isin:

    print(df.loc[df['B'].isin(['one','three'])])

yields

         A      B  C   D
    0  foo    one  0   0
    1  bar    one  1   2
    3  bar  three  3   6
    6  foo    one  6  12
    7  foo  three  7  14

Note, however, that if you wish to do this many times, it is more efficient to make an index first, and then use df.loc:

    df = df.set_index(['B'])
    print(df.loc['one'])

yields

           A  C   D
    B              
    one  foo  0   0
    one  bar  1   2
    one  foo  6  12

or, to include multiple values from the index use df.index.isin:

    df.loc[df.index.isin(['one','two'])]

yields

           A  C   D
    B              
    one  foo  0   0
    one  bar  1   2
    two  foo  2   4
    two  foo  4   8
    two  bar  5  10
    one  foo  6  12

From: stackoverflow.com/q/17071871

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