How to Select Specific Rows in Pandas DataFrames Based on Column Values?

Selecting Rows Based on Column Values in Pandas DataFrames

When working with Pandas DataFrames, there often arises a need to filter rows based on specific values in a particular column. This mimics SQL queries where rows are retrieved using filters like WHERE column_name = some_value.

Scalar Values

To select rows where a column value matches a scalar value, some_value, use the equality operator ==:

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

Iterable Values

To select rows where a column value is in an array, some_values, use the isin method:

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

Combining Conditions

Multiple conditions can be combined using the logical & operator:

df.loc[(df['column_name'] >= A) & (df['column_name'] <= B)]

Note: Use parentheses to ensure operator precedence is correct.

Negations

To select rows where a column value does not equal some_value, use the inequality operator !=:

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

For isin, negate the result using ~:

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

Examples

Consider the following DataFrame:

import pandas as pd

df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
                   'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
                   'C': np.arange(8),
                   'D': np.arange(8) * 2})
print(df)

Select rows where A is foo:

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

Select rows where B is one or three:

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

Create an index and select rows using it:

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

Select rows with multiple indexed values:

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

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