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

Selecting Rows Based on Column Values in Pandas

In Pandas, filtering a DataFrame to select specific rows based on column values can be done using a combination of comparison operators and Boolean indexing.

Comparing Column Values

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

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

To select rows where a column value is in a list or other iterable value, use the isin operator:

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

Combining Conditions

Multiple conditions can be combined using the & operator to select rows that satisfy all conditions:

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

Note that parentheses are necessary to ensure proper operator precedence.

Negating Conditions

To select rows that do not match a certain value or are not in a specific list, negate the condition using != or ~:

df.loc[df['column_name'] != some_value]
df = df.loc[~df['column_name'].isin(some_values)] # In-place replacement requires `loc`

Index Optimization

For efficient filtering on frequently used criteria, it can be beneficial to create an index on the column. This allows for faster lookups using df.loc:

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

Examples

Consider the following DataFrame:

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})

To select rows where column 'A' equals 'foo':

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

To select rows where column 'B' is in ['one', 'three']:

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

To select rows where column 'B' is 'one' or 'two':

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

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