How to Efficiently Select Rows in Pandas MultiIndex DataFrames?

Select Rows in Pandas MultiIndex DataFrame

Problem Summary

Given a Pandas DataFrame with a MultiIndex, how can we select rows based on specific values/labels in each index level?

Slicing with loc

df.loc[key, :]

• key is a tuple of labels, one for each index level.
• This provides a convenient and concise way to select rows based on specific values in different levels.

Slicing with xs

df.xs(level_key, level=level_name, drop_level=True/False)

• level_key is the key for the specific index level.
• drop_level controls whether the level should be dropped from the resulting DataFrame.
• xs is particularly useful when slicing on a single level.

Filtering with query

df.query("condition")

• condition is a Boolean expression that specifies the filtering criteria.
• Supports flexible filtering across multiple index levels.

Using get_level_values

mask = df.index.get_level_values(level_name).isin(values_list)
selected_rows = df[mask]

• Creates a boolean mask based on the values in a specific index level.
• Useful for more complex filtering operations or when slicing on multiple values.

Examples

Example 1: Selecting rows with specific values in level 'one' and 'two':

# Using loc
selected_rows = df.loc[['a'], ['t', 'u']]

# Using xs
selected_rows = df.xs('a', level='one', drop_level=False)
selected_rows = selected_rows.xs(['t', 'u'], level='two')

# Using query
selected_rows = df.query("one == 'a' and two.isin(['t', 'u'])")

# Using get_level_values
one_mask = df.index.get_level_values('one') == 'a'
two_mask = df.index.get_level_values('two').isin(['t', 'u'])
selected_rows = df[one_mask & two_mask]

Example 2: Filtering rows based on a numerical inequality in level 'two':

# Using query
selected_rows = df.query("two > 5")

# Using get_level_values
two_mask = df.index.get_level_values('two') > 5
selected_rows = df[two_mask]

Tips and Considerations

• Consider the complexity of the slicing/filtering operation and choose the appropriate method accordingly.
• For simple slicing on a single or few levels, loc or xs are preferred.
• For complex filtering or slicing on multiple values, consider using query or get_level_values as they provide more flexibility.
• Mind the use of pd.IndexSlice to specify complex slicing operations with loc.
• sort_index() can improve performance for large DataFrames with unsorted MultiIndexes.

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