Use a list of values to select rows from a Pandas dataframe

Extended row selection tricks

For complex requirements, utilize boolean operators (& for AND, | for OR) to chain various conditions:

df[(df['column1'].isin(['list'])) & (df['column2'] == 'some_value')]


Excluding rows can be as simple as opting for negation (~):

df[~df['column'].isin(['list'])] # even the rows you don't want have feelings


Range queries are a breeze when you select numerical data within a specific range using between() and query():

df.query('column_name.between(@lower_bound, @upper_bound)')  # Goldilocks would be proud of you


When you need to scan multiple columns with list of values, go for the any() and all() methods. This command filters rows if any of the specified columns contain the values:

df[df[['col1', 'col2']].isin(['value1', 'value2']).any(axis=1)]  # any port in a storm


And if you need those rows where all columns match the list values, simply switch any() with all():

df[df[['col1', 'col2']].isin(['value1', 'value2']).all(axis=1)]  # teamwork makes the dream work


The loc key to ordered row selection

The loc method comes to the rescue when maintaining the order of rows or when tackling complex row selection is on the cards:

df.loc[df['column'].isin(['list']), :]  # you're the captain now, decide the order of the rows


Its functionalities include maintaining a consistent row order and catering for conditional logic within indexing.

The query move for less overhead

The query method creates an easer-to-read syntax and can deliver a minor speedup by curtailing the overhead:

df.query('column in @list_of_values')  # @ mention to the rescue, as always


The @ symbol paves the way to reference external variables , especially beneficial when dealing with long and complex lists.

Efficient filtering in the wild

Use these pointers to optimize performance with isin:

• Pre-filter: Reduce your dataframe size for a speedy filtering.
• Indexes: Accelarate operations by setting the filtering column as an index if filtering is a frequent task.
• Categorical data: Change filtering columns with a limited number of unique text values to a categorical type to speed up the operation.
• apply() resistance: Although using apply() with a lambda for custom checks seems appealing, resist the urge unless essential. Opt for isin to save your machine some cycles.