filter_on(df: pandas.core.frame.DataFrame, criteria: str, complement: bool = False) → pandas.core.frame.DataFrame¶
Return a dataframe filtered on a particular criteria.
This method does not mutate the original DataFrame.
This is super-sugary syntax that wraps the pandas .query() API, enabling users to use strings to quickly specify filters for filtering their dataframe. The intent is that filter_on as a verb better matches the intent of a pandas user than the verb query.
Let’s say we wanted to filter students based on whether they failed an exam or not, which is defined as their score (in the “score” column) being less than 50.
df = (pd.DataFrame(...) .filter_on('score < 50', complement=False) ...) # chain on more data preprocessing.
This stands in contrast to the in-place syntax that is usually used:
df = pd.DataFrame(...) df = df[df['score'] < 3]
As with the filter_string function, a more seamless flow can be expressed in the code.
Functional usage syntax:
df = filter_on(df, 'score < 50', complement=False)
Method chaining syntax:
df = (pd.DataFrame(...) .filter_on('score < 50', complement=False))
Credit to Brant Peterson for the name.
df – A pandas DataFrame.
criteria – A filtering criteria that returns an array or Series of booleans, on which pandas can filter on.
complement – Whether to return the complement of the filter or not.
A filtered pandas DataFrame.