filter_column_isin(df: pandas.core.frame.DataFrame, column_name: Hashable, iterable: Iterable, complement: bool = False) → pandas.core.frame.DataFrame¶
Filter a dataframe for values in a column that exist in another iterable.
This method does not mutate the original DataFrame.
Assumes exact matching; fuzzy matching not implemented.
The below example syntax will filter the DataFrame such that we only get rows for which the “names” are exactly “James” and “John”.
df = ( pd.DataFrame(...) .clean_names() .filter_column_isin(column_name="names", iterable=["James", "John"] ) )
This is the method chaining alternative to:
df = df[df['names'].isin(['James', 'John'])]
If “complement” is true, then we will only get rows for which the names are not James or John.
df – A pandas DataFrame
column_name – The column on which to filter.
iterable – An iterable. Could be a list, tuple, another pandas Series.
complement – Whether to return the complement of the selection or not.
A filtered pandas DataFrame.
ValueError – if
iterabledoes not have a length of