group_similar_strings

Arguments

## Arguments

group_similar_strings(strings_to_group: pd.Series,
                      string_ids: Optional[pd.Series],
                      **kwargs) -> Union[pd.DataFrame, pd.Series]

Result

Takes a single Series of strings (strings_to_group) and groups them by assigning to each string one string from strings_to_group chosen as the group-representative for each group of similar strings found. (See tutorials/group_representatives.md for details on how the the group-representatives are chosen.)

If ignore_index=True, the output is a Series (with the same name as strings_to_group prefixed by the string 'group_rep_') of the same length and index as strings_to_group containing the group-representative strings. If strings_to_group has no name then the name of the returned Series is 'group_rep'.

For example, an input Series with values: ['foooo', 'foooob', 'bar'] will return ['foooo', 'foooo', 'bar']. Here 'foooo' and 'foooob' are grouped together into group 'foooo' because they are found to be similar. Another example can be found below.

If ignore_index=False, the output is a DataFrame containing the above output Series as one of its columns with the same name. The remaining column(s) correspond to the index (or index-levels) of strings_to_group and contain the index-labels of the group-representatives as values. These columns have the same names as their counterparts prefixed by the string 'group_rep_'.

If strings_id is also given, then the IDs from strings_id corresponding to the group-representatives are also returned in an additional column (with the same name as strings_id prefixed as described above). If strings_id has no name, it is assumed to have the name 'id' before being prefixed.