The contains() method tests if the given regular expression pattern matches string values in the column.
Example
>>> df = DataFrame('sales')
>>> accounts = df['accounts']
>>> df.assign(drop_columns = True, Accounts = accounts, has_llc = accounts.str.contains('LLC')) Accounts has_llc 0 Alpha Co 0 1 Blue Inc 0 2 Yellow Inc 0 3 Jones LLC 1 4 Red Inc 0 5 Orange Inc 0
Examples using the case parameter
The contains() method has a case parameter to toggle case-sensitive matching on or off. The default value is on.
>>> df = DataFrame('sales')
>>> accounts = df['accounts']
>>> df.assign(drop_columns = True, Accounts = accounts, has_llc = accounts.str.contains('llc', case=True)) Accounts has_llc 0 Blue Inc 0 1 Alpha Co 0 2 Jones LLC 0 3 Yellow Inc 0 4 Orange Inc 0 5 Red Inc 0
>>> df.assign(drop_columns = True, Accounts = accounts, has_llc = accounts.str.contains('llc', case=False)) Accounts has_llc 0 Blue Inc 0 1 Alpha Co 0 2 Jones LLC 1 3 Yellow Inc 0 4 Orange Inc 0 5 Red Inc 0
Example using the na parameter
Use the na parameter to specify an optional fill value for columns that have a NULL value. You can pass numeric, string, or bool literals.
>>> df = DataFrame('employee_info')
>>> df first_name marks dob joined_date employee_no 112 None None None 18/12/05 101 abcde None None 02/12/05 100 abcd None None None
>>> df.assign(has_name = df.first_name.str.contains('abcd', na = 'FNU')) first_name marks dob joined_date has_name employee_no 112 None None None 18/12/05 FNU 101 abcde None None 02/12/05 1 100 abcd None None None 1