Use the contains() method to test if the given regular expression pattern matches string values in the column.
Example 1: Test if column contains a given expression
>>> 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
Example 2: Test using the case parameter
Uses the case parameter to toggle case-sensitive matching on or off. The default value is on.
>>> df = DataFrame('sales')
>>> accounts = df['accounts']
Example 2.1: Test when case is set to True.
>>> 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
Example 2.1: Test when case is set to False.
>>> 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 3: Test 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