pandas.Series.str.contains¶
-
Series.str.
contains
(pat, case=True, flags=0, na=nan, regex=True)[source]¶ Test if pattern or regex is contained within a string of a Series or Index.
Return boolean Series or Index based on whether a given pattern or regex is contained within a string of a Series or Index.
Parameters: pat : str
Character sequence or regular expression.
case : bool, default True
If True, case sensitive.
flags : int, default 0 (no flags)
Flags to pass through to the re module, e.g. re.IGNORECASE.
na : default NaN
Fill value for missing values.
regex : bool, default True
If True, assumes the pat is a regular expression.
If False, treats the pat as a literal string.
Returns: Series or Index of boolean values
A Series or Index of boolean values indicating whether the given pattern is contained within the string of each element of the Series or Index.
See also
match
- analogous, but stricter, relying on re.match instead of re.search
Examples
Returning a Series of booleans using only a literal pattern.
>>> s1 = pd.Series(['Mouse', 'dog', 'house and parrot', '23', np.NaN]) >>> s1.str.contains('og', regex=False) 0 False 1 True 2 False 3 False 4 NaN dtype: object
Returning an Index of booleans using only a literal pattern.
>>> ind = pd.Index(['Mouse', 'dog', 'house and parrot', '23.0', np.NaN]) >>> ind.str.contains('23', regex=False) Index([False, False, False, True, nan], dtype='object')
Specifying case sensitivity using case.
>>> s1.str.contains('oG', case=True, regex=True) 0 False 1 False 2 False 3 False 4 NaN dtype: object
Specifying na to be False instead of NaN replaces NaN values with False. If Series or Index does not contain NaN values the resultant dtype will be bool, otherwise, an object dtype.
>>> s1.str.contains('og', na=False, regex=True) 0 False 1 True 2 False 3 False 4 False dtype: bool
Returning ‘house’ and ‘parrot’ within same string.
>>> s1.str.contains('house|parrot', regex=True) 0 False 1 False 2 True 3 False 4 NaN dtype: object
Ignoring case sensitivity using flags with regex.
>>> import re >>> s1.str.contains('PARROT', flags=re.IGNORECASE, regex=True) 0 False 1 False 2 True 3 False 4 NaN dtype: object
Returning any digit using regular expression.
>>> s1.str.contains('\d', regex=True) 0 False 1 False 2 False 3 True 4 NaN dtype: object
Ensure pat is a not a literal pattern when regex is set to True. Note in the following example one might expect only s2[1] and s2[3] to return True. However, ‘.0’ as a regex matches any character followed by a 0.
>>> s2 = pd.Series(['40','40.0','41','41.0','35']) >>> s2.str.contains('.0', regex=True) 0 True 1 True 2 False 3 True 4 False dtype: bool