pandas.Series.str.contains

Series.str.contains(pat, case=True, flags=0, na=None, regex=True)

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

patstr

Character sequence or regular expression.

casebool, default True

If True, case sensitive.

flagsint, default 0 (no flags)

Flags to pass through to the re module, e.g. re.IGNORECASE.

nascalar, optional

Fill value for missing values. The default depends on dtype of the array. For object-dtype, numpy.nan is used. For StringDtype, pandas.NA is used.

regexbool, 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.

Series.str.startswith

Test if the start of each string element matches a pattern.

Series.str.endswith

Same as startswith, but tests the end of string.

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’ or ‘dog’ when either expression occurs in a string.

>>> s1.str.contains('house|dog', regex=True)
0    False
1     True
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

pandas.Series.str.center pandas.Series.str.count