Skip to main content
`Ctrl`+`K`

# pandas.Series.str.contains

Series.str.contains(pat, case=True, flags=0, na=<no_default>, 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 the nullable `StringDtype`, `pandas.NA` is used. For the `"str"` dtype, `False` 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