Pandas Series: str.contains() function

Last update on September 15 2022 12:55:04 (UTC/GMT +8 hours)

Series-str.contains() function

The str.contains() function is used to 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.

Syntax:

Series.str.contains(self, pat, case=True, flags=0, na=nan, regex=True)

Parameters:

Name	Description	Type/Default Value	Required / Optional
pat	Character sequence or regular expression.	str	Required
case	If True, case sensitive.	bool Default Value: True	Required
flags	Flags to pass through to the re module, e.g. re.IGNORECASE.	int Default Value: 0 (no flags)	Required
na	Fill value for missing values.	Default Value: None	Required
regex	If True, assumes the pat is a regular expression. If False, treats the pat as a literal string.	bool Default Value: True	Required

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.

Example - Returning a Series of booleans using only a literal pattern:

Python-Pandas Code:

import numpy as np
import pandas as pd
s1 = pd.Series(['Tiger', 'fox', 'house and men', '20', np.NaN])
s1.str.contains('ox', regex=False)

Output:

0    False
1     True
2    False
3    False
4      NaN
dtype: object

Example - Returning an Index of booleans using only a literal pattern:

Python-Pandas Code:

import numpy as np
import pandas as pd
s1 = pd.Series(['Tiger', 'fox', 'house and men', '20', np.NaN])
ind = pd.Index(['Tiger', 'fox', 'house and men', '20.0', np.NaN])
ind.str.contains('20', regex=False)

Output:

Index([False, False, False, True, nan], dtype='object')

Example - Specifying case sensitivity using case:

Python-Pandas Code:

import numpy as np
import pandas as pd
s1 = pd.Series(['Tiger', 'fox', 'house and men', '20', np.NaN])
ind = pd.Index(['Tiger', 'fox', 'house and men', '20.0', np.NaN])
s1.str.contains('oX', case=True, regex=True)

Output:

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.

Python-Pandas Code:

import numpy as np
import pandas as pd
s1 = pd.Series(['Tiger', 'fox', 'house and men', '20', np.NaN])
ind = pd.Index(['Tiger', 'fox', 'house and men', '20.0', np.NaN])
s1.str.contains('ox', na=False, regex=True)

Output:

0    False
1     True
2    False
3    False
4    False
dtype: bool

Example - Returning ‘house’ or ‘fox’ when either expression occurs in a string:

Python-Pandas Code:

import numpy as np
import pandas as pd
s1 = pd.Series(['Tiger', 'fox', 'house and men', '20', np.NaN])
ind = pd.Index(['Tiger', 'fox', 'house and men', '20.0', np.NaN])
s1.str.contains('house|fox', regex=True)

Output:

0    False
1     True
2     True
3    False
4      NaN
dtype: object

Example - Ignoring case sensitivity using flags with regex:

Python-Pandas Code:

import numpy as np
import pandas as pd
s1 = pd.Series(['Tiger', 'fox', 'house and men', '20', np.NaN])
ind = pd.Index(['Tiger', 'fox', 'house and men', '20.0', np.NaN])
import re
s1.str.contains('MEN', flags=re.IGNORECASE, regex=True)

Output:

0    False
1    False
2     True
3    False
4      NaN
dtype: object

Example - Returning any digit using regular expression:

Python-Pandas Code:

import numpy as np
import pandas as pd
s1 = pd.Series(['Tiger', 'fox', 'house and men', '20', np.NaN])
ind = pd.Index(['Tiger', 'fox', 'house and men', '20.0', np.NaN])
import re
s1.str.contains('\d', regex=True)

Output:

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

Python-Pandas Code:

import numpy as np
import pandas as pd
s1 = pd.Series(['Tiger', 'fox', 'house and men', '20', np.NaN])
ind = pd.Index(['Tiger', 'fox', 'house and men', '20.0', np.NaN])
import re
s2 = pd.Series(['60', '60.0', '61', '61.0', '45'])
s2.str.contains('.0', regex=True)

Output:

0     True
1     True
2    False
3     True
4    False
dtype: bool

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