Pandas Series.str.fullmatch() Method

The Series.str.fullmatch() method in Pandas is used to determine if each string in the Series entirely matches a specified regular expression pattern or not.

This method is useful when you want to verify if entire strings conform to a given format or pattern. And equivalent to applying re.fullmatch() to each string in the Series.

Syntax

Following is the syntax of the Pandas Series.str.fullmatch() method −

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

Parameters

The Series.str.fullmatch() method accepts the following parameters −

• pat − A string representing the character sequence or regular expression pattern to match against.

• case − A boolean value, default is True. If True, the match is case sensitive.

• flags − An optional integer, default is 0. Flags from the re module, such as re.IGNORECASE, to modify the pattern matching behavior.

• na − An optional scalar value used for missing values. If not specified, the default depends on the dtype of the Series. For object-dtype, numpy.nan is used. For StringDtype, pandas.NA is used.

Return Value

The Series.str.fullmatch() method returns a Series or Index of boolean values. Each boolean value indicates whether the corresponding string in the Series entirely matches the given regular expression pattern.

Example 1

This example demonstrates checking if each string in a Series fully matches the regular expression pattern for a valid email address.

import pandas as pd

# Create a Series of strings
s = pd.Series(['user@example.com', 'user@domain', 'example.com', 'test@tutorialspoint.com'])

# Check if each string fully matches the pattern for an email address
result = s.str.fullmatch(r'\w+@\w+\.\w+')

print("Input Series:")
print(s)
print("\nFull Match Results:")
print(result)

When we run the above code, it produces the following output −

Input Series:
0           user@example.com
1                user@domain
2                example.com
3    test@tutorialspoint.com
dtype: object

Full Match Results:
0     True
1    False
2    False
3     True
dtype: bool

The output shows that only the strings that fully match the email pattern are marked as True.

Example 2

This example demonstrates checking if each string fully matches the pattern for a date in the format 'YYYY-MM-DD' using the Series.str.fullmatch() method.

import pandas as pd

# Create a Series of strings
s = pd.Series(['2024-07-29', '2024-07-29 00:00:00', '2024-07-29T00:00:00', '07-29-2024'])

# Check if each string fully matches the date pattern
result = s.str.fullmatch(r'\d{4}-\d{2}-\d{2}')

print("Input Series:")
print(s)
print("\nFull Match Results:")
print(result)

When we run the above code, it produces the following output −

Input Series:
0             2024-07-29
1    2024-07-29 00:00:00
2    2024-07-29T00:00:00
3             07-29-2024
dtype: object

Full Match Results:
0     True
1    False
2    False
3    False
dtype: bool

Example 3

This example demonstrates checking if each string in a DataFrame column fully matches a date pattern, while handling missing values.

import pandas as pd

# Create a DataFrame with date strings
df = pd.DataFrame({
    'date': ['2024-07-29', '2024-07-29 00:00:00', '2024-07-29', None]
})

# Check if each string fully matches the date pattern, treating NaNs as True
result = df['date'].str.fullmatch(r'\d{4}-\d{2}-\d{2}', na=True)

print("Input DataFrame:")
print(df)
print("\nFull Match Results:")
print(result)

When we run the above code, it produces the following output −

Input DataFrame:
                  date
0           2024-07-29
1  2024-07-29 00:00:00
2           2024-07-29
3                 None

Full Match Results:
0     True
1    False
2     True
3     True
Name: date, dtype: bool

In this case, the NaN value is treated as True due to the na=True parameter, while other strings are matched according to the pattern.