Pandas Series.str.findall() Method

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The Series.str.findall() method in Python Pandas is used to find all occurrences of a pattern or regular expression within each string in the Series or Index. This method is equivalent to applying re.findall() to all elements in the Series/Index.

The method returns a Series or Index of lists, where each list contains all non-overlapping matches of the pattern or regular expression found in the corresponding string. And it is useful for finding and extracting all non-overlapping occurrences of a specified pattern or regular expression from each string in a Pandas Series, Index, or a DataFrame column.

Syntax

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

Series.str.findall(pat, flags=0)

Parameters

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

• pat − A string representing the pattern or regular expression to be searched for.

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

Return Value

The Series.str.findall() method returns a Series or Index of lists of strings. Each list contains all non-overlapping matches of the pattern or regular expression found in the corresponding string. If no matches are found, an empty list is returned for those elements.

Example 1

This example demonstrates finding all occurrences of the substring 't' in each string element in a Series.

import pandas as pd

# Create a Series of strings
s = pd.Series(['tutorials', 'articles', 'Examples'])

# Find all occurrences of the substring 't' in each string
result = s.str.findall('t')

print("Input Series:")
print(s)
print("\nOccurrences of 't':")
print(result)

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

Input Series:
0    tutorials
1     articles
2     Examples
dtype: object

Occurrences of 't':
0    [t, t]
1       [t]
2        []
dtype: object

An empty list [] indicates that there are no occurrences of the pattern in the element.

Example 2

This example demonstrates finding all occurrences of a pattern using a regular expression. Here, we look for all substrings starting with 't' followed by any character.

import pandas as pd

# Create a Series of strings
s = pd.Series(['tutorials', 'testing', 'test cases'])

# Find all substrings starting with 't' followed by any character
result = s.str.findall(r't.')

print("Input Series:")
print(s)
print("\nOccurrences of pattern 't.':")
print(result)

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

Input Series:
0    tutorials
1      testing
2   test cases
dtype: object

Occurrences of pattern 't.':
0    [tu, to]
1    [te, ti]
2    [te, t ]
dtype: object

The output shows lists of matches for the regular expression pattern 't.' where each element represents substrings that match the pattern.

Example 3

This example demonstrates applying the Series.str.findall() method to a DataFrame. We find all email addresses in a DataFrame that match a specified pattern.

import pandas as pd

# Create a DataFrame 
df = pd.DataFrame({
    'Email': ['user1@example.com', 'info@tutorialspoint.com', 'contact@website.org']
})

# Find all occurrences of the pattern 'tutorialspoint.com' in the 'Email' column
result = df['Email'].str.findall('tutorialspoint.com')

print("Input DataFrame:")
print(df)
print("\nOccurrences of 'tutorialspoint.com':")
print(result)

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

Input DataFrame:
                      Email
0          user1@example.com
1  info@tutorialspoint.com
2       contact@website.org

Occurrences of 'tutorialspoint.com':
0    []
1    [tutorialspoint.com]
2    []
Name: Email, dtype: object

The output shows that the pattern 'tutorialspoint.com' is found in the second email address only.