Replace whole String if it contains Substring in Pandas

# Table of Contents

# Replace whole String if it contains Substring in Pandas

To replace whole strings if they contain a substring in Pandas:

Use the str.contains() method to check if each string contains a substring.
Assign a new value to the matching strings.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'job': ['dev', 'web dev', 'accountant', 'dev']
})

print(df)

df.loc[df['job'].str.contains('dev'), 'job'] = 'developer'

print('-' * 50)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

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    name         job
0  Alice         dev
1  Bobby     web dev
2   Carl  accountant
3    Dan         dev
--------------------------------------------------
    name         job
0  Alice   developer
1  Bobby   developer
2   Carl  accountant
3    Dan   developer

replace whole string if it contains substring in pandas

The DataFrame.loc indexer enables us to access a group of rows and columns by label(s) or a boolean array.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'job': ['dev', 'web dev', 'accountant', 'dev']
})

#     name         job
# 0  Alice         dev
# 1  Bobby     web dev
# 2   Carl  accountant
# 3    Dan         dev
print(df)


print('-' * 50)

# 0        dev
# 1    web dev
# 3        dev
# Name: job, dtype: object
print(df.loc[df['job'].str.contains('dev'), 'job'])

using-loc-indexer-to-access-group-of-rows-cols-by-label

The code for this article is available on GitHub

Once we have the group of rows that contain the string "dev" in the job column, we update their values to "developer".

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'job': ['dev', 'web dev', 'accountant', 'dev']
})

# 0  Alice         dev
# 1  Bobby     web dev
# 2   Carl  accountant
# 3    Dan         dev
print(df)

df.loc[df['job'].str.contains('dev'), 'job'] = 'developer'

print('-' * 50)

#     name         job
# 0  Alice   developer
# 1  Bobby   developer
# 2   Carl  accountant
# 3    Dan   developer
print(df)

The code for this article is available on GitHub

You can see that the rows with values dev and web dev were updated to developer.

The str.contains() method takes a pattern as a parameter and checks if the supplied pattern or regex is contained within a string.

The method returns a boolean Series or Index indicating the result.

# Replace whole String if it contains Substring in Pandas ignoring the case

If you need to replace a whole string if it contains a substring in pandas, ignoring the case, set the case argument to False when calling str.contains().

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'job': ['DEV', 'web Dev', 'accountant', 'dev']
})

print(df)

df.loc[df['job'].str.contains('dev', case=False), 'job'] = 'developer'

print('-' * 50)

#     name         job
# 0  Alice   developer
# 1  Bobby   developer
# 2   Carl  accountant
# 3    Dan   developer
print(df)

replace whole string if it contains substring ignoring case

The code for this article is available on GitHub

Notice that the values in the job column are not consistently cased.

The str.contains method takes a case argument that can be used to make the method case-insensitive.

By default, the case argument is set to True, which means that str.contains() is case-sensitive.

Setting the argument to False means that the case is ignored when matching the substring in each string.

# Replace whole string if it contains substring in Pandas using Regex

You can also use a regular expression to replace a whole string if it contains a substring in pandas.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'job': ['dev', 'web dev', 'accountant', 'dev']
})

print(df)

print('-' * 50)

df['job'] = df.job.str.replace(
    r'(^.*dev.*$)',
    'developer',
    regex=True
)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name         job
0  Alice         dev
1  Bobby     web dev
2   Carl  accountant
3    Dan         dev
--------------------------------------------------
    name         job
0  Alice   developer
1  Bobby   developer
2   Carl  accountant
3    Dan   developer

replace whole string if it contains substring using regex

We passed a regular expression as the first parameter to the str.replace() method.

main.py

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df['job'] = df.job.str.replace(
    r'(^.*dev.*$)',
    'developer',
    regex=True
)

The regular expression matches a string that contains the substring dev.

The replacement string is provided as the second argument.

Notice that we also had to set the regex keyword argument to True.

The regex argument determines if the supplied pattern is a regular expression.

By default, the argument is set to False, so it has to be explicitly specified when using a regex with str.replace().

If you want to ignore the case when matching the substring in the strings, set the flags keyword argument to re.IGNORECASE

main.py

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import re
import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'job': ['DEV', 'web Dev', 'accountant', 'dev']
})

print(df)

print('-' * 50)

df['job'] = df.job.str.replace(
    r'(^.*dev.*$)',
    'developer',
    regex=True,
    flags=re.IGNORECASE
)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name         job
0  Alice         DEV
1  Bobby     web Dev
2   Carl  accountant
3    Dan         dev
--------------------------------------------------
    name         job
0  Alice   developer
1  Bobby   developer
2   Carl  accountant
3    Dan   developer

When the flags argument is set to re.IGNORECASE, the substring is matched in the string in a case-insensitive manner.

# Replace whole string if it contains substring in Pandas using `apply()`

You can also use the DataFrame.apply() method to replace a whole string if it contains a substring in Pandas.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'job': ['dev', 'web dev', 'accountant', 'dev']
})

print(df)

print('-' * 50)

df['job'] = df.job.apply(
    lambda x: 'developer' if 'dev' in x else x
)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name         job
0  Alice         dev
1  Bobby     web dev
2   Carl  accountant
3    Dan         dev
--------------------------------------------------
    name         job
0  Alice   developer
1  Bobby   developer
2   Carl  accountant
3    Dan   developer

replace whole string if it contains substring using apply

The DataFrame.apply method applies a function along an axis of the DataFrame.

main.py

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df['job'] = df.job.apply(
    lambda x: 'developer' if 'dev' in x else x
)

Our lambda function returns the string developer if the substring dev is contained in the current value, otherwise, the current value is returned.

If you need to ignore the case when matching the substring in the string, use the str.lower() method.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'job': ['DEV', 'web Dev', 'accountant', 'dev']
})

print(df)

print('-' * 50)

df['job'] = df.job.apply(
    lambda x: 'developer'
    if 'dev'.lower() in x.lower()
    else x
)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
    name         job
0  Alice         DEV
1  Bobby     web Dev
2   Carl  accountant
3    Dan         dev
--------------------------------------------------
    name         job
0  Alice   developer
1  Bobby   developer
2   Carl  accountant
3    Dan   developer

Converting the substring we're checking for and the current string to lowercase enables us to perform a case-insensitive test whether the substring is contained in the string.

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Replace whole String if it contains Substring in Pandas

# Table of Contents

# Replace whole String if it contains Substring in Pandas

# Replace whole String if it contains Substring in Pandas ignoring the case

# Replace whole string if it contains substring in Pandas using Regex

# Replace whole string if it contains substring in Pandas using apply()

# Additional Resources

Borislav Hadzhiev

# Replace whole string if it contains substring in Pandas using `apply()`