How to Replace String in Pandas DataFrame

In pandas, to replace a string in the DataFrame column, you can use either the replace() function or the str.replace() method along with lambda methods.

In this article, I will explain how to replace strings in a Pandas offers several methods for replacing strings within DataFrame columns. The primary method is to use the replace() function.

Key Points –

• Utilize the replace() function in Pandas to substitute specific strings with other strings within DataFrame columns.
• Specify the target string(s) and replacement string(s) as arguments within the replace() function.
• Using regular expressions for handling complex string replacement patterns effectively.
• Ensure to assign the modified DataFrame back to the original DataFrame or a new variable to retain changes.
• Ensure to use the inplace=True parameter to modify the DataFrame in place or assign the result to a new DataFrame if needed.

Quick Examples of Replacing String

Following are quick examples of replacing a string.

# Quick examples to replace string

# Example 1: Replace string 
# Using DataFrame.replace() method
df2 = df.replace('Py','Python with ', regex=True)

# Example 2: Replace pattern of string 
# Using regular expression.
df2 = df.replace({'Courses': 'Py', 'Duration': 'days'}, 
    {'Courses': 'Python with', 'Duration': ' Days'}, regex=True)

# Example 3: Replace pattern of string 
# Using regular expression
df2=df.replace(regex=['Language'],value='Lang')

# Example 4: By using str.replace()
df['Courses'] = df['Courses'].str.replace('Language','Lang')

# Example 5: Replace String 
# Using apply() function with lambda
df2 = df.apply(lambda x: x.replace({'Py':'Python with', 'Language':'Lang'}, regex=True))

Now, let’s create a DataFrame with a few rows and columns, execute these examples, and validate results. Our DataFrame contains column names Courses, Fee, Duration, and Discount.

# Create a pandas DataFrame.
import pandas as pd
import numpy as np
technologies= {
    'Courses':["Spark","PySpark","Spark","Java Language","PySpark","PHP Language"],
    'Fee' :[22000,25000,23000,24000,26000,27000],
    'Duration':['30days','50days','30days','60days','35days','30days']
          }
df = pd.DataFrame(technologies)
print("Create DataFrame:\n", df)

Yields below output.

Pandas Replace String Example

You can utilize the DataFrame.replace() function to replace strings within a Pandas DataFrame column. This function updates the specified value with another specified value and returns a new DataFrame. In order to update on existing DataFrame use inplace=True.

# Replace string using DataFrame.replace() method.
df2 = df.replace('PySpark','Python with Spark')
print("After replacing the string values of a single column:\n", df2)

In the above example, you create a DataFrame df with columns Courses, Fee, and Duration. Then you use the DataFrame.replace() method to replace PySpark with Python with Spark in the Courses column. This example yields the below output.

Replace Multiple Strings

Now let’s see how to replace multiple string column(s), In this example, I will also show how to replace part of the string by using regex=True param. To update multiple string columns, use the dict with a key-value pair. The below example updates Py with Python with on Courses column and days with Days on Duration column.

# Replace pattern of string using regular expression.
df2 = df.replace({'Courses': 'Py', 'Duration': 'days'}, 
    {'Courses': 'Python with ', 'Duration': ' Days'}, regex=True)
print("After replacing the string values of multiple columns:\n", df2)

Yields below output.

# Output:
# After replacing the string values of multiple columns
             Courses    Fee Duration
0              Spark  22000  30 Days
1  Python with Spark  25000  50 Days
2              Spark  23000  30 Days
3      Java Language  24000  60 Days
4  Python with Spark  26000  35 Days
5       PHP Language  27000  30 Days

Using Regular Expression

Alternatively, using regular expressions you can replace matching strings with other strings within a Pandas DataFrame. The below example finds a string Language and replace it with Lan.

# Replace pattern of string using regular expression
df2=df.replace(regex=['Language'],value='Lang')
print("After replacing the string values of a single column:\n", df2)

Yields below output.

# Output:
# After replacing the string values of a single column:
     Courses    Fee Duration
0      Spark  22000   30days
1    PySpark  25000   50days
2      Spark  23000   30days
3  Java Lang  24000   60days
4    PySpark  26000   35days
5   PHP Lang  27000   30days

Use str.replace() Function

To use str.replace() on a DataFrame, you would first access the column containing the strings you want to replace using square brackets ([]), then apply the str.replace() method.

# By using str.replace()
df['Courses'] = df['Courses'].str.replace('Language','Lang')
print("After replacing the string values of a single column:\n", df)

In the above example, you create a DataFrame df with a column Courses containing strings. Then, you use the str.replace() method directly on the Courses column of the DataFrame to replace Language with Lang.

Using apply() Function Along with Lambda

Similarly, you can use DataFrame.apply() with a lambda expression to replace strings. The apply() method in Pandas permits the application of a method along one of the DataFrame axes. By default, the axis is set to 0, representing the index (row) axis.

# Replace String using apply() function with lambda
df2 = df.apply(lambda x: x.replace({'Py':'Python with', 'Language':'Lang'}, regex=True))
print("After replacing the string values of a single column:\n", df2)

Yields below output.

# Output:
# After replacing the string values of a single column:
            Courses    Fee Duration
0             Spark  22000   30days
1  Python withSpark  25000   50days
2             Spark  23000   30days
3         Java Lang  24000   60days
4  Python withSpark  26000   35days
5          PHP Lang  27000   30days

Complete Example

# Create a pandas DataFrame
import pandas as pd
import numpy as np
technologies= {
    'Courses':["Spark","PySpark","Spark","P","PySpark","P"],
    'Fee' :[22000,25000,23000,24000,26000,27000],
    'Duration':['30days','50days','30days','60days','35days','30days']
          }
df = pd.DataFrame(technologies)
print(df)

# Replace string using DataFrame.replace() method
df2 = df.replace('Py','Python with ', regex=True)
print(df2)

# Replace pattern of string using regular expression
df2 = df.replace({'Courses': 'Py', 'Duration': 'days'}, 
    {'Courses': 'Python with', 'Duration': ' Days'}, regex=True)
print(df2)

# Replace pattern of string using regular expression.
df2=df.replace(regex=['Language'],value='Lang')
print(df2)

# By using str.replace()
df['Courses'] = df['Courses'].str.replace('Language','Lang')
print(df)

# Replace String using apply() function with lambda
df2 = df.apply(lambda x: x.replace({'Py':'Python with', 'Language':'Lang'}, regex=True))
print(df2)

FAQs on Replace String

Conclusion

In this article, you have learned to replace the string in the Pandas column by using DataFrame.replace() and str.replace() with lambda function.

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• Pandas Replace substring in DataFrame
• Replace NaN Values with Zero in a Column

References

• https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.replace.html