Using Pandas.DataFrame.apply()
method you can execute a function to a single column, all, and a list of multiple columns (two or more). In this article, I will cover how to apply() a function on values of a selected single, multiple, and all columns. For example, let’s say we have three columns and would like to apply a function on a single column without touching the other two columns and return a DataFrame with three columns.
1. Quick Examples of Pandas Apply Function to a Column
If you are in a hurry, below are some quick examples of how to apply a function to single and multiple columns (two or more) in Pandas DataFrame.
# Below are some quick examples
# Example 1: Using Dataframe.apply() to apply function add column
def add_3(x):
return x+3
df2 = df.apply(add_3)
# Example 2: Using apply function single column
def add_4(x):
return x+4
df["B"] = df["B"].apply(add_4)
# Example 3: Apply to multiple columns
df[['A','B']] = df[['A','B']].apply(add_3)
# Example 4: Apply a lambda function to each column
df2 = df.apply(lambda x : x + 10)
# Example 5: Using Dataframe.apply() and lambda function
df["A"] = df["A"].apply(lambda x: x-2)
# Example 6: Using Dataframe.apply() & [] operator
df['A'] = df['A'].apply(np.square)
# Example 7: Using numpy.square() and [] operator
df['A'] = np.square(df['A'])
# Example 8: Apply function NumPy.square() to square the values of two rows
#'A'and'B
df2 = df.apply(lambda x: np.square(x) if x.name in ['A','B'] else x)
# Example 9: Apply function single column using transform()
def add_2(x):
return x+2
df = df.transform(add_2)
# Example 10: Using DataFrame.map() to Single Column
df['A'] = df['A'].map(lambda A: A/2.)
# Example 11: Using DataFrame.assign() and Lambda
df2 = df.assign(B=lambda df: df.B/2)
2. Syntax of Pandas.DataFrame.apply() Function
If you are a learner let’s see the syntax of the apply() method and execute some examples of how to apply it on a single column, multiple, and all columns.
Below is a syntax of pandas.DataFrame.apply()
# Syntax of apply() function
DataFrame.apply(func, axis=0, raw=False, result_type=None, args=(), **kwargs)
Let’s create a sample DataFrame with column names A
, B
, and C
.
# Create DataFrame
import pandas as pd
import numpy as np
data = [(3,5,7), (2,4,6),(5,8,9)]
df = pd.DataFrame(data, columns = ['A','B','C'])
print("Create DataFrame:\n", df)
Yields below output.

3. Pandas Apply Function to Single Column
You can define a function add_4()
that adds value 4 for every value in a specified column value and uses this on the apply() function. To apply it to a single column, qualify the column name using df["col_name"]
. The example below applies a function to a column B
.
# Using apply function single column
def add_4(x):
return x+4
df["B"] = df["B"].apply(add_4)
print("After applying a function on a single column:\n", df)
Yields below output. This applies the function to every row in DataFrame for a specified column.

4. Pandas Apply Function to All Columns
In some cases we would want to apply a function on all pandas columns, you can do this using the apply() function. Here the add_3() function will be applied to all DataFrame columns.
# Using Dataframe.apply() to apply function add column
def add_3(x):
return x+3
df2 = df.apply(add_3)
print("After applying a function on multiple columns:\n", df2)
Yields below output.
# Output:
# After applying a function on multiple columns:
A B C
0 6 8 10
1 5 7 9
2 8 11 12
5. Pandas Apply Function to Lists of Columns
Similarly, you can apply a function on a selected list of columns. In this case, this function will apply to only the selected list of columns without touching the rest of the columns.
# Apply() function on selected list of multiple columns
df = pd.DataFrame(data, columns = ['A','B','C'])
df[['A','B']] = df[['A','B']].apply(add_3)
print("After applying a function on list of columns:\n", df)
Yields below output
# Output:
# After applying a function on list of columns:
A B C
0 6 8 7
1 5 7 6
2 8 11 9
6. Apply the Lambda Function to Each Column
You can also use a lambda expression with the apply() method to manipulate each column value in a DataFrame, the Below example, adds 10 to all column values.
# Apply a lambda function to each column
df2 = df.apply(lambda x : x + 10)
print("After applying a lambda function:\n", df2)
Yields below output.
# Output:
# After applying a lambda function:
A B C
0 13 15 17
1 12 14 16
2 15 18 19
7. Apply the Lambda Function to a Single Column
You can apply the lambda function for a single column in the DataFrame. The following example subtracts every cell value by 2 for column A – df["A"]=df["A"].apply(lambda x:x-2)
.
# Using Dataframe.apply() and lambda function
df["A"] = df["A"].apply(lambda x: x-2)
print("After applying a lambda function to a single column:\n", df)
Yields below output.
# Output:
# After applying a lambda function to a single column:
A B C
0 1 5 7
1 0 4 6
2 3 8 9
Similarly, you can also apply the Lambda function to all & multiple columns in pandas, I will leave this to you to explore.
8. Using pandas.DataFrame.transform() to Apply Function Column
Using DataFrame.apply()
method & lambda functions the resultant DataFrame can be any number of columns whereas with transform()
function the resulting DataFrame must have the same length as the input DataFrame.
# Using DataFrame.transform()
def add_2(x):
return x+2
df = df.transform(add_2)
print("After applying a function to every column:\n", df)
Yields below output.
# Output:
# After applying a function to every column:
A B C
0 5 7 9
1 4 6 8
2 7 10 11
9. Using pandas.DataFrame.map() to Single Column
Here is another alternative using map()
method.
# Using DataFrame.map() to Single Column
df['A'] = df['A'].map(lambda A: A/2.)
print("After applying a function to a single column:\n", df)
Yields below output.
# Output:
# After applying a function to a single column:
A B C
0 1.5 5 7
1 1.0 4 6
2 2.5 8 9
10. DataFrame.assign() to Apply Lambda Function
You can also try assign()
# Using DataFrame.assign() and Lambda
df2 = df.assign(B=lambda df: df.B/2)
print("After applying a function to a single column:\n", df2)
Yields below output.
# Output:
# After applying a function to a single column:
A B C
0 3 2.5 7
1 2 2.0 6
2 5 4.0 9
11. Using the Numpy function on a single Column
Use df['A']=df['A'].apply(np.square)
to select the column from DataFrame as a series using the [] operator and apply NumPy.square()
method.
# Using Dataframe.apply() & [] operator
df['A'] = df['A'].apply(np.square)
print("After applying a function to a column:\n", df)
Yields below output.
# Output:
# After applying a function to a column:
A B C
0 9 5 7
1 4 4 6
2 25 8 9
12. Using NumPy.square() Method
You can also do the same without using the apply() function and directly using Numpy.
# Using numpy.square() and [] operator
df['A'] = np.square(df['A'])
print("After applying a function to a column:\n", df)
Yields the same output as above.
13. Multiple columns Using NumPy.square() and Lambda Function
Apply a lambda function to multiple columns in DataFrame using Dataframe apply(), lambda, and Numpy functions.
# Apply function NumPy.square() to square the values of two rows
'A'and'B
df2 = df.apply(lambda x: np.square(x) if x.name in ['A','B'] else x)
print("After applying a lambda function to multiple columns:\n", df2)
Yields below output.
# Output:
# After applying a lambda function to multiple columns:
A B C
0 9 25 7
1 4 16 6
2 25 64 9
Frequently Asked Questions on Pandas apply() Function
apply()
is a Pandas DataFrame method used to apply a function along the axis of a DataFrame. It can be used to perform operations on both rows and columns.
When applied to a single column, apply()
applies the specified function to each element in that column. For example, df['column'] = df['column'].apply(lambda x: x * 2)
you can use apply()
on multiple columns by specifying the axis parameter. When applying to columns, use axis=0
. For example, df[['column1', 'column2']] = df[['column1', 'column2']].apply(lambda x: x * 2, axis=0)
You can use any custom function with apply()
. Just define your function and pass it as an argument. For example, def add_3(x):<br/> return x+3<br/> df2 = df.apply(add_3)
Conclusion
In this article, you have learned how to apply a function to a single column, all, and multiple columns (two or more) of Pandas DataFrame using apply()
, transform()
, NumPy.square()
, map()
, transform()
, and assign()
methods.
Happy Learning !!
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