Pandas apply() Function to Single & Multiple Column(s)

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() 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.

Key Points –

• Pandas’ apply() function is a powerful tool for applying a function along one or more axes of a DataFrame.
• When applied to a single column, apply() iterates over each element of the column, applying the specified function.
• For multiple columns, apply() can operate on either rows or columns, based on the axis parameter.
• The apply() function can significantly enhance the efficiency of data manipulation tasks by allowing custom operations on DataFrame elements

Quick Examples of apply() Function to Single & Multiple Column(s)

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.

# Quick examples of pandas apply() function 

# 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)

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.

# Syntax of apply() function
DataFrame.apply(func, axis=0, raw=False, result_type=None, args=(), **kwargs)

To run some examples of pandas applying functions to single & multiple column(s), let’s create 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.

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.

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)

# Output:
# After applying a function on multiple columns:
#   A   B   C
#0  6   8  10
#1  5   7   9
#2  8  11  12

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)

# Output:
# After applying a function on list of columns:
#   A   B  C
#0  6   8  7
#1  5   7  6
#2  8  11  9

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)

# Output:
# After applying a lambda function:
#    A   B   C
#0  13  15  17
#1  12  14  16
#2  15  18  19

Apply the Lambda Function to a Single Column

Applying a lambda function to a single column allows you to perform elelement-wise operations on that particular column. For instance, you applied the lambda function lambda x: x - 2 to the column A, subtracting 2 from each element in that column.

# 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)

# 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

Using DataFrame.transform() to Apply() Function

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)

# Output:
# After applying a function to every column:
#   A   B   C
#0  5   7   9
#1  4   6   8
#2  7  10  11

Single Column Using DataFrame.map()

Here is another alternative using map() function to a single column in a DataFrame. For instance, the lambda function lambda x: x/2 is applied to each element in the column A, dividing each value by 2.0.

# 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)

# 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

Using DataFrame.assign() and Lambda Function

You can also try assign() with a lambda function to apply it to a single column in a DataFrame. For instance, the lambda function lambda x: x['B']/2 is applied within assign() to create a new DataFrame (df2) where the column B is modified by dividing each value by 2.0.

# 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)

# 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

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)

# Output:
# After applying a function to a column:
#    A  B  C
#0   9  5  7
#1   4  4  6
#2  25  8  9

Using NumPy.square() Method

You can use NumPy’s square() method to square the values in a DataFrame column. For example, np.square(df['A']) square each element in column A, and the result is assigned back to column A in the DataFrame.

 
# 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.

Using NumPy.square() & Lambda Function to Multiple Columns

Use apply() and a lambda function to conditionally square the values of columns A and B using NumPy’s square() function. For instance, the lambda function checks if the name of the current column (x.name) is either A or B. If so, it applies np.square(x) to square the values in that column. Otherwise, it leaves the column unchanged.

# 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)

# Output:
# After applying a lambda function to multiple columns:
#    A   B  C
#0   9  25  7
#1   4  16  6
#2  25  64  9

FAQ on Pandas apply() Function

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 !!

Related Articles

• pandas map() Function – Examples
• Pandas apply map (applymap()) Explained
• Pandas apply() return multiple columns
• Pandas Series apply() function usage
• pandas.DataFrame.where() Examples
• Set Order of Columns in Pandas DataFrame
• pandas.DataFrame.mean() Examples
• pandas head() – Returns Top N Rows
• How to apply multiple filters to Pandas DataFrame or Series
• Difference between map, applymap and apply Methods

References

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