Using pandas.DataFrame.apply() method you can execute a function to a single column, all and 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, all columns. For example, let’s say we have three columns and would like to apply a function on a single column without touching 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 of the quick examples of how to apply a function to a single and multiple columns (two or more) in pandas DataFrame.
# Below are quick examples
# Using Dataframe.apply() to apply function add column
def add_3(x):
return x+3
df2 = df.apply(add_3)
# Using apply function single column
def add_4(x):
return x+4
df["B"] = df["B"].apply(add_4)
# Apply to multiple columns
df[['A','B']] = df[['A','B']].apply(add_3)
# apply a lambda function to each column
df2 = df.apply(lambda x : x + 10)
# Using Dataframe.apply() and lambda function
df["A"] = df["A"].apply(lambda x: x-2)
# Using Dataframe.apply() & [] operator
df['A'] = df['A'].apply(np.square)
# Using numpy.square() and [] operator
df['A'] = np.square(df['A'])
# 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)
# Apply function single column using transform()
def add_2(x):
return x+2
df = df.transform(add_2)
# Using DataFrame.map() to Single Column
df['A'] = df['A'].map(lambda A: A/2.)
# Using DataFrame.assign() and Lambda
df2 = df.assign(B=lambda df: df.B/2)
2. pandas.DataFrame.apply() Function Syntax
If you are a learner let’s see the syntax of apply() method and executing some examples of how to apply it on a single column, multiple, and all columns. Our DataFrame contains column names A, B, and C.
Below is a syntax of pandas.DataFrame.apply()
DataFrame.apply(func, axis=0, raw=False, result_type=None, args=(), **kwargs)
Let’s create a sample DataFrame to work with some examples.
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(df)
Yields below output.
A B C
0 3 5 7
1 2 4 6
2 5 8 9
3. Pandas Apply Function to Single Column
We will create a function add_3() which adds value 3 column value and use this on apply() function. To apply it to a single column, qualify the column name using df["col_name"]. The below example 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(df)
Yields below output. This applies the function to every row in DataFrame for a specified column.
A B C
0 3 9 7
1 2 8 6
2 5 12 9
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 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(df2)
Yields below output.
A B C
0 6 8 10
1 5 7 9
2 8 11 12
5. Pandas Apply Function to Multiple List of Columns
Similarly using apply() method, you can apply a function on a selected multiple list of columns. In this case, the function will apply to only selected two 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(df)Yields below output
A B C
0 6 8 7
1 5 7 6
2 8 11 9
6. Apply Lambda Function to Each Column
You can also apply a lambda expression using the apply() method, the Below example, adds 10 to all column values.
# apply a lambda function to each column
df2 = df.apply(lambda x : x + 10)
print(df2)
Yields below output.
A B C
0 13 15 17
1 12 14 16
2 15 18 19
7. Apply Lambda Function to 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(df)
Yields below output.
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(df)
Yields below output.
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(df)
Yields below output.
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(df2)
Yields below output.
A B C
0 3 2.5 7
1 2 2.0 6
2 5 4.0 9
11. Using Numpy function on single Column
Use df['A']=df['A'].apply(np.square) to select the column from DataFrame as series using the [] operator and apply NumPy.square() method.
# Using Dataframe.apply() & [] operator
df['A'] = df['A'].apply(np.square)
print(df)
Yields below output.
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 apply() function and directly using Numpy.
# Using numpy.square() and [] operator
df['A'] = np.square(df['A'])
print(df)
Yields 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(df2)
Yields below output.
A B C
0 9 25 7
1 4 16 6
2 25 64 9
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() and NumPy.square(), map(), transform() and assign() methods.
Happy Learning !!
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