Pandas DataFrame apply() Method
In this tutorial, we will learn the python pandas DataFrame.apply()
method. Using this method we can apply different functions on rows and columns of the DataFrame. The objects passed to the method are Series objects whose index is either the DataFrame’s index (axis=0
) or the DataFrame’s columns (axis=1
).
After applying the method, it returns the Series
or DataFrame
along the given axis of the DataFrame. This function can be used when we want to alter a particular column without affecting other columns.
The below shows the syntax of the DataFrame.apply()
method.
Syntax
DataFrame.apply(func, axis=0, raw=False, result_type=None, args=(), **kwds)
Parameters
func: It represents the function to apply to each column or row.
axis: It represents the axis along which the function is applied, 0 or ‘index’: apply the function to each column, 1 or ‘columns’: apply the function to each row.
result_type: It includes the ‘expand’, ‘reduce’, ‘broadcast’, None, and the default value is None.
These only act when axis=1
(columns):
-
‘expand’: The list-like results will be turned into columns.
-
‘reduce’: This is the opposite of ‘expand’ and it returns a Series if possible rather than expanding list-like results.
-
‘broadcast’: The results will be broadcast to the original shape of the DataFrame, the original index and columns will be retained.
Example 1: Applying a np.sum
function to all the elements of DataFrame using the DataFrame.apply()
Method
The below example shows how we can apply the function to all the elements of the DataFrame over the axis. Here, in this example, we choose to function as np.sum
, with default axis(axis=0)
and axis=1
.
import pandas as pd
import numpy as np
df=pd.DataFrame([[10,11,12],[20,21,22]],columns=['A','B','C'])
print("Applying sum function to all the elements of DataFrame")
print(df.apply(np.sum))
print(df.apply(np.sum,axis=1))
Once we run the program we will get the following output.
![](data:image/png;base64,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)
Example 2: Applying user defined function to the DataFrame using DataFrame.apply()
Method
We can pass a user-defined function as a parameter to the DataFrame.apply()
function. The below example shows the same.
import pandas as pd
df=pd.DataFrame([[10,11,12],[20,21,22]],columns=['A','B','C'])
print(df)
def add(x):
return x+1
print(df.apply(add))
Once we run the program we will get the following output.
![](data:image/png;base64,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)
Example 3: Applying lambda function to all the elements of DataFrame using the DataFrame.apply()
Method
We can pass the lambda function to the DataFrame.apply()
method. Here, we choose to function as lambda function
, axis=1
, result_type='expand'
and this parameter converts list-like results to columns. The below example shows with and without result_type
parameter.
df=pd.DataFrame([[10,11,12],[20,21,22]],columns=['A','B','C'])
print(df.apply(lambda x: [1,2,3], axis=1))
print(df.apply(lambda x: [1,2,3], axis=1, result_type='expand'))
Once we run the program we will get the following output.
![](data:image/png;base64,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)
Example 4: Applying a specific function to a selected column using DataFrame.apply()
Method
If we want to change a particular column without affecting others, we can use DataFrame.apply()
method like this. In the below example we are altering only the 'Name'
column by passing the lambda function to the DataFrame.apply()
method and printing the output.
import pandas as pd
df1 = pd.DataFrame([['Abhishek',75,80,90], ['Anurag',80,90,95],['Bavya',80,82,85],['Bavana',95,92,95],['Chetan',85,90,89]], columns=['Name','Maths','Science','Social'])
df1['Name'] = df1['Name'].apply(lambda x: x.upper())
print(df1)
Once we run the program we will get the following output.
![](data:image/png;base64,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)
Example 5: Applying a specific function to a selected column using DataFrame.apply()
Method
In this example, we are altering the 'Science'
column passing the lambda function
to the DataFrame.apply()
method.
import pandas as pd
df1 = pd.DataFrame([['Abhishek',75,80,90], ['Anurag',80,90,95],['Bavya',80,82,85],['Bavana',95,92,95],['Chetan',85,90,89]], columns=['Name','Maths','Science','Social'])
df1['Science'] = df1['Science'].apply(lambda x: x+10)
print(df1)
Once we run the program we will get the following output.
![](data:image/png;base64,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)
Conclusion
In this tutorial, we learned the python pandas DataFrame.apply()
method to the DataFrame. We learned syntax, parameters, and passing different functions, axis, and result types to the DataFrame.apply()
method, we solved examples. This function is very useful when cleaning up the data like altering particular columns, changing formats, etc.