Return multiple columns using Pandas apply() method
Objects passed to the pandas.apply() are Series objects whose index is either the DataFrame’s index (axis=0) or the DataFrame’s columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the result_type argument.
Syntax: DataFrame.apply(func, axis=0, broadcast=None, raw=False, reduce=None, result_type=None, args=(), **kwds)
Below are some programs which depicts the use of pandas.DataFrame.apply()
Example 1:
# Program to illustrate the use of # pandas.DataFrame.apply() method # Importing required Libraries import pandas import numpy # Creating dataframe dataFrame = pandas.DataFrame([[ 4 , 9 ], ] * 3 , columns = [ 'A' , 'B' ]) print ( 'Data Frame:' ) display(dataFrame) # Using pandas.DataFrame.apply() on the data frame print ( 'Returning multiple columns from Pandas apply()' ) dataFrame. apply (numpy.sqrt) |
Output:
Using a numpy universal function (in this case the same as numpy.sqrt(dataFrame)).
Example 2:
# Program to illustrate the use of # pandas.DataFrame.apply() method # Importing required Libraries import pandas import numpy # Creating dataframe dataFrame = pandas.DataFrame([[ 4 , 9 ], ] * 3 , columns = [ 'A' , 'B' ]) print ( 'Data Frame:' ) display(dataFrame) # Using pandas.DataFrame.apply() on the data frame print ( 'Returning multiple columns from Pandas apply()' ) dataFrame. apply (numpy. sum , axis = 0 ) |
Output:
Using a reducing function on columns.
Example 3:
# Program to illustrate the use of # pandas.DataFrame.apply() method # Importing required Libraries import pandas import numpy # Creating dataframe dataFrame = pandas.DataFrame([[ 4 , 9 ], ] * 3 , columns = [ 'A' , 'B' ]) print ( 'Data Frame:' ) display(dataFrame) # Using pandas.DataFrame.apply() on the data frame print ( 'Returning multiple columns from Pandas apply()' ) dataFrame. apply (numpy. sum , axis = 1 ) |
Output:
Using a reducing function on rows.
Example 4:
# Program to illustrate the use of # pandas.DataFrame.apply() method # Importing required Libraries import pandas import numpy # Creating dataframe dataFrame = pandas.DataFrame([[ 4 , 9 ], ] * 3 , columns = [ 'A' , 'B' ]) print ( 'Data Frame:' ) display(dataFrame) # Using pandas.DataFrame.apply() on the data frame print ( 'Returning multiple columns from Pandas apply()' ) dataFrame. apply ( lambda x: [ 1 , 2 ], axis = 1 ) |
Output:
Returning a list-like will result in a Series.
Example 5:
# Program to illustrate the use of # pandas.DataFrame.apply() method # Importing required Libraries import pandas import numpy # Creating dataframe dataFrame = pandas.DataFrame([[ 4 , 9 ], ] * 3 , columns = [ 'A' , 'B' ]) print ( 'Data Frame:' ) display(dataFrame) # Using pandas.DataFrame.apply() on the data frame print ( 'Returning multiple columns from Pandas apply()' ) dataFrame. apply ( lambda x: [ 1 , 2 ], axis = 1 , result_type = 'expand' ) |
Output:
Passing result_type=’expand’ will expand list-like results to columns of a Dataframe.
Example 6:
# Program to illustrate the use of # pandas.DataFrame.apply() method # Importing required Libraries import pandas import numpy # Creating dataframe dataFrame = pandas.DataFrame([[ 4 , 9 ], ] * 3 , columns = [ 'A' , 'B' ]) print ( 'Data Frame:' ) display(dataFrame) # Using pandas.DataFrame.apply() on the data frame print ( 'Returning multiple columns from Pandas apply()' ) dataFrame. apply ( lambda x: pandas.Series( [ 1 , 2 ], index = [ 'foo' , 'bar' ]), axis = 1 ) |
Output:
Returning a Series inside the function is similar to passing result_type=’expand’. The resulting column names will be the Series index.
Example 7:
# Program to illustrate the use of # pandas.DataFrame.apply() method # Importing required Libraries import pandas import numpy # Creating dataframe dataFrame = pandas.DataFrame([[ 4 , 9 ], ] * 3 , columns = [ 'A' , 'B' ]) print ( 'Data Frame:' ) display(dataFrame) # Using pandas.DataFrame.apply() on the data frame print ( 'Returning multiple columns from Pandas apply()' ) dataFrame. apply ( lambda x: [ 1 , 2 ], axis = 1 , result_type = 'broadcast' ) |
Output:
Passing result_type=’broadcast’ will ensure the same shape result, whether list-like or scalar is returned by the function, and broadcast it along the axis. The resulting column names will be the originals.