There are different ways to apply a function to each row or column in DataFrame. We will learn about various ways in this post. Let’s create a small dataframe first and see that.
Python3
# import pandas and numpy library import pandas as pd import numpy as np # list of tuples matrix = [(1,2,3,4), (5,6,7,8,), (9,10,11,12), (13,14,15,16) ] # Create a Dataframe object df = pd.DataFrame(matrix, columns = list('abcd')) # Output df |
Output :
Method 1: Applying lambda function to each row/column.
Example 1: For Column
Python3
# import pandas and numpy library import pandas as pd import numpy as np # list of tuples matrix = [(1,2,3,4), (5,6,7,8,), (9,10,11,12), (13,14,15,16) ] # Create a Dataframe object df = pd.DataFrame(matrix, columns = list('abcd')) # Applying a lambda function to each # column which will add 10 to the value new_df = df.apply(lambda x : x + 10) # Output new_df |
Output :
Example 2: For Row
Python3
# import pandas and numpy library import pandas as pd import numpy as np # list of tuples matrix = [(1,2,3,4), (5,6,7,8,), (9,10,11,12), (13,14,15,16) ] # Creating a Dataframe object df = pd.DataFrame(matrix, columns = list('abcd')) # Applying a lambda function to each # row which will add 5 to the value new_df = df.apply(lambda x: x + 5, axis = 1) # Output new_df |
Output :
Method 2: Applying user defined function to each row/column
Example 1: For Column
Python3
# function to returns x*x def squareData(x): return x * x # import pandas and numpy packages import pandas as pd import numpy as np # list of tuples matrix = [(1,2,3,4), (5,6,7,8,), (9,10,11,12), (13,14,15,16) ] # Creating a Dataframe object df = pd.DataFrame(matrix, columns = list('abcd')) # Applying a user defined function to # each column that will square the given # value new_df = df.apply(squareData) # Output new_df |
Output :
Example 2: For Row
Python3
# function to returns x*X def squareData(x): return x * x # import pandas and numpy library import pandas as pd import numpy as np # List of tuples matrix = [(1,2,3,4), (5,6,7,8,), (9,10,11,12), (13,14,15,16) ] # Creating a Dataframe object df = pd.DataFrame(matrix, columns = list('abcd')) # Applying a user defined function # to each row that will square the given value new_df = df.apply(squareData, axis = 1) # Output new_df |
Output :
In the above examples, we saw how a user defined function is applied to each row and column. We can also apply user defined functions which take two arguments.
Example 1: For Column
Python3
# function to returns x+y def addData(x, y): return x + y # import pandas and numpy library import pandas as pd import numpy as np # list of tuples matrix = [(1,2,3,4), (5,6,7,8,), (9,10,11,12), (13,14,15,16) ] # Creating a Dataframe object df = pd.DataFrame(matrix, columns = list('abcd')) # Appling a user defined function to each # column which will add value in each # column by given number new_df = df.apply(addData, args = [1]) # Output print(new_df) |
Output :
Example 2: For Row
Python3
# function to returns x+y def addData(x, y): return x + y # import pandas and numpy library import pandas as pd import numpy as np # List of tuples matrix = [(1,2,3,4), (5,6,7,8,), (9,10,11,12), (13,14,15,16) ] # Creating a Dataframe object df = pd.DataFrame(matrix, columns = list('abcd')) # Appling a user defined function to each # row which will add value in each row by # given number new_df = df.apply(addData, axis = 1, args = [3]) # Output new_df |
Output :
Method 3: Applying numpy function to each row/column
Example 1: For Column
Python3
# import pandas and numpy library import pandas as pd import numpy as np # list of tuples matrix = [(1,2,3,4), (5,6,7,8,), (9,10,11,12), (13,14,15,16) ] # Creating a dataframe object df = pd.DataFrame(matrix, columns = list('abcd')) # Applying a numpy function to each # column by squaring each value new_df = df.apply(np.square) # Output new_df |
Output :
Example 2: For Row
Python3
# import pandas and numpy library import pandas as pd import numpy as np # List of tuples matrix = [(1,2,3,4), (5,6,7,8,), (9,10,11,12), (13,14,15,16) ] # Creating a dataframe object df = pd.DataFrame(matrix, columns = list('abcd')) # Apply a numpy function to each row # to find square root of each value new_df = df.apply(np.sqrt, axis = 1) # Output new_df |
Output :
Method 4: Applying a Reducing function to each row/column
A Reducing function will take row or column as series and returns either a series of same size as that of input row/column or it will return a single variable depending upon the function we use.
Example 1: For Column
Python3
# import pandas and numpy library import pandas as pd import numpy as np # List of tuples matrix = [(1,2,3,4), (5,6,7,8,), (9,10,11,12), (13,14,15,16) ] # Creating a Dataframe object df = pd.DataFrame(matrix, columns = list('abcd')) # Applying a numpy function to get the sum # of all values in each column new_df = df.apply(np.sum) # Output new_df |
Output :
Example 2: For Row
Python3
# import pandas and numpy library import pandas as pd import numpy as np # List of tuples matrix = [(1,2,3,4), (5,6,7,8,), (9,10,11,12), (13,14,15,16) ] # Creating a Dataframe object df = pd.DataFrame(matrix, columns = list('abcd')) # Applying a numpy function to get t # he sum of all values in each row new_df = df.apply(np.sum, axis = 1) # Output new_df |
Output :
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