By applying a lambda function to each row
Example
import pandas as pd
df = pd.DataFrame([(10, 3, 13),(0, 42, 11),(26, 52, 1)], columns=list('xyz'))
print("Existing matrix")
print(df)
NewMatrix = df.apply(lambda a: a + 10, axis=1)
print("Modified Matrix")
print(NewMatrix)Output
Running the above code gives us the following result −
Existing matrix x y z 0 10 3 13 1 0 42 11 2 26 5 21 Modified Matrix x y z 0 20 13 23 1 10 52 21 2 36 62 11
By applying a User Defined function
Example
import pandas as pd
def SquareData(x):
return x * x
df = pd.DataFrame([(10, 3, 13), (0, 42, 11), (26, 52, 1)], columns=list('xyz'))
print("Existing matrix")
print(df)
NewMatrix = df.apply(SquareData, axis=1)
print("Modified Matrix")
print(NewMatrix)Output
Running the above code gives us the following result −
Existing matrix x y z 0 10 3 13 10 42 1 1 2 26 52 1 Modified Matrix x y z 0 100 9 169 1 0 1764 121 2 676 2704 1