Apply function to column and row in the Dataframe
Here. we will see how to apply a function to more than one row and column using df.apply() method.
For Column
Here, we applied the function to the x, and y columns.
Python3
# import pandas and numpy libraryimport pandas as pdimport numpy as np# List of Tuplesmatrix = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]# Create a DataFrame objectdf = pd.DataFrame(matrix, columns=list('xyz'), index=list('abc'))# Apply function numpy.square()# for square the values of# two columns 'x' and 'y'new_df = df.apply(lambda x: np.square(x) if x.name in ['x', 'y'] else x)# Outputprint(new_df) |
Output:
x y z
a 1 4 3
b 16 25 6
c 49 64 9
For Row
Here, we applied the function to the b, and c rows.
Python3
# import pandas and numpy libraryimport pandas as pdimport numpy as np# List of Tuplesmatrix = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]# Create a DataFrame objectdf = pd.DataFrame(matrix, columns=list('xyz'), index=list('abc'))# Apply function numpy.square() to# square the values of two rows# 'b' and 'c'new_df = df.apply(lambda x: np.square(x) if x.name in ['b', 'c'] else x, axis=1)# Outputprint(new_df) |
Output:
x y z
a 1 2 3
b 16 25 36
c 49 64 81
Apply a function to single or selected columns or rows in Pandas Dataframe
In this article, we will learn different ways to apply a function to single or selected columns or rows in Dataframe. We will use Dataframe/series.apply() method to apply a function.