Transform pandas DataFrames using apply() and applymap() functions

Overview:

A function can be applied on a pandas DataFrame object to transform its elements. The pandas DataFrame is designed in such a way that each call to the applied function, can be passed of data: either row-wise, column-wise or element wise.
When the function applied on the DataFrame is designed to take a pandas series or an ndarray, the method to be used is DataFrame.apply().
On the other hand if the function to be applied on the DataFrame takes only one Python object as a parameter then the method to be used is DataFrame.applymap().

Applying a Python function to each row or column of a pandas DataFrame:

The example below sums the elements of a pandas DataFrame both row-wise as well column-wise using the numpy.sum() function.
The Python code uses the method apply(), which takes a function as parameter and applies the function on each column or each row of the DataFrame. Method apply() works on a pandas series or a ndarray.

Example:

# Example Python program to apply a function

# that transforms the elements of a pandas DataFrame

import pandas as pd

import math

import numpy as np

numericData = [(2, 4, 6),

(8, 10, 12),

(45, 50, 55)];

dataFrame = pd.DataFrame(data=numericData);

sum = dataFrame.apply(np.sum);

print("DataFrame:");

print(dataFrame);

print("Sum of each DataFrame column:")

print(sum);

print(type(sum));

print("Sum of each DataFrame row:")

sum = dataFrame.apply(np.sum, axis=1);

print(sum);

print(type(sum));

Output:

DataFrame:

0 1 2

0 2 4 6

1 8 10 12

2 45 50 55

Sum of each DataFrame column:

0 55

1 64

2 73

dtype: int64

Sum of each DataFrame row:

0 12

1 30

2 150

dtype: int64

Applying a Python function to each element of a pandas DataFrame:

The example below applies math.sin() function on a DataFrame that has angle data using the method DataFrame.applymap(). For each element of the DataFrame the function math.sin() is called once. The result is returned as a new DataFrame instance that has the transformed elements.

Example:

# Example Python program to apply a function
# that transforms the elements of a pandas DataFrame

import pandas as pd
import math

panelAngles = [(15, 20, 25),
(30, 35, 40),
(45, 50, 55)];

rows = ["R1", "R2", "R3"];
columnNames = ["C1", "C2", "C3"];

dataFrame = pd.DataFrame(data=panelAngles, index = rows, columns = columnNames);
sinValues = dataFrame.applymap(math.sin);

print("DataFrame:");
print(dataFrame);

print("The function math.sine applied on the DataFrame elements:")
print(sinValues);

Output:

DataFrame:

C1 C2 C3

R1 15 20 25

R2 30 35 40

R3 45 50 55

The function math.sine applied on the DataFrame elements:

C1 C2 C3

R1 0.650288 0.912945 -0.132352

R2 -0.988032 -0.428183 0.745113

R3 0.850904 -0.262375 -0.999755

Transform Pandas DataFrames Using Apply() And Applymap() Functions

Overview:

Applying a Python function to each row or column of a pandas DataFrame:

Example:

Output:

Applying a Python function to each element of a pandas DataFrame:

Example:

Output: