Apply log2 transformation to a pandas DataFrame

I want to apply log2 with applymap and np2.log2to a data and show it using boxplot, here is the code I have written:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

data = pd.read_csv('testdata.csv')
df = pd.DataFrame(data)

################################
    # a.
df.boxplot()
plt.title('Raw Data')

################################
    # b.
df.applymap(np.log2)
df.boxplot()
plt.title('Normalized Data')

and below is the boxplot I get for my RAW data which is okay, but I do get the same boxplot after applying log2 transformation !!! can anyone please tell me what I am doing wrong and what should be corrected to get the normalized data with applymap and np.log2

Answer

According to API Reference DataFrame.applymap(func)

Apply a function to a DataFrame that is intended to operate elementwise, i.e. like doing map(func, series) for each series in the DataFrame

It won’t change the DataFrame you need to get the return value and use it.