Transformations

In this section, we will consider three transformations:

• Log transformation
• Square root transformation
• Cube root transformation

First, we will import the numpy package to create a random sample drawn from a Beta distribution. The documentation on Beta distributions can be found here:

https://numpy.org/doc/stable/reference/random/generated/numpy.random.beta.html

The sample, df, has 10,000 values. We also use matplotlib.pyplot to create different histogram plots. Second, we transform the original data by using a log transformation, square root transformation, and cube root transformation, and we draw four histograms:

import numpy as np
import matplotlib.pyplot as plt
np.random.seed(42) # for reproducible purpose
# create a random data
df = np.random.beta(a=1, b=10, size = 10000)
df_log = np.log(df) #log transformation
df_sqrt = np.sqrt(df) # Square Root transformation
df_cbrt = np.cbrt(df) # Cube Root transformation
plt.figure(figsize ...