Question

Transform the values of a dataframe to exponential, logarithmic, and square root.

Answer 1

To transform dataframe values to exponential, logarithmic, and square root, use NumPy functions exp, log, and sqrt respectively, applying them to the desired dataframe column.

Step-by-step explanation:

To transform the values of a dataframe to exponential, logarithmic, and square root, you can use various functions from a data manipulation library such as pandas in Python. Assuming you have a pandas dataframe, you can apply these transformations to a column of your choice. Let's consider a dataframe column df['values'].

• To transform the values to exponential, you can use the NumPy library's exp function:

import numpy as np
df['exponential'] = np.exp(df['values'])

• For logarithmic transformation, ensuring that all values are positive, apply the log function:

df['logarithmic'] = np.log(df['values'])

• To get the square root, use the sqrt function:

df['square_root'] = np.sqrt(df['values'])

It's important to handle any potential issues such as negative values for the log transformation or zero and negative values for the square root transformation, as these will result in undefined or NaN values.