I am trying to generate a normally distributed data frame in Python with 3 columns. Each column requires a separate input of mean, standard deviation, lower and upper values. I am trying to generate sales data of Unique Coke products, Unique Products and Total Revenue which are normally distributed. Also, how can I find the mean of the entire normally distributed data frame?
A possibility in the following code snippet.
import pandas as pd import numpy as np df = pd.DataFrame() mean = [0,1,2] scale = [1,2,3] max_val = [2,3,4] min_val = [-2,-1,0] for n in range(3): df[n] = np.random.normal(loc=mean[n],scale=scale[n], size=100) df.loc[df[n] < min_val[n],n]= min_val[n] df.loc[df[n] > max_val[n],n]= max_val[n] print('The Dataframes mean is: ',df.mean().mean())