Generate szenarios with differnet means from data frame

I have the following data frame:

           Cluster  OPS(4)  mean(ln)  std(ln)
0           5-894  5-894a     2.203    0.775
1           5-894  5-894b     2.203    0.775
2           5-894  5-894c     2.203    0.775
3           5-894  5-894d     2.203    0.775
4           5-894  5-894e     2.203    0.775

For each surgery type (in column OPS(4)) I would like to generate 10.000 scenarios which should be stored in another data frame.

I know, that I can create scenarios with:

 num_reps = 10.000
 scenarios  = np.ceil(np.random.lognormal(mean, std, num_reps))

And the new data frame should look like this whith 10,000 scenarios in each column:

scen_per_surg = pd.DataFrame(index=range(num_reps), columns=merged_information['OPS(4)'])

OPS(4) 5-894a 5-894b 5-894c 5-894d 5-894e 
0         NaN    NaN    NaN    NaN    NaN    
1         NaN    NaN    NaN    NaN    NaN    
2         NaN    NaN    NaN    NaN    NaN    
3         NaN    NaN    NaN    NaN    NaN    
4         NaN    NaN    NaN    NaN    NaN    
5         NaN    NaN    NaN    NaN    NaN    
...

Unfortunately, I don't know how to iterate over the rows of the first data frame to create the scenarios.

Can somebody help me? Best regards

Solution

Create some experimenting data

import pandas as pd
df = pd.DataFrame(data=[
                          [ '5-894' , '5-894a'  ,   2.0 ,   0.70],
                          [ '5-894' , '5-894b'  ,   2.1 ,   0.71],
                          [ '5-894' , '5-894c'  ,   2.2 ,   0.72],
                          [ '5-894' , '5-894d'  ,   2.3 ,   0.73],
                          [ '5-894' , '5-894e'  ,   2.4 ,   0.74] ], columns =['Cluster', 'OPS(4)', 'mean(ln)', 'std(ln)'])
print(df)

create an empty dataframe

new_df = pd.DataFrame()

Define a function that will be applied to each row of the original df and generates the random values required and assign it to a column in new df

import numpy as np
def geb_scenarios(row):
  # print(row)
  col, mean, std = row[1:]
  new_df[col] = np.ceil(np.random.lognormal(mean, std, 10))

Apply the function

df.apply(geb_scenarios, axis=1)
print(new_df)