How to run assembled sample data - pandas

Question

I have a pd df assembled from various samples that I randomly picked. Now, I want to run 10,000 times and get mean values for column ['MP_learning'] and ['LCC_saving'] for each row.
How should I write the code?
I tried
output=np.mean(df), but it didn't work.
PC EL MP_Learning LCC _saving
0 1 0 24 95
1 1 1 35 67
2 1 2 12 23
3 1 3 23 45
4 2 0 36 67
5 2 1 74 10
6 2 2 80 23
np.random.seed()
output=[]
for i in range (10000):
output=np.mean(df)
output

Answer 1

For your code, you did not post the entire code. Thus, I don't know where the data come from. However, I replicated something similar and here is the solution. For you loop code though, you suppose to append to output. Use only one of those two lines in the "for" loop code, unless you need them both.
import pandas as pd
import numpy as np
df =\
pd.DataFrame([[1,0,24,95],
[1,1,35,67],
[1,2,12,23],
[1,3,23,45],
[2,0,36,67],
[2,1,74,10],
[2,2,80,23]],
columns = ["PC","EL","MP_Learning","LCC_saving"],
index = [0,1,2,3,4,5,6]
).T
output = []
for i in range (10000):
# Use the line below to get mean for both column
output.append(np.mean([df.loc["MP_Learning"],df.loc["LCC_saving"]]))
# Use the line below to get mean for one column
output.append(np.mean(df.loc["MP_Learning"]))
print(output)