Myujth

I am trying to take the sum of four columns in a pandas dataframe (which are determined by a random number) and simulate this process 1000 times. I want this to give me 1000 rows each with different results, for each column.

I essentially want to say something like the following:

for i in range(1000):
 np.sum(df['A']) = iterations[i, j]

where df['A'] is one of the columns I want to sum for each iteration. That is, 'for each iteration, sum the column values and 'place' this result in a new dataframe called 'iterations', specifying where the result is going to go'. I understand the code doesn't make sense but it describes what I am trying to achieve. To be clear, I do not want to write the result to a csv or txt file.

Thank you in advance for your advice.

Take the sum of four columns in a pandas dataframe (which are determined by a random number) and simulate this process 1000 times. This should give me 1000 rows each with different results, for each column. We can write:

import os
import pandas as pd
import numpy as np
import random
from tqdm import tqdm

df_output = 

for i in tqdm(range(1000)):
 sample_matrix = np.random.rand(60,4)
 df = pd.DataFrame(sample_matrix)
 df.columns = ['V_' + str(col) for col in df.columns]
 df_output.append(np.array(df.sum()))

df_output

df_output will be a matrix, where the number of rows is 1000 (= the number of simulations)

Without knowing how/why you plan on randomizing each column each iteration, this will work:

df = pd.DataFrame(np.random.rand(500,4)) # initialize with random data

iterations = [df.sum()]
for i in range(999):
 iterations = np.vstack([iterations, df.sum()])

iterations = pd.DataFrame(iterations)