从 while 循环返回 pandas dataframe

Question

I have a while loop that randomly returns three items from a list. And then do the same again until the list is empty. I would like to collect all these 16 rows in a pandas dataframe.

from random import randint

colors = ['Pink', 'Purple', 'Green', 'Skyblue', 'Blue', 'Grey'] * 8

while colors:
    lst = [colors.pop(randint(0, len(colors) - 1)) for _ in range(3)]
    print(lst)

I aim to return from the while loop a pandas dataframe with four columns and 16 rows.

pd.DataFrame(index=np.arange(1,17), columns = ["Store", "Bar 1", "Bar 2", "Bar 3"])

I don't seeam to be able to figure out how to get a while loop to return such a dataframe. Any help would be much appreciated.

Answer 1

There are several things that you've not specified in your question, like:

• Is using a while loop mandatory?
• You talk about 3 items per row, then you add another column to your dataframe for which you don't have a value (talking about that store column).

Taking the first one as "yes" and the second one as "the first column will be another value that you will append with some modification to your code", an option would be:

1. Generate a single list with the data that you want to load to your dataframe.
2. Generate the dataframe with it as source. Dataframe columns need to be the same amount than data width available in the incoming data.

import numpy as np
import pandas as pd
from random import randint

colors = ['Pink', 'Purple', 'Green', 'Skyblue', 'Blue', 'Grey'] * 8
data = []

while colors:
    lst = [colors.pop(randint(0, len(colors) - 1)) for _ in range(3)]
    data.append(lst)

myDataFrame = pd.DataFrame(data, columns = ["Bar 1", "Bar 2", "Bar 3"])

print(myDataFrame)

Execute it to get:

      Bar 1    Bar 2    Bar 3
0      Blue   Purple  Skyblue
1      Blue     Blue     Grey
2    Purple     Pink     Grey
3      Blue     Pink    Green
4     Green  Skyblue    Green
5      Pink     Pink     Grey
6      Pink   Purple    Green
7   Skyblue     Blue  Skyblue
8    Purple     Blue   Purple
9      Grey    Green  Skyblue
10    Green   Purple    Green
11     Grey    Green     Grey
12  Skyblue  Skyblue   Purple
13  Skyblue     Blue     Pink
14     Blue     Pink     Grey
15     Grey   Purple     Pink