I would like to randomly select a value in consideration of weightings using Pandas.
df:
0 1 2 3 4 5
0 40 5 20 10 35 25
1 24 3 12 6 21 15
2 72 9 36 18 63 45
3 8 1 4 2 7 5
4 16 2 8 4 14 10
5 48 6 24 12 42 30
I am aware of using np.random.choice, e.g:
x = np.random.choice(
['0-0','0-1',etc.],
1,
p=[0.4,0.24 etc.]
)
And so, I would like to get an output, in a similar style/alternative method to np.random.choice from df, but using Pandas. I would like to do so in a more efficient way in comparison to manually inserting the values as I have done above.
Using np.random.choice I am aware that all values must add up to 1. I'm not sure as to how to go about solving this, nor randomly selecting a value based on weightings using Pandas.
When referring to an output, if the randomly selected weight was for example, 40, then the output would be 0-0 since it is located in that column 0, row 0 and so on.