Numpy: TypeError: 'float' object cannot be interpreted as an integer

Question

Given that i have a dataset as below:

dict = {
    "No": list(range(1,9)),
    "Pro":  [0.6703557312252965, 0.20395256916996046, 0.0782608695652174, 0.03241106719367589, 0.009486166007905139, 0.0031620553359683794, 0.0015810276679841897, 0.0007905138339920949]
}

dt = pd.DataFrame(dict)

And i need to generate a number from "NO" column based on "Pro" column:

noList = dt["No"].tolist()
pro = dt["Pro"].tolist()
print("NoList", noList, " Pro: ", pro)
randomPolypNo = np.random.choice(noList, pro)

But, it complains with:

TypeError: 'float' object cannot be interpreted as an integer

What is the problem?

From the docs: parameter 2 is size, not the weights list. Try: np.random.choice(dt["No"].tolist(), 1, p=dt["Pro"].tolist()) — ggorlen
– ggorlen, Commented Jun 11, 2020 at 20:47

Luke WIlliams · Accepted Answer · 2020-06-11 20:52:43Z

2

I think you need to specify that pro is your probability distribution.

randomPolypNo = np.random.choice(noList, p=pro)

As is, it's interpreting pro as the size of the desired sample, which needs to be an int.

answered Jun 11, 2020 at 20:52

Luke WIlliams

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alefianrahman · Accepted Answer · 2020-06-11 20:59:02Z

2

You need to specify the arguments. Did you mean "Pro" as the probabilities? You can solve it by this.

randomPolypNo = np.random.choice(a=noList, p=pro)

edited Jun 11, 2020 at 20:59

answered Jun 11, 2020 at 20:53

alefianrahman

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