为什么我会收到 numpy.exp 的“ufunc 循环不支持 int 类型的参数 0”错误？

Question

I have a dataframe and I'd like to perform exponential calculation on a subset of rows in a column. I've tried three versions of code and two of them worked. But I don't understand why one version gives me the error.

import numpy as np

Answer 1

I just saw your post and would like to answer.

I guess your problem occurs because some numpy functions require float type argument explicity, whereas your such use of the code as np.exp(test) puts int data into the argument.

Solution could be:

import numpy as np

your_array = your_array.float()
output = np.exp(your_array)

# OR

def exp_test(x)
  x.float()
  return np.exp(x)

output = exp_test(your_array)

Would you check if it works to you? I'd be glad to help.

Answer 2

The root cause of the problem was correct in Yoshiaki's answer

I guess your problem occurs because some numpy functions require float type argument explicity, whereas your such use of the code as np.exp(test) puts int data into the argument.

However, his solution didn't work for me so I adjusted it a little bit and got it work for me

your_array = your_array.astype(float)
output = np.exp(your_array)

Answer 3

Although this question has already been adequately answered, I'd like to share my experience with this issue in the hope of shedding some more light on this sort of problems and what causes them. From what I gathered, the problem is related to "numpy vs non-numpy datatypes". Here's a minimal example:

import numpy as np

arr_float = np.array([1., 2., 3.], dtype=object)
arr_float64 = arr_float.astype(float)  # The solution proposed in other answers
np.exp(arr_float)  # This throws the TypeError
np.exp(arr_float64)  # This works!

There can be various reasons for ending up with a "float-looking" object-type array, likely related to the analyzed data being pulled from a DataFrame where it was stored with an incorrect type (due to the presence of inconvertible entries), or some back-and-forth transitions between numpy and another medium (like pandas).

In conclusion - be careful with datatypes , float ≠ np.float64 !

Answer 4

test = pd.loc[(pd['a'] > 0) & (pd['a'] < 650), 'a'].values