Python Numpy how to change the data types inside an array-CodePudding

I am trying to calculate information from an array that contains integers, however when I do a calculation the results are foat's. How do I change the ndarry to accept 0.xxx numbers as a input. Currently I am only getting 0's. Here is the code I have been trying to get working:

        ham_fields = np.array([], dtype=float)   # dtype specifies the type of the elements
        ham_total = np.array([], dtype=float)  # dtype specifies the type of the elements
        ham_fields = data[data[:, 0] == 0]  # All the first column of the dataset doing a check if they are true or false
        ham_sum = np.delete((ham_fields.sum(0)),0)  # Boolean indices are treated as a mask of elements to remove none Ham items
        ham_total = np.sum(ham_sum)
        ham_len = len(ham_sum)



        for i in range(ham_len):
            ham_sum[i] = (ham_sum[i]   self.alpha) / (ham_total   (ham_len * self.alpha))

CodePudding user response：

ham_fields = np.array([], dtype=float)

ham_fields = data[data[:, 0] == 0] 
ham_sum = np.delete((ham_fields.sum(0)),0)

This line assigns a new array object to ham_fields. The first assignment did nothing for you. In Python variables are not declared at the start.

If data has a int dtype, then so does ham_fields. You could change that with a another assignment

ham_fields = ham_fields.astype(float)

ham_sum has the same dtype as ham_fields, from which it's derived.

Assigning a float to an element of a int dtype array will not change the dtype.

    for i in range(ham_len):
        ham_sum[i] = (ham_sum[i]   self.alpha) / (ham_total   (ham_len * self.alpha))

If self.alpha, ham_total are scalar then you should be able to do

ham_sum = (ham_sum   self.alpha)/(ham_toal   (ham_len * self.alpha))

This makes a new array, which will be float, and assigns it to ham_sum variable. It's a new assignment (not modification) so the float dtype is preserved. Or to make things clear, assign it to a new variable name.

CodePudding user response：

You can use astype(int) to convert it to an int array after the calculation

import numpy as np

array1 = np.array([1, 2, 3])
print(array1.dtype)
#output: int64

array2 = np.array([2, 3, 4])
print(array2.dtype)
#output: int64

array3 = array1 / array2
print(array3.dtype)
#output: float64

array4 = array3.astype(int)
print(array3.dtype)
#output: int64

You could also do that inside of your calculation by working with brackets:

array3 = (array1 / array2).astype(int)