Convert an array of floats to integer

Question

Suppose the following:

import numpy as np

a = np.array ( [1.1, 2.2, 3.3] )

How to convert this array to int without having to iterate each element or using another array?

Why do:

b = int(a)

Gives an error because it is only possible to convert an array of length 1 to int. So I try:

for i in a:
    int(i)
    # ou
    # i = int(i)

does not solve, because a, after the loop remains with the elements in float.

You would then have to use one more array to do this and iterate over this array:

b = np.zeros( a.shape )
for i in xrange(0, len(a))
    b[i] = int(a[i])

Returns me an array of integer numbers, yes, but still of type float, note the point...

print b
[1., 2., 3.]

How to then convert to int?

Answer 1

Try this:

a = array( [1.1, 2.2, 3.3] )
b = array(a, 'int')
File "<stdin>", line 1, in ? TypeError: Array can not be safely cast to required type

b = a.astype(Int)

Answer 2

The arrays of the type numpy.ndarray has a method for type conversion:

import numpy as np

a = np.array( [1.1, 2.2, 3.3] )
b = a.astype('int')
print b
[1, 2, 3]

Care should be taken, however, when dealing with very large arrays, since the astype creates a copy of the array in memory.

Answer 3

I think an iteration by list already solves your problem without needing the astype.

Using list compression it is possible to perform this conversion in a much simpler way:

a = [1.1,2.2,3.3]
b = [int(x) for x in a]

The result of print b will be:

>>[1, 2, 3]

Of course, this will only be valid if they are valid elements to convert, if there are invalid elements (which cannot be converted to integer type), an exception will be returned. In case the ideal would be using a block try\except, if you are not sure of the list elements:

a = [1.1,2.2,3.3]
try:
  b = [int(x) for x in a]
except:
  print 'Nao foi possivel converter o numero.'

Answer 4

An alternative way, using map:

map(int, [1.2, 2.1, 3.1])

The map applies the function passed in the first parameter in each element of the list of the second parameter.

Answer 5

The method array class numpy supports in all 6 types of parameters. Among these parameters we have the dtype. This, in turn, serves to identify the type of data desired for the object array.

See here more details.

This way we can implement an algorithm that converts all elements to the type int. This algorithm is...

from numpy import array

a = array([1.1, 2.2, 3.3])
b = array(a, int)
print(b)

Answer 6

Numpy arrays have an associated type, and the default is float. To create an ints array, you will invariably have to create an array new. On the other hand, this is quite easy:

b = np.array( a, dtype=np.int32)

Answer 7

For some value to be valid to transform into int, it has to be a "practically int".

just separate the whole part of the decimal part:

int = float - (float % 1);

(I do not know how is mod in python, I believe it is %.)