Trying to understand Numpy/array

[nabuchadresar]

Hi guys, it's on my first exercise list with Numpy.
That problem:
Consider the 3 arrays below
Return the value of the xarr array if the value is True in the cond array. Otherwise, return the value of the yarr array.

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import numpy as np   xarr = np.array([1.1, 1.2, 1.3, 1.4, 1.5]) yarr = np.array([2.1, 2.2, 2.3, 2.4, 2.5]) cond = np.array([True, False, True, True, False])   #my answer to the problem   result1 = np.arange(cond.size) a=0 for i in cond:     if i == True:         #print(xarr[a])         result1[a] = xarr[a]         a=a+1     else:         #print(yarr[a])         result1[a] = yarr[a]         a=a+1 print(result1)

In response I get this:

1	[1 2 1 1 2]

But I would like to get this:

1	[1.1 2.2 1.3 1.4 2.5]

I'm starting study with Data Science, this is the first list of exercises I've done with NumPy. I was confused because when I use the "prints" that are inside the "if/else", they return the value I want, but when I assign them to the new "array" they don't go as I imagine. What logic am I wrong or missing? from now on, grateful

[buran]

Numpy array is not like list. All elements in the array are of same type and the type is fixed, unless you cast it to different dtype.
You can specify the dtype when you create the array with arange, but you do not do so and numpy infer the dtype from other arguments and in this case the dtype of result1 is int32. You can do result1 = np.arange(cond.size, dtype=float) and to get dtype float64. You can use other types

[nabuchadresar]

Very thanks

a lesson learned!!