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I was working on this piece of code and was stuck here. 

import numpy as np
a = np.arange(10)
a[7:] = np.nan

By theory, it should insert missing values starting from index 7 until the end of the array. However, when I ran the code, some random values are inserted into the array instead of NA's.

Can someone explain what happened here and how should I insert missing values intentionally into numpy arrays?

jpp
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vivek
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    `how should I insert missing values intentionally into numpy arrays` - Change dtype to float. – Divakar Jul 31 '18 at 11:14
  • Possible duplicate of [Numpy integer nan](https://stackoverflow.com/questions/12708807/numpy-integer-nan) – jpp Jul 31 '18 at 11:16

1 Answers1

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Not-a-number (NA) is a special type of floating point number. By default, np.arange() creates an array of type int. Casting this to float should allow you to add NA's:

import numpy as np
a = np.arange(10).astype(float)
a[7:] = np.nan
Marijn van Vliet
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