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1st case :

>>> import numpy as np
>>> x=np.array(0)
>>> x=np.append(x,1)
>>> x
array([0, 1])

x contains 2 elements. Why is that ?!

2nd case :

>>> x=np.array([])
>>> x=np.append(x,1)
>>> x
array([ 1.])

x contains 1 element, as expected.

What's the difference between np.array(0) and np.array([]) ?

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  • 7
    np.array(0) is an array containing 0. whereas np.array([]) is an empty array containing nothing. Commented Nov 28, 2014 at 9:00
  • 3
    The real lesson to learn here is to check your results in between evaluations, it would have became obvious. Commented Nov 28, 2014 at 10:28

1 Answer 1

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In your first case, you are creating an array called x that will containing one value, which is 0.

In your second case you are creating an empty array called x that will contain no values, but is still an array.

FIRST CASE

So when you append x = np.append(x,1), the value 1 get's appended to your array (which already contains 0) i.e. it now contains 0 and 1

SECOND CASE

Since you have no values in the empty array, when you append x=np.append(x,1) the value 1 get's appended and the length of x becomes 1 (i.e. it now contains only 1)

P.S. I believe you might have thought that calling x = np.array(0) with the 0 would make it an empty array, it doesn't!! In Python, 0 is still taken to be a number and is appended to the array.

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5 Comments

Are you sure the 1st case create a 1-item array ? x.shape is ().
@dplamp yes, you are true.. But as mentioned in my answer, when you create x = np.array(0) you are creating x with the value 0. So whatever you are going to append further to x will contain 0 along with the appended value
That is not quite true, I think. My mistake is that array() takes an array-like object as its 1st argument, which is not the case in my '1st case'. Yet, it silently accepts it, which leads to a strange behaviour down the line. Hard to admit for a C++ guy...
@dplamp This is expected behavior. array(0) a 'zero-dimensional' array, while array([0]) is a one-dimensional array.
@dplamp x = np.array(0) creates a scalar, which is still an array (hence, type(x) would return numpy.ndarray), but is of empty shape. The whole handling of scalars vs. one-dimensional arrays seems a bit messy in numpy in general. Have a look at the docs or this answer to get a bit more insight.

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