Where clause with numpy with single array and / or empty_like

Question

I am trying to figure out how the np.where clause works. I create a simple df:

np.random.seed(1)
df = pd.DataFrame(np.random.randint(0, 10, size=(3, 4)), columns=list('ABCD'))
print(df)

   A  B  C  D
0  5  8  9  5
1  0  0  1  7
2  6  9  2  4

Now when I implement:

print(np.where(df.values, 1, np.nan))

I receive:

[[  1.   1.   1.   1.]
 [ nan  nan   1.   1.]
 [  1.   1.   1.   1.]]

But when I create an empty_like array from df: and put it into where clause I receive this:

print(np.where(np.empty_like(df.values), 1, np.nan))

[[ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]]

Really could use help on explaining how where clause works on a single array.

Answer 1

np.empty_like()

Docs:-

numpy.empty_like(prototype, dtype=None, order='K', subok=True)

Return a new array with the same shape and type as a given array.

>>> a = ([1,2,3], [4,5,6])                         # a is array-like
>>> np.empty_like(a)
array([[-1073741821, -1073741821,           3],    #random
       [          0,           0, -1073741821]])

np.empty_like() creates an array of the same shape and type as the given array but with random numbers. This array now goes into np.where()

numpy.where()

Docs:-

numpy.where(condition[, x, y])

Return elements that are chosen from x or y depending on condition.

Example:-

>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.where(a < 5, a, 10*a)
array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])
>>>np.where(a,1,np.nan)
array([nan,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])

In Python any number other than zero is considered to be TRUE whereas zero is considered to FALSE.

When np.where() gets a np.array it checks for the condition, Here the array itself acts as condition i.e, the np.where evaluates to TRUE when the array elements are not zero and FALSE when they are 0. So the "True" elements are replaced by 1 and "False" elements by np.nan.

Reference:-

1. numpy.where()
2. numpy.empty_like()