I wonder if there is a rationale behind the fact that np.size('')
returns 1
, given the fact that len('')
or np.size([])
, for instance, both return 0
.
The np. size() function count items from a given array and give output in the form of a number as size. If you want to count how many items in a row or a column of NumPy array.
reshape(-1, 1) if your data has a single feature or array. reshape(1, -1) if it contains a single sample. We could change our Series into a NumPy array and then reshape it to have two dimensions. However, as you saw above, there's an easier way to make x a 2D object.
"NP" (Not Proficient) The "NP" grade is used only for 1001-level and below English, Math, and Statistics courses that require a level of proficiency to move through the sequence and that are approved by the appropriate College committees.
Size of the first dimension of the NumPy array: len() len() is the Python built-in function that returns the number of elements in a list or the number of characters in a string. For numpy. ndarray , len() returns the size of the first dimension.
np.size
of any str
is 1. This is also true of most Python objects which are not lists.
Calling help
on it prints:
Help on function size in module numpy:
size(a, axis=None)
Return the number of elements along a given axis.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which the elements are counted. By default, give
the total number of elements.
Returns
-------
element_count : int
Number of elements along the specified axis.
...
From this we see that the provided first argument ought to be "array_like", and so should not be a str
in any case.
The source code of the body of np.size
is:
if axis is None:
try:
return a.size
except AttributeError:
return asarray(a).size
else:
try:
return a.shape[axis]
except AttributeError:
return asarray(a).shape[axis]
When provided a str
, it calls asarray
on the object. This results in a 0-dimensional array being created, which will always have a size of 1.
>>> a = np.asarray('')
>>> a
array('', dtype='<U1')
>>> a.size
1
>>> a.ndim
0
>>>
>>> b = np.asarray('example str')
>>> b
array('example str', dtype='<U11')
>>> b.size
1
>>> b.ndim
0
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