This rule raises an issue when a generator is passed to
Why is this an issue?
The creation of a NumPy array can be done in several ways, for example by passing a Python list to the
np.array function. Another way
would be to pass a generator to the
np.array function, but doing so creates a 0-dimensional array of objects and may not be the intended
goal. This NumPy array will have a have a data type (dtype) of
object and could hold any Python objects.
One of the characteristics of NumPy arrays is homogeneity, meaning all its elements are of the same type. Creating an array of objects allows the
user to create heterogeneous array without raising any errors and creating such an array can lead to bugs further in the program.
arr = np.array(x**2 for x in range(10))
arr.put(indices=1, values=3) # No issues raised.
The NumPy array
arr shown above now holds 2 values: a generator and the number 3.
How to fix it
To fix this issue, either:
- pass a Python list instead of a generator to the
np.array function or,
- explicitly show the intention to create a 0-dimensional array of objects by either adding
Any as the type hint of the generator or
by specifying the
dtype parameter of the NumPy array as
Noncompliant code example
arr = np.array(x**2 for x in range(10)) # Noncompliant: the resulting array will be of the data type: object.
gen = (x*2 for x in range(5))
arr = np.array(gen) # Noncompliant: the resulting array will be of the data type: object.
from typing import Any
arr = np.array([x**2 for x in range(10)]) # Compliant: a list of 10 elements is passed to the np.array function.
arr = np.array(x**2 for x in range(10), dtype=object) # Compliant: the dtype parameter of np.array is set to object.
gen: Any = (x*2 for x in range(5))
arr = np.array(gen) # Compliant: the generator is explicitly type hinted with Any.