NumPyArray Iterating
Iterating Arrays
Iterating means going through elements one by one.
As we deal with multi-dimensional arrays in numpy, we can do this using basicfor loop of python.
If we iterate on a 1-D array it will go through each element one by one.
Example
Iterate on the elements of the following 1-D array:
arr = np.array([1, 2, 3])
for x in arr:
print(x)
Iterating 2-D Arrays
In a 2-D array it will go through all the rows.
Example
Iterate on the elements of the following 2-D array:
arr = np.array([[1, 2, 3], [4, 5, 6]])
for x in arr:
print(x)
If we iterate on an-D array it will go through n-1th dimension one by one.
To return the actual values, the scalars, we have to iterate the arrays in each dimension.
Example
Iterate on each scalar element of the 2-D array:
arr = np.array([[1, 2, 3], [4, 5, 6]])
for x in arr:
for y in x:
print(y)
Iterating 3-D Arrays
In a 3-D array it will go through all the 2-D arrays.
Example
Iterate on the elements of the following 3-D array:
arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
for x in arr:
print(x)
To return the actual values, the scalars, we have to iterate the arrays in each dimension.
Example
Iterate down to the scalars:
arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
for x in arr:
for y in x:
for z in y:
print(z)
Iterating Arrays Using nditer()
The functionnditer() is a helping function that can be used from very basic to very advanced iterations. It solves some basic issues which we face in iteration, lets go through it with examples.
Iterating on Each Scalar Element
In basicfor loops, iterating through each scalar of an array we need to usenfor loops which can be difficult to write for arrays with very high dimensionality.
Example
Iterate through the following 3-D array:
arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
for x in np.nditer(arr):
print(x)
Iterating Array With Different Data Types
We can useop_dtypes argument and pass it the expected datatype to change the datatype of elements while iterating.
NumPy does not change the data type of the element in-place (where the element is in array) so it needs some other space to perform this action, that extra space is called buffer, and in order to enable it innditer() we passflags=['buffered'].
Example
Iterate through the array as a string:
arr = np.array([1, 2, 3])
for x in np.nditer(arr, flags=['buffered'], op_dtypes=['S']):
print(x)
Iterating With Different Step Size
We can use filtering and followed by iteration.
Example
Iterate through every scalar element of the 2D array skipping 1 element:
arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
for x in np.nditer(arr[:, ::2]):
print(x)
Enumerated Iteration Using ndenumerate()
Enumeration means mentioning sequence number of somethings one by one.
Sometimes we require corresponding index of the element while iterating, thendenumerate() method can be used for those usecases.
Example
Enumerate on following 1D arrays elements:
arr = np.array([1, 2, 3])
for idx, x in np.ndenumerate(arr):
print(idx, x)
Example
Enumerate on following 2D array's elements:
arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
for idx, x in np.ndenumerate(arr):
print(idx, x)
