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Update README.md
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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David Rotermund
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@ -405,6 +405,185 @@ A = np.array(1 + 0.5j)
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print(A.conj()) # -> (1-0.5j)
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```
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## [numpy.ndarray.sort](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.sort.html)
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```python
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ndarray.sort(axis=-1, kind=None, order=None)
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```
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> Sort an array **in-place**. Refer to numpy.sort for full documentation.
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```python
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import numpy as np
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A = np.arange(0, 6)
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A = np.concatenate((A, A))
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print(A) # -> [0 1 2 3 4 5 0 1 2 3 4 5]
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print()
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A.sort()
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print(A) # -> [0 0 1 1 2 2 3 3 4 4 5 5]
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```
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## [numpy.ndarray.argsort](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.argsort.html)
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```python
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ndarray.argsort(axis=-1, kind=None, order=None)
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```
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> Returns the indices that would sort this array.
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```python
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import numpy as np
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A = np.arange(0, 6)
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A = np.concatenate((A, A))
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print(A) # -> [0 1 2 3 4 5 0 1 2 3 4 5]
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print()
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idx = A.argsort()
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print(idx) # -> [ 0 6 1 7 2 8 3 9 4 10 5 11]
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```
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## [numpy.ndarray.sum](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.sum.html)
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```python
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ndarray.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)
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```
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> Return the sum of the array elements over the given axis.
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```python
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import numpy as np
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A = np.arange(0, 6).reshape((2, 3))
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print(A.sum()) # -> 15
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print(A.sum(axis=0)) # -> [3 5 7]
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print(A.sum(axis=0).shape) # -> (3,)
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print(A.sum(axis=1)) # -> [ 3 12]
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print(A.sum(axis=1).shape) # -> (2,)
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print(A.sum(axis=0, keepdims=True))
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print(A.sum(axis=0, keepdims=True).shape) # -> (1, 3)
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print()
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print(A.sum(axis=1, keepdims=True))
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print(A.sum(axis=0, keepdims=True).shape) # -> (1, 3)
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```
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Output:
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```python
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[[3 5 7]]
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[[ 3]
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[12]]
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```
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## [numpy.ndarray.cumsum](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.cumsum.html)
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```python
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ndarray.cumsum(axis=None, dtype=None, out=None)
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```
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> Return the cumulative sum of the elements along the given axis.
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```python
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import numpy as np
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A = np.arange(0, 6).reshape((2, 3))
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print(A)
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print()
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print(A.cumsum()) # -> [ 0 1 3 6 10 15]
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print(A.cumsum().shape) # -> (6,)
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print(A.cumsum(axis=0))
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print()
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print(A.cumsum(axis=0).shape) # -> (2, 3)
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print(A.cumsum(axis=1))
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print(A.cumsum(axis=1).shape) # -> (2, 3)
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```
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Output:
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```python
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[[0 1 2]
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[3 4 5]]
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[[0 1 2]
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[3 5 7]]
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[[ 0 1 3]
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[ 3 7 12]]
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```
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## [numpy.ndarray.prod](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.prod.html)
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```python
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ndarray.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)
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```
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> Return the product of the array elements over the given axis
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```python
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import numpy as np
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A = np.arange(1, 7).reshape((2, 3))
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print(A)
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print(A.prod()) # -> 720
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print(A.prod().shape) # -> ()
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print(A.prod(axis=0)) # -> [ 4 10 18]
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print(A.prod(axis=0).shape) # -> (3,)
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print(A.prod(axis=1)) # -> [ 6 120]
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print(A.prod(axis=1).shape) # -> (2,)
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```
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Output:
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```python
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[[1 2 3]
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[4 5 6]]
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```
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## [numpy.ndarray.cumprod](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.cumprod.html)
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```python
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ndarray.cumprod(axis=None, dtype=None, out=None)
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```
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> Return the cumulative product of the elements along the given axis.
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```python
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import numpy as np
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A = np.arange(1, 7).reshape((2, 3))
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print(A)
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print()
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print(A.cumprod()) # -> [ 1 2 6 24 120 720]
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print(A.cumprod().shape) # -> (6,)
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print(A.cumprod(axis=0))
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print()
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print(A.cumprod(axis=0).shape) # -> (2, 3)
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print(A.cumprod(axis=1))
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print(A.cumprod(axis=1).shape) # -> (2, 3)
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```
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Output:
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```python
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[[1 2 3]
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[4 5 6]]
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[[ 1 2 3]
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[ 4 10 18]]
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[[ 1 2 6]
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[ 4 20 120]]
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```
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## [Array methods](https://numpy.org/doc/stable/reference/arrays.ndarray.html#array-methods)
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### [Array conversion](https://numpy.org/doc/stable/reference/arrays.ndarray.html#array-conversion)
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