davrot/pytutorial
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Update README.md

Browse source 
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
This commit is contained in:
David Rotermund 2023-12-29 17:36:43 +01:00 committed by GitHub
parent 258986b0e4
commit 3d7f5b71fd
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 70 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

70
numpy/stack_split/README.md
View file

@ -84,3 +84,73 @@ Output:
[0 1 2 3 4 5 6 7 8 9]]
```
## [numpy.vstack](https://numpy.org/doc/stable/reference/generated/numpy.vstack.html)
```python
numpy.vstack(tup, *, dtype=None, casting='same_kind')
```
> Stack arrays in sequence vertically (row wise).
>
> This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by vsplit.
>
> This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.
>
> np.row_stack is an alias for vstack. They are the same function.
```python
import numpy as np
a = np.zeros((2, 3, 4))
print(a.shape) # -> (2, 3, 4)
b = np.vstack((a, a))
print(b.shape) # -> (4, 3, 4)
```
## [numpy.hstack](https://numpy.org/doc/stable/reference/generated/numpy.hstack.html)
```python
numpy.hstack(tup, *, dtype=None, casting='same_kind')
```
> Stack arrays in sequence horizontally (column wise).
>
> This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis. Rebuilds arrays divided by hsplit.
>
> This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.
```python
import numpy as np
a = np.zeros((2, 3, 4))
print(a.shape) # -> (2, 3, 4)
b = np.hstack((a, a))
print(b.shape) # -> (2, 6, 4)
```
## [numpy.dstack](https://numpy.org/doc/stable/reference/generated/numpy.dstack.html)
```python
numpy.dstack(tup)
```
> Stack arrays in sequence depth wise (along third axis).
>
> This is equivalent to concatenation along the third axis after 2-D arrays of shape (M,N) have been reshaped to (M,N,1) and 1-D arrays of shape (N,) have been reshaped to (1,N,1). Rebuilds arrays divided by dsplit.
>
> This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.
```python
import numpy as np
a = np.zeros((2, 3, 4))
print(a.shape) # -> (2, 3, 4)
b = np.dstack((a, a))
print(b.shape) # -> (2, 3, 8)
```