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 2023-12-14 16:05:43 +01:00 committed by GitHub
parent 79bcc8a898
commit 6bc2902c5c
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 116 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

116
numpy/bool_matrix/README.md
View file

@ -11,6 +11,122 @@
Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
## Boolean matrices
There are different ways to get a Boolean matrix. For example the result of a **np.isfinite()** (checks if the values in a matrix are infite values) is a Boolean matrix.
```python
import numpy as np
a = 1.0 / np.arange(0, 6).reshape((2, 3))
print(a)
print()
print(np.isfinite(a))
```
Output:
```python
[[ inf 1. 0.5 ]
[0.33333333 0.25 0.2 ]]
[[False True True]
[ True True True]]
<ipython-input-3-7ead4ee291d3>:4: RuntimeWarning: divide by zero encountered in divide
a = 1.0 / np.arange(0, 6).reshape((2, 3))
```
However, there are other ways to produce Boolean matrixes:
```python
import numpy as np
a = np.arange(0, 6).reshape((2, 3))
print(a)
print()
print((a > 1))
```
Output:
```python
[[0 1 2]
[3 4 5]]
[[False False True]
[ True True True]]
```
## Calculating with boolean matrices
You can treat them a matricies that are filled with 0 (False) and 1 (True):
```python
import numpy as np
a = np.arange(0, 6).reshape((2, 3))
print(a)
print()
print((a > 1) * (a < 4))
```
Output:
```python
[[0 1 2]
[3 4 5]]
[[False False True]
[ True False False]]
```
Or you can use Boolean logical matrix functions:
```python
import numpy as np
a = np.arange(0, 6).reshape((2, 3))
print(a)
print()
print(np.logical_and((a > 1), (a < 4)))
```
Output:
```python
[[0 1 2]
[3 4 5]]
[[False False True]
[ True False False]]
```
## [np.nonzero()](https://numpy.org/doc/stable/reference/generated/numpy.nonzero.html)
```python
numpy.nonzero(a)
```
> Return the indices of the elements that are non-zero.
We can use nonzero for extracting the positions where the boolean value is true:
```python
import numpy as np
a = np.arange(0, 6).reshape((2, 3))
print(a)
print()
print(np.nonzero(a > 1))
```
Output:
```python
[[0 1 2]
[3 4 5]]
(array([0, 1, 1, 1]), array([2, 0, 1, 2]))
```
## [Logic functions](https://numpy.org/doc/stable/reference/routines.logic.html)