Skip to content

numpy.random: fix broken link and update syntax to use new RNG system #185

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jan 28, 2022
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Original file line number Diff line number Diff line change
Expand Up @@ -150,19 +150,22 @@ Numpy has other functions for creating sequential arrays, such as producing an a
## Creating Arrays Using Random Sampling
Several functions can be accessed from `np.random`, which populate arrays of a user-specified shape by drawing randomly from a specified statistical distribution:
```python
# construct a new random number generator
>>> rng = np.random.default_rng()

# create a shape-(3,3) array by drawing its entries randomly
# from the uniform distribution [0, 1)
>>> np.random.rand(3,3)
>>> rng.random((3, 3))
array([[ 0.09542611, 0.13183498, 0.39836068],
[ 0.7358235 , 0.77640024, 0.74913595],
[ 0.37702688, 0.86617624, 0.39846429]])

# create a shape-(5,) array by drawing its entries randomly
# from a mean-0, variance-1 normal (a.k.a. Gaussian) distribution
>>> np.random.randn(5)
>>> rng.normal(size=(5,))
array([-1.11262121, -0.35392007, 0.4245215 , -0.81995588, 0.65412323])
```
There are [many more functions](https://numpy.org/doc/stable/reference/routines.random.html#distributions) to read about that allow you to draw from a wide variety of statistical distributions. This only scratches the surface of random number generation in NumPy.
There are [many more functions](https://numpy.org/doc/stable/reference/random/generator.html#simple-random-data) to read about that allow you to draw from a wide variety of statistical distributions. This only scratches the surface of random number generation in NumPy.

<!-- #endregion -->

Expand Down