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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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## Goal
If you don't see something like **np.random.default_rng()** in your code then you are probably using the old [Legacy Random Generation](https://numpy.org/doc/stable/reference/random/legacy.html#legacy-random-generation).
**Don't use the legacy methods** for new source code!!!
**Don't use the [legacy](https://numpy.org/doc/stable/reference/random/legacy.html) methods** for new source code!!!
**numpy.random.random() == old == bad == don't use​**
Do it like this:
```python
@ -65,13 +67,13 @@ print(rng) # -> Generator(PCG64)
```
If you don't like it there are other options:
| |
| -------------|
|[MT19937](https://numpy.org/doc/stable/reference/random/bit_generators/mt19937.html)|
|[PCG64](https://numpy.org/doc/stable/reference/random/bit_generators/pcg64.html)|
|[PCG64DXSM](https://numpy.org/doc/stable/reference/random/bit_generators/pcg64dxsm.html)|
|[Philox](https://numpy.org/doc/stable/reference/random/bit_generators/philox.html)|
|[SFC64](https://numpy.org/doc/stable/reference/random/bit_generators/sfc64.html)|
|||
|---|---|
|[PCG64](https://numpy.org/doc/stable/reference/random/bit_generators/pcg64.html) -- **The default**| A fast generator that can be advanced by an arbitrary amount. See the documentation for advance. PCG-64 has a period of 2^128. See the PCG author’s page for more details about this class of PRNG.​|
|[MT19937](https://numpy.org/doc/stable/reference/random/bit_generators/mt19937.html)| The standard Python BitGenerator. Adds a MT19937.jumped function that returns a new generator with state as-if 2^128 draws have been made.|
|[PCG64DXSM](https://numpy.org/doc/stable/reference/random/bit_generators/pcg64dxsm.html)| An upgraded version of PCG-64 with better statistical properties in parallel contexts. See Upgrading PCG64 with PCG64DXSM for more information on these improvements.​|
|[Philox](https://numpy.org/doc/stable/reference/random/bit_generators/philox.html)|A counter-based generator capable of being advanced an arbitrary number of steps or generating independent streams. See the Random123 page for more details about this class of bit generators.​|
|[SFC64](https://numpy.org/doc/stable/reference/random/bit_generators/sfc64.html)|A fast generator based on random invertible mappings. Usually the fastest generator of the four. See the SFC author’s page for (a little) more detail.​|
## [Distributions](https://numpy.org/doc/stable/reference/random/generator.html#distributions) (you will use)
The most important ones are in **bold**. If you see a function argument *out*, then you can reuse an existing np array (i.e. [in-place operation](https://numpy.org/doc/stable/reference/random/generator.html#in-place-vs-copy)) as target.