Add code examples for NumPy RNG tutorial (#390)

* Add code examples for NumPy RNG tutorial

* Final QA

---------

Co-authored-by: Geir Arne Hjelle <[email protected]>

Author: martin-martin

numpy-rng/README.md

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+# NumPy Random Number Generation
+
+This folder contains supplementary code for the Real Python tutorial on [NumPy's pseudo-random number generator](https://realpython.com/numpy-random-number-generator/). You can copy the code examples, or continue your learning by experimenting more with them.
+
+## Setup
+
+Create and activate a virtual environment, then install the requirements:
+
+```bash
+$ python -m venv venv
+$ source venv/bin/activate
+(venv) $ python -m pip install -r requirements.txt -c constraints.txt
+```
+
+For most of the examples, you only need a version of NumPy>=1.17 installed, but you'll use `matplotlib` and `scipy` in the final section of the tutorial.
+
+## Usage
+
+After creating and activating your virtual environment, and installing the dependencies, you should be able to run each individual file normally:
+
+```bash
+(venv) $ python filename.py
+```
+
+You can find more information and context on the code blocks in [Using the NumPy Random Number Generator](https://realpython.com/numpy-random-number-generator/).

numpy-rng/array_constraints.py

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+from numpy.random import Generator, PCG64DXSM
+
+rng = Generator(PCG64DXSM())
+
+print(rng.integers(size=(2, 2), low=1, high=5))
+print(rng.uniform(size=(2, 2), low=1, high=5))

numpy-rng/arrays.py

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+import numpy as np
+
+rng = np.random.default_rng()
+
+print(rng.random(size=(5,)))
+print(rng.random(size=(5, 3)))
+print(rng.random(size=(3, 4, 2)))

numpy-rng/cars_per_minute.py

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+import math
+
+lam = 4
+cars_per_minute = [0, 4, 8]
+
+for cars in cars_per_minute:
+    probability = lam**cars * math.exp(-lam) / math.factorial(cars)
+    print(f"P({cars}) = {probability:.1%}")

numpy-rng/choice.py

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+import numpy as np
+
+rng = np.random.default_rng()
+input_array_1d = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
+
+print(rng.choice(input_array_1d, size=3, replace=False))
+print(rng.choice(input_array_1d, size=(2, 3), replace=False))

numpy-rng/choice_shuffle.py

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+import numpy as np
+
+input_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
+
+rng = np.random.default_rng()
+print(rng.choice(input_array, size=2))
+print(rng.choice(input_array, size=2, axis=1))
+print(rng.choice(input_array, size=3, replace=False))
+
+rng = np.random.default_rng(seed=100)
+print(rng.choice(input_array, size=3, replace=False, shuffle=False))
+
+rng = np.random.default_rng(seed=100)
+print(rng.choice(input_array, size=3, replace=False, shuffle=True))
+
+rng = np.random.default_rng(seed=100)
+print(rng.choice(input_array, size=3, replace=True, shuffle=False))
+
+rng = np.random.default_rng(seed=100)
+print(rng.choice(input_array, size=3, replace=True, shuffle=True))

numpy-rng/constraints.txt

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+contourpy==1.0.7
+cycler==0.11.0
+fonttools==4.39.4
+kiwisolver==1.4.4
+matplotlib==3.7.1
+numpy==1.24.3
+packaging==23.1
+Pillow==9.5.0
+pyparsing==3.0.9
+python-dateutil==2.8.2
+scipy==1.10.1
+six==1.16.0

numpy-rng/decks.py

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+import numpy as np
+
+
+def create_deck():
+    RANKS = "2 3 4 5 6 7 8 9 10 J Q K A".split()
+    SUITS = "♣ ♢ ♡ ♠".split()
+    return np.array([r + s for s in SUITS for r in RANKS])
+
+
+def create_high_cards():
+    HIGH_CARDS = "10 J Q K A".split()
+    SUITS = "♣ ♢ ♡ ♠".split()
+    return np.array([r + s for s in SUITS for r in HIGH_CARDS])
+
+
+if __name__ == "__main__":
+    print("Full deck:\n", create_deck(), end="\n\n")
+    print("High cards:\n", create_high_cards(), end="\n\n")

numpy-rng/default_rng.py

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+import numpy as np
+
+default_rng = np.random.default_rng()
+print(default_rng)
+print(default_rng.random())

numpy-rng/generator_swap_and_seeds.py

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+from numpy.random import PCG64DXSM, Generator
+
+pcg64dxsm_rng = Generator(PCG64DXSM())
+print(pcg64dxsm_rng.random())
+
+rng1 = Generator(PCG64DXSM(seed=100))
+print(rng1.random())
+print(rng1.random())
+
+rng2 = Generator(PCG64DXSM(seed=100))
+print(rng2.random())
+print(rng2.random())