Merge branch 'master' into python-class

Author: lpozo

build-a-rest-api-frontend/README.md

@@ -1,6 +1,6 @@
 # Build a Front End for a REST API
 
-This repository contains code related to the Real Python tutorial on [building a front end for a REST API](https://realpython.com/build-a-rest-api-frontend/).
+This repository contains code related to the Real Python tutorial on [building a front end for a REST API](https://realpython.com/flask-javascript-frontend-for-rest-api/).
 
 ## Setup
 

numpy-random-normal/README.md

@@ -0,0 +1,46 @@
+# How to Get Normally Distributed Random Numbers With NumPy
+
+This repository holds the code for the Real Python [How to Get Normally Distributed Random Numbers With NumPy](https://realpython.com/numpy-random-normal/) tutorial.
+
+## Dependencies
+
+The scripts use NumPy, Matplotlib, and SciPy. You should first create a virtual environment:
+
+```console
+$ python -m venv venv
+$ source venv/bin/activate
+```
+
+You can then install the necessary packages with `pip`:
+
+```console
+(venv) $ python -m pip install numpy matplotlib scipy
+```
+
+Alternatively, you can install from [`requirements.txt`](requirements.txt) in order to get the same versions of dependencies that were used when developing and testing the code:
+
+```console
+(venv) $ python -m pip install -r requirements.txt
+```
+
+Use only one of the `pip install` commands.
+
+## Run the Code
+
+There are four scripts in this repository. The first three—[`normal_distribution.py`](normal_distribution.py), [`weights.py`](weights.py), and [`dice.py`](dice.py)—show code discussed in the tutorial. The final script, [`plot_normal_dist.py`](plot_normal_dist.py), contains the code used to generate the first figure in the tutorial.
+
+Each script can be run with `python` inside your virtual environment:
+
+```console
+(venv) $ python normal_distribution.py
+```
+
+You can run the other scripts with a similar command.
+
+## Author
+
+- **Geir Arne Hjelle**, E-mail: [[email protected]]([email protected])
+
+## License
+
+Distributed under the MIT license. See [`LICENSE`](../LICENSE) for more information.

numpy-random-normal/dice.py

@@ -0,0 +1,12 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+rng = np.random.default_rng(seed=2310)
+
+rolls = rng.integers(low=1, high=6, endpoint=True, size=(10_000, 2))
+plt.hist(rolls.mean(axis=1), bins=21)
+plt.show()
+
+rolls = rng.integers(low=1, high=6, endpoint=True, size=(10_000, 10))
+plt.hist(rolls.mean(axis=1), bins=41)
+plt.show()

numpy-random-normal/normal_distribution.py

@@ -0,0 +1,25 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.stats
+
+rng = np.random.default_rng(seed=2310)
+numbers = rng.normal(size=10_000)
+
+print(f"Mean of numbers: {numbers.mean():.3f}")
+print(f"Standard deviation of numbers: {numbers.std():.3f}")
+
+bins = 100
+bin_width = (numbers.max() - numbers.min()) / bins
+hist_area = len(numbers) * bin_width
+
+plt.hist(numbers, bins=bins)
+plt.show()
+
+x = np.linspace(-4, 4, 101)
+plt.plot(x, scipy.stats.norm.pdf(x))
+plt.show()
+
+x = np.linspace(numbers.min(), numbers.max(), 101)
+plt.hist(numbers, bins=bins)
+plt.plot(x, scipy.stats.norm.pdf(x) * hist_area)
+plt.show()

numpy-random-normal/plot_normal_dist.py

@@ -0,0 +1,23 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.stats
+
+x = np.linspace(-4, 4, 201)
+y = scipy.stats.norm.pdf(x)
+
+fig, ax = plt.subplots()
+
+ax.fill_between(x, y, 0, alpha=0.2, color="green")
+ax.fill_between(x[75:126], y[75:126], 0, alpha=1, color="green")
+ax.plot(x, y, "k")
+ax.plot([-1, -1], [0, y[75]], "k--")
+ax.plot([1, 1], [0, y[125]], "k--")
+ax.annotate(
+    "68%",
+    xy=(0.1, 0.25),
+    xytext=(2, 0.3),
+    arrowprops=dict(facecolor="black", shrink=0.005),
+)
+ax.set_yticks([])
+
+plt.show()

numpy-random-normal/requirements.in

@@ -0,0 +1,3 @@
+numpy
+matplotlib
+scipy

numpy-random-normal/requirements.txt

@@ -0,0 +1,34 @@
+#
+# This file is autogenerated by pip-compile with python 3.11
+# To update, run:
+#
+#    pip-compile requirements.in
+#
+contourpy==1.0.7
+    # via matplotlib
+cycler==0.11.0
+    # via matplotlib
+fonttools==4.39.3
+    # via matplotlib
+kiwisolver==1.4.4
+    # via matplotlib
+matplotlib==3.7.1
+    # via -r requirements.in
+numpy==1.24.2
+    # via
+    #   -r requirements.in
+    #   contourpy
+    #   matplotlib
+    #   scipy
+packaging==23.0
+    # via matplotlib
+pillow==9.5.0
+    # via matplotlib
+pyparsing==3.0.9
+    # via matplotlib
+python-dateutil==2.8.2
+    # via matplotlib
+scipy==1.10.1
+    # via -r requirements.in
+six==1.16.0
+    # via python-dateutil

numpy-random-normal/weights.py

@@ -0,0 +1,11 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+rng = np.random.default_rng(seed=2310)
+weights = rng.normal(1023, 19, size=5_000)
+
+print(f"Mean of weights: {weights.mean():.2f}g")
+print(f"Standard deviation of weights: {weights.std():.2f}g")
+
+plt.hist(weights, bins=50)
+plt.show()