Add runnable scripts to numpy-reshape materials

numpy-reshape/README.md

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-# Image Repository For Using NumPy reshape() to Change the Shape of an Array
+# Using NumPy reshape() to Change the Shape of an Array
 
-This image repository accompanies [Using NumPy reshape() to Change the Shape of an Array](https://realpython.com/numpy-reshape/). It contains an image that you'll need while exploring the code in the tutorial.
+This folder contains the sample code and image that accompany the Real Python
+tutorial [Using NumPy reshape() to Change the Shape of an Array](https://realpython.com/numpy-reshape/).
+
+## Setup
+
+Create and activate a virtual environment, then install the requirements:
+
+```console
+$ python -m venv venv
+$ source venv/bin/activate
+(venv) $ python -m pip install -r requirements.txt
+```
+
+## Scripts
+
+Each script is self-contained and mirrors one section of the tutorial. The
+scripts that use random data seed the generator so that the output is
+reproducible.
+
+| Script | Tutorial section |
+| --- | --- |
+| `array_shape.py` | Understand the shape of NumPy arrays |
+| `change_shape.py` | Change an array's shape using `reshape()` |
+| `reduce_dimensions.py` | Reduce an array's number of dimensions |
+| `increase_dimensions.py` | Increase an array's number of dimensions |
+| `compatible_shapes.py` | Ensure the new shape is compatible |
+| `order_parameter.py` | Control how data is rearranged using `order` |
+| `color_image.py` | Reduce a 3D color image to two dimensions |
+| `wildcard.py` | Use `-1` as an argument in `reshape()` |
+
+Run any script with Python:
+
+```console
+(venv) $ python change_shape.py
+```
+
+`color_image.py` reads `poppy.jpg` from this folder and opens the reshaped
+images in your default image viewer, so run it from this directory.
 
 ## Image Credit
+
 - poppy.jpg: [Pixabay](https://pixabay.com/photos/poppy-summer-red-nature-flower-2381645/) by [kellepics](https://pixabay.com/users/kellepics-4893063/)

numpy-reshape/array_shape.py

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+"""Inspect the shape and number of dimensions of a NumPy array."""
+
+import numpy as np
+
+numbers = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
+print(numbers)
+print(f"Shape: {numbers.shape}")
+print(f"Number of dimensions: {numbers.ndim}")

numpy-reshape/change_shape.py

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+"""Change an array's shape with reshape() without changing its data.
+
+The random generator is seeded so that the output is reproducible.
+"""
+
+import numpy as np
+
+rng = np.random.default_rng(seed=42)
+results = rng.integers(0, 100, size=(5, 10))
+print(f"Original results, shape {results.shape}:")
+print(results)
+
+# Reshape the five classes of ten scores into a single row
+year_results = results.reshape((1, 50))
+print(f"\nReshaped to one row, shape {year_results.shape}:")
+print(year_results)
+
+# year_results is still 2D, so you index it with both a row and a column
+print(f"\nFirst score: {year_results[0, 0]}")

numpy-reshape/color_image.py

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+"""Reshape a 3D color image into a 2D triptych of its color channels.
+
+Run this script from the folder that contains poppy.jpg. Each reshaped
+image opens in your default image viewer.
+"""
+
+import numpy as np
+from PIL import Image
+
+with Image.open("poppy.jpg") as photo:
+    image_array = np.array(photo)
+
+print(f"Image shape: {image_array.shape}")
+height, width, _ = image_array.shape
+
+# The default order="C" interleaves each pixel's color channels
+triptych_c = image_array.reshape((height, 3 * width))
+print(f"Reshaped shape: {triptych_c.shape}")
+Image.fromarray(triptych_c).show()
+
+# order="F" places the red, green, and blue channels side by side
+triptych_f = image_array.reshape((height, 3 * width), order="F")
+Image.fromarray(triptych_f).show()

numpy-reshape/compatible_shapes.py

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+"""Reshape into compatible shapes by trimming or extending the data."""
+
+import numpy as np
+
+rng = np.random.default_rng(seed=42)
+temperatures = rng.normal(18, 1, size=200)
+
+# An incompatible shape raises a ValueError
+try:
+    temperatures.reshape((3, 7, 8))
+except ValueError as error:
+    print(f"ValueError: {error}")
+
+days_per_week = 7
+readings_per_day = 8
+number_of_weeks = len(temperatures) // (days_per_week * readings_per_day)
+trimmed_length = number_of_weeks * days_per_week * readings_per_day
+
+# Option 1: trim the data so that it fits a whole number of weeks
+temperatures_week = temperatures[:trimmed_length].reshape(
+    (number_of_weeks, days_per_week, readings_per_day)
+)
+print(f"Trimmed shape: {temperatures_week.shape}")
+
+# Option 2: extend the data with np.nan filler values
+extended_length = (number_of_weeks + 1) * (days_per_week * readings_per_day)
+additional_length = extended_length - len(temperatures)
+temperatures_extended = np.append(
+    temperatures, np.full(additional_length, np.nan)
+)
+temperatures_week = temperatures_extended.reshape(
+    (number_of_weeks + 1, days_per_week, readings_per_day)
+)
+print(f"Extended shape: {temperatures_week.shape}")

numpy-reshape/increase_dimensions.py

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+"""Increase the number of dimensions of an array with reshape()."""
+
+import numpy as np
+
+rng = np.random.default_rng(seed=42)
+temperatures = rng.normal(18, 1, size=200)
+print(f"Original shape: {temperatures.shape}")
+
+# 200 readings, eight per day, gives 25 rows of eight columns
+temperatures_day = temperatures.reshape((25, 8))
+print(f"Reshaped into days, shape: {temperatures_day.shape}")
+print("Second day's readings:")
+print(temperatures_day[1])

numpy-reshape/order_parameter.py

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+"""Control how reshape() rearranges data with the order parameter."""
+
+import numpy as np
+
+numbers = np.array([1, 2, 3, 4, 5, 6, 7, 8])
+
+# Row-major (C) order fills each row before moving to the next
+print('order="C":')
+print(numbers.reshape((2, 4), order="C"))
+
+# Column-major (F) order fills each column before moving to the next
+print('order="F":')
+print(numbers.reshape((2, 4), order="F"))

numpy-reshape/reduce_dimensions.py

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+"""Reduce the number of dimensions of an array with reshape()."""
+
+import numpy as np
+
+rng = np.random.default_rng(seed=42)
+results = rng.integers(0, 100, size=(5, 10))
+
+# Pass a one-element tuple to reshape the 2D array into a 1D array
+year_results = results.reshape((50,))
+print(f"Shape: {year_results.shape}, dimensions: {year_results.ndim}")
+print(f"First score: {year_results[0]}")
+
+# Passing a single integer gives the same 1D result
+same_results = results.reshape(50)
+print(f"Integer argument gives the same shape: {same_results.shape}")

numpy-reshape/requirements.txt

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+numpy==2.5.0
+Pillow==12.2.0

numpy-reshape/wildcard.py

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+"""Use -1 as a wildcard dimension in reshape()."""
+
+import numpy as np
+
+rng = np.random.default_rng(seed=42)
+temperatures = rng.normal(18, 1, size=200)
+
+# Let reshape() infer the first dimension's length with -1
+temperatures_day = temperatures.reshape((-1, 8))
+print(f"Inferred shape: {temperatures_day.shape}")
+
+# Use -1 to flatten an array of any shape into a single dimension
+numbers = rng.integers(1, 100, (2, 4, 3, 3))
+print(f"Original shape: {numbers.shape}")
+numbers_flattened = numbers.reshape(-1)
+print(f"Flattened shape: {numbers_flattened.shape}")