How can the Euclidean distance be calculated with NumPy?

Going multidimensional

Dealing with higher dimensions? No problem! From 3D all the way to the multiverse, np.linalg.norm holds:

# Bee goes brrrrrrr in 3D!
distance = np.linalg.norm(np.array([x1, y1, z1]) - np.array([x2, y2, z2]))
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In np.linalg.norm the default is 'l2 norm', aka our delightful Euclidean distance.

Massively efficient vectorization

If you're swimming in a sea of points, try vectorization for a performance speedboat:

# Like a game of darts, but with multidimensional arrays
points_a = np.array([(1, 2, 3), (4, 5, 6)])
points_b = np.array([(7, 8, 9), (10, 11, 12)])

# One line, many distances, such wow
distances = np.linalg.norm(points_a - points_b, axis=1)
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Level up: advanced use cases

Optimize your buzz

Hive-scale performance needs some optimizing nectar. Try these:

• Continuous buzz: Keep your data in contiguous memory arrays for faster fetching.
• Square dance: Just comparing magnitudes? Avoid the square root in np.linalg.norm by using keepdims=True.
• Einstein, no... Einsum!: For memory-efficient and speedier computations on complex problems, trust our bee-friend np.einsum.

Practical pollen source

Euclidean distance is king in pollen locations (K-Nearest Neighbors). Here are some honey-tips:

• Preprocessing: Normalizing or scaling data for better bee-haviour in accuracy.
• Batch buzzing: Calculate distances in batches to make better use of CPU and memory.
• Benchmarking: Different flowers? Try perfplot to test various methods' performance with distinct datasets.

Bee aware of pitfalls

Escape from the spider webs that slay performance:

• Casting np.sqrt and np.sum redundantly when np.linalg.norm gives you both.
• Misinterpreting bee directions (axis parameter) when calculating mutual distances between flowers, leading bees off-track.