NumPy library

NumPy is an open-source project, with fiscal sponsorship from the nonprofit NumFOCUS.

NumPy (imported as np) is Python's standard library for fast numeric arrays — the foundation nearly every other data or scientific library in Python is built on. It's a third-party package, not part of the standard library. A NumPy ndarray looks similar to a list, but every element is the same type and math operations apply to the whole array at once, instead of one item at a time.

Install

pip install numpy

Import

np is the near-universal alias for NumPy — used throughout this page and in virtually every codebase that imports it.

import numpy as np

Type	Holds	Math operations
`list`	Any mix of types	Element-by-element, usually with a loop
`ndarray`	One type, fixed size	Applied to the whole array at once ("vectorized")

Creating arrays

np.array() builds an ndarray from an existing list — every value gets converted to the same type.

import numpy as np

lengths_ft = np.array([4.5, 12, 8, 6])
print(lengths_ft)
print(lengths_ft.dtype)

Building arrays without a list

np.zeros(n) builds an array of n zeros as a starting point to fill in later. np.arange(stop) counts up from 0 to (but not including) stop, just like the built-in range() — with an optional start and step, exactly like range() too.

np.zeros(4)      # array([0., 0., 0., 0.])
np.arange(4)     # array([0, 1, 2, 3])
np.arange(0, 10, 2)    # array([0, 2, 4, 6, 8])

Run a creating arrays example

All the examples above, combined into one script:

import numpy as np

lengths_ft = np.array([4.5, 12, 8, 6])
print(lengths_ft)
print(lengths_ft.dtype)

import numpy as np

print(np.zeros(4))
print(np.arange(4))
print(np.arange(0, 10, 2))

Array operations

A math operation on an array applies to every element at once — no loop required, and considerably faster than looping over a plain list.

import numpy as np

lengths_ft = np.array([4.5, 12, 8, 6])
lengths_m = lengths_ft * 0.3048

print(lengths_m)

Aggregating an array

Collapses an entire array down to a single summary number. .mean(), .max(), .min(), and .sum() — the same idea as Python's built-in sum() and max(), but computed directly on the array without converting it back to a list first.

lengths_ft = np.array([4.5, 12, 8, 6])
lengths_ft.mean()    # 7.625
lengths_ft.max()     # 12.0
lengths_ft.sum()     # 30.5

Filtering with a boolean mask

Comparing an array to a number produces a same-size array of True/False values — a boolean mask. Indexing the array with that mask keeps only the elements where it's True. This is the standard way to filter a NumPy array, instead of writing an explicit loop with an if inside it.

lengths_ft = np.array([4.5, 12, 8, 6])
lengths_ft > 7             # array([False, True, True, False])
lengths_ft[lengths_ft > 7]  # array([12., 8.])

Run an array operations example

All the examples above, combined into one script:

import numpy as np

lengths_ft = np.array([4.5, 12, 8, 6])
lengths_m = lengths_ft * 0.3048

print(lengths_m)

import numpy as np

lengths_ft = np.array([4.5, 12, 8, 6])
print(lengths_ft.mean())
print(lengths_ft.max())
print(lengths_ft.sum())

import numpy as np

lengths_ft = np.array([4.5, 12, 8, 6])
mask = lengths_ft > 7
print(mask)
print(lengths_ft[mask])