Arrays with NumPy

NumPy is a Python library for working with arrays, with functions for linear algebra, Fourier transforms, and matrices. It was created in 2005 by Travis Oliphant and is open source.

Why NumPy

Python lists can act like arrays, but they are slow for numeric work. NumPy's array object, the ndarray, is up to 50 times faster and comes with a large set of helper functions.

import numpy as np      # the usual alias
 
print(np.__version__)

Creating an ndarray

Pass a list, tuple, or other array-like object to np.array.

import numpy as np
 
arr = np.array([101, 201, 301, 401, 501])
print(arr)
print(type(arr))        # class 'numpy.ndarray'

Dimensions

A dimension is one level of array depth.

import numpy as np
 
a = np.array(42)                       # 0-D, a scalar
b = np.array([1, 2, 3, 4, 5])          # 1-D
c = np.array([[1, 2, 3], [4, 5, 6]])   # 2-D, a matrix
d = np.array([[[1, 2], [3, 4]],
              [[5, 6], [7, 8]]])       # 3-D
 
for name, x in {"a": a, "b": b, "c": c, "d": d}.items():
    print(name, x.ndim)                # 0 1 2 3

ndim reports the number of dimensions; shape reports the size along each.

Indexing and slicing

Index from 0, or from the end with negative numbers. For multiple dimensions, separate indices with commas.

import numpy as np
 
arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
print(arr[1, 3])        # 9
print(arr[0, -1])       # 5, last of the first row

Slicing takes a range with [start:end], optionally [start:end:step]. Missing start means 0, missing end means the end, missing step means 1.

import numpy as np
 
arr = np.array([1, 2, 3, 4, 5, 6, 7])
print(arr[1:5])         # [2 3 4 5]
print(arr[4:])          # [5 6 7]
print(arr[:4])          # [1 2 3 4]

Iterating

Loop over an array with for. For multi-dimensional arrays, nest the loops to reach scalars.

import numpy as np
 
arr = np.array([[1, 2, 3], [4, 5, 6]])
for row in arr:
    for value in row:
        print(value)

Joining and splitting

concatenate merges arrays; array_split breaks one apart.

import numpy as np
 
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
joined = np.concatenate((arr1, arr2))   # [1 2 3 4 5 6]
 
parts = np.array_split(joined, 3)        # three sub-arrays
print(parts[1])                          # [3 4]

Practice

In a Week_7 notebook: build a 2-D array of five students' scores across three subjects, print its shape, the second student's scores, the highest score in the array, and the array split into individual rows.