NumPy Cheatsheet with Examples

 

This includes a cheatsheet-like code with examples for numpy.

To make this work, first install numpy:

pip3 install numpy

Examples:

import numpy as np


def multiply_by_scalars():
    print("=== multiply by scalars ===")
    x = np.array([1., 2., 3.])
    weight = np.array([4., 5., 6.])

    print("x", x)
    print("weight", weight)

    # same as: output = x.dot(weight)
    output = x @ weight
    print(output)


def multiply_matrix():
    print("=== multiply matrix ===")
    x = np.ones(([2, 3]), dtype=int)
    y = np.ones(([3, 4]), dtype=int)
    print(x @ y)

    print("transpose to multiply")
    print(x @ x.transpose())


def multiple_dimensions_array():
    print("=== multiple dimensions array ===")
    array_2d = np.array([[1, 2, 3], [4, 5, 6]])
    print("2D array", array_2d)
    print("index access", array_2d[1, 2])


def convert_types():
    print("=== convert types ===")
    a = np.array([1, 2, 3])

    print("before convert")
    print("a", a)
    print("a type", a.dtype)

    print("after convert (better to work with float32 on GPU)")
    a = a.astype(np.float32)
    print("a", a)
    print("a type", a.dtype)


def check_dimensions():
    print("=== check dimensions ===")
    array_3d = np.array([
        [
            [1, 2, 3, 4],
            [5, 6, 7, 8],
            [9, 10, 11, 12],
        ],
        [
            [1001, 1002, 1003, 1004],
            [1005, 1006, 1007, 1008],
            [1009, 10010, 10011, 10012],
        ],
    ])
    print(array_3d)
    print("dimensions", array_3d.ndim)
    print("shape", array_3d.shape)


def create_predefined_arrays():
    print("=== create predefined arrays ===")

    print("ones")
    print(np.ones((2, 3), dtype=np.int32))

    print("zeros")
    print(np.zeros((2, 3), dtype=np.int32))

    print("constants")
    print(42 + np.zeros((2, 3), dtype=np.int32))

    print("I matrix")
    print(np.eye(4, dtype=np.int32))

    print("diagonal")
    print(np.diag((5, 6, 7)))

    print("configure range and step size")
    print(np.arange(10., 5., -0.5))

    print("configure number of elements within range")
    print(np.linspace(0., 20., num=4))

    print("random arrays")
    np.random.seed(42)
    print(np.random.rand(3, 2))


def array_slices():
    print("=== array slices ===")
    a = np.array([[1, 2, 3], [4, 5, 6]])

    print("slice by index")
    print(a[0])

    print("slice from end")
    print(a[-1])

    print("slice by range")
    print(a[1, 0:2])

    print("slice by column")
    print(a[:, 0])


def array_math():
    print("=== array math ===")
    a = np.array([[1, 2, 3], [4, 5, 6]])

    print(a)

    print("add")
    print(a + 1)

    print("power")
    print(a ** 2)

    print("sum by rows")
    print(np.add.reduce(a, axis=0))

    print("sum by columns")
    print(np.add.reduce(a, axis=1))

    print("max by columns")
    print(np.max(a, axis=1))


def broadcast():
    print("=== broadcast ===")
    a = np.array([[1, 1, 1], [7, 7, 7]])
    b = np.array([1, 2, 3])
    print(a + b)


def slice_changes_original():
    print("=== slice changes original ===")
    a = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])

    print("default slice is a view on the original")
    first_row = a[0]
    first_row += 1
    print(a)

    print("using copy() duplicates memory")
    second_row = a[1].copy()
    second_row += 1
    print(a)

    print("fancy index (non continuous) always uses a copy")
    fancy_slice = a[[0, 2]]
    fancy_slice += 100
    print(fancy_slice)
    print(a)


def boolean_masks():
    print("=== boolean masks ===")
    a = np.array([[1, 5, 2], [8, 4, 7]])
    print(a >= 5)

    print("multiple conditions")
    complex_condition = (a >= 5) & (a < 8)
    print(complex_condition)

    print("select only the elements that match a condition (this is fancy condition)")
    print(a[complex_condition])

    print("check how many elements answer a condition")
    print(complex_condition.sum())

    print("update by boolean condition")
    print(np.where(a >= 5, 100, 55))


def reshape_array():
    print("=== reshape array ===")
    a = np.random.rand(12)
    print(a)

    array_2d = a.reshape(3, 4)
    print(array_2d)

    print("reshape shares the memory of the original")
    print(np.may_share_memory(a, array_2d))

    print("reshape without knowing one dimension")
    print(a.reshape(-1, 6))

    print("flatten array")
    i = np.eye(3)
    # same as: print(i.reshape(-1))
    print(i.flatten())

    print("create vector")
    a = np.array([1, 2, 3])
    # same as: print(a.reshape(-1, 1))
    # same as: print(a[:, None])
    print(a[:, np.newaxis])


def concatenate_array():
    print("=== concatenate array ===")
    a = np.array([[1, 2, 3], [4, 5, 6]])

    print("add rows")
    print(np.concatenate((a, a), axis=0))

    print("add columns")
    print(np.concatenate((a, a), axis=1))


multiply_by_scalars()
multiply_matrix()
multiple_dimensions_array()
convert_types()
check_dimensions()
create_predefined_arrays()
array_slices()
array_math()
broadcast()
slice_changes_original()
boolean_masks()
reshape_array()
concatenate_array()