NumPy Arrays and Syntax

Creates array from list

np.array([1, 2, 3])

Creates evenly spaced array

np.arrange(start, stop, step)

Creates evenly spaced array

np.linspace(start, stop, number_of_elements)

Creates array of 0s

np.zeros(number_of_0)

Creates array of 1s

np.ones(number_of_1)

Creates array of specific value

np.full(shape, fill_value)

Creates identity matrix

np.eye(size)

Creates uniform distribution [0, 1)

np.random.rand(number_of_terms)

Creates random array

np.random.ranint(min, max, size=x)

Addition

a + b

Subtraction

a - b

Element-wise multiplication

a * b

Matrix multiplication

a % b

Division

a / b

Square root

np.sqrt(a)

Scalar addition

a + 10

Scalar multiplication

a * 2

Scalar power

a ** 3

Element comparisons

a > 2, a == 3, a <= 4

Reshapes to n x m array

[array].reshape(n, m)

Converts to 1D

[array].flatten()

Reshapes to n rows

[array].reshape(n, -1)

Transpose array

[array].T

Vertical concatenation

np.vstack([array1], [array2])

Horizontal concatenation

np.hstack([array1], [array2])

Sum

np.sum([array])

Mean

np.mean([array])

Median

np.median([array])

Standard deviation

np.std([array])

Variance (range)

np.var([array])

Minimum value

np.min([array])

Maximum value

np.max([array])

Rows

axis=0

Columns

axis=1

Sorts array

np.sort([array])

Keeps only unique values

np.unique([array])

Searches for specific values

np.where([condition])