NumPy Matrix Library - Rookie Nerd

numpy.array() in Python

numpy.concatenate() in Python

numpy.append() in Python

numpy.reshape() in Python

numpy.sum() in Python

numpy.random() in Python

numpy.zeros() in Python

numpy.log() in Python

numpy.where() in Python

numpy.argsort() in Python

numpy.transpose() in Python

numpy.mean() in Python

numpy.unique() in Python

numpy.ndarray.tolist() in Python

numpy.dot() in Python

numpy.loadtxt() in Python

numpy.clip() in Python

numpy.ndarray.flatten() in Python

numpy.meshgrid() in Python

NumPy standard deviation

numpy.argmax in Python

numpy.diff() in Python

numpy.empty() in Python

numpy.histogram() in Python

numpy.sort in Python

numpy.average() in Python

numpy.pad() in Python

numpy.ravel() in Python

numpy.save() in Python

NumPy arccos() method

NumPy arcsin() method

NumPy arctan() method

NumPy degrees() method

NumPy tan() method

NumPy deg2rad() method

NumPy hypot() method

NumPy rad2deg() method

NumPy radians() method

NumPy arcsinh()

NumPy arctanh()

NumPy ceil()

NumPy fix()

NumPy floor()

NumPy rint()

NumPy tanh()

NumPy trunc()

numpy.matlib.empty()

numpy.matlib.eye()

numpy.matlib.identity()

numpy.matlib.ones()

numpy.matlib.zeros()

numpy.arrange()

numpy.asarray()

numpy.frombuffer()

numpy.fromiter()

numpy.linspace()

numpy.logspace()

NumPy Matrix Library

NumPy contains a matrix library, i.e. numpy.matlib which is used to configure matrices instead of ndarray objects.

numpy.matlib.empty() function

This function is used to return a new matrix with the uninitialized entries. The syntax to use this function is given below.

snippet

numpy.matlib.empty(shape, dtype, order)

It accepts the following parameter.

shape: It is the tuple defining the shape of the matrix.
dtype: It is the data type of the matrix.
order: It is the insertion order of the matrix, i.e. C or F.

Consider the following example.

Example

snippet

import numpy as np

import numpy.matlib

print(numpy.matlib.empty((3,3)))

Output:

Output

[[6.90262230e-310 6.90262230e-310 6.90262304e-310] [6.90262304e-310 6.90261674e-310 6.90261552e-310] [6.90261326e-310 6.90262311e-310 3.95252517e-322]]

numpy.matlib.zeros() function

This function is used to create the matrix where the entries are initialized to zero.

Consider the following example.

Example

snippet

import numpy as np

import numpy.matlib

print(numpy.matlib.zeros((4,3)))

Output:

Output

[[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]]

numpy.matlib.ones() function

This function returns a matrix with all the elements initialized to 1.

Consider the following example.

Example

snippet

import numpy as np

import numpy.matlib

print(numpy.matlib.ones((2,2)))

Output:

Output

[[1. 1.] [1. 1.]]

numpy.matlib.eye() function

This function returns a matrix with the diagonal elements initialized to 1 and zero elsewhere. The syntax to use this function is given below.

snippet

numpy.matlib.eye(n, m, k, dtype)

It accepts the following parameters.

n: It represents the number of rows in the resulting matrix.
m: It represents the number of columns, defaults to n.
k: It is the index of diagonal.
dtype: It is the data type of the output

Consider the following example.

Example

snippet

import numpy as np

import numpy.matlib

print(numpy.matlib.eye(n = 3, M = 3, k = 0, dtype = int))

Output:

Output

[[1 0 0] [0 1 0] [0 0 1]]

numpy.matlib.identity() function

This function is used to return an identity matrix of the given size. An identity matrix is the one with diagonal elements initializes to 1 and all other elements to zero.

Consider the following example.

Example

snippet

import numpy as np

import numpy.matlib

print(numpy.matlib.identity(5, dtype = int))

Output:

Output

[[1 0 0 0 0] [0 1 0 0 0] [0 0 1 0 0] [0 0 0 1 0] [0 0 0 0 1]]

numpy.matlib.rand() function

This function is used to generate a matrix where all the entries are initialized with random values.

Consider the following example.

Example

snippet

import numpy as np

import numpy.matlib

print(numpy.matlib.rand(3,3))

Output:

Output

[[0.86201511 0.86980769 0.06704884] [0.80531086 0.53814098 0.84394673] [0.85653048 0.8146121 0.35744405]]