NumPy Tutorial

Python NumPy Tutorial numpy.empty() in Python numpy.empty_like() in Python numpy.eye() in Python numpy.identity() in Python numpy.ones() in Python numpy.ones_like() in Python numpy.zeros in Python numpy.zeros_like() in Python numpy.full() in Python numpy.full_like() in Python numpy.asarray() in Python numpy.frombuffer() in Python numpy.fromiter() in Python numpy.fromstring () in Python numpy.asanyarray() in Python with Example numpy.ascontiguousarray() in Python with Example Numpy.asmatrix() in Python with Example Numpy.copy() in Python with Example numpy.loadtxt() Python numpy.arrange() in Python numpy.linspace() in Python numpy.logspace() in Python numpy.geomspace() in Python numpy.meshgrid() in Python numpy.diag() in Python numpy.diagflat() in Python numpy.tri() in Python numpy.tril() in Python numpy.copyto() in Python numpy.reshape() in Python numpy.ravel() in Python numpy.ndarray.flat() in Python numpy.ndarray.flatten() in Python numpy.rollaxis() in Python numpy.swapaxes() in Python numpy.ndarray.T in Python numpy.transpose() in Python numpy.atleast_1d() in Python numpy.atleast_2d() in Python numpy.atleast_3d() in Python numpy.broadcast_to() in Python numpy.broadcast_arrays() in Python numpy.expand_dims() in Python numpy.squeeze() in Python numpy.asarray_chkfinite() in Python numpy.asscalar() in Python numpy.concatenate() in Python numpy.stack() in Python numpy.column_stack() in Python numpy.dstack() in Python numpy.hstack() in Python numpy.vstack() in Python numpy.split() in Python numpy.tile() in Python numpy.repeat() in Python numpy.delete() in Python numpy.append() in Python numpy.resize() in Python numpy.trim_zeros() in Python numpy.unique() in Python numpy.flip() in Python NumPy vs SciPy

Misc

Numpy Attributes

numpy.diag() in Python

numpy.diag() in Python

The diag() function of Python numpy class extracts and construct a diagonal array.

Syntax

numpy.diag(v, k=0)

Parameter

a: It represents the array_like.

k: It represents the diagonal value that we require. It is an optional parameter and its default value is 0. If k>0, the diagonal is above the main diagonal or vice versa.

Return

This function returns the extracted diagonal or constructed diagonal array.

Example 1

# Python Programming explaining
# numpy.diag() function
import numpy as np
# matrix creation by array input
num = np.matrix([[11, 121, 130], 
                 [613 ,34, 13], 
                 [514, 50, 16]])
print("Main Diagnol elements : \n", np.diag(num))
print("\nDiagnol elements above main diagnol : \n", np.diag(num, 1) )
print("\nDiagnol elements below main diagnol : \n", np.diag(num, -1))

Output

Main Diagnol elements :
[11 34 16]
Diagnol elements above main diagnol :
[121  13]
Diagnol elements below main diagnol :
[613  50]