top of page

Numpy Basics and Functions

Numpy and Scipy Library

Numpy and Scipy are the fundamental python packages for scientific computation and programing.If we want to do scientific computation in python we have to use these libraries

 import numpy as np
 import scipy as sp

Why Numpy and Scipy ?

The Basics scientific programming operations are Arrays, matrices, integration ,differential equation solver , statistics etc. By default python does not have any of these built in except some basic mathematical operations that only deals with variables not with array and matrices , so we are using Numpy and Scipy. Here we are going to see Numpy.

Numpy Arrays

Numpy Arrays are called n dimensional array(nd). It holds the data of same data type. These are some functions used in Numpy .


import numpy as np


Help on built-in function array in module numpy:

    array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0)
    Create an array.
    object : array_like
        An array, any object exposing the array interface, an object whose
        __array__ method returns an array, or any (nested) sequence.
    dtype : data-type, optional
        The desired data-type for the array.  If not given, then the type will
        be determined as the minimum type required to hold the objects in the
    copy : bool, optional
        If true (default), then the object is copied.  Otherwise, a copy will
        only be made if __array__ returns a copy, if obj is a nested sequence,
        or if a copy is needed to satisfy any of the other requirements
        (`dtype`, `order`, etc.).
    order : {'K', 'A', 'C', 'F'}, optional
        Specify the memory layout of the array. If object is not an array, the
        newly created array will be in C order (row major) unless 'F' is
        specified, in which case it will be in Fortran order (column major).
        If object is an array the following holds.
        ===== ========= ===================================================
        order  no copy                     copy=True
        ===== ========= ===================================================
        'K'   unchanged F & C order preserved, otherwise most similar order
        'A'   unchanged F order if input is F and not C, otherwise C order
        'C'   C order   C order
        'F'   F order   F order
        ===== ========= ===================================================
        When ``copy=False`` and a copy is made for other reasons, the result is
        the same as if ``copy=True``, with some exceptions for `A`, see the
        Notes section. The default order is 'K'.
    subok : bool, optional
        If True, then sub-classes will be passed-through, otherwise
        the returned array will be forced to be a base-class array (default).
    ndmin : int, optional
        Specifies the minimum number of dimensions that the resulting
        array should have.  Ones will be pre-pended to the shape as
        needed to meet this requirement.
    out : ndarray
        An array object satisfying the specified requirements.
    See Also
    empty_like : Return an empty array with shape and type of input.
    ones_like : Return an array of ones with shape and type of input.
    zeros_like : Return an array of zeros with shape and type of input.
    full_like : Return a new array with shape of input filled with value.
    empty : Return a new uninitialized array.
    ones : Return a new array setting values to one.
    zeros : Return a new array setting values to zero.
    full : Return a new array of given shape filled with value.
    When order is 'A' and `object` is an array in neither 'C' nor 'F' order,
    and a copy is forced by a change in dtype, then the order of the result is
    not necessarily 'C' as expected. This is likely a bug.
    >>> np.array([1, 2, 3])
    array([1, 2, 3])
    >>> np.array([1, 2, 3.0])
    array([ 1.,  2.,  3.])
    More than one dimension:
    >>> np.array([[1, 2], [3, 4]])
    array([[1, 2],
           [3, 4]])
    Minimum dimensions 2:
    >>> np.array([1, 2, 3], ndmin=2)
    array([[1, 2, 3]])
    Type provided:
    >>> np.array([1, 2, 3], dtype=complex)
    array([ 1.+0.j,  2.+0.j,  3.+0.j])
    Data-type consisting of more than one element:
    >>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
    >>> x['a']
    array([1, 3])
    Creating an array from sub-classes:
    >>> np.array(np.mat('1 2; 3 4'))
    array([[1, 2],
           [3, 4]])
    >>> np.array(np.mat('1 2; 3 4'), subok=True)
    matrix([[1, 2],
            [3, 4]])

Arange() :- Creates Array of evenly spaced values


arange ([start,] stop[, step,], dtype =None)


import numpy as np 
np.arange(1,10,2 ,"float")


array([1., 3., 5., 7., 9.])

Linspace ():- creates Array filled evenly spaced values


linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)




(array([ 1, 10, 20, 30, 40, 50, 60, 70, 80, 90]), 9.9)

Eye ():- Returns array filled with zeros except in the k-th diagonal

import numpy as np


array([[0., 0., 0., 0.],
       [1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.]])

Numpy Random Module

1) rand()



array([0.90600296, 0.84742763, 0.21140014, 0.71782607])

2) randn()



array([-1.36980338,  1.42326917,  2.14822216, -0.69217009])

3) ranf()



array([0.28127124, 0.41282905, 0.48456846, 0.25986008])

4) randint()



array([[0, 1, 3],
       [0, 3, 1]])



These are the different ways to create Numpy Array in python.I hope it will be useful for the beginners.

39 views0 comments

Recent Posts

See All

Beginner Friendly Java String Interview Questions

Hello Everyone! Welcome to the second section of the Java Strings blog. Here are some interesting coding questions that has been solved with different methods and approaches. “Better late than never!”


Avaliado com 0 de 5 estrelas.
Ainda sem avaliações

Adicione uma avaliação
bottom of page