NumPy - Introduction

NumPy is a Python package. It stands for 'Numerical Python'. It is a library consisting of multidimensional array objects and a collection of routines for processing of array.
Numeric, the ancestor of NumPy, was developed by Jim Hugunin. Another package Numarray was also developed, having some additional functionalities.

NumPy - Environment

Try it Option Online

We have set up the NumPy Programming environment online, so that you can compile and execute all the available examples online. It gives you confidence in what you are reading and enables you to verify the programs with different options. Feel free to modify any example and execute it online.

NumPy - Ndarray Object

The most important object defined in NumPy is an N-dimensional array type called ndarray. It describes the collection of items of the same type. Items in the collection can be accessed using a zero-based index.
Every item in an ndarray takes the same size of block in the memory. Each element in ndarray is an object of data-type object (called dtype).

NumPy - Data Types

NumPy supports a much greater variety of numerical types than Python does. The following table shows different scalar data types defined in NumPy.

S.No	Data Types & Description
1.	bool_ Boolean (True or False) stored as a byte

NumPy - Array Attributes

In this chapter, we will discuss the various array attributes of NumPy.

ndarray.shape

This array attribute returns a tuple consisting of array dimensions. It can also be used to resize the array.

Example 1

import numpy as np 
a = np.array([[1,2,3],[4,5,6]]) 
print a.shape

The output is as follows −

(2, 3)

NumPy - Array Creation Routines

A new ndarray object can be constructed by any of the following array creation routines or using a low-level ndarray constructor.

numpy.empty

It creates an uninitialized array of specified shape and dtype. It uses the following constructor −

numpy.empty(shape, dtype = float, order = 'C')

The constructor takes the following parameters.

NumPy - Array From Existing Data

In this chapter, we will discuss how to create an array from existing data.

numpy.asarray

This function is similar to numpy.array except for the fact that it has fewer parameters. This routine is useful for converting Python sequence into ndarray.

numpy.asarray(a, dtype = None, order = None)

The constructor takes the following parameters.

NumPy - Array From Numerical Ranges

In this chapter, we will see how to create an array from numerical ranges.

numpy.arange

This function returns an ndarray object containing evenly spaced values within a given range. The format of the function is as follows −

numpy.arange(start, stop, step, dtype)

The constructor takes the following parameters.

NumPy - Indexing & Slicing

Contents of ndarray object can be accessed and modified by indexing or slicing, just like Python's in-built container objects.
As mentioned earlier, items in ndarray object follows zero-based index. Three types of indexing methods are available − field access, basic slicing and advanced indexing.

NumPy - Advanced Indexing

It is possible to make a selection from ndarray that is a non-tuple sequence, ndarray object of integer or Boolean data type, or a tuple with at least one item being a sequence object. Advanced indexing always returns a copy of the data. As against this, the slicing only presents a view.
There are two types of advanced indexing − Integer and Boolean.

NumPy - Broadcasting

The term broadcasting refers to the ability of NumPy to treat arrays of different shapes during arithmetic operations. Arithmetic operations on arrays are usually done on corresponding elements. If two arrays are of exactly the same shape, then these operations are smoothly performed.

NumPy - Iterating Over Array

NumPy package contains an iterator object numpy.nditer. It is an efficient multidimensional iterator object using which it is possible to iterate over an array. Each element of an array is visited using Python’s standard Iterator interface.
Let us create a 3X4 array using arange() function and iterate over it using nditer.

NumPy - Array Manipulation

Several routines are available in NumPy package for manipulation of elements in ndarray object. They can be classified into the following types −

Changing Shape

S.No	Shape & Description
1.	reshape Gives a new shape to an array without changing its data

NumPy - Binary Operators

Following are the functions for bitwise operations available in NumPy package.

S.No	Operation & Description
1.	bitwise_and Computes bitwise AND operation of array elements

NumPy - String Functions

The following functions are used to perform vectorized string operations for arrays of dtype numpy.string_ or numpy.unicode_. They are based on the standard string functions in Python's built-in library.

NumPy - Mathematical Functions

Quite understandably, NumPy contains a large number of various mathematical operations. NumPy provides standard trigonometric functions, functions for arithmetic operations, handling complex numbers, etc.

NumPy - Arithmetic Operations

Input arrays for performing arithmetic operations such as add(), subtract(), multiply(), and divide() must be either of the same shape or should conform to array broadcasting rules.

Example

import numpy as np 
a = np.arange(9, dtype = np.float_).reshape(3,3) 

NumPy - Statistical Functions

NumPy has quite a few useful statistical functions for finding minimum, maximum, percentile standard deviation and variance, etc. from the given elements in the array. The functions are explained as follows −

NumPy - Sort, Search & Counting Functions

A variety of sorting related functions are available in NumPy. These sorting functions implement different sorting algorithms, each of them characterized by the speed of execution, worst case performance, the workspace required and the stability of algorithms. Following table shows the comparison of three sorting algorithms.

NumPy - Byte Swapping

We have seen that the data stored in the memory of a computer depends on which architecture the CPU uses. It may be little-endian (least significant is stored in the smallest address) or big-endian (most significant byte in the smallest address).