NumPy - Quick Guide

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.
In 2005, Travis Oliphant created NumPy package by incorporating the features of Numarray into Numeric package. There are many contributors to this open source project.

Operations using NumPy

Using NumPy, a developer can perform the following operations −

• Mathematical and logical operations on arrays.
• Fourier transforms and routines for shape manipulation.
• Operations related to linear algebra. NumPy has in-built functions for linear algebra and random number generation.

NumPy – A Replacement for MatLab

NumPy is often used along with packages like SciPy (Scientific Python) and Mat−plotlib (plotting library). This combination is widely used as a replacement for MatLab, a popular platform for technical computing. However, Python alternative to MatLab is now seen as a more modern and complete programming language.
It is open source, which is an added advantage of NumPy.

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.
Try the following example using our online compiler available at CodingGround

import numpy as np 
a = 'hello world' 
print a

For most of the examples given in this tutorial, you will find a Try it option in our website code sections at the top right corner that will take you to the online compiler. So just make use of it and enjoy your learning.

Standard Python distribution doesn't come bundled with NumPy module. A lightweight alternative is to install NumPy using popular Python package installer, pip.

pip install numpy

The best way to enable NumPy is to use an installable binary package specific to your operating system. These binaries contain full SciPy stack (inclusive of NumPy, SciPy, matplotlib, IPython, SymPy and nose packages along with core Python).

Windows

Anaconda (from www.continuum.io) is a free Python distribution for SciPy stack. It is also available for Linux and Mac.
Canopy (www.enthought.com/products/canopy/) is available as free as well as commercial distribution with full SciPy stack for Windows, Linux and Mac.
Python (x,y): It is a free Python distribution with SciPy stack and Spyder IDE for Windows OS. (Downloadable from www.python-xy.github.io/)

Linux

Package managers of respective Linux distributions are used to install one or more packages in SciPy stack.

For Ubuntu

sudo apt-get install python-numpy 
python-scipy python-matplotlibipythonipythonnotebook python-pandas 
python-sympy python-nose

For Fedora

sudo yum install numpyscipy python-matplotlibipython 
python-pandas sympy python-nose atlas-devel

Building from Source

Core Python (2.6.x, 2.7.x and 3.2.x onwards) must be installed with distutils and zlib module should be enabled.
GNU gcc (4.2 and above) C compiler must be available.
To install NumPy, run the following command.

Python setup.py install

To test whether NumPy module is properly installed, try to import it from Python prompt.
If it is not installed, the following error message will be displayed.

Traceback (most recent call last): 
   File "<pyshell#0>", line 1, in <module> 
      import numpy 
ImportError: No module named 'numpy'

Alternatively, NumPy package is imported using the following syntax −

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).
Any item extracted from ndarray object (by slicing) is represented by a Python object of one of array scalar types. The following diagram shows a relationship between ndarray, data type object (dtype) and array scalar type −
An instance of ndarray class can be constructed by different array creation routines described later in the tutorial. The basic ndarray is created using an array function in NumPy as follows −

numpy.array 

It creates an ndarray from any object exposing array interface, or from any method that returns an array.

numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)

The above constructor takes the following parameters −

S.No	Parameter & Description
1.	object Any object exposing the array interface method returns an array, or any (nested) sequence.
2.	dtype Desired data type of array, optional
3.	copy Optional. By default (true), the object is copied
4.	order C (row major) or F (column major) or A (any) (default)
5.	subok By default, returned array forced to be a base class array. If true, sub-classes passed through
6.	ndimin Specifies minimum dimensions of resultant array

Take a look at the following examples to understand better.

Example 1

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

The output is as follows −

[1, 2, 3]

Example 2

# more than one dimensions 
import numpy as np 
a = np.array([[1, 2], [3, 4]]) 
print a

The output is as follows −

[[1, 2] 
 [3, 4]]

Example 3

# minimum dimensions 
import numpy as np 
a = np.array([1, 2, 3,4,5], ndmin = 2) 
print a

The output is as follows −

[[1, 2, 3, 4, 5]]

Example 4

# dtype parameter 
import numpy as np 
a = np.array([1, 2, 3], dtype = complex) 
print a

The output is as follows −

[ 1.+0.j,  2.+0.j,  3.+0.j]

The ndarray object consists of contiguous one-dimensional segment of computer memory, combined with an indexing scheme that maps each item to a location in the memory block. The memory block holds the elements in a row-major order (C style) or a column-major order (FORTRAN or MatLab style).

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
2.	int_ Default integer type (same as C long; normally either int64 or int32)
3.	intc Identical to C int (normally int32 or int64)
4.	intp Integer used for indexing (same as C ssize_t; normally either int32 or int64)
5.	int8 Byte (-128 to 127)
6.	int16 Integer (-32768 to 32767)
7.	int32 Integer (-2147483648 to 2147483647)
8.	int64 Integer (-9223372036854775808 to 9223372036854775807)
9.	uint8 Unsigned integer (0 to 255)
10.	uint16 Unsigned integer (0 to 65535)
11.	uint32 Unsigned integer (0 to 4294967295)
12.	uint64 Unsigned integer (0 to 18446744073709551615)
13.	float_ Shorthand for float64
14.	float16 Half precision float: sign bit, 5 bits exponent, 10 bits mantissa
15.	float32 Single precision float: sign bit, 8 bits exponent, 23 bits mantissa
16.	float64 Double precision float: sign bit, 11 bits exponent, 52 bits mantissa
17.	complex_ Shorthand for complex128
18.	complex64 Complex number, represented by two 32-bit floats (real and imaginary components)
19.	complex128 Complex number, represented by two 64-bit floats (real and imaginary components)

NumPy numerical types are instances of dtype (data-type) objects, each having unique characteristics. The dtypes are available as np.bool_, np.float32, etc.

Data Type Objects (dtype)

A data type object describes interpretation of fixed block of memory corresponding to an array, depending on the following aspects −

• Type of data (integer, float or Python object)
• Size of data
• Byte order (little-endian or big-endian)
• In case of structured type, the names of fields, data type of each field and part of the memory block taken by each field.
• If data type is a subarray, its shape and data type

The byte order is decided by prefixing '<' or '>' to data type. '<' means that encoding is little-endian (least significant is stored in smallest address). '>' means that encoding is big-endian (most significant byte is stored in smallest address).
A dtype object is constructed using the following syntax −

numpy.dtype(object, align, copy)

The parameters are −

• Object − To be converted to data type object
• Align − If true, adds padding to the field to make it similar to C-struct
• Copy − Makes a new copy of dtype object. If false, the result is reference to builtin data type object

Example 1

# using array-scalar type 
import numpy as np 
dt = np.dtype(np.int32) 
print dt

The output is as follows −

int32

Example 2

#int8, int16, int32, int64 can be replaced by equivalent string 'i1', 'i2','i4', etc. 
import numpy as np 

dt = np.dtype('i4')
print dt 

The output is as follows −

int32

Example 3

# using endian notation 
import numpy as np 
dt = np.dtype('>i4') 
print dt

The output is as follows −

>i4

The following examples show the use of structured data type. Here, the field name and the corresponding scalar data type is to be declared.

Example 4

# first create structured data type 
import numpy as np 
dt = np.dtype([('age',np.int8)]) 
print dt 

The output is as follows −

[('age', 'i1')] 

Example 5

# now apply it to ndarray object 
import numpy as np 

dt = np.dtype([('age',np.int8)]) 
a = np.array([(10,),(20,),(30,)], dtype = dt) 
print a

The output is as follows −

[(10,) (20,) (30,)]

Example 6

# file name can be used to access content of age column 
import numpy as np 

dt = np.dtype([('age',np.int8)]) 
a = np.array([(10,),(20,),(30,)], dtype = dt) 
print a['age']

The output is as follows −

[10 20 30]

Example 7

The following examples define a structured data type called student with a string field 'name', an integer field 'age' and a float field 'marks'. This dtype is applied to ndarray object.

import numpy as np 
student = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')]) 
print student

The output is as follows −

[('name', 'S20'), ('age', 'i1'), ('marks', '<f4')])

Example 8

import numpy as np 

student = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')]) 
a = np.array([('abc', 21, 50),('xyz', 18, 75)], dtype = student) 
print a

The output is as follows −

[('abc', 21, 50.0), ('xyz', 18, 75.0)]

Each built-in data type has a character code that uniquely identifies it.

• 'b' − boolean
• 'i' − (signed) integer
• 'u' − unsigned integer
• 'f' − floating-point
• 'c' − complex-floating point
• 'm' − timedelta
• 'M' − datetime
• 'O' − (Python) objects
• 'S', 'a' − (byte-)string
• 'U' − Unicode
• 'V' − raw data (void)