Python Programming – NumPy

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Learn NumPy Library in Python – Complete Guide

Creating Numpy Arrays

  • Create NumPy Arrays from list, tuple, or list of lists
  • Create NumPy Arrays from a range of evenly spaced numbers using np.arrange().
  • Create NumPy Array of zeros (0’s) using np.zeros()
  • Create 1D / 2D NumPy Array filled with ones (1’s) using np.ones()
  • Create NumPy Array of different shapes & initialize with identical values using numpy.full()
  • Create NumPy Array with same sized samples over an interval in Python using numpy.linspace()
  • Create a NumPy Array of bool value.

Adding Elements in Numpy Array

Searching in Numpy Arrays

Get Metadata of Numpy Array

Selecting elements from Numpy Array

Modifying a Numpy Array

Converting NumPy Array to Other Data Structures

Numpy Array and File I/O

Verify Contents of Numpy Array

Counting Elements in Numpy Array

Advance Topics about Numpy Array

  • What is a Structured Numpy Array and how to create and sort it in Python?
  • numpy.zeros() & numpy.ones() | Create a numpy array of zeros or ones

Python Programming – NumPy

NUMPY

As discussed previously, simple one dimensional array operations can be executed using list, tuple etc. But carrying out multi-dimensional array operations using list is not easy. Python has an array module which provides methods for creating array, but they are slower to index than list. A good choice for carrying array operations is by using “NumPy” package.

NumPy is a Python package (licensed under the BSD license) which is helpful in scientific computing by providing multi-dimensional array object, various derived objects (such as masked arrays and matrices), and collection of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, basic linear algebra, basic statistical operations, and many more. At the core of the NumPy package, there is array object which encapsulates n-dimensional arrays of homogeneous data types. There are several important differences between the NumPy array and the standard Python sequence:

  • NumPy array has a fixed size at creation, unlike Python list (which can grow dynamically). Changing the size of a ndarray will create a new array and delete the original.
  • All elements in a NumPy array are required to be of the same data type.
  • NumPy array facilitates advanced mathematical and other types of operations on large numbers of data. Typically, such operations are executed more efficiently and with less code than is possible using Python’s built-in sequences.

History

NumPy is built on (and is a successor to) the successful “Numeric” package. Numeric was reasonably complete and stable, remains available, but is now obsolete. Numeric was originally written in 1995 largely by Jim Hugunin, while he was a graduate student at MIT. In 2001, Travis Oliphant along with Eric Jones and Pearu Peterson created “SciPy”, which had the the strenght of Numeric package along additional functionality. At about the same time as SciPy was being built, some Numeric users were hitting up against the limited capabilities of Numeric.

As a result, “numarray” (now obselete) was created by Perry Greenfield, Todd Miller, and RickWhite at the Space Science Telescope Institute as a replacement for Numeric. In early 2005, Travis Oliphant initiated an effort to bring the diverging community together under a common framework. The effort was paid off with the release of a new package Numpy (with version 0.9.2) in early 2006, which is an amalgam of the code base of Numeric with additional features of numarray. The NumPy name was christened from the unofficial name of “Numerical Python”.

Universal functions

NumPy provides familiar mathematical functions such as sin ( ), cos ( ), exp ( ), etc. In NumPy, these are called “universal functions”. Within NumPy, these functions operate element-wise on an array, producing an array as output.

>>> a=np . arange ( 3 ) 
>>> a 
array ( [ 0 , 1 , 2 ] ) 
>>> np . exp ( a ) 
array ( [ 1 . , 2 . 71828183 , 7 . 3890561 ] )
>>> np . sqrt ( a ) 
array ( [ 0 . , 1 . , 1 . 41421356 ] )

The Matrix Class

There is also a matrix class, which returns a matrix from an array-like object, or from a string of data. A matrix is a specialized 2-D array that retains its 2-D nature through operations.

>>> np . matrix ( [ [ 1 . 0 , 2 . 0 ] , [ 3 . 0 , 4 . 0 ] ] ) 
matrix ( [ [ 1 . , 2 . ] , 
[ 3 . , 4 . ] ] )
>>> a=np . matrix ( ' 1 . 0 2 . 0 ; 3 . 0 4 . 0 ' ) 
>>> a
matrix ( [ [ 1 . , 2 . ] , 
[ 3 . , 4 . ] ] )
>>> a . T                                                                           # Transpose of a matrix
matrix ( [ [ 1 . , 3 . ] ,
[ 2 . , 4 .] ] ) 
>>> x=np . matrix ( ' 5 . 0 7 . 0 ' )
>>> y=x.T
>>> y
matrix ( [ [ 5 . ] ,
[ 7 . ] ] )
>>> a*y                                                                          # Matrix multiplication
matrix ( [ [ 19 . ] ,
[ 43 . ] ] )
>>> a.I                                                                           # Inverse of a matrix
matrix ( [ [ -2 . , 1 . ] ,
[ 1 . 5 , -0 . 5 ] ] )

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