2. Numpy
• Numpy package provides convenient and fast arbitrary-dimensional
array manipulation routines.
• The NumPy arrays have three fundamental properties.
• shape: The shape attribute of any arrys describes its size along all of its
dimensions
• ndim: The number of dimensions (often also called directions or axes)
• dtype: The data type of the array elements.
from numpy import *
3. • We can create array, matrix and manipulate them accordingly in with
the operation and function available in numpy.
• Array manipulation routines
• Basic operations
• Changing array shape
• Transpose-like operations
• Changing number of dimensions
• Changing kind of array
• Joining arrays
• Splitting arrays
• Tiling arrays
• Adding and removing elements
• Rearranging elements
4. Universal functions (ufunc) in Numpy
A universal function (or ufunc for short) is a function that operates on
ndarrays in an element-by-element fashion, supporting array
broadcasting, type casting, and several other standard features.
There are various ufun available for :
• Broadcasting
• Output type determination
• Use of internal buffers
• Error handling
• Casting Rules
• Math operations
• Trigonometric functions
• Comparison functions etc.
5. Scipy
• The scipy package contains various toolboxes dedicated to common
issues in scientific computing. Its different submodules correspond to
different applications, such as interpolation, integration, optimization,
image processing, statistics, special functions, etc
• http://scipy-lectures.github.io/intro/scipy.html
