NumPy is a powerful Python library for numerical computing that provides support for n-dimensional arrays and a variety of mathematical functions. It offers flexibility and speed, making it accessible for programmers. The tutorial covers array creation, manipulation, matrix operations, and advanced features like reshaping, indexing, and linear algebra, along with practical applications and exercises.
NumPy Tutorial – Python Library
NumPy is a general-purpose array-processing Python library which provides handy methods/functions for working n-dimensional arrays. NumPy is a short form for “Numerical Python“. It provides various computing tools such as comprehensive mathematical functions, and linear algebra routines.
- NumPy provides both the flexibility of Python and the speed of well-optimized compiled C code.
 - Its easy-to-use syntax makes it highly accessible and productive for programmers from any background.
 
This NumPy tutorial helps you learn the fundamentals of NumPy from Basics to Advanced, like operations on NumPy array, creating and plotting random data sets, and working with NumPy functions.
Why Numpy ?
NumPy revolutionized the way we handle numerical data in Python. It is created to address the limitations of traditional Python lists when it comes to numerical computing. It is developed by Travis Olliphant in 2005.
Numpy Array in Python
- create a array object
 - create numpy array from list
 - create a numpy array from Tuple
 - create a numpy using 
fromtier() - create a numpy array using 
arange() - create a numpy array using 
linespace - Create a numpy array using 
nump.empty( ) - create a numpy array using 
nump.one( ) - create a numpy array using 
nump.zero( ) - Create a Numpy Array using 
nump.eye( ) - Create 1D NumPy Array using 
nump.random.rand() - Create a Full NumPy Array using 
np.full() - Create Numpy from 
numpy.core.fromrecords() 
NumPy Array Manipulation
new_array = np.copy(original_array)new_array = original_array.view()new_array.base