Numpy-with-Python

                                                  Learn Numpy Basic

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.The ancestor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers. In 2005, Travis Oliphant created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications. NumPy is open-source software and has many contributors.

NumPy is the fundamental package for scientific computing with Python. It contains among other things:

A powerful N-dimensional array object
Sophisticated (broadcasting) functions
Tools for integrating C/C++ and Fortran code
Useful linear algebra, Fourier transform, and random number capabilities Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data.
Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
NumPy is licensed under the BSD license, enabling reuse with few restrictions.

Prequest for Learn Numpy

Python>=3.5
How to insatll Numpy
pip install numpy

Different Data Structure In Numpy and Pandas and Python

Different Types Of Array Creations and Reshaping:

Name		Name	Last commit message	Last commit date
Latest commit History 19 Commits
Chapter-1 Arrays		Chapter-1 Arrays
Chapter-2 Indexing on ndarrays		Chapter-2 Indexing on ndarrays
Chapter-3 Statistics		Chapter-3 Statistics
Chapter-4 I_O with NumPy		Chapter-4 I_O with NumPy
Chapter-5 Broadcasting		Chapter-5 Broadcasting
Chapter-6 Copies and views		Chapter-6 Copies and views
Chapter-7 Shape Manipulation		Chapter-7 Shape Manipulation
Images		Images
Statistics		Statistics
.gitignore		.gitignore
0 Introduction-to-NumPy.ipynb		0 Introduction-to-NumPy.ipynb
0. Jupyter Notebook Users Manual.ipynb		0. Jupyter Notebook Users Manual.ipynb
1. The-Basics-Of-NumPy-Arrays.ipynb		1. The-Basics-Of-NumPy-Arrays.ipynb
1. What is numpy.ipynb		1. What is numpy.ipynb
1.1 NumPy Basic 41 exercises & solution.ipynb		1.1 NumPy Basic 41 exercises & solution.ipynb
10. Polynomial .ipynb		10. Polynomial .ipynb
11. Matrix .ipynb		11. Matrix .ipynb
12. Tricks and Tips.ipynb		12. Tricks and Tips.ipynb
2. Installation of Numpy.ipynb		2. Installation of Numpy.ipynb
2.1 Scalars Vectors Matrices and Tensors.ipynb		2.1 Scalars Vectors Matrices and Tensors.ipynb
2.2 Multiplying Matrices and Vectors.ipynb		2.2 Multiplying Matrices and Vectors.ipynb
2.3 Identity and Inverse Matrices.ipynb		2.3 Identity and Inverse Matrices.ipynb
2.4 Linear Dependence and Span.ipynb		2.4 Linear Dependence and Span.ipynb
3. The Basics Numpy and Universal Functions.ipynb		3. The Basics Numpy and Universal Functions.ipynb
4. Indexing, Slicing and Iterating¶.ipynb		4. Indexing, Slicing and Iterating¶.ipynb
5. Shape Manipulation.ipynb		5. Shape Manipulation.ipynb
6. Copies and Views.ipynb		6. Copies and Views.ipynb
7. Broadcasting rules.ipynb		7. Broadcasting rules.ipynb
8. Fancy indexing and index tricks.ipynb		8. Fancy indexing and index tricks.ipynb
9. Linear Algebra.ipynb		9. Linear Algebra.ipynb
Boolean_Arrays_and_Masks.ipynb		Boolean_Arrays_and_Masks.ipynb
Computation_on_arrays_Universal_Functions.ipynb		Computation_on_arrays_Universal_Functions.ipynb
Computation_on_arrays_aggregates.ipynb		Computation_on_arrays_aggregates.ipynb
Computation_on_arrays_broadcasting.ipynb		Computation_on_arrays_broadcasting.ipynb
Fancy_Indexing.ipynb		Fancy_Indexing.ipynb
LICENSE		LICENSE
Linear_Algebra_with_Python.ipynb		Linear_Algebra_with_Python.ipynb
Overview of numpy.ipynb		Overview of numpy.ipynb
README.md		README.md
Sorting.ipynb		Sorting.ipynb
Structured_Data_NumPy.ipynb		Structured_Data_NumPy.ipynb

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Numpy-with-Python

Prequest for Learn Numpy

Different Data Structure In Numpy and Pandas and Python

Different Types Of Array Creations and Reshaping:

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