This repository contains a series of Jupyter notebooks with various aspects of using the NumPy library in Python. Each notebook is self-contained and focuses on a specific topic within NumPy, starting from the basics and moving towards more complex operations.

1. What is NumPy in Python?
   Introduction to NumPy and its significance in scientific computing.

2. NumPy Array vs Python List
   A comparison between NumPy arrays and Python lists, highlighting the performance benefits and use cases of NumPy arrays.

3. Creating NumPy Arrays
   Various methods to create NumPy arrays, including array initialization and converting other data structures to arrays.

4. NumPy Array Using NumPy Functions
   Utilizing built-in NumPy functions to create and manipulate arrays.

5. NumPy Array with Random Numbers
   Generating arrays of random numbers and understanding the use of the random module in NumPy.

6. Datatype of NumPy Arrays
   Understanding the various data types available in NumPy and how to work with them.

7. NumPy Arithmetic Operations
   Performing basic arithmetic operations like addition, subtraction, multiplication, and division with arrays.

8. Arithmetic Functions in NumPy
   Exploring built-in arithmetic functions such as max(), min(), argmin(), argmax(), sqrt(), sin(), cumsum() and more.

9. Shape and Reshaping in NumPy
   Understanding the shape of arrays and how to reshape them without changing the data.

10. Broadcasting in NumPy Array
    Learning about broadcasting, a powerful mechanism that allows NumPy to work with arrays of different shapes during arithmetic operations.

11. Indexing and Slicing in NumPy Arrays
    Dive deep into indexing and slicing to manipulate NumPy array elements

12. Iterating NumPy Arrays
    Learn how to iterate over NumPy arrays using nditer(), ndenumerate().

13. Copy vs Views in NumPy
    Understanding the difference between copying and viewing arrays and their implications using copy() and view().

14. Joining & Splitting NumPy Arrays
    Techniques for concatenating and splitting arrays using various functions like concatenate(), stack(), hstack(), dstack(), vstack() and split().

15. NumPy Array Functions
    Exploreed a range of functions for array manipulation, where(), sort(), searchsorted(), shuffle(), unique(), resize, flatten() and ravel()

16. NumPy Insert and Delete Arrays Functions
    Learnt how to insert and delete elements in arrays efficiently using insert(), append(), and delete()

To get started with these notebooks, clone this repository and install the required packages using the following commands:

git clone https://github.com/Biotechnologyguy/numpy.git
cd numpy
pip install numpy