Introduction to NumPy

NumPy, short for Numerical Python, is a powerful library in Python used for numerical computing. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy forms the foundation for many scientific computing and data analysis libraries in Python.

Key Features of NumPy:

1. Arrays: NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers.
2. Efficient Operations: NumPy provides efficient mathematical operations on arrays, enabling you to perform complex computations with ease and speed.
3. Broadcasting: Broadcasting is a powerful mechanism that allows NumPy to work with arrays of different shapes when performing arithmetic operations, making code more concise and readable.
4. Vectorized Operations: NumPy operations are vectorized, meaning they are performed element-wise on arrays, which often leads to cleaner and more concise code compared to traditional iterative approaches.
5. Integration with Other Libraries: NumPy integrates seamlessly with other Python libraries such as SciPy (for scientific computing), Matplotlib (for plotting and visualization), and Pandas (for data manipulation and analysis).

How to Install NumPy:

There are 2 ways to install NumPy:

1. pip install numpy
2. Install Anaconda and open Jupyter Notebook

How to Import NumPy:

import numpy as np

import numpy

That’s it for now, let’s deep dive into NumPy in my upcoming blogs.

And can learn more from my Github profile:

GitHub - vandanasri/NumPyLearnings

Also, Matplotlib learnings from github:

GitHub - vandanasri/MatplotlibLearnings: Learning practice for Matplotlib

Pandas Learnings from github:

GitHub - vandanasri/PandasLearnings: Learn Pandas

Check some of my other blogs in this series:

• Basic concept of NumPy
• Arrays vs NumPy Arrays vs List
• Creation of NumPy

Happy Learning:)