Assignment 2 - Numpy Array Operations

This assignment is part of the course "Data Analysis with Python: Zero to Pandas". The objective of this assignment is to develop a solid understanding of Numpy array operations. In this assignment you will:

1. Pick 5 interesting Numpy array functions by going through the documentation: https://numpy.org/doc/stable/reference/routines.html
2. Run and modify this Jupyter notebook to illustrate their usage (some explanation and 3 examples for each function). Use your imagination to come up with interesting and unique examples.
3. Upload this notebook to your Jovian profile using jovian.commit and make a submission here: https://jovian.ml/learn/data-analysis-with-python-zero-to-pandas/assignment/assignment-2-numpy-array-operations
4. (Optional) Share your notebook online (on Twitter, LinkedIn, Facebook) and on the community forum thread: https://jovian.ml/forum/t/assignment-2-numpy-array-operations-share-your-work/10575 .
5. (Optional) Check out the notebooks shared by other participants and give feedback & appreciation.

The recommended way to run this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks.

Try to give your notebook a catchy title & subtitle e.g. "All about Numpy array operations", "5 Numpy functions you didn't know you needed", "A beginner's guide to broadcasting in Numpy", "Interesting ways to create Numpy arrays", "Trigonometic functions in Numpy", "How to use Python for Linear Algebra" etc.

NOTE: Remove this block of explanation text before submitting or sharing your notebook online - to make it more presentable.

Getting Started with Numpy

Getting to know different numpy methods

Numpy is a python library used for scientific calculations. The library provides multidimensional array object which we can manipulate to our requirement. Numpy makes working with arrays simpler and much faster than python built-in list data structure as the underlying implementation of numpy is written in C++ making it extremely fast(almost 10 times faster) in comparison to interpreted code of python.

• add
• str_len
• equal
• eye
• isreal