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.

Beginner's guide to NUMPY :-

About Numpy :

NumPy is a Python library that is the core library for scientific computing in Python. It contains a collection of tools and techniques that can be used to solve on a computer mathematical models of problems in Science and Engineering

Some Basic Useful Functions to Use in Numpy :

• sum: np.sum() is an inbuilt numpy function which returns the sum of all of the elements present in the array when summation is possible, i.e. elements of the array are integer or float which can be added.
• arange: arange() is an inbuilt numpy function that returns an ndarray object containing evenly spaced values within a defined interval. For instance, you want to create values from 1 to 10; you can use numpy. arange() function.
• copy: copy() method in Python returns a copy of the Set
• lexsort: The lexsort() Given multiple sorting keys, which can be interpreted as columns in a spreadsheet, lexsort returns an array of integer indices that describes the sort order by multiple columns.
• percentile: Percentile is an interesting application to use. It is useful generally as a relative ranking rather than an absolute percentage.

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.

Let's begin by importing Numpy and listing out the functions covered in this notebook.