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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:

Pick 5 interesting Numpy array functions by going through the documentation: https://numpy.org/doc/stable/reference/routines.html

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.

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

(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 .

(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.

Numpy: A must know Python library

If you have something with math, leave native python and just go with Numpy.

Numpy is a python library, that does almost all kinds of mathematical operations from simple to sophisticated ones. And yes, performance and speed is it's key. We may come up with its alternatives, but in terms of speed and performance, no other python stuff can beat it. So, for currently trending topic of Data Science, one hardly believes if one does numerical computations in python without Numpy.

Here are the 5 Numpy functions (including some more which works great when working together) that I chose for the purpose of this assignment. And yes it does help with my Data Science course that I am taking right now. You really wouldn't know how interesting it is, until you use them practically.

np.linspace
np.random.binomial & np.random.normal
np.expand_dims & np.squeeze
np.hstack & np.vstack
np.polyfit & np.polyval

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.

!pip install jovian --upgrade -q

import jovian

jovian.commit(project='numpy-array-operations')

[jovian] Attempting to save notebook..
[jovian] Updating notebook "suyogya-tamrakar/numpy-array-operations" on https://jovian.ml/
[jovian] Uploading notebook..
[jovian] Capturing environment..
[jovian] Committed successfully! https://jovian.ml/suyogya-tamrakar/numpy-array-operations

'https://jovian.ml/suyogya-tamrakar/numpy-array-operations'

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