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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.

Five useful functions of numpy

Numpy

Numpy stands for numerical pytho. So, by name in itself says that numpy is related to the numerical programming. Numpy is a pthon library for working with arrays and other data structures like matrices linear algebra and so on. Numpy is fast and very convenient in use compared to array functions. As we have heard this very common heavy slogan 'with big data comes big resposibility' numpy just gives smooth working on such data operations and analysis experiences. In data science numpy library contributes alot to speed up the algorithms and calculations giving many useful dynamic fucntions.

Following are five useful functions in Numpy.

np.reshape
np.amax
np.amin
np.matmul
np.vstack

!pip install jovian --upgrade -q

import jovian

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

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

'https://jovian.ml/jaycessunuwar/numpy-array-operations'

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