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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.

5 useful Numpy functions

Introduction on Numpy

NumPy is a Python package. It stands for 'Numerical Python'. It is a library consisting of multidimensional array objects and a collection of routines for processing of array.

Operations using NumPy:

Using NumPy, a developer can perform the following operations
Mathematical and logical operations on arrays.
Fourier transforms and routines for shape manipulation.
Operations related to linear algebra. NumPy has in-built functions for linear algebra and random number generation.

NumPy – A Replacement for MatLab:
NumPy is often used along with packages like SciPy (Scientific Python) and Mat−plotlib (plotting library). This combination is widely used as a replacement for MatLab, a popular platform for technical computing. However, Python alternative to MatLab is now seen as a more modern and complete programming language.

order statistcs-percentile
averages and variances- var
Searching-argmax
solving equations-linalg.solve
Datetime Support Functions -numpy.datetime_as_string

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 "ma19m002/numpy-array-operations" on https://jovian.ml/
[jovian] Uploading notebook..
[jovian] Capturing environment..
[jovian] Committed successfully! https://jovian.ml/ma19m002/numpy-array-operations

'https://jovian.ml/ma19m002/numpy-array-operations'

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