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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:

  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.

5 Numpy Functions you wish you knew were important

Subtitle Here

Numpy is a Python library used in scientific computing and data analysis which has numerous linear algebra, calculus, fourier transform, statistics and random number capabilities. It supports vectorized code which means that operations can be performed on numpy arrays with short, concise and easy to read code. Since all its in-built functions are internally implemented using pre-compiled code written in C language, interpreting the code is not needed which makes them 100 times faster as compared to traditional way of using inefficient nested for loops.

  • matmul
  • function 2
  • function 3
  • function 4
  • function 5

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] Creating a new project "abdullah-ikram-0599/numpy-array-operations" [jovian] Uploading notebook.. [jovian] Uploading additional files... [jovian] Committed successfully! https://jovian.ai/abdullah-ikram-0599/numpy-array-operations

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