Programming For Data Analysis Assessment - numpy.random

Table of Contents

1. Description
2. How To Get The Respoistory on Your Machine
3. Running Jupyter Notebook
4. Technologies Used
5. Libraries Used
6. nbview

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Description

This document is relating to numpy.random assessment for Programming For Data Analysis module. The aim of this project is to research and investigate the numpy.random package.

This README contains the documentation for technologies and libraries used for the project.

The Jupyter Notebook and images used can be found at https://github.com/kmcd14/ProgrammingDA-assessment.

Objectives and to do list:

• Explain the overall purpose of the package.
• Explain the use of the “Simple random data” and “Permutations” functions.
• Explain the use and purpose of at least five “Distributions” functions.
• Explain the use of seeds in generating pseudorandom numbers.

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How To Get The Respoistory on Your Machine

1. Create a folder on your desktop where you wish to store the code
2. Navigate to the repoistory https://github.com/kmcd14/ProgrammingDA-assessment.
   
   
3. Copy the repository address, as seen in the above picture. Using either SSH or HTTPS
4. Open the terminal on your desktop and navigate to the folder you created earlier.
5. Once in the folder use

   $git clone git@github.com:kmcd14/ProgrammingDA-assessment
   

6. Set up pull mode and pull down the contents

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To access this notebook - numpy-random.ipynb - you will need to install Python preferably through Anaconda distribution.

Running Jupyter Notebook

From the command line navigate to the folder you have cloned the repository.

Type jupyter lab or jupyer notebook into the command line and press enter

Open the numpy-random.ipynb notebook in the browser

To run the code in a cell hold down the shift key and press enter.

To change between edit and read mode at any time press the ESC key.

When you have finished, close the web browser and press Press Ctrl + C on the command line to terminate the programme.

Technologies Used:

Google Docs: an online word processor used to write my documentation before transfering into this README file. https://www.google.com/docs/about/

Anaconda: the easiest way to perfrom Python data science machine learning on Windows, Linux and Mac OS. This script was created using Version 4.9.2. https://www.anaconda.com/distribution/

Python: an interpreted, object-oriented, high-level programming language with dynamic semantics. This script was created using Version 3.8.5. https://www.python.org/

GitHub: is a code hosting platform for collaboration and version control. https://github.com/

Jupyter Lab/Jupyter Notebook: a web-based interactive development environment for Jupyter notebooks, code, and data. https://jupyter.org/

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Libraries Used:

“Python is a widely-used, interpreted, object-oriented, and high-level programming language with dynamic semantics, used for general-purpose programming. It was created by Guido van Rossum, and first released on February 20, 1991” (Python Institute, 2021). It is suitable for research, prototyping and building production systems, which eliminates the need for using different languages for both. Python has a vast and continuously growing library to choose from which makes it perfect for data analysis, such as Numpy and Pandas. It is a robust, flexible and efficient language which provides many solutions and avenues to approach and solve problems.

Numpy is a Python library used for working with arrays. It produces a narray object. Numpy arrays are faster and more efficient than using python lists. It does this by storing arrays in one place in memory, so they can be accessed and manipulated quickly http://www.numpy.org/

Mathplotlib is a python library used to create plots, graphs, charts etc. https://matplotlib.org/

Seaborn is based on matplotlib and is also used to create data visualisations. “It provides a high-level interface for drawing attractive and informative statistical graphics.” https://seaborn.pydata.org/

If your system does not have these libaries installed enter the below command from the command line:

    $pip install <library name>

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nbviewer:

Additionally, you can view the notebook by clicking on the following icon: