Data Analysis and Visualisation

About

This coding example is part of a Udemy Python course using Python Pandas library with JustPy to plot graphs from DataFrames onto web pages for interactive data visualisation. The source data is imported into a Pandas DataFrame from a .csv file and presented on a web page using JustPy Quasar pages and HighCharts.

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Technologies

Languages

• Python3
  • Used to create the main application functionality

Libraries

• Pandas
  • Used to manipulate raw data from a csv file in a DataFrame
• JustPy
  • Quasar WebPages are used to render http output pages
  • HighCharts JS code is used to render the charts within the WebPage.

Tools

• Jupyter Notebook
  • Used to create the code and explanation

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Deployment

The website was developed using Gitpod using Git pushed to GitHub, which hosts the repository. I made the following steps to deploy the site:

Cloning Data_Analysis_and_Visualisation from GitHub

Prerequisites

Ensure the following are installed locally on your computer:

• Python 3.6 or higher
• PIP3 Python package installer
• Git Version Control

Cloning the GitHub repository

• navigate to simonjvardy/Data_Analysis_and_Visualisation GitHub repository.
• Click the Code button
• Copy the clone url in the dropdown menu
• Using your favourite IDE open up your preferred terminal.
• Navigate to your desired file location.

Copy the following code and input it into your terminal to clone Sportswear-Online:

git clone https://github.com/simonjvardy/Data_Analysis_and_Visualisation.git

Creation of a Python Virtual Environment

Note: The process may be different depending upon your own OS - please follow this Python help guide to understand how to create a virtual environment.

Install the App dependencies and external libraries

• In your IDE terminal window, install the dependencies from the requirements.txt file with the following command:

pip3 install -r requirements.txt

Build the individual Quasar WebPages

• In your IDE terminal window, enter:

python3 1-average-rating-day.py

Output runs locally in a web browser using URL http://127.0.0.1:8000/ to show the Average Rating by Day graph.

python3 2-average-rating-week.py

Output runs locally in a web browser using URL http://127.0.0.1:8000/ to show the Average Rating by Week graph.

python3 3-average-rating-month.py

Output runs locally in a web browser using URL http://127.0.0.1:8000/ to show the Average Rating by Month graph.

python3 4-average-rating-month-course.py

Output runs locally in a web browser using URL http://127.0.0.1:8000/ to show the Average Rating by Month by Course graph.

python3 5-average-rating-month-course-stream.py

Output runs locally in a web browser using URL http://127.0.0.1:8000/ to show the Average Rating by Month by Course graph.

python3 6-happiest-day-of-week.py

Output runs locally in a web browser using URL http://127.0.0.1:8000/ to show the Average Rating by Day of the Week graph indicating when the Students are happiest to leave a good review.

python3 7-rating-count-by-course-pie.py

Output runs locally in a web browser using URL http://127.0.0.1:8000/ to show the Rating Count by Course pie chart.

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Acknowledgements

• Udemy: The Python Mega Course - Build 10 Real World Applications Credit: Ardit Sulce