blog, data, data science, python, Statistics

Project:- Data Analysis of Global Weather Trends Compared to Edinburgh, in Python

The Project

The project is part of the Udacity Data Analysis Nanodegree. The project requires the student to extract Global average temperature data and the average temperature of a local city. In this case I chose Edinburgh. We then need to discuss what questions we want to ask of the data, analyse the data, visualise the data and draw our conclusions.

I extracted the data from the Udacity virtual environment using SQL. I then imported the data into Python so we could use a Jupyter Notebook to create the required report, which allows us to document and code in the same document, great for presenting back findings and visualisations from the data.

I approached the project by first deciding what questions to ask of the data. I then imported the data to Python using Pandas and carried out some rudimentary exploration of the data to understand its layout, structure, number of records and so on . To prepare the data for visualisation, I then applied the rolling() function to the data to smooth out some of the jagged changes in the data

With the data now using a rolling window, I then visualised the data in both a Line chart and a Box Plot for the Global data and Local data so that we can compare, ascertain trends and answer the questions posed.

Finally, I drew conclusions and answered our questions we posed at the start.

The PDF report written to communicate my project and findings can also be found here

What We Learned

  • How to approach an analysis project, posing questions and drawing conclusions
  • Manipulating data in Python
  • Creating a rolling average in Python using the rolling() function
  • Utilising Matplotlib to visualise data in Line charts and Box plots, complete with customised colour, axis, labels and title

The Code and the Report

  • GitHub repository for the data, SQL, PDF report and Jupyter Notebook
  • the PDF report can also be found here


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