Hello, @DevJames1. As @Maryann-Onyinye stated in the project page, Phabricator isn't really the place to do introductions, you should do that in Zulip.

In T359186#9609870, @Ederporto wrote:

Hello, @DevJames1. As @Maryann-Onyinye stated in the project page, Phabricator isn't really the place to do introductions, you should do that in Zulip.

Ok, Well understood, thanks for the clarification.
I was thinking I could create a template here to update steadily and ask for a review for my final application,
I will now invalidate this sub-task,
Thanks

I will now regularly update the weekly project progress report here

Weekly Internship Report

Week 1 : May 27 - June 2
Overview of Tasks Completed:

• Community Bonding
• Create a proposal on Phabricator to track progress using the comment section
• Create a wiki meta user page
• Complete the first Introductory blog post
• Study how to get structured information from Wikidata, Learning about SPARQL queries for Wikidata which has data stored in the Resource Description Framework (RDF) format.

Challenges Faced:

• Wikidata linked data format and SPARQL learning curve, but by going through the tutorial on WDQS tutorial I was able to understand the basics of SPARQL and Wikidata and it's concepts.

Learnings and Skills Gained:

• SPARQL syntax used to query and manipulate Wikidata data.
• Knowledge about Structured data on Wikidata and how to access it.
• Writing Skills

Weekly Internship Report

Week 2 : June 3 - June 9
Overview of Tasks Completed:

• Write Python scripts to fetch and test the structured data from Wikidata
• The Technical Plan laid out and discussed with my mentors
• Research what happens when NaNs are in a bar chart race structured data frame. - how does it act when you leave the NaNs or when you do ffill/bfill
• Start Benchmarking existing tools and technologies for infographic creation and Wikidata integration.

Challenges Faced:

• Pandas converting my integers to float type after pivoting the table, but I was able to solve this by leaving the Year column as string type and explicitly converting the Population column to integer type using astype(int)
• Dealing with NaN values on the data frame and how to appropriately fill these cells

Learnings and Skills Gained:

• Benchmarking/Research skills
• More Python data skills especially using the Pandas package
• Writing Skills

Weekly Internship Report

Week 3 : June 10 - June 16
Overview of Tasks Completed:

• Fill out the technical requirement section of the technical plan
• Write a blog post about an open-source vocabulary term
• Conclude benchmarking existing tools and technologies for infographic creation and Wikidata integration.
• Refactor the query code to fill the NaNs before the first value in a column with zero in the data frame and properly handle subsequent NaN values

Challenges Faced:

• Dealing with NaN values on the data frame and how to appropriately interpolate and extrapolate between NaNs between valid values and after valid values

Learnings and Skills Gained:

• Documentation skills
• More Python data skills especially using the Pandas package
• Interpolation in Pandas data frame

Weekly Internship Report

Week 4 : June 17 - June 23
Overview of Tasks Completed:

• Fill out the technical documentation for processes and tools used so far In the project
• Brush up skills in Flask
• Learn about MariaDB, which is commonly used in Wikimedia projects
• Update technical plan with comparisons between Django vs Flask, for the best fit for this project

Challenges Faced:

• None

Learnings and Skills Gained:

• Flask
• MariaDB
• Research skills and documentation