Data preparation, data manipulation and visual analytics were the main focus for this project.

Before I present my analysis, allow me to provide context and explain what SpaceBudz is.

“ SpaceBudz was launched on March 24th of 2021 on the Cardano blockchain. It grew quickly to a beloved Non-fungible Token project in the Cardano community and is known for its pioneering work. Minting one of the first NFT collections, creating the NFT metadata standard and launching the first fully smart contract based marketplace got people excited about SpaceBudz.”

“Zieg and Alessandro the founders of SpaceBudz created the project out of curiosity about Cardano and its technology. We’ve always been passionate about crypto and tried to make things the right way. All we’ve done so far is open-source, we truly believe in decentralization and want to preserve these values at the core of the SpaceBudz project.”

You can check out and or learn more about the entire SpaceBudz collection by clicking here or clicking the hyperlink down below.

Project steps taken:

1. Sourced and safely stored the collection’s extended metadata on my local machine.

I began my project by identifying which data was necessary to conduct a complete analysis on the 10k collection. After communicating with members of the community via Discord and exploring Alessandro Konrad’s Github profile ( the co-founder), I was able to locate the SpaceBudz “metadata_extended.json” file.

2. Conducted an exploratory analysis on the metadata_extended.json file.

Once the metadata_extended.json file had securely been downloaded, I conducted an exploratory analysis in order to understand the raw data structure. This was done by opening the file using the Atom IDE.

Within the metadata, SpaceBud token ( #0 — #9999) lists each of its specific individual traits and extended properties. Token traits include: gadgets, specie type, type color, gloves color, suit, emotion, background, and gadget hand position.

i.e.

Below we can see SpaceBudz token #34’s metadata.

SpaceBudz Token #34:

3. Converted the downloaded JSON file into a CSV text file.

I converted the json file to csv (comma separated values) using an open source file convertor:

Converting the file, the extended metadata now appeared such as:

Summary stats:

Columns: 72

Cell Count: 211,788

4. Conducted an exploratory analysis on the newly converted metadata.

I immediately noticed columns inefficiently reporting similar groups of data. Such as the traits/1 column, extendedproperties/2/trait column , and extendedproperties/3/trait column.

Additionally at first glance, the dataset appears to contain missing values. However after further review, I was able to realize that data is not missing but more so formatted based off conditional token properties.

For example when comparing SpaceBud #4 and SpaceBud #5, we can see that SpaceBud #5 is missing cells: D7, E7, F7, N7, O7, and P7. The data is sequentially organized by traits/0, traits/1, traits/2, and traits/3. Columns that follow, further describe traits by extendedproperties/X/trait and extendedproperties/X/color.

For SpaceBud #4, traits/0 is represented as “special background”. Whereas SpaceBud #5 has “belt” represented in its traits/0 cell. Because of this and specific token traits, SpaceBud #5 will have blank values in specific column cells.

4. Transformed the dataset into a more readable layout

To improve readability, I decided to create additional columns, re-title existing columns, and re-arrange the dataset’s column order. In doing so, I was able to reduce the total amount of columns from 72 to 64. Cell count was increased from 211,788 to 640,064. Columns that previously had been reporting similar information and that contained “blank” values were consolidated using the filter drop tool option.

For example, to fill in the blank cells within the “Chestplate” column, I would filter out rows that contained a chestplate trait cell value. In doing so, I would be able to visualize the cells that did not contain a chestplate trait. The data from these filtered rows, were then collected and inserted into their proper trait columns. Once existing data had been re-organized, cell values were deleted and replaced with “none”.

(Before data manipulation)

(After data manipulation)

This filtering process was done to organize the metadata in a more legible format and to ensure compatibility with the Tableau platform.

[Before data manipulation]

[After data manipulation]

5. Created data visualizations using Tableau.

The goal was to create beautiful, minimalistic, and informative data visualizations for the community. Across this sample of visualizations, you may notice small differences in design, format, and colors. To get the community involved I posted a survey poll on Twitter asking for design feedback. You can check out the poll by clicking here and or by visiting my profile down below.

SpaceBudz Data Visualizations:

To further examine these data visualizations you can click the hyper link below, and or click here to visit my Tableau profile.

https://public.tableau.com/app/profile/isaac.arriaga

6. Shared final product with community stakeholders.

In true decentralization style, I have open sourced this work and data onto my Github profile.

A big thank you to both Alessandro and Zieg for open sourcing the project’s metadata!

Thank you all for the support!