Non Fungible Token, often known as NFT, has been a popular term recently. The NFT marketplace is expanding at a quick pace, and certain collections, such as the Bored Ape Yacht Club, have achieved total sales in excess of $1 billion, which is rather remarkable.
Data scientist expert defines NFTs as digital works of art that may be purchased or sold on a blockchain, to give them a specific context. The blockchain technology, which is essentially a decentralised record of all of the transactions that take place on a network of computers runs NFTs as its underlying technology. So, if you also have interest in creating your NFT in Python specifically then this guide is for you. Here, the write up explores best advice from the data scientist in nft collection creation using Python.
Scripting procedure
Firstly, you have to start with creating a unique avatar image by joining various characteristics altogether.
Data usage
As per data scientist expert, you’ll need to get the data from Substrapunks’ repository that’s available on the web. Next, you have to download the repository and receive the zip file in your local device. Further, you’ll have to perform important packages’ import. For creating NFTs in Python you’ll need to use the below given packages:
- PIL
- IPython
- Random
- Json
- OS
Further to learn about these packages in depth, take up data science certifications courses if accessible.
Trait Rarity
Every digital avatar contains these 5 traits:
- Face
- Ears
- Hair
- Mouth
- Nose
Rarity is crucial as per data scientist, in nft collection creation because it brings out the scarcity of the NFT art which increases its value. Ensure that weights assigned across multiple types inside one trait must make up to 100 as total weight. However, joining a data scientist certifications program will help you to understand the concept in-depth.
Traits classification
Further, you can use dictionaries to redirect the names of the trait to the name of their respective files.
Images traits in details
Every avatar image you build will consist of 6 more images over one another: eyes, nose, face, ears, mouth. Here, with programming you can use For loop to combine these traits within a sole image for a defined number of images.
Uniqueness validation
As per data scientist expert, each NFT avatar needs to be unique. Through Python programming you can leverage its function that lists down the images in the storage and returns back the copied images. Once done add one-of-a-kind identifier to each NFT avatar.
Counting of Trait
Usually, the assigning of traits depends on random function and predefined weights. Hence, it indicates that you might not have 60 image faces even after your defined weight count is 60 for image faces. Therefore to keep the track of exact numbers of the trait you’ll need to check the present number of traits in your image collection. For this, You’ll have to write the code in Python to achieve the following:
- Defining a dictionary for every trait along with their classifications.
- Run the loop for your created images and put them into their respective trait dictionary.
Images generations
Finally, you are now at the stage where NFT image generation will take place.
For every image, the programming script will run the following:
- Opening files of the image traits as defined earlier.
- Next, using the PIL package you can pick out the respective trait image from the directory.
- Then, blending all the selected traits within a single image.
- Now, converting it to RGB which makes up the most suitable color model.
- Save the file to your computer.
Necessary Tools
The following is a list of the tools that may be used to do this task:
- Dataset derived from the Copernicus Program.
- Python — Libraries such as Xarray, Pandas, Matplotlib, Geopandas, Shapely, Rasterio, and Cdsapi are all available for use with Python.
- For logo design, Adobe Photoshop and Canva are recommended.
- Metamask is a cryptocurrency wallet that utilises the Polygon blockchain technology.
- Introducing the NFT marketplace at Opensea.
There are already a plethora of guides available online on how to start your own NFT project, so you won’t have to spend much time getting into the specifics of setting up the wallet and minting the coins. If you want to create your own NFT, one option is to look at generative models such as GANs. However, models such as GANs need GPUs and a significant amount of computing power.
Thoughts on NFT
The concepts of NFT, digital ownership, and smart contracts are intriguing, as is the notion of moving towards a decentralised ecosystem and employing tools to facilitate the creation of such an environment (like cryptocurrency, NFT, etc). At the same time, there are certain drawbacks to this method of operation.
The act of minting an NFT, which involves uploading the picture information to the blockchain, consumes energy (both in terms of electricity and computer power), and if the demand for crypto art develops, this might have a considerable carbon footprint. Ethereum, a cryptocurrency that is commonly used to sell and exchange NFTs, has a power usage that is comparable to Kazakhstan’s total electricity use. There have been several suggestions for how to make the minting process a little more environmentally friendly. In the future years, we will see the crypto economy become more environmentally friendly.
Conclusion
Create an instructive NFT collection that is backed up by climatic data in order to learn more about the field of NFT as a Data Scientist. Climate change is real, and the rise in global temperatures has something to say about it! It would be a rewarding experience to use the Python programming language and its libraries to create something unique and instructive, and this would be a nice learning experience. There are several opportunities for Data Scientists to produce digital art using a variety of technologies at their disposal, and this show will only feature one of those tools. In order to produce an NFT, you do not need to be an artist; you only need to think beyond the box. Follow the GLOBAL TECH COUNCIL to learn more.
