The Timescale NFT Starter Kit is a step-by-step guide to get up and running with collecting, storing, analyzing and visualizing NFT data from OpenSea, using PostgreSQL and TimescaleDB.

Overview

Timescale NFT Starter Kit

The Timescale NFT Starter Kit is a step-by-step guide to get up and running with collecting, storing, analyzing and visualizing NFT data from OpenSea, using PostgreSQL and TimescaleDB.

The NFT Starter Kit will give you a foundation for analyzing NFT trends so that you can bring some data to your purchasing decisions, or just learn about the NFT space from a data-driven perspective. It also serves as a solid foundation for your more complex NFT analysis projects in the future.

We recommend following along with the NFT Starter Kit tutorial to get familar with the contents of this repository.

For more information about the NFT Starter Kit, see the announcement blog post.

Project components

Earn a Time Travel Tiger NFT

Time Travel Tigers is a collection of 20 hand-crafted NFTs featuring Timescale’s mascot: Eon the friendly tiger, as they travel through space and time, spreading the word about time-series data wearing various disguises to blend in. The first 20 people to complete the NFT Starter Kit tutorial can earn a limited edition NFT from the collection, for free! Simply download the NFT Starter Kit, complete the tutorial and fill out this form, and we’ll send one of the limited-edition Eon NFTs to your ETH address (at no cost to you!).

Get started

Clone the nft-starter-kit repository:

git clone https://github.com/timescale/nft-starter-kit.git
cd nft-starter-kit

Setting up the pre-built Superset dashboards

This part of the project is fully Dockerized. TimescaleDB and the Superset dashboard is built out automatically using docker-compose. After completing the steps below, you will have a local TimescaleDB and Superset instance running in containers - containing 500K+ NFT transactions from OpenSea.

The Docker service uses port 8088 (for Superset) and 6543 (for TimescaleDB) so make sure there's no other services using those ports before starting the installation process.

Prerequisites

  • Docker

  • Docker compose

    Verify that both are installed:

    docker --version && docker-compose --version

Instructions

  1. Run docker-compose up --build in the /pre-built-dashboards folder:

    cd pre-built-dashboards
    docker-compose up --build

    See when the process is done (it could take a couple of minutes):

    timescaledb_1      | PostgreSQL init process complete; ready for start up.
  2. Go to http://0.0.0.0:8088/ in your browser and login with these credentials:

    user: admin
    password: admin
    
  3. Open the Databases page inside Superset (http://0.0.0.0:8088/databaseview/list/). You will see exactly one item there called NFT Starter Kit.

  4. Click the edit button (pencil icon) on the right side of the table (under "Actions").

  5. Don't change anything in the popup window, just click Finish. This will make sure the database can be reached from Superset.

  6. Go check out your NFT dashboards!

    Collections dashboard: http://0.0.0.0:8088/superset/dashboard/1

    Assets dashboard: http://0.0.0.0:8088/superset/dashboard/2

Running the data ingestion script

If you'd like to ingest data into your database (be it a local TimescaleDB, or in Timescale Cloud) straight from the OpenSea API, follow these steps to configure the ingestion script:

Prerequisites

Instructions

  1. Go to the root folder of the project:
    cd nft-starter-kit
  2. Create a new Python virtual environment and install the requirements:
    virtualenv env && source env/bin/activate
    pip install -r requirements.txt
  3. Replace the parameters in the config.py file:
    DB_NAME="tsdb"
    HOST="YOUR_HOST_URL"
    USER="tsdbadmin"
    PASS="YOUR_PASSWORD_HERE"
    PORT="PORT_NUMBER"
    OPENSEA_START_DATE="2021-10-01T00:00:00" # example start date (UTC)
    OPENSEA_END_DATE="2021-10-06T23:59:59" # example end date (UTC)
  4. Run the Python script:
    python opensea_ingest.py
    This will start ingesting data in batches, ~300 rows at a time:
    Start ingesting data between 2021-10-01 00:00:00+00:00 and 2021-10-06 23:59:59+00:00
    ---
    Fetching transactions from OpenSea...
    Data loaded into temp table!
    Data ingested!
    Data has been backfilled until this time: 2021-10-06 23:51:31.140126+00:00
    ---
    You can stop the ingesting process anytime (Ctrl+C), otherwise the script will run until all the transactions have been ingested from the given time period.

Ingest the sample data

If you don't want to spend time waiting until a decent amount of data is ingested, you can just use our sample dataset which contains 500K+ sale transactions from OpenSea (this sample was used for the Superset dashboard as well)

Prerequisites

Instructions

  1. Go to the folder with the sample CSV files (or you can also download them from here):
    cd pre-built-dashboards/database/data
  2. Connect to your database with PSQL:
    psql -x "postgres://host:port/tsdb?sslmode=require"
    If you're using Timescale Cloud, the instructions under How to Connect provide a customized command to run to connect directly to your database.
  3. Import the CSV files in this order (it can take a few minutes in total):
    \copy accounts FROM 001_accounts.csv CSV HEADER;
    \copy collections FROM 002_collections.csv CSV HEADER;
    \copy assets FROM 003_assets.csv CSV HEADER;
    \copy nft_sales FROM 004_nft_sales.csv CSV HEADER;
  4. Try running some queries on your database:
    SELECT count(*), MIN(time) AS min_date, MAX(time) AS max_date FROM nft_sales 
Geocoding library for Python.

geopy geopy is a Python client for several popular geocoding web services. geopy makes it easy for Python developers to locate the coordinates of addr

geopy 3.8k Jan 02, 2023
NumPy and Pandas interface to Big Data

Blaze translates a subset of modified NumPy and Pandas-like syntax to databases and other computing systems. Blaze allows Python users a familiar inte

Blaze 3.1k Jan 01, 2023
🎨 Python3 binding for `@AntV/G2Plot` Plotting Library .

PyG2Plot 🎨 Python3 binding for @AntV/G2Plot which an interactive and responsive charting library. Based on the grammar of graphics, you can easily ma

hustcc 990 Jan 05, 2023
Pydrawer: The Python package for visualizing curves and linear transformations in a super simple way

pydrawer 📐 The Python package for visualizing curves and linear transformations in a super simple way. ✏️ Installation Install pydrawer package with

Dylan Tintenfich 56 Dec 30, 2022
Official Matplotlib cheat sheets

Official Matplotlib cheat sheets

Matplotlib Developers 6.7k Jan 09, 2023
The official colors of the FAU as matplotlib/seaborn colormaps

FAU - Colors The official colors of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) as matplotlib / seaborn colormaps. We support the old colo

Machine Learning and Data Analytics Lab FAU 9 Sep 05, 2022
Practical-statistics-for-data-scientists - Code repository for O'Reilly book

Code repository Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python by Peter Bruce, Andrew Bruce, and Peter Gedeck Pub

1.7k Jan 04, 2023
Python support for Godot 🐍🐍🐍

Godot Python, because you want Python on Godot ! The goal of this project is to provide Python language support as a scripting module for the Godot ga

Emmanuel Leblond 1.4k Jan 04, 2023
Data aggregated from the reports found at the MCPS COVID Dashboard into a set of visualizations.

Montgomery County Public Schools COVID-19 Visualizer Contents About this project Data Support this project About this project Data All data we use can

James 3 Jan 19, 2022
Customizing Visual Styles in Plotly

Customizing Visual Styles in Plotly Code for a workshop originally developed for an Unconference session during the Outlier Conference hosted by Data

Data Design Dimension 9 Aug 03, 2022
Render Jupyter notebook in the terminal

jut - JUpyter notebook Terminal viewer. The command line tool view the IPython/Jupyter notebook in the terminal. Install pip install jut Usage $jut --

Kracekumar 169 Dec 27, 2022
Pyan3 - Offline call graph generator for Python 3

Pyan takes one or more Python source files, performs a (rather superficial) static analysis, and constructs a directed graph of the objects in the combined source, and how they define or use each oth

Juha Jeronen 235 Jan 02, 2023
Extract and visualize information from Gurobi log files

GRBlogtools Extract information from Gurobi log files and generate pandas DataFrames or Excel worksheets for further processing. Also includes a wrapp

Gurobi Optimization 56 Nov 17, 2022
FairLens is an open source Python library for automatically discovering bias and measuring fairness in data

FairLens FairLens is an open source Python library for automatically discovering bias and measuring fairness in data. The package can be used to quick

Synthesized 69 Dec 15, 2022
Editor and Presenter for Manim Generated Content.

Editor and Presenter for Manim Generated Content. Take a look at the Working Example. More information can be found on the documentation. These Browse

Manim Community 149 Dec 29, 2022
Small U-Net for vehicle detection

Small U-Net for vehicle detection Vivek Yadav, PhD Overview In this repository , we will go over using U-net for detecting vehicles in a video stream

Vivek Yadav 91 Nov 03, 2022
Lightweight data validation and adaptation Python library.

Valideer Lightweight data validation and adaptation library for Python. At a Glance: Supports both validation (check if a value is valid) and adaptati

Podio 258 Nov 22, 2022
Python code for solving 3D structural problems using the finite element method

3DFEM Python 3D finite element code This python code allows for solving 3D structural problems using the finite element method. New features will be a

Rémi Capillon 6 Sep 29, 2022
✅ Today I Learn

Today I Learn EDA numpy_100ex numpy_0~10 airline_satisfaction_prediction BERT_naver_movie_classification NLP_prepare NLP_Tweet_Emotion_Recognition tex

Yeonghoo_Ahn 3 Dec 15, 2022
1900-2016 Olympic Data Analysis in Python by plotting different graphs

🔥 Olympics Data Analysis 🔥 In Data Science field, there is a big topic before creating a model for future prediction is Data Analysis. We can find o

Sayan Roy 1 Feb 06, 2022