kopia lustrzana https://github.com/bugout-dev/moonstream
1233 wiersze
92 KiB
Plaintext
1233 wiersze
92 KiB
Plaintext
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"cells": [
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"id": "e2c7afd6-752c-477a-adcc-417eefd575f1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import sqlite3\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import nfts.dataset\n",
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"import numpy as np\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "9f0e7f34-591b-4694-99d7-e20535afe33a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"The Moonstream NFTs dataset\n",
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"===========================\n",
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"\n",
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"To load the NFTs dataset from a SQLite file, run:\n",
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">>> ds = nfts.dataset.FromSQLite(<path to sqlite database>)\n",
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"\n",
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"This dataset consists of the following dataframes:\n",
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"\n",
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"Dataframe: nfts\n",
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"Load using:\n",
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">>> nfts_df = ds.load_dataframe(<sqlite connection or path to sqlite db>, \"nfts\")\n",
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"\n",
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"Describes the NFT contracts represented in this dataset, with a name and symbol if they were available at time of crawl.\n",
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"\n",
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"Columns:\n",
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"1. address: The Ethereum address of the NFT contract.\n",
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"2. name: The name of the collection of NFTs that the contract represents.\n",
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"3. symbol: The symbol of the collection of NFTs that the contract represents.\n",
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"\n",
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"- - -\n",
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"\n",
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"Dataframe: mints\n",
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"Load using:\n",
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">>> mints_df = ds.load_dataframe(<sqlite connection or path to sqlite db>, \"mints\")\n",
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"\n",
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"All token mint events crawled in this dataset.\n",
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"\n",
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"Columns:\n",
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"1. event_id: A unique event ID associated with the event.\n",
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"2. transaction_hash: The hash of the transaction which triggered the event.\n",
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"3. block_number: The transaction block in which the transaction was mined.\n",
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"4. nft_address: The address of the NFT collection containing the minted token.\n",
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"5. token_id: The ID of the token that was minted.\n",
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"6. from_address: The \"from\" address for the transfer event. For a mint, this should be the 0 address: 0x0000000000000000000000000000000000000000.\n",
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"7. to_address: The \"to\" address for the transfer event. This represents the owner of the freshly minted token.\n",
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"8. transaction_value: The amount of WEI that were sent with the transaction in which the token was minted.\n",
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"9. timestamp: The time at which the mint operation was mined into the blockchain (this is the timestamp for the mined block).\n",
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"\n",
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"- - -\n",
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"\n",
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"Dataframe: transfers\n",
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"Load using:\n",
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">>> transfers_df = ds.load_dataframe(<sqlite connection or path to sqlite db>, \"transfers\")\n",
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"\n",
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"All token transfer events crawled in this dataset.\n",
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"\n",
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"Columns:\n",
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"1. event_id: A unique event ID associated with the event.\n",
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"2. transaction_hash: The hash of the transaction which triggered the event.\n",
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"3. block_number: The transaction block in which the transaction was mined.\n",
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"4. nft_address: The address of the NFT collection containing the transferred token.\n",
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"5. token_id: The ID of the token that was transferred.\n",
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"6. from_address: The \"from\" address for the transfer event. This is the address that owned the token at the *start* of the transfer.\n",
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"7. to_address: The \"to\" address for the transfer event. This is the address that owned the token at the *end* of the transfer.\n",
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"8. transaction_value: The amount of WEI that were sent with the transaction in which the token was transferred.\n",
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"9. timestamp: The time at which the transfer operation was mined into the blockchain (this is the timestamp for the mined block).\n",
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"\n",
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"- - -\n",
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"\n",
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"Dataframe: current_owners\n",
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"Load using:\n",
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">>> current_owners_df = ds.load_dataframe(<sqlite connection or path to sqlite db>, \"current_owners\")\n",
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"\n",
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"This table is derived from the nfts, mints, and transfers tables. It represents the current owner of each token in the dataset.\n",
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"\n",
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"Columns:\n",
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"1. nft_address: The address of the NFT collection containing the token whose ownership we are denoting.\n",
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"2. token_id: The ID of the token (inside the collection) whose ownership we are denoting.\n",
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"3. owner: The address that owned the token at the time of construction of this dataset.\n",
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"\n",
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"- - -\n",
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"\n",
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"Dataframe: current_market_values\n",
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"Load using:\n",
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">>> current_market_values_df = ds.load_dataframe(<sqlite connection or path to sqlite db>, \"current_market_values\")\n",
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"\n",
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"This table is derived from the nfts, mints, and transfers tables. It represents the current market value (in WEI) of each token in the dataset.\n",
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"\n",
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"Columns:\n",
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"1. nft_address: The address of the NFT collection containing the token whose market value we are denoting.\n",
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"2. token_id: The ID of the token (inside the collection) whose market value we are denoting.\n",
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"3. market_value: The estimated market value of the token at the time of construction of this dataset.\n",
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"\n",
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"For this dataset, we estimate the market value as the last non-zero transaction value for a transfer involving this token.\n",
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"This estimate may be inaccurate for some transfers (e.g. multiple token transfers made by an escrow contract in a single transaction)\n",
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"but ought to be reasonably accurate for a large majority of tokens.\n",
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"\n",
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"- - -\n",
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"\n",
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"Dataframe: transfer_statistics_by_address\n",
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"Load using:\n",
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">>> transfer_statistics_by_address_df = ds.load_dataframe(<sqlite connection or path to sqlite db>, \"transfer_statistics_by_address\")\n",
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"\n",
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"This table is derived from the nfts, mints, and transfers tables. For each address that participated in\n",
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"at least one NFT transfer between April 1, 2021 and September 25, 2021, this table shows exactly how many NFTs that address transferred to\n",
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"other addresses and how many NFT transfers that address was the recipient of.\n",
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"\n",
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"Columns:\n",
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"1. address: An Ethereum address that participated in at least one NFT transfer between April 1, 2021 and September 25, 2021.\n",
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"2. transfers_out: The number of NFTs that the given address transferred to any other address between April 1, 2021 and September 25, 2021.\n",
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"3. transfers_in: The number of NFTs that any other address transferred to given address between April 1, 2021 and September 25, 2021.\n",
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"\n",
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"- - -\n",
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"\n",
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"Matrix: ownership_transitions\n",
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"\n",
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"ownership_transitions is an adjacency matrix which counts the number of times that a token was transferred from a source address (indexed by the rows of the matrix) to a target address (indexed by the columns of the matrix).\n",
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"\n",
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"These counts only include data about mints and transfers made between April 1, 2021 and September 25, 2021. We also denote the current owners of an NFT as having transitioned\n",
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"the NFT from themselves back to themselves. This gives some estimate of an owner retaining the NFT in the given time period.\n",
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"\n",
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"Load this matrix as follows:\n",
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">>> indexed_addresses, transitions = ds.load_ownership_transitions()\n",
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"\n",
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"- \"indexed_addresses\" is a list denoting the address that each index (row/column) in the matrix represents.\n",
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"- \"transitions\" is a numpy ndarray containing the matrix, with source addresses on the row axis and target addresses on the column axis.\n",
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"\n",
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"- - -\n"
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]
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}
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],
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"source": [
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"nfts.dataset.explain()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "b25c369a-3751-4e18-a539-f8e950982537",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Path to SQLite database containing the NFTs dataset: /home/neeraj/data/nfts/nfts.sqlite\n"
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]
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}
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],
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"source": [
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"DATABASE = os.path.expanduser(\"~/data/nfts/nfts.sqlite\")\n",
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"print(f\"Path to SQLite database containing the NFTs dataset: {DATABASE}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "b7a608aa-d304-4c53-9073-bb3d2379482c",
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"metadata": {},
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"outputs": [],
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"source": [
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"ds = nfts.dataset.FromSQLite(DATABASE)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8977af98-ff38-48c9-bc3a-7a11d2b7e8fc",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Who owns NFTs?"
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]
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},
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{
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"cell_type": "markdown",
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"id": "17564e85-99bc-4456-8353-ef892b042921",
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"metadata": {},
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "4793b4e8-3138-4a85-8266-09c42b29eb3e",
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"metadata": {},
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"outputs": [],
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"source": [
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"current_owners_df = ds.load_dataframe(\"current_owners\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "a52f616b-0441-46a1-b8b3-c117464b35d8",
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"metadata": {},
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"outputs": [
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"source": [
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"current_owners_df.head()"
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"source": [
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"top_owners_df = current_owners_df.groupby([\"owner\"], as_index=False).size().rename(columns={\"size\": \"num_tokens\"})"
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"top_owners_df.sort_values(\"num_tokens\", inplace=True, ascending=False)"
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]
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},
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"metadata": {},
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"source": [
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"#### Top 20 NFT owners"
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]
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},
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{
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"cell_type": "code",
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|
|
"0 0x0000000000000000000000000000000000000000 83548\n",
|
|
"470227 0xE052113bd7D7700d623414a0a4585BCaE754E9d5 51028\n",
|
|
"562045 0xcDA72070E455bb31C7690a170224Ce43623d0B6f 50131\n",
|
|
"369228 0x96bEE49d3386d674bF4E956D9B3ce61b9540409D 36751\n",
|
|
"439086 0xC69b4c6fFDBaF843A0d0588c99E3C67f27069BEa 32905\n",
|
|
"427270 0xBa0d01220a7CeA942596123102535F800f558763 32691\n",
|
|
"7 0x000000000000000000000000000000000000dEaD 19758\n",
|
|
"277 0x0008d343091EF8BD3EFA730F6aAE5A26a285C7a2 12137\n",
|
|
"454705 0xD387A6E4e84a6C86bd90C158C6028A58CC8Ac459 11497"
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"top_owners_df.head(10)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3590f26f-d486-4477-bf1d-b849ecf0f19b",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"#### NFT ownership histogram\n",
|
|
"\n",
|
|
"The following is the cumulative distribution of the number of addressses owning $n$ NFTs for each $n \\geq 1$."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "89e9fa88-8997-4e89-a1a6-5f4dc0912be0",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.xlabel(\"Number of tokens owned - n\")\n",
|
|
"plt.ylabel(\"Number of addresses owning n tokens (log scale)\")\n",
|
|
"_ = plt.hist(top_owners_df[\"num_tokens\"], bins=100, log=True)\n",
|
|
"plt.savefig(\"img/tokens_owned_histogram_log.png\", transparent=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e98d9d53-4068-4178-9cc8-603dc6ed824c",
|
|
"metadata": {},
|
|
"source": [
|
|
"The *overwhelming* number of NFT owners each only own a small number of tokens. There are very few addresses that own hundreds or even thousands of tokens.\n",
|
|
"\n",
|
|
"**Note:** This histogram has been charted on a logarithmic scale. We have done this because the true distribution of the count of number of NFTs owned by each address follows an [exponential distribution](https://en.wikipedia.org/wiki/Exponential_distribution). It would be difficult to visually tell apart the differences on ownership patterns over all owners if we charted this distribution using a linear scale."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "11989c70-fe49-41c1-b28a-77d9b26fd465",
|
|
"metadata": {},
|
|
"source": [
|
|
"Any address which owns thousands of tokens is either purchasing those tokens automatically (if they exist on multiple contracts) or is financing the collections in which they own tokens. First, let us analye the ownership trends amount addresses which do not own large numbers of tokens. This will help us estimate trends in NFT ownership among non-algorithmic and non-smart contract owners.\n",
|
|
"\n",
|
|
"For this, we set a `scale_cutoff` and only consider addresses which own a number of tokens not exceeding that cutoff.\n",
|
|
"\n",
|
|
"This allows us to estimate on a linear scale, rather than a logarithmic one, how NFT ownership is distributed among human owners."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "6fd8e3ba-a943-4afc-8d9f-82d0f74e2d3f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"scale_cutoff = 1500"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "93681f2b-f6a8-440f-a831-ad43f5beb946",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"low_scale_owners = [num_tokens for num_tokens in top_owners_df[\"num_tokens\"] if num_tokens <= scale_cutoff]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "ad904ada-cffa-4dbe-8bd8-23a3cb33af50",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.xlabel(f\"Number of tokens owned - n <= {scale_cutoff}\")\n",
|
|
"plt.ylabel(\"Number of addresses owning n tokens\")\n",
|
|
"_ = plt.hist(low_scale_owners, bins=int(scale_cutoff/5))\n",
|
|
"plt.savefig(\"img/tokens_owned_histogram_low_scale.png\", transparent=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3ea46e96-4771-4ef3-ac4a-a6cb310b362c",
|
|
"metadata": {},
|
|
"source": [
|
|
"Even at this scale, it is more instructive to view the distribution on a logarithmic scale:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "3a9415c3-a5f0-4c87-a6e7-69112c2e6c0f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.xlabel(f\"Number of tokens owned - n <= {scale_cutoff}\")\n",
|
|
"plt.ylabel(\"Number of addresses owning n tokens (log scale)\")\n",
|
|
"_ = plt.hist(low_scale_owners, bins=int(scale_cutoff/50), log=True)\n",
|
|
"plt.savefig(\"img/tokens_owned_histogram_log_low_scale.png\", transparent=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1febf072-b4be-4460-8ef5-88715cb31230",
|
|
"metadata": {},
|
|
"source": [
|
|
"This analysis shows that the *decentralized* NFT market is indeed decentralized, with proportionally few NFTs being held by addresses which are minting and purchasing NFTs at industrial scale.\n",
|
|
"\n",
|
|
"**There are vanishingly few large scale NFT owners on the Ethereum blockchain.**\n",
|
|
"\n",
|
|
"Note that this is an analysis of addresses, not real-world entities. It is possible for a single person or organization to use a distinct Ethereum address to control each NFT they own. This would currently be difficult enough operationally that only a handful of players in the NFT market are probably doing it. Even this would yield to a further network analysis of where the *funds* for each NFT purchase were coming from."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0a597bc5-24a5-49a1-b6b5-082770d36ee4",
|
|
"metadata": {},
|
|
"source": [
|
|
"### The shapes of NFT collections\n",
|
|
"\n",
|
|
"NFTs are released in collections, with a single contract accounting for multiple tokens.\n",
|
|
"\n",
|
|
"Are there differences between ownership distributions of NFTs like the [Ethereum Name Service (ENS)](https://ens.domains/), which have utility beyond their artistic value, and those that do not currently have such use cases?\n",
|
|
"\n",
|
|
"One way we can answer this question is to see how much information each NFT collection gives us about individual owners of tokens in that collection. We will do this by treating each collection as a probability distribution over owners of tokens from that collection. If the collection $C$ consists of $n$ tokens and an address $A$ owns $m$ of those tokens, we will assign that address a probability of $p_A = m/n$ in the collection's associated probability distribution. Then we will calculate the entropy:\n",
|
|
"\n",
|
|
"$$H(C) = - \\sum_{A} p_A \\log(p_A).$$\n",
|
|
"\n",
|
|
"Here, the sum is over all addresses $A$ that own at least one token from $C$.\n",
|
|
"\n",
|
|
"$H(C)$ simultaneously contains information about:\n",
|
|
"1. How many tokens were issued as part of the collection $C$.\n",
|
|
"2. How evenly the tokens in $C$ are distributed over the addresses $A$ which own those tokens."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "8a08e304-b5ff-4e45-a12c-6fd562fe5255",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"contract_owners_df = current_owners_df.groupby([\"nft_address\", \"owner\"], as_index=False).size().rename(columns={\"size\": \"num_tokens\"})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "9fd82a56-fe0c-4f02-b996-c5bf0feea5e3",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
|
" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>nft_address</th>\n",
|
|
" <th>owner</th>\n",
|
|
" <th>num_tokens</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
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" <th>0</th>\n",
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" <td>0x00000000000b7F8E8E8Ad148f9d53303Bfe20796</td>\n",
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" <td>0x429a635eD4DaF9529C07d5406D466B349EC34361</td>\n",
|
|
" <td>3</td>\n",
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|
" </tr>\n",
|
|
" <tr>\n",
|
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" <th>1</th>\n",
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" <td>0x00000000000b7F8E8E8Ad148f9d53303Bfe20796</td>\n",
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" <td>0x5e5C817E9264B46cBBB980198684Ad9d14f3e0B4</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>0x00000000000b7F8E8E8Ad148f9d53303Bfe20796</td>\n",
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" <td>0x8376f63c13b99D3eedfA51ddd77Ff375279B3Ba0</td>\n",
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" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>0x00000000000b7F8E8E8Ad148f9d53303Bfe20796</td>\n",
|
|
" <td>0x83D7Da9E572C5ad14caAe36771022C43AF084dbF</td>\n",
|
|
" <td>5</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>0x00000000000b7F8E8E8Ad148f9d53303Bfe20796</td>\n",
|
|
" <td>0x8A73024B39A4477a5Dc43fD6360e446851AD1D28</td>\n",
|
|
" <td>5</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" nft_address \\\n",
|
|
"0 0x00000000000b7F8E8E8Ad148f9d53303Bfe20796 \n",
|
|
"1 0x00000000000b7F8E8E8Ad148f9d53303Bfe20796 \n",
|
|
"2 0x00000000000b7F8E8E8Ad148f9d53303Bfe20796 \n",
|
|
"3 0x00000000000b7F8E8E8Ad148f9d53303Bfe20796 \n",
|
|
"4 0x00000000000b7F8E8E8Ad148f9d53303Bfe20796 \n",
|
|
"\n",
|
|
" owner num_tokens \n",
|
|
"0 0x429a635eD4DaF9529C07d5406D466B349EC34361 3 \n",
|
|
"1 0x5e5C817E9264B46cBBB980198684Ad9d14f3e0B4 5 \n",
|
|
"2 0x8376f63c13b99D3eedfA51ddd77Ff375279B3Ba0 1 \n",
|
|
"3 0x83D7Da9E572C5ad14caAe36771022C43AF084dbF 5 \n",
|
|
"4 0x8A73024B39A4477a5Dc43fD6360e446851AD1D28 5 "
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"contract_owners_df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "7192cc40-c04f-4e91-b731-5ba6ee749fde",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"contract_owners_groups = contract_owners_df.groupby([\"nft_address\"])\n",
|
|
"\n",
|
|
"entropies = {}\n",
|
|
"\n",
|
|
"for contract_address, owners_group in contract_owners_groups:\n",
|
|
" total_supply = owners_group[\"num_tokens\"].sum()\n",
|
|
" owners_group[\"p\"] = owners_group[\"num_tokens\"]/total_supply\n",
|
|
" owners_group[\"log(p)\"] = np.log2(owners_group[\"p\"])\n",
|
|
" owners_group[\"-plog(p)\"] = (-1) * owners_group[\"p\"] * owners_group[\"log(p)\"]\n",
|
|
" entropy = owners_group[\"-plog(p)\"].sum()\n",
|
|
" entropies[contract_address] = entropy"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "2dc080a0-8dd1-49ab-be2d-cc1eed5d0ff4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.xlabel(f\"Ownership entropy of NFT collection\")\n",
|
|
"plt.ylabel(\"Number of NFT collections\")\n",
|
|
"_ = plt.hist(entropies.values(), bins=80)\n",
|
|
"plt.savefig(\"img/ownership_entropy.png\", transparent=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d197f6b6-65d8-4f84-9f12-9d31840adf34",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Collections at the extremes\n",
|
|
"\n",
|
|
"It is interesting to get a sense of what the collections look like at either extreme of this entropy spectrum."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "9e1bca5a-be9d-46c6-b21a-ae1a648b7fa7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sorted_entropies = [it for it in entropies.items()]\n",
|
|
"sorted_entropies.sort(key=lambda it: it[1], reverse=True)\n",
|
|
"entropies_df = pd.DataFrame.from_records(sorted_entropies, columns=[\"nft_address\", \"entropy\"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c060fa53-245b-4774-8fba-0bb0aa2aed8d",
|
|
"metadata": {},
|
|
"source": [
|
|
"##### Highest entropy"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "c80abbc5-2268-47a7-b2df-f93450a4a7d0",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>nft_address</th>\n",
|
|
" <th>entropy</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>0x57f1887a8BF19b14fC0dF6Fd9B2acc9Af147eA85</td>\n",
|
|
" <td>13.864019</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>0x60F80121C31A0d46B5279700f9DF786054aa5eE5</td>\n",
|
|
" <td>13.831032</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>0xC36442b4a4522E871399CD717aBDD847Ab11FE88</td>\n",
|
|
" <td>13.742724</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>0xabc207502EA88D9BCa29B95Cd2EeE5F0d7936418</td>\n",
|
|
" <td>13.714889</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>0x5537d90A4A2DC9d9b37BAb49B490cF67D4C54E91</td>\n",
|
|
" <td>13.285761</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" nft_address entropy\n",
|
|
"0 0x57f1887a8BF19b14fC0dF6Fd9B2acc9Af147eA85 13.864019\n",
|
|
"1 0x60F80121C31A0d46B5279700f9DF786054aa5eE5 13.831032\n",
|
|
"2 0xC36442b4a4522E871399CD717aBDD847Ab11FE88 13.742724\n",
|
|
"3 0xabc207502EA88D9BCa29B95Cd2EeE5F0d7936418 13.714889\n",
|
|
"4 0x5537d90A4A2DC9d9b37BAb49B490cF67D4C54E91 13.285761"
|
|
]
|
|
},
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"entropies_df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e0726c04-1349-4b8d-919e-7547cfffd6e7",
|
|
"metadata": {},
|
|
"source": [
|
|
"[`0x57f1887a8BF19b14fC0dF6Fd9B2acc9Af147eA85`](https://etherscan.io/address/0x57f1887a8BF19b14fC0dF6Fd9B2acc9Af147eA85) is the [Ethereum Name Service](https://ens.domains/).\n",
|
|
"\n",
|
|
"[`0x60F80121C31A0d46B5279700f9DF786054aa5eE5`](https://etherscan.io/address/0x60F80121C31A0d46B5279700f9DF786054aa5eE5) is [Rarible's](https://rarible.com/) governance token ([details](https://www.notion.so/rarible/Rarible-com-FAQ-a47b276aa1994f7c8e3bc96d700717c5)). Their aidrops are the cause of this high entropy.\n",
|
|
"\n",
|
|
"[`0xC36442b4a4522E871399CD717aBDD847Ab11FE88`](https://etherscan.io/address/0xC36442b4a4522E871399CD717aBDD847Ab11FE88) is [Uniswap's](https://uniswap.org/) position NFT, representing [non-fungible liquidity positions](https://uniswap.org/blog/uniswap-v3/) on Uniswap v3.\n",
|
|
"\n",
|
|
"[`0xabc207502EA88D9BCa29B95Cd2EeE5F0d7936418`](https://etherscan.io/address/0xabc207502EA88D9BCa29B95Cd2EeE5F0d7936418) are badges for [Yield Guild Games](https://yieldguild.io/), which seem to have been airdropped to many existing NFT holders.\n",
|
|
"\n",
|
|
"[`0x5537d90A4A2DC9d9b37BAb49B490cF67D4C54E91`](https://etherscan.io/address/0x5537d90A4A2DC9d9b37BAb49B490cF67D4C54E91) is the [OneDayPunk](https://punkscape.xyz/) collection, which has gained popularity as a down-market Crypto Punks alternative.\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "786574c8-4226-43a4-b3e3-81fb8667583f",
|
|
"metadata": {},
|
|
"source": [
|
|
"##### Zero entropy"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "d4c9d052-f9dd-4e4f-9716-31ddd8692294",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>nft_address</th>\n",
|
|
" <th>entropy</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>9287</th>\n",
|
|
" <td>0xfe00276E0A87E5e54ADD7C5FC6cdD80B363DEe04</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9288</th>\n",
|
|
" <td>0xfe6b0dAccBAE832b0283CfBFEBe9543B6b7B10a8</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9289</th>\n",
|
|
" <td>0xff881E3008f081707bdDA1644e6c92DB9599C1C0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9290</th>\n",
|
|
" <td>0xffC6c59F34Cd9f8861012FDDd0c7F1323082Ab86</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9291</th>\n",
|
|
" <td>0xffCb352Fb3FdbEAab3F662378db28B8D151f210c</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" nft_address entropy\n",
|
|
"9287 0xfe00276E0A87E5e54ADD7C5FC6cdD80B363DEe04 0.0\n",
|
|
"9288 0xfe6b0dAccBAE832b0283CfBFEBe9543B6b7B10a8 0.0\n",
|
|
"9289 0xff881E3008f081707bdDA1644e6c92DB9599C1C0 0.0\n",
|
|
"9290 0xffC6c59F34Cd9f8861012FDDd0c7F1323082Ab86 0.0\n",
|
|
"9291 0xffCb352Fb3FdbEAab3F662378db28B8D151f210c 0.0"
|
|
]
|
|
},
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"entropies_df.tail()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "49008c09-4fec-482a-b080-8836922de57b",
|
|
"metadata": {},
|
|
"source": [
|
|
"[`0xfe00276E0A87E5e54ADD7C5FC6cdD80B363DEe04`](https://etherscan.io/address/0xfe00276E0A87E5e54ADD7C5FC6cdD80B363DEe04).\n",
|
|
"\n",
|
|
"[`0xfe6b0dAccBAE832b0283CfBFEBe9543B6b7B10a8`](https://etherscan.io/address/0xfe6b0dAccBAE832b0283CfBFEBe9543B6b7B10a8).\n",
|
|
"\n",
|
|
"[`0xff881E3008f081707bdDA1644e6c92DB9599C1C0`](https://etherscan.io/address/0xff881E3008f081707bdDA1644e6c92DB9599C1C0).\n",
|
|
"\n",
|
|
"[`0xffC6c59F34Cd9f8861012FDDd0c7F1323082Ab86`](https://etherscan.io/address/0xffC6c59F34Cd9f8861012FDDd0c7F1323082Ab86).\n",
|
|
"\n",
|
|
"[`0xffCb352Fb3FdbEAab3F662378db28B8D151f210c`](https://etherscan.io/address/0xffCb352Fb3FdbEAab3F662378db28B8D151f210c).\n",
|
|
"\n",
|
|
"All these projects are NFTs that did see release in the time period for which we collected data, but saw no further activity. That means that these are either failed projects or projects that have not yet done an official launch."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "598be2d9-5ade-45cd-8777-70a0d61cae34",
|
|
"metadata": {},
|
|
"source": [
|
|
"##### Low entropy"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "ca65053e-9cb7-4698-94b6-7da01e509bb7",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>nft_address</th>\n",
|
|
" <th>entropy</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>4025</th>\n",
|
|
" <td>0x08CdCF9ba0a4b5667F5A59B78B60FbEFb145e64c</td>\n",
|
|
" <td>2.004886</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4026</th>\n",
|
|
" <td>0xA4fF6019f9DBbb4bCC61Fa8Bd5C39F36ee4eB164</td>\n",
|
|
" <td>2.003856</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4027</th>\n",
|
|
" <td>0xB66c7Ca15Af1f357C57294BAf730ABc77FF94940</td>\n",
|
|
" <td>2.003756</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4028</th>\n",
|
|
" <td>0x5f98B87fb68f7Bb6F3a60BD6f0917723365444C1</td>\n",
|
|
" <td>2.002227</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4029</th>\n",
|
|
" <td>0x374DBF0dF7aBc89C2bA776F003E725177Cb35750</td>\n",
|
|
" <td>2.001823</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" nft_address entropy\n",
|
|
"4025 0x08CdCF9ba0a4b5667F5A59B78B60FbEFb145e64c 2.004886\n",
|
|
"4026 0xA4fF6019f9DBbb4bCC61Fa8Bd5C39F36ee4eB164 2.003856\n",
|
|
"4027 0xB66c7Ca15Af1f357C57294BAf730ABc77FF94940 2.003756\n",
|
|
"4028 0x5f98B87fb68f7Bb6F3a60BD6f0917723365444C1 2.002227\n",
|
|
"4029 0x374DBF0dF7aBc89C2bA776F003E725177Cb35750 2.001823"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"entropies_df.loc[entropies_df[\"entropy\"] > 2].tail()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0100251f-e2f3-4665-98cf-81594ecc0145",
|
|
"metadata": {},
|
|
"source": [
|
|
"[`0x08CdCF9ba0a4b5667F5A59B78B60FbEFb145e64c`](https://etherscan.io/address/0x08CdCF9ba0a4b5667F5A59B78B60FbEFb145e64c) is called [WorldCupToken](https://coinclarity.com/dapp/worldcuptoken/) and was last active 4 years ago. Their recent increase in activity could be in anticipation of the next soccer world cup in 2022.\n",
|
|
"\n",
|
|
"[`0xA4fF6019f9DBbb4bCC61Fa8Bd5C39F36ee4eB164`](https://etherscan.io/address/0xA4fF6019f9DBbb4bCC61Fa8Bd5C39F36ee4eB164) is associated with a project called [instigators](https://instigators.network/).\n",
|
|
"\n",
|
|
"[`0xB66c7Ca15Af1f357C57294BAf730ABc77FF94940`](https://etherscan.io/address/0xB66c7Ca15Af1f357C57294BAf730ABc77FF94940) is a token associated with something called the [Gems of Awareness Benefit](https://nftcalendar.io/event/gems-of-awareness-benefit-for-entheon-art-by-alex-grey-x-allyson-grey/).\n",
|
|
"\n",
|
|
"[`0x5f98B87fb68f7Bb6F3a60BD6f0917723365444C1`](https://etherscan.io/address/0x5f98B87fb68f7Bb6F3a60BD6f0917723365444C1) is [SHADYCON, an NFT associated with Eminem which seems to have been marketed on Nifty Gateway](https://www.eminem.com/news/shadycon-x-nifty-gateway).\n",
|
|
"\n",
|
|
"[`0x374DBF0dF7aBc89C2bA776F003E725177Cb35750`](https://etherscan.io/address/0x374DBF0dF7aBc89C2bA776F003E725177Cb35750) is [WyldFrogz](https://twitter.com/WyldFrogz), a cryptopunks derivative that seems to have some kind of planet-saving theme."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "23ab64f9-85fb-4a12-87ef-a5f6295b43ce",
|
|
"metadata": {},
|
|
"source": [
|
|
"##### Medium entropy"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "09997f43-5eeb-43a1-9e01-4076b7a4bc8e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>nft_address</th>\n",
|
|
" <th>entropy</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>1564</th>\n",
|
|
" <td>0x0ae3c3A1504E41a6877De1B854C000EC64894bEa</td>\n",
|
|
" <td>6.021144</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1565</th>\n",
|
|
" <td>0x1ECA43C93D8e06FB91489818B4967014D748Da53</td>\n",
|
|
" <td>6.017002</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1566</th>\n",
|
|
" <td>0xc57605Bef27ef91DbECc839e71E49574b98857Fc</td>\n",
|
|
" <td>6.011324</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1567</th>\n",
|
|
" <td>0xd3f69F10532457D35188895fEaA4C20B730EDe88</td>\n",
|
|
" <td>6.010405</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1568</th>\n",
|
|
" <td>0xba61aEF92ebF174DbB39C97Dd29D0F2bd3D83d33</td>\n",
|
|
" <td>6.009679</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
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"text/plain": [
|
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" nft_address entropy\n",
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"1564 0x0ae3c3A1504E41a6877De1B854C000EC64894bEa 6.021144\n",
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"1565 0x1ECA43C93D8e06FB91489818B4967014D748Da53 6.017002\n",
|
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"1566 0xc57605Bef27ef91DbECc839e71E49574b98857Fc 6.011324\n",
|
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"1567 0xd3f69F10532457D35188895fEaA4C20B730EDe88 6.010405\n",
|
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"1568 0xba61aEF92ebF174DbB39C97Dd29D0F2bd3D83d33 6.009679"
|
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]
|
|
},
|
|
"execution_count": 23,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
|
],
|
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"source": [
|
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"entropies_df.loc[entropies_df[\"entropy\"] > 6].tail()"
|
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]
|
|
},
|
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{
|
|
"cell_type": "markdown",
|
|
"id": "f3f2dc22-d111-472b-a28f-076f12f98047",
|
|
"metadata": {},
|
|
"source": [
|
|
"[`0x0ae3c3A1504E41a6877De1B854C000EC64894bEa`](https://etherscan.io/address/0x0ae3c3A1504E41a6877De1B854C000EC64894bEa) is the [Circleorzo NFT](https://opensea.io/collection/circleorzo), a collection of images of procedurally generated circles.\n",
|
|
"\n",
|
|
"[`0x1ECA43C93D8e06FB91489818B4967014D748Da53`](https://etherscan.io/address/0x1ECA43C93D8e06FB91489818B4967014D748Da53) is [Cowboy Punks](https://twitter.com/cowboypunks?lang=en), which appeals to blockheads that prefer westerns to cyberpunk.\n",
|
|
"\n",
|
|
"[`0xc57605Bef27ef91DbECc839e71E49574b98857Fc`](https://etherscan.io/address/0xc57605Bef27ef91DbECc839e71E49574b98857Fc) seems to be associated with the [Enigma Project](https://www.producthunt.com/posts/enigma-project) and control access to puzzle games.\n",
|
|
"\n",
|
|
"[`0xd3f69F10532457D35188895fEaA4C20B730EDe88`](https://etherscan.io/address/0xd3f69F10532457D35188895fEaA4C20B730EDe88) is something called hte [RTFKT Capsule Space Drip](https://rtfkt.com/spacedrip) which I do not understand and feel too old to have a hope of ever understanding. The important thing is that it seems these NFTs can be redeemed for a physical object called a space drip. [Here's a blog post about it](https://www.one37pm.com/nft/gaming/space-drip-rtfkt-loopify).\n",
|
|
"\n",
|
|
"[`0xba61aEF92ebF174DbB39C97Dd29D0F2bd3D83d33`](https://etherscan.io/address/0xba61aEF92ebF174DbB39C97Dd29D0F2bd3D83d33) is an NFT project called [Dommies](https://twitter.com/DommiesNFT)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "158b1714-083d-48fa-820a-c238b510de29",
|
|
"metadata": {},
|
|
"source": [
|
|
"##### Entropy as a measure of quality\n",
|
|
"\n",
|
|
"Based on this analysis, the ownership entropy of an NFT collection shows promise as a measure of its quality. There are certainly examples of high entropy NFT collections (like Rarible's governance token) which have that kind of entropy simply because they have been airdropped at scale. It remains to be seen what the value of these mass airdropped tokens will be in the long term.\n",
|
|
"\n",
|
|
"At the very least, the entropy measurement indicates that there is a lot of money behind those releases. This is in contrast to lower entropy releases promising thousands of tokens and only minting tens of them."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
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}
|