kopia lustrzana https://github.com/bugout-dev/moonstream
833 wiersze
238 KiB
Plaintext
833 wiersze
238 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import sqlite3\n",
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"import numpy as np\n",
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"from matplotlib.pyplot import figure\n",
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"\n",
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"import warnings # current version of seaborn generates a bunch of warnings that we'll ignore\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"sns.set(style=\"white\", color_codes=True)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"conTXs = sqlite3.connect('../../../../../../datasets/nfts.sqlite')"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"transfers = pd.read_sql_query(\"SELECT * FROM transfers\", conTXs)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"num_df = (transfers[[\"transaction_value\", \"timestamp\"]].apply(pd.to_numeric, errors='coerce'))\n",
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"num_df[\"timestamp\"] = pd.to_datetime(num_df.timestamp, unit='s', errors='coerce')\n",
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"num_df.set_index(\"timestamp\")\n",
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"num_df = num_df.resample(\"1440min\", label='right', on='timestamp').sum()\n"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Text(0.5, 1.0, 'NFT transfers value over time')"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 864x432 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Set the width and height of the figure\n",
|
|
"plt.figure(figsize=(12,6))\n",
|
|
"# Line chart showing the number of visitors to each museum over time\n",
|
|
"ax = sns.lineplot(data=num_df, x=\"timestamp\", y=\"transaction_value\")\n",
|
|
"ax.set(xlabel='timestamp', ylabel='Total value')\n",
|
|
"plt.title(\"NFT transfers value over time\")\n",
|
|
"# Add title"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"number of unique addresses: 6765\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(\"number of unique addresses:\", transfers[\"nft_address\"].nunique())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# num_df = (transfers[[\"nft_address\", \"transaction_value\", \"timestamp\"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#create data frame where group together from_addresses and count size of each group (how many TX each address did in total)\n",
|
|
"from_series = transfers[\"from_address\"].groupby(transfers[\"from_address\"]).size()\n",
|
|
"#create data frame where group together from_addresses and count size of each group (how many TX each address did in total)\n",
|
|
"to_series = transfers[\"to_address\"].groupby(transfers[\"to_address\"]).size()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df = pd.DataFrame()\n",
|
|
"df = df.join(to_series.rename(\"to_count\"), how='outer')\n",
|
|
"df = df.join(from_series.rename('from_count'), how='outer')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1728x1728 with 0 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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|
|
"text/plain": [
|
|
"<Figure size 1728x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.figure(figsize=(24,24))\n",
|
|
"fig, axs = plt.subplots(1, 2)\n",
|
|
"fig.set_size_inches(24, 5, forward=True)\n",
|
|
"fig.suptitle('Numbers of NFT transactions per address')\n",
|
|
"axs[0].hist(df[\"from_count\"], density=False, alpha=0.75, log=True, bins=20, color='orange')\n",
|
|
"axs[0].set_title(\"NFTs Sent from an address\")\n",
|
|
"axs[1].hist(df[\"to_count\"], density=False, alpha=0.75, log=True, bins=20)\n",
|
|
"axs[1].set_title(\"NFTs received to an address\")\n",
|
|
"plt.setp(axs[0], xlabel='Number of transactions out', ylabel='Number of addresses')\n",
|
|
"plt.setp(axs[1], xlabel='Number of transactions in', ylabel='Number of addresses')\n",
|
|
"print(\"\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df_small=df[df[\"to_count\"]<10]\n",
|
|
"df_small=df_small[df_small[\"from_count\"]<10]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1728x1728 with 0 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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|
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"text/plain": [
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"<Figure size 1728x360 with 2 Axes>"
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]
|
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},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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}
|
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],
|
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"source": [
|
|
"plt.figure(figsize=(24,24))\n",
|
|
"fig, axs = plt.subplots(1, 2)\n",
|
|
"fig.set_size_inches(24, 5, forward=True)\n",
|
|
"fig.suptitle('Numbers of NFT transactions per address')\n",
|
|
"axs[0].hist(df_small[\"from_count\"], density=False, alpha=0.75, log=False, bins=9, color='orange')\n",
|
|
"axs[0].set_title(\"NFTs Sent from an address\")\n",
|
|
"axs[1].hist(df_small[\"to_count\"], density=False, alpha=0.75, log=False, bins=9)\n",
|
|
"axs[1].set_title(\"NFTs received to an address\")\n",
|
|
"plt.setp(axs[0], xlabel='Number of transactions out', ylabel='Number of addresses')\n",
|
|
"plt.setp(axs[1], xlabel='Number of transactions in', ylabel='Number of addresses')\n",
|
|
"print(\"\")"
|
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]
|
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"whales_tx=df[df[\"from_count\"]>60000]\n",
|
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"whales_rx=df[df[\"to_count\"]>60000]"
|
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]
|
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},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\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",
|
|
"\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>to_count</th>\n",
|
|
" <th>from_count</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0x0000000000000000000000000000000000000000</th>\n",
|
|
" <td>119978.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0xC69b4c6fFDBaF843A0d0588c99E3C67f27069BEa</th>\n",
|
|
" <td>74608.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0xcDA72070E455bb31C7690a170224Ce43623d0B6f</th>\n",
|
|
" <td>76645.0</td>\n",
|
|
" <td>36116.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" to_count from_count\n",
|
|
"0x0000000000000000000000000000000000000000 119978.0 NaN\n",
|
|
"0xC69b4c6fFDBaF843A0d0588c99E3C67f27069BEa 74608.0 1.0\n",
|
|
"0xcDA72070E455bb31C7690a170224Ce43623d0B6f 76645.0 36116.0"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"whales_rx"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Whales RX info:\n",
|
|
"0x0000000000000000000000000000000000000000 - burn address\n",
|
|
"\n",
|
|
"[0xC69b4c6fFDBaF843A0d0588c99E3C67f27069BEa](https://etherscan.io/address/0xC69b4c6fFDBaF843A0d0588c99E3C67f27069BEa) / [creator](https://etherscan.io/address/0xC69b4c6fFDBaF843A0d0588c99E3C67f27069BEa) / info: `ENS: ETH Registrar Controller `\n",
|
|
"\n",
|
|
"[0xcDA72070E455bb31C7690a170224Ce43623d0B6f](https://etherscan.io/address/0xcDA72070E455bb31C7690a170224Ce43623d0B6f) / [creator](https://etherscan.io/address/0x95271d54d6e0d88b3825f89a766f97b8b7e8af82) / info: https://foundation.app"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
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|
"<style scoped>\n",
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|
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" }\n",
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|
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" .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>to_count</th>\n",
|
|
" <th>from_count</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0x283Af0B28c62C092C9727F1Ee09c02CA627EB7F5</th>\n",
|
|
" <td>2.0</td>\n",
|
|
" <td>140875.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0x327305A797d92a39cEe1a225D7E2A1cC42B1a8fA</th>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>106620.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" to_count from_count\n",
|
|
"0x283Af0B28c62C092C9727F1Ee09c02CA627EB7F5 2.0 140875.0\n",
|
|
"0x327305A797d92a39cEe1a225D7E2A1cC42B1a8fA NaN 106620.0"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"whales_tx"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Whales TX info:\n",
|
|
"\n",
|
|
"[0x283Af0B28c62C092C9727F1Ee09c02CA627EB7F5](https://etherscan.io/address/0x283Af0B28c62C092C9727F1Ee09c02CA627EB7F5) / [creator](https://etherscan.io/address/0x4fe4e666be5752f1fdd210f4ab5de2cc26e3e0e8) / info: `ENS: ETH Registrar Controller`\n",
|
|
"\n",
|
|
"[0x327305A797d92a39cEe1a225D7E2A1cC42B1a8fA](https://etherscan.io/address/0x327305A797d92a39cEe1a225D7E2A1cC42B1a8fA) not a contract! / info: `???`"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1728x1728 with 0 Axes>"
|
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
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{
|
|
"data": {
|
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\n",
|
|
"text/plain": [
|
|
"<Figure size 1728x360 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"sharks_tx=df[df[\"from_count\"]>20e3]\n",
|
|
"sharks_rx=df[df[\"to_count\"]>20e3]\n",
|
|
"sharks_tx=sharks_tx[sharks_tx[\"from_count\"]<60e3]\n",
|
|
"sharks_rx=sharks_tx[sharks_tx[\"to_count\"]<60e3]\n",
|
|
"\n",
|
|
"plt.figure(figsize=(24,24))\n",
|
|
"fig, axs = plt.subplots(1, 2)\n",
|
|
"fig.set_size_inches(24, 5, forward=True)\n",
|
|
"fig.suptitle('Numbers of NFT transactions per address')\n",
|
|
"axs[0].hist(sharks_tx[\"from_count\"], density=False, alpha=0.75, log=False, bins=100, color='orange')\n",
|
|
"axs[0].set_title(\"NFTs Sent from an address\")\n",
|
|
"axs[1].hist(sharks_rx[\"to_count\"], density=False, alpha=0.75, log=False, bins=9)\n",
|
|
"axs[1].set_title(\"NFTs received to an address\")\n",
|
|
"plt.setp(axs[0], xlabel='Number of transactions out', ylabel='Number of addresses')\n",
|
|
"plt.setp(axs[1], xlabel='Number of transactions in', ylabel='Number of addresses')\n",
|
|
"print(\"\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"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>to_count</th>\n",
|
|
" <th>from_count</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0xE052113bd7D7700d623414a0a4585BCaE754E9d5</th>\n",
|
|
" <td>9552.0</td>\n",
|
|
" <td>31967.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0xb1690C08E213a35Ed9bAb7B318DE14420FB57d8C</th>\n",
|
|
" <td>14124.0</td>\n",
|
|
" <td>23128.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0xcDA72070E455bb31C7690a170224Ce43623d0B6f</th>\n",
|
|
" <td>76645.0</td>\n",
|
|
" <td>36116.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" to_count from_count\n",
|
|
"0xE052113bd7D7700d623414a0a4585BCaE754E9d5 9552.0 31967.0\n",
|
|
"0xb1690C08E213a35Ed9bAb7B318DE14420FB57d8C 14124.0 23128.0\n",
|
|
"0xcDA72070E455bb31C7690a170224Ce43623d0B6f 76645.0 36116.0"
|
|
]
|
|
},
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"sharks_tx"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
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"### Sharks TX info:\n",
|
|
"\n",
|
|
"[0xE052113bd7D7700d623414a0a4585BCaE754E9d5](https://etherscan.io/address/0xE052113bd7D7700d623414a0a4585BCaE754E9d5) / not a contract! / info: `Nifty Gateway: Omnibus `\n",
|
|
"\n",
|
|
"[0xb1690C08E213a35Ed9bAb7B318DE14420FB57d8C](https://etherscan.io/address/0xb1690C08E213a35Ed9bAb7B318DE14420FB57d8C) / not a contract! / info: `CryptoKitties: Sales Auction `\n",
|
|
"\n",
|
|
"[0xb1690C08E213a35Ed9bAb7B318DE14420FB57d8C](https://etherscan.io/address/0xcDA72070E455bb31C7690a170224Ce43623d0B6f) / [creator](https://etherscan.io/address/0x95271d54d6e0d88b3825f89a766f97b8b7e8af82) / info: https://foundation.app"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"data": {
|
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"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\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",
|
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" }\n",
|
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
|
" <th>to_count</th>\n",
|
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" <th>from_count</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0xE052113bd7D7700d623414a0a4585BCaE754E9d5</th>\n",
|
|
" <td>9552.0</td>\n",
|
|
" <td>31967.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0xb1690C08E213a35Ed9bAb7B318DE14420FB57d8C</th>\n",
|
|
" <td>14124.0</td>\n",
|
|
" <td>23128.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" to_count from_count\n",
|
|
"0xE052113bd7D7700d623414a0a4585BCaE754E9d5 9552.0 31967.0\n",
|
|
"0xb1690C08E213a35Ed9bAb7B318DE14420FB57d8C 14124.0 23128.0"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"sharks_rx"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Sharks RX info:\n",
|
|
"\n",
|
|
"[0xE052113bd7D7700d623414a0a4585BCaE754E9d5](https://etherscan.io/address/0xE052113bd7D7700d623414a0a4585BCaE754E9d5) / not a contract! / info: `Nifty Gateway: Omnibus `\n",
|
|
"\n",
|
|
"[0xb1690C08E213a35Ed9bAb7B318DE14420FB57d8C](https://etherscan.io/address/0xb1690C08E213a35Ed9bAb7B318DE14420FB57d8C) / not a contract! / info: `CryptoKitties: Sales Auction `\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#transfers not transactions\n",
|
|
"transactions_per_nft = transfers[\"nft_address\"].groupby(transfers[\"nft_address\"]).size()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0 0.000000e+00\n",
|
|
"1 0.000000e+00\n",
|
|
"2 0.000000e+00\n",
|
|
"3 0.000000e+00\n",
|
|
"4 0.000000e+00\n",
|
|
"5 6.180000e+18\n",
|
|
"6 0.000000e+00\n",
|
|
"7 0.000000e+00\n",
|
|
"8 3.000000e+16\n",
|
|
"9 0.000000e+00\n",
|
|
"10 0.000000e+00\n",
|
|
"11 0.000000e+00\n",
|
|
"12 0.000000e+00\n",
|
|
"13 0.000000e+00\n",
|
|
"14 0.000000e+00\n",
|
|
"Name: transaction_value, dtype: float64"
|
|
]
|
|
},
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"transfers[\"transaction_value\"] = pd.to_numeric(transfers[\"transaction_value\"])\n",
|
|
"transfers[\"transaction_value\"] = transfers[\"transaction_value\"].fillna(0)\n",
|
|
"transfers[\"transaction_value\"].head(15)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"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>transaction_value</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>nft_address</th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0x00000000000b7F8E8E8Ad148f9d53303Bfe20796</th>\n",
|
|
" <td>0.000000e+00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0x000000000437b3CCE2530936156388Bff5578FC3</th>\n",
|
|
" <td>4.175880e+18</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0x000000000A42C2791eEc307FFf43Fa5c640e3Ef7</th>\n",
|
|
" <td>0.000000e+00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0x000000F36EDb9d436Be73cDBf0DCa7dF3E6F3A50</th>\n",
|
|
" <td>0.000000e+00</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" transaction_value\n",
|
|
"nft_address \n",
|
|
"0x00000000000b7F8E8E8Ad148f9d53303Bfe20796 0.000000e+00\n",
|
|
"0x000000000437b3CCE2530936156388Bff5578FC3 4.175880e+18\n",
|
|
"0x000000000A42C2791eEc307FFf43Fa5c640e3Ef7 0.000000e+00\n",
|
|
"0x000000F36EDb9d436Be73cDBf0DCa7dF3E6F3A50 0.000000e+00"
|
|
]
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"total_value_per_nft = transfers[[\"nft_address\", \"transaction_value\"]].groupby(transfers[\"nft_address\"]).sum()\n",
|
|
"total_value_per_nft.head(4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"nft_address\n",
|
|
"0xa7d8d9ef8D8Ce8992Df33D8b8CF4Aebabd5bD270 2.296429e+23\n",
|
|
"0xBC4CA0EdA7647A8aB7C2061c2E118A18a936f13D 1.588150e+23\n",
|
|
"0x60E4d786628Fea6478F785A6d7e704777c86a7c6 7.530649e+22\n",
|
|
"Name: transaction_value, dtype: float64"
|
|
]
|
|
},
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"most_popular_nfts = transactions_per_nft.sort_values(ascending=False).head(8)\n",
|
|
"most_valuable_nfts = total_value_per_nft[\"transaction_value\"].sort_values(ascending=False).head(3)\n",
|
|
"most_valuable_nfts"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#get frame with values over time\n",
|
|
"num_df = pd.DataFrame()\n",
|
|
"#cast to numeric\n",
|
|
"num_df = (transfers[[\"transaction_value\", \"timestamp\"]].apply(pd.to_numeric, errors='coerce'))\n",
|
|
"#add nft_address column to it\n",
|
|
"num_df[\"nft_address\"]=transfers[\"nft_address\"]\n",
|
|
"#filter out only ones that are in most_popular_nft variable\n",
|
|
"num_df = num_df[num_df.nft_address.isin(list(most_valuable_nfts.index))]\n",
|
|
"#convert timestamp in to date time\n",
|
|
"num_df[\"timestamp\"] = pd.to_datetime(num_df.timestamp, unit='s', errors='coerce')\n",
|
|
"#set index as timestamp\n",
|
|
"num_df = num_df.set_index(\"timestamp\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#group timestamps by day, create column per each nft_address, aggregate transaction value by count and sum\n",
|
|
"new_df = num_df.groupby([pd.Grouper(freq='d'), 'nft_address'])['transaction_value'].agg(transaction_value=\"sum\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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|
|
"text/plain": [
|
|
"<Figure size 1728x864 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.figure(figsize=(24, 12))\n",
|
|
"# new_df.unstack()\n",
|
|
"ax = sns.lineplot(data=new_df, x='timestamp', y='transaction_value', hue='nft_address',)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"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": "3.8.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|