import pandas as pd
import matplotlib.pyplot as pltTop 100 Ether balances obtained from Ethereum public datasets (the corresponding table contains Ether balances of all addresses, updated daily) on Google Cloud using BigQuery:
SELECT *
FROM `bigquery-public-data.crypto_ethereum.balances`
WHERE eth_balance > 0
ORDER BY eth_balance DESC
LIMIT 100bq_eth_df = pd.read_json('daily_top_100_eth_balances_[25 Oct 2021, 13:49:26 UTC+1]_results-20211026-090241.json')
bq_eth_df.rename(columns={'eth_balance': 'balance'}, inplace=True)
bq_eth_df['balance'] /= 10**18 # convert Wei to Ether
bq_eth_df| address | balance | |
|---|---|---|
| 0 | 0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2 | 8.139444e+06 |
| 1 | 0x00000000219ab540356cbb839cbe05303d7705fa | 8.011266e+06 |
| 2 | 0xbe0eb53f46cd790cd13851d5eff43d12404d33e8 | 2.296896e+06 |
| 3 | 0x73bceb1cd57c711feac4224d062b0f6ff338501e | 1.874773e+06 |
| 4 | 0x4ddc2d193948926d02f9b1fe9e1daa0718270ed5 | 1.499808e+06 |
| ... | ... | ... |
| 95 | 0xd05e6bf1a00b5b4c9df909309f19e29af792422b | 1.500000e+05 |
| 96 | 0x2fa9f9efc767650aace0422668444c3ff63e1f8d | 1.485317e+05 |
| 97 | 0xd57479b8287666b44978255f1677e412d454d4f0 | 1.476994e+05 |
| 98 | 0xc6dcb7ac175636cbc3d5f32c68865c24ca7d74d4 | 1.461931e+05 |
| 99 | 0x4baf012726cb5ec7dda57bc2770798a38100c44d | 1.450001e+05 |
100 rows × 2 columns
The following is obtained from Etherscan.io:
etherscan_df = pd.read_csv('etherscan_top_100_balances_[2021-10-26 10:56:12 IST].csv', header=0)
etherscan_df| address | balance | |
|---|---|---|
| 0 | 0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2 | 8.175899e+06 |
| 1 | 0x00000000219ab540356cbb839cbe05303d7705fa | 8.018114e+06 |
| 2 | 0xbe0eb53f46cd790cd13851d5eff43d12404d33e8 | 2.296896e+06 |
| 3 | 0x73bceb1cd57c711feac4224d062b0f6ff338501e | 1.874773e+06 |
| 4 | 0x4ddc2d193948926d02f9b1fe9e1daa0718270ed5 | 1.485087e+06 |
| ... | ... | ... |
| 95 | 0x2fa9f9efc767650aace0422668444c3ff63e1f8d | 1.485317e+05 |
| 96 | 0xd57479b8287666b44978255f1677e412d454d4f0 | 1.476994e+05 |
| 97 | 0xc6dcb7ac175636cbc3d5f32c68865c24ca7d74d4 | 1.461931e+05 |
| 98 | 0x4baf012726cb5ec7dda57bc2770798a38100c44d | 1.450001e+05 |
| 99 | 0x3dfd23a6c5e8bbcfc9581d2e864a68feb6a076d3 | 1.443559e+05 |
100 rows × 2 columns
plt.rcParams.update({'font.size': 20})
plt.figure(figsize=(12, 8))
plt.title('Top 100 Ether balances on 2021-10-26')
plt.xlabel('Addresses')
plt.ylabel('Balance [Ether]')
plt.xticks(color='w')
plt.plot(bq_eth_df['balance'], marker='o', markersize='8', color='blue', linestyle='', label='BigQuery')
plt.plot(etherscan_df['balance'], linestyle='-', linewidth=3, color='orange', label='Etherscan')
plt.legend()
plt.show()
To get top Ether balances for a given date (e.g. 2018-11-12), the following query shall be used (see awesome-bigquery-views for details):
#standardSQL
with double_entry_book as (
-- debits
select to_address as address, value as value
from `bigquery-public-data.crypto_ethereum.traces`
where to_address is not null
and status = 1
and value > 0
and date(block_timestamp) < "2018-11-13"
and (call_type not in ('delegatecall', 'callcode', 'staticcall') or call_type is null)
union all -- combines results from different 'selects' with duplicates
-- credits
select from_address as address, -value as value
from `bigquery-public-data.crypto_ethereum.traces`
where from_address is not null
and status = 1
and value > 0
and date(block_timestamp) < "2018-11-13"
and (call_type not in ('delegatecall', 'callcode', 'staticcall') or call_type is null)
union all
-- transaction fees debits
-- select miner as address, sum(cast(receipt_gas_used as numeric) * cast(gas_price as numeric)) as value
select miner as address, sum(cast(receipt_gas_used as numeric) * cast((receipt_effective_gas_price - coalesce(base_fee_per_gas, 0)) as numeric)) as value
from `bigquery-public-data.crypto_ethereum.transactions` as transactions
join `bigquery-public-data.crypto_ethereum.blocks` as blocks on blocks.number = transactions.block_number
group by blocks.miner
union all
-- transaction fees credits
select from_address as address, -(cast(receipt_gas_used as numeric) * cast(gas_price as numeric)) as value
from `bigquery-public-data.crypto_ethereum.transactions`
)
select address, sum(value) as balance
from double_entry_book
group by address
order by balance desc
limit 100The resultts for this query are as follows:
bq_eth_df = pd.read_json('top_100_eth_balances_[2018-11-12]_results-20211026-103524.json')
bq_eth_df['balance'] /= 10**18 # convert Wei to Ether
bq_eth_df| address | balance | |
|---|---|---|
| 0 | 0x742d35cc6634c0532925a3b844bc454e4438f44e | 1.653529e+06 |
| 1 | 0x281055afc982d96fab65b3a49cac8b878184cb16 | 1.538423e+06 |
| 2 | 0x6f46cf5569aefa1acc1009290c8e043747172d89 | 1.510066e+06 |
| 3 | 0x90e63c3d53e0ea496845b7a03ec7548b70014a91 | 1.507810e+06 |
| 4 | 0x53d284357ec70ce289d6d64134dfac8e511c8a3d | 1.378754e+06 |
| ... | ... | ... |
| 95 | 0x47029dc4f3922706bf670d335c45550cff4f6a35 | 1.228616e+05 |
| 96 | 0xddf744374b46aa980ddce4a5aa216478bf925cd1 | 1.218604e+05 |
| 97 | 0xd224ca0c819e8e97ba0136b3b95ceff503b79f53 | 1.149793e+05 |
| 98 | 0x376c3e5547c68bc26240d8dcc6729fff665a4448 | 1.149390e+05 |
| 99 | 0xa1a45e91164cdab8fa596809a9b24f8d4fdbe0f3 | 1.137622e+05 |
100 rows × 2 columns
plt.rcParams.update({'font.size': 20})
plt.figure(figsize=(12, 8))
plt.title('Top 100 Ether balances on 2018-11-12')
plt.xlabel('Addresses')
plt.ylabel('Balance [Ether]')
plt.xticks(color='w')
plt.plot(bq_eth_df['address'], bq_eth_df['balance'], color='blue', linestyle='-', linewidth=3, label='BigQuery')
plt.legend()
plt.show()
To get top Bitcoin balances, we can use (see awesome-bigquery-views for details):
WITH double_entry_book AS (
-- debits
SELECT array_to_string(inputs.addresses, ",") as address, inputs.type, -inputs.value as value
FROM `bigquery-public-data.crypto_bitcoin.inputs` as inputs
-- WHERE DATE(inputs.block_timestamp) < "2018-11-13"
UNION ALL
-- credits
SELECT array_to_string(outputs.addresses, ",") as address, outputs.type, outputs.value as value
FROM `bigquery-public-data.crypto_bitcoin.outputs` as outputs
-- WHERE DATE(outputs.block_timestamp) < "2018-11-13"
)
SELECT address, type, sum(value) as balance
FROM double_entry_book
GROUP BY address, type
ORDER BY balance DESC
LIMIT 100which gave the following results
bq_btc_df = pd.read_json('daily_top_100_btc_balances_[25 Oct 2021, 13:36:04 UTC+1 ]_results-20211026-104433.json')
bq_btc_df['balance'] /= 10**8 # convert Satoshi to BTC
bq_btc_df| address | type | balance | |
|---|---|---|---|
| 0 | 34xp4vRoCGJym3xR7yCVPFHoCNxv4Twseo | scripthash | 288126.209985 |
| 1 | bc1qgdjqv0av3q56jvd82tkdjpy7gdp9ut8tlqmgrpmv24... | witness_v0_scripthash | 178010.985449 |
| 2 | 1P5ZEDWTKTFGxQjZphgWPQUpe554WKDfHQ | pubkeyhash | 106354.574363 |
| 3 | 37XuVSEpWW4trkfmvWzegTHQt7BdktSKUs | scripthash | 94505.114067 |
| 4 | 38UmuUqPCrFmQo4khkomQwZ4VbY2nZMJ67 | scripthash | 93566.021928 |
| ... | ... | ... | ... |
| 95 | bc1qyr9dsfyst3epqycghpxshfmgy8qfzadfhp8suk | witness_v0_keyhash | 9900.451786 |
| 96 | bc1q8qg2eazryu9as20k3hveuvz43thp200g7nw7qy | witness_v0_keyhash | 9900.098341 |
| 97 | bc1q4ffskt6879l4ewffrrflpykvphshl9la4q037h | witness_v0_keyhash | 9895.369770 |
| 98 | bc1qa56lzf7d05ttur003zmuzwrgl3x0qau5zm2qzn | witness_v0_keyhash | 9887.714116 |
| 99 | 134ooU6EQmCFFzEPN7yga3S1XikcBtynco | pubkeyhash | 9866.702280 |
100 rows × 3 columns
The following is obtained from BTC.com:
btc_df = pd.read_csv('BTCcom_top_100_balances_[2021-10-26 12:07:14 IST].csv', header=0)
btc_df| address | balance | |
|---|---|---|
| 0 | 34xp4vRoCGJym3xR7yCVPFHoCNxv4Twseo | 293427.057899 |
| 1 | bc1qgdjqv0av3q56jvd82tkdjpy7gdp9ut8... | 166510.985836 |
| 2 | 35hK24tcLEWcgNA4JxpvbkNkoAcDGqQPsP | 115451.667857 |
| 3 | 1P5ZEDWTKTFGxQjZphgWPQUpe554WKDfHQ | 112155.617716 |
| 4 | 37XuVSEpWW4trkfmvWzegTHQt7BdktSKUs | 94505.831979 |
| ... | ... | ... |
| 95 | bc1qh4cpaydaqlzez8ekkasm3ygj4us7gwx... | 9111.905706 |
| 96 | 17CzhFvGwH6TT46JtzhnhMpTw4mHYpEGCR | 9107.364474 |
| 97 | bc1q78wmm8xnhveklsparpeq6drlg9wlq4l... | 9098.999109 |
| 98 | 1Cr7EjvS8C7gfarREHCvFhd9gT3r46pfLb | 9049.832927 |
| 99 | 1J1F3U7gHrCjsEsRimDJ3oYBiV24wA8FuV | 9000.000000 |
100 rows × 2 columns
plt.rcParams.update({'font.size': 20})
plt.figure(figsize=(12, 8))
plt.title('Top 100 Bitcoin balances on 2021-10-26')
plt.xlabel('Addresses')
plt.ylabel('Balance [BTC]')
plt.xticks(color='w')
plt.plot(bq_btc_df['balance'], marker='o', markersize='8', color='blue', linestyle='', label='BigQuery')
plt.plot(btc_df['balance'], linestyle='-', linewidth=3, color='orange', label='BTC.com')
plt.legend()
plt.show()