import pandas as pd
import matplotlib.pyplot as plt

Top 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 100

bq_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 100

The 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 100

which 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()