origin_from_address as user,

count(distinct tx_hash) as tx_count

kaia.core.fact_event_logs

where

contract_address in ('0x84f8c3c8d6ee30a559d73ec570d574f671e82647', '0x990d8cfbce8892e2126f85637f3728b53266b45e', '0x63d4f17d2a8a729fd050f7679d961b1dfbb1e3af',

'0x6b684816884def88cbc72e1bfa47242ff27fd0de', '0x9bcb2efc545f89986cf70d3adc39079a1b730d63', '0x8da74ba3db7b440264421373d2cbfca2b10e2e3b', '0x26e7e1f2351a39c3b718512bb4480aa6892bef9b',

'0x976232eb7eb92287ff06c5d145bd0d1c033eca58', '0xa9dc3a0a9b0fd00f5572996cfb1fac79a78eb9f1', '0xaeb0c547c3175aed37f67f4565a5064ee48b8cae', '0xb97c9b2a4ea46672f46e05740621a12c7928d73e',

'0xe777abd2594c81149d1b0dd35203ccbafaa234fd', '0x2e5be2ad0473ec82fd66f2f476a510c9f3962583', '0x144Fa704D6d7A12950AC4EaD5C48cB5D5e79cD14', '0x52678aeff4b82b0b167ca3e37c272f574ca9fde0',

'0x9d0c0675a995d5F12b03E880763F639d0628b5C6', '0x3b58a4C865B568a2F6a957C264F6b50CbA35D8CE', '0x0A6cE134Bcb399D0F7a25D2d776fFbed4557c7AF', '0xabbF528374c9f431dFd45E25c01490389Ff8fd6a',

'0x1a818d53ecdcdb9a3af102a9df4436122b9a643c', '0x8D967653D64Ad05c57927995866DCB45399210aA', '0x13AF3A8f1428dDC20be8Bd47821F69779e24F80c')

group by

user

select

case

when tx_count < 2 then 'A. 1'

when tx_count < 4 then 'B. 2-3'

when tx_count < 8 then 'C. 4-7'

when tx_count < 16 then 'D. 8-15'

when tx_count < 32 then 'E. 16-31'

when tx_count < 64 then 'F. 32-63'

when tx_count < 128 then 'G. 64-127'

when tx_count < 256 then 'H. 128-255'

when tx_count < 512 then 'I. 256-511'

else 'J. more than 512' end as dis,

count(distinct user) as user_count,

100.0 * count(distinct user) / sum(count(distinct user)) over () as user_count_percentage

from per_user

group by 1