<jupyter_start><jupyter_text>Metacartel VenturesFirst block: 9484668 (Feb-15-2020 01:32:52 AM +UTC)https://etherscan.io/block/9484668Other blocks:10884668 (Sep-18-2020 06:57:11 AM +UTC)Recent block (for reference): 13316507 (Sep-28-2021 08:58:06 PM +UTC)summoner: 0x8c8b237bea3c23317d08b73d7137e90cafdf68e6<jupyter_code># Approximate seconds per block

sec_per_block = (datetime(2021, 9, 28, 8, 58, 6) - datetime(2020, 2, 15, 1, 32, 52)).seconds / (13316507 - 9484668)

print("approx. this many blocks per days:", sec_per_block * 86400)
# 86400 seconds per day

# >>> from datetime import datetime
# >>> a = datetime(2011,11,24,0,0,0)
# >>> b = datetime(2011,11,17,23,59,59)
# >>> a-b
# datetime.timedelta(6, 1)
# >>> (a-b).days
# 6
9484668 + 602

# Load data
with open("./data/10884668-results.json", "r") as f:
    results_09182020 = json.load(f)
f.close()

with open("./data/13316507-results.json", "r") as f:
    results_09282021 = json.load(f)
f.close()
df_09182020_members = pd.DataFrame.from_dict(results_09182020["data"]["moloches"][0]["members"])
df_09182020_proposals = pd.DataFrame.from_dict(results_09182020["data"]["moloches"][0]["proposals"])
df_09182020_members["id"][0].split('-')[2]
df_09182020_members["id"] = df_09182020_members.apply(lambda row: row["id"].split('-')[2], axis=1)
df_09182020_members
print("Number of members:", df_09182020_members["id"].nunique())
df_09182020_proposals
cols_proposal = ["applicant", "details", "didPass", "lootRequested", "sharesRequested", "aborted", "cancelled", "createdAt", "yesShares", "yesVotes", "noShares", "noVotes", "maxTotalSharesAndLootAtYesVote"]
df_09182020_proposals[cols_proposal]
# df_09182020_proposals.columns
df_09182020_members.columns
df_09182020_member_proposals = pd.merge(df_09182020_members, df_09182020_proposals[cols_proposal], how="left", left_on=["id"], right_on = ["applicant"]).sort_values(["createdAt_y", "applicant"])
df_09182020_member_proposals.head()

df_09182020_member_proposals[["id", "applicant"]].groupby("id").count()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17229564366128242, 'fraction_numerical': 0.17572197748409202, 'mean_word_length': 8.044247787610619, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 20, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '668645', 'license': 'MIT', 'n_tokens_mistral': 1047, 'n_tokens_neox': 824, 'n_words': 130, 'path': 'MetacartelVentures/MetacartelVentures DAO Analysis.ipynb', 'repo_name': 'Xqua/dao-research', 'size': '2043'}