analyze_transactions

[GypsyS44]

pip install pandas openpyxl

[GypsyS44]

export-0xdadb0d80178819f2319190d340ce9a924f783711.csv

[GypsyS44]

import pandas as pd

Load your data (update filename/path as needed)

file_path = "transactions.xlsx" # or "transactions.csv"
df = pd.read_excel(file_path) # for CSV use pd.read_csv(file_path)

Quick data check

print("First 5 rows:")
print(df.head())

Convert DateTime column to datetime if not already

df['DateTime (UTC)'] = pd.to_datetime(df['DateTime (UTC)'])

Clean 'Amount' column to extract numeric value (assuming ETH values like "0.0123 ETH")

df['Amount_numeric'] = df['Amount'].str.extract(r'([0-9.]+)').astype(float)

Clean 'Txn Fee' column similarly

df['Txn_Fee_numeric'] = df['Txn Fee'].astype(str).str.extract(r'([0-9.]+)').astype(float)

Total number of transactions

total_txns = len(df)
print(f"Total transactions: {total_txns}")

Total amount transferred

total_amount = df['Amount_numeric'].sum()
print(f"Total amount transferred (ETH): {total_amount:.6f}")

Average transaction fee

avg_fee = df['Txn_Fee_numeric'].mean()
print(f"Average transaction fee (ETH): {avg_fee:.8f}")

Group by date to see daily transaction count

df['Date'] = df['DateTime (UTC)'].dt.date
daily_counts = df.groupby('Date').size()
print("\nTransactions per day:")
print(daily_counts)

Save daily counts to CSV

daily_counts.to_csv("daily_transaction_counts.csv")
print("\nDaily transaction counts saved to 'daily_transaction_counts.csv'")