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import pandas as pd

# Sample bank transactions
bank_data = {
    "Date": ["2024-06-01", "2024-06-02", "2024-06-03"],
    "Description": ["Deposit", "Withdrawal", "Payment"],
    "Amount": [1000, -200, -150]
}
bank_df = pd.DataFrame(bank_data)

# Sample ledger transactions
ledger_data = {
    "Date": ["2024-06-01", "2024-06-02", "2024-06-04"],
    "Description": ["Deposit", "Withdrawal", "Fee"],
    "Amount": [1000, -200, -50]
}
ledger_df = pd.DataFrame(ledger_data)

# Merge on all columns to find exact matches
merged = pd.merge(bank_df, ledger_df, how="outer", indicator=True)

# Find transactions not matched in both datasets
unmatched = merged[merged["_merge"] != "both"]

print("Unmatched transactions:")
print(unmatched)

              
1234567891011121314151617181920
            
import pandas as pd

# Example datasets
bank = pd.DataFrame({
    "Date": ["2024-06-01", "2024-06-02"],
    "Amount": [500, -100]
})
ledger = pd.DataFrame({
    "Date": ["2024-06-01", "2024-06-03"],
    "Amount": [500, -50]
})

# Merge and flag discrepancies
merged = pd.merge(bank, ledger, on=["Date", "Amount"], how="outer", indicator=True)
discrepancies = merged[merged["_merge"] != "both"]

# Report discrepancies
for idx, row in discrepancies.iterrows():
    source = "Bank" if row["_merge"] == "left_only" else "Ledger"
    print(f"Discrepancy found in {source}: Date {row['Date']}, Amount {row['Amount']}")