# How to correct Syntax Error

**URL:** <https://discuss.python.org/t/how-to-correct-syntax-error/105797>\
**Category:** Python Help\
**Created:** [January 19, 2026, 9:10pm UTC](https://discuss.python.org/t/how-to-correct-syntax-error/105797 "2026-01-19T21:10:50Z")\
**Posts on this page:** 6\
**Page:** 1

<div class="post-metadata">

**Author:** ![gbm570](https://avatars.discourse-cdn.com/v4/letter/g/67e7ee/32.png) [@gbm570](https://discuss.python.org/u/gbm570)\
**Post date:** [January 19, 2026, 9:10pm UTC](https://discuss.python.org/t/how-to-correct-syntax-error/105797/1 "2026-01-19T21:10:50Z")

</div>

Hello,

I am a brand new Python user. I used ChatGPT to develop the code below. However, I am getting the following error SyntaxError: multiple statements found while compiling a single statement. I have tried to edit but I keep coming back to the error.

Any ideas or guidance on how to correct this are appreciated

Here is the code:

```python
import pandas as pd

from rapidfuzz import process, fuzz

‘Single Line’

# — CONFIGURATION —

bank_statement_file = ‘bank_statement.csv’

book_ledger_file = ‘book_ledger.csv’

output_reconciliation_file = ‘bank_reconciliation_final.xlsx’

fuzzy_threshold = 85 # similarity score threshold

# — LOAD DATA —

bank_df = pd.read_csv(bank_statement_file)

book_df = pd.read_csv(book_ledger_file)

# — CLEAN DATA —

def clean_data(df):

df\['Date'\] = pd.to_datetime(df\['Date'\])

df\['Description'\] = df\['Description'\].astype(str).str.strip().str.lower()

df\['Amount'\] = df\['Amount'\].astype(float)

return df

bank_df = clean_data(bank_df)

book_df = clean_data(book_df)

# — EXACT MATCHES —

bank_df[‘Match_Key’] = bank_df[‘Date’].astype(str) + ‘|’ + bank_df[‘Amount’].astype(str)

book_df[‘Match_Key’] = book_df[‘Date’].astype(str) + ‘|’ + book_df[‘Amount’].astype(str)

exact_matched = pd.merge(

bank_df, book_df,

on='Match_Key',

how='inner',

suffixes=('\_bank', '\_book')

)

exact_matched = exact_matched[exact_matched[‘Description_bank’] == exact_matched[‘Description_book’]]

unmatched_bank = bank_df[~bank_df[‘Match_Key’].isin(exact_matched[‘Match_Key’])]

unmatched_book = book_df[~book_df[‘Match_Key’].isin(exact_matched[‘Match_Key’])]

# — FUZZY MATCHES —

fuzzy_matches = []

for idx_b, bank_row in unmatched_bank.iterrows():

candidates = unmatched_book\[unmatched_book\['Amount'\] == bank_row\['Amount'\]\]

if candidates.empty:

    continue

match_desc, score, match_idx = process.extractOne(

    bank_row\['Description'\],

    candidates\['Description'\],

    scorer=fuzz.token_sort_ratio

)

if score >= fuzzy_threshold:

    matched_book_row = candidates.loc\[match_idx\]

    fuzzy_matches.append({

        'Date_bank': bank_row\['Date'\],

        'Description_bank': bank_row\['Description'\],

        'Amount_bank': bank_row\['Amount'\],

        'Date_book': matched_book_row\['Date'\],

        'Description_book': matched_book_row\['Description'\],

        'Amount_book': matched_book_row\['Amount'\],

        'Match_Score': score

    })

fuzzy_matched_df = pd.DataFrame(fuzzy_matches)

# Remove fuzzy-matched rows from unmatched sets

if not fuzzy_matched_df.empty:

matched_bank_keys = set(zip(fuzzy_matched_df\['Date_bank'\], fuzzy_matched_df\['Amount_bank'\], fuzzy_matched_df\['Description_bank'\]))

matched_book_keys = set(zip(fuzzy_matched_df\['Date_book'\], fuzzy_matched_df\['Amount_book'\], fuzzy_matched_df\['Description_book'\]))

unmatched_bank = unmatched_bank\[\~unmatched_bank.apply(lambda x: (x\['Date'\], x\['Amount'\], x\['Description'\]) in matched_bank_keys, axis=1)\]

unmatched_book = unmatched_book\[\~unmatched_book.apply(lambda x: (x\['Date'\], x\['Amount'\], x\['Description'\]) in matched_book_keys, axis=1)\]

# — CATEGORIZE UNMATCHED ITEMS —

def categorize_unmatched(bank_df, book_df):

categories = \[\]

for \_, row in book_df.iterrows():

    if row\['Amount'\] < 0:

        categories.append('Outstanding Check')

    else:

        categories.append('Deposit in Transit')

book_df = book_df.copy()

book_df\['Category'\] = categories

categories = \[\]

for \_, row in bank_df.iterrows():

    if row\['Amount'\] < 0:

        categories.append('Unrecorded Bank Withdrawal')

    else:

        categories.append('Unrecorded Bank Deposit')

bank_df = bank_df.copy()

bank_df\['Category'\] = categories

return bank_df, book_df

unmatched_bank, unmatched_book = categorize_unmatched(unmatched_bank, unmatched_book)

# — FLAG POTENTIAL DUPLICATES —

def flag_duplicates(df, label):

dup = df\[df.duplicated(subset=\['Date', 'Amount'\], keep=False)\]

dup = dup.assign(Potential_Duplicate_Label=label)

return dup

duplicates_bank = flag_duplicates(bank_df, ‘Bank’)

duplicates_book = flag_duplicates(book_df, ‘Book’)

# — SUMMARY —

summary = {

'Total Bank Transactions': \[len(bank_df)\],

'Total Book Transactions': \[len(book_df)\],

'Exact Matches': \[len(exact_matched)\],

'Fuzzy Matches': \[len(fuzzy_matched_df)\],

'Unmatched in Bank': \[len(unmatched_bank)\],

'Unmatched in Book': \[len(unmatched_book)\],

'Bank Duplicates': \[len(duplicates_bank)\],

'Book Duplicates': \[len(duplicates_book)\],

'Difference in Total Amount': \[round(bank_df\['Amount'\].sum() - book_df\['Amount'\].sum(), 2)\]

}

summary_df = pd.DataFrame(summary)

# — EXPORT RESULTS —

with pd.ExcelWriter(output_reconciliation_file) as writer:

summary_df.to_excel(writer, sheet_name='Summary', index=False)

exact_matched.to_excel(writer, sheet_name='Exact_Matched', index=False)

fuzzy_matched_df.to_excel(writer, sheet_name='Fuzzy_Matched', index=False)

unmatched_bank.to_excel(writer, sheet_name='Unmatched_Bank', index=False)

unmatched_book.to_excel(writer, sheet_name='Unmatched_Book', index=False)

duplicates_bank.to_excel(writer, sheet_name='Potential_Duplicates_Bank', index=False)

duplicates_book.to_excel(writer, sheet_name='Potential_Duplicates_Book', index=False)

print(“Bank recon done”)

print(f"Reconciliation report saved to: {output_reconciliation_file}")

```

---

<div class="post-metadata">

**Author:** ![onePythonUser](https://sea2.discourse-cdn.com/flex002/user_avatar/discuss.python.org/onepythonuser/32/19158_2.png) [@onePythonUser](https://discuss.python.org/u/onePythonUser)\
**Post date:** [January 19, 2026, 10:40pm UTC](https://discuss.python.org/t/how-to-correct-syntax-error/105797/2 "2026-01-19T22:40:53Z")

</div>

Are you attempting to run this code from the **IDLE** shell - pasting directly? From an editor? Which one?

Save the script as a file and run it directly as a file.

By the way, when pasting your script here, after having done so, select what you have just pasted, then click the `</>` symbol tool on the upper bar so that it is formatted properly.

---

<div class="post-metadata">

**Author:** ![tjwilli58](https://sea2.discourse-cdn.com/flex002/user_avatar/discuss.python.org/tjwilli58/32/31145_2.png) [@tjwilli58](https://discuss.python.org/u/tjwilli58)\
**Post date:** [January 19, 2026, 10:43pm UTC](https://discuss.python.org/t/how-to-correct-syntax-error/105797/3 "2026-01-19T22:43:10Z")

</div>

Not sure what’s wrong with your program, but you may get more help if you posted the code properly.

Check this out:  
[Python Discourse Quick Start - Welcome to Discourse! - Discussions on Python.org](https://discuss.python.org/t/python-discourse-quick-start/116#p-341-how-do-i-post-code-snippets-error-messages-etc-4)

---

<div class="post-metadata">

**Author:** ![gbm570](https://avatars.discourse-cdn.com/v4/letter/g/67e7ee/32.png) [@gbm570](https://discuss.python.org/u/gbm570)\
**Post date:** [January 19, 2026, 11:21pm UTC](https://discuss.python.org/t/how-to-correct-syntax-error/105797/4 "2026-01-19T23:21:52Z")

</div>

I am trying to run it directly in IDLE. Copy and paste into IDLE.

Here is the code with the \</\> update.

```python
import pandas as pd

from rapidfuzz import process, fuzz

 

# --- CONFIGURATION ---

bank_statement_file = 'bank_statement.csv'

book_ledger_file = 'book_ledger.csv'

output_reconciliation_file = 'bank_reconciliation_final.xlsx'

fuzzy_threshold = 85 # similarity score threshold

 

# --- LOAD DATA ---

bank_df = pd.read_csv(bank_statement_file)

book_df = pd.read_csv(book_ledger_file)

 

# --- CLEAN DATA ---

def clean_data(df):

    df['Date'] = pd.to_datetime(df['Date'])

    df['Description'] = df['Description'].astype(str).str.strip().str.lower()

    df['Amount'] = df['Amount'].astype(float)

    return df

 

bank_df = clean_data(bank_df)

book_df = clean_data(book_df)

 

# --- EXACT MATCHES ---

bank_df['Match_Key'] = bank_df['Date'].astype(str) + '|' + bank_df['Amount'].astype(str)

book_df['Match_Key'] = book_df['Date'].astype(str) + '|' + book_df['Amount'].astype(str)

 

exact_matched = pd.merge(

    bank_df, book_df,

    on='Match_Key',

    how='inner',

    suffixes=('_bank', '_book')

)

exact_matched = exact_matched[exact_matched['Description_bank'] == exact_matched['Description_book']]

 

unmatched_bank = bank_df[~bank_df['Match_Key'].isin(exact_matched['Match_Key'])]

unmatched_book = book_df[~book_df['Match_Key'].isin(exact_matched['Match_Key'])]

 

# --- FUZZY MATCHES ---

fuzzy_matches = []

 

for idx_b, bank_row in unmatched_bank.iterrows():

    candidates = unmatched_book[unmatched_book['Amount'] == bank_row['Amount']]

    if candidates.empty:

        continue

    match_desc, score, match_idx = process.extractOne(

        bank_row['Description'],

        candidates['Description'],

        scorer=fuzz.token_sort_ratio

    )

    if score >= fuzzy_threshold:

        matched_book_row = candidates.loc[match_idx]

        fuzzy_matches.append({

            'Date_bank': bank_row['Date'],

            'Description_bank': bank_row['Description'],

            'Amount_bank': bank_row['Amount'],

            'Date_book': matched_book_row['Date'],

            'Description_book': matched_book_row['Description'],

            'Amount_book': matched_book_row['Amount'],

            'Match_Score': score

        })

 

fuzzy_matched_df = pd.DataFrame(fuzzy_matches)

 

# Remove fuzzy-matched rows from unmatched sets

if not fuzzy_matched_df.empty:

    matched_bank_keys = set(zip(fuzzy_matched_df['Date_bank'], fuzzy_matched_df['Amount_bank'], fuzzy_matched_df['Description_bank']))

    matched_book_keys = set(zip(fuzzy_matched_df['Date_book'], fuzzy_matched_df['Amount_book'], fuzzy_matched_df['Description_book']))

    unmatched_bank = unmatched_bank[~unmatched_bank.apply(lambda x: (x['Date'], x['Amount'], x['Description']) in matched_bank_keys, axis=1)]

    unmatched_book = unmatched_book[~unmatched_book.apply(lambda x: (x['Date'], x['Amount'], x['Description']) in matched_book_keys, axis=1)]

 

# --- CATEGORIZE UNMATCHED ITEMS ---

def categorize_unmatched(bank_df, book_df):

    categories = []

    for _, row in book_df.iterrows():

        if row['Amount'] < 0:

            categories.append('Outstanding Check')

        else:

            categories.append('Deposit in Transit')

    book_df = book_df.copy()

    book_df['Category'] = categories

 

    categories = []

    for _, row in bank_df.iterrows():

        if row['Amount'] < 0:

            categories.append('Unrecorded Bank Withdrawal')

        else:

            categories.append('Unrecorded Bank Deposit')

    bank_df = bank_df.copy()

    bank_df['Category'] = categories

    return bank_df, book_df

 

unmatched_bank, unmatched_book = categorize_unmatched(unmatched_bank, unmatched_book)

 

# --- FLAG POTENTIAL DUPLICATES ---

def flag_duplicates(df, label):

    dup = df[df.duplicated(subset=['Date', 'Amount'], keep=False)]

    dup = dup.assign(Potential_Duplicate_Label=label)

    return dup

 

duplicates_bank = flag_duplicates(bank_df, 'Bank')

duplicates_book = flag_duplicates(book_df, 'Book')

 

# --- SUMMARY ---

summary = {

    'Total Bank Transactions': [len(bank_df)],

    'Total Book Transactions': [len(book_df)],

    'Exact Matches': [len(exact_matched)],

    'Fuzzy Matches': [len(fuzzy_matched_df)],

    'Unmatched in Bank': [len(unmatched_bank)],

    'Unmatched in Book': [len(unmatched_book)],

    'Bank Duplicates': [len(duplicates_bank)],

    'Book Duplicates': [len(duplicates_book)],

    'Difference in Total Amount': [round(bank_df['Amount'].sum() - book_df['Amount'].sum(), 2)]

}

summary_df = pd.DataFrame(summary)

 

# --- EXPORT RESULTS ---

with pd.ExcelWriter(output_reconciliation_file) as writer:

    summary_df.to_excel(writer, sheet_name='Summary', index=False)

    exact_matched.to_excel(writer, sheet_name='Exact_Matched', index=False)

    fuzzy_matched_df.to_excel(writer, sheet_name='Fuzzy_Matched', index=False)

    unmatched_bank.to_excel(writer, sheet_name='Unmatched_Bank', index=False)

    unmatched_book.to_excel(writer, sheet_name='Unmatched_Book', index=False)

    duplicates_bank.to_excel(writer, sheet_name='Potential_Duplicates_Bank', index=False)

    duplicates_book.to_excel(writer, sheet_name='Potential_Duplicates_Book', index=False)

 

print("Bank recon done")

print(f"Reconciliation report saved to: {output_reconciliation_file}")

```

---

<div class="post-metadata">

**Author:** ![gbm570](https://avatars.discourse-cdn.com/v4/letter/g/67e7ee/32.png) [@gbm570](https://discuss.python.org/u/gbm570)\
**Post date:** [January 19, 2026, 11:24pm UTC](https://discuss.python.org/t/how-to-correct-syntax-error/105797/5 "2026-01-19T23:24:20Z")

</div>

Thanks - I updated the post to show code in the proper format

---

<div class="post-metadata">

**Author:** ![onePythonUser](https://sea2.discourse-cdn.com/flex002/user_avatar/discuss.python.org/onepythonuser/32/19158_2.png) [@onePythonUser](https://discuss.python.org/u/onePythonUser)\
**Post date:** [January 19, 2026, 11:28pm UTC](https://discuss.python.org/t/how-to-correct-syntax-error/105797/6 "2026-01-19T23:28:41Z")

</div>

> [@gbm570](#):
>
> I am trying to run it directly in IDLE. Copy and paste into IDLE.

The `IDLE` shell only accepts single line statements. What you need to do is save your script as a file. To do so, go to `File` \> `New File`. Paste your script there. Next, go to `Run` \> `Run Module`. It will prompt you to give it a name if not already done so.

 ![steps_to_run_script](https://us1.discourse-cdn.com/flex002/uploads/python1/original/3X/f/c/fc02e15b34408afec9b82841d41d89a987b4f43f.png)