> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lendpathway.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Export data

> Download parsed results as CSV, Excel, or raw JSON.

## CSV export (UW table)

The fastest way to get a structured spreadsheet of the bank statement data.

```bash theme={null}
# Months as rows (one row per statement period per account)
curl "https://api.lendpathway.com/api/books/{book_id}/csv-export" \
  -H "Authorization: Bearer pat_xxx" \
  -o uw.csv

# Pivoted (metrics as rows, months as columns)
curl "https://api.lendpathway.com/api/books/{book_id}/csv-export?table_format=month_as_col" \
  -H "Authorization: Bearer pat_xxx" \
  -o uw_pivoted.csv
```

**`month_as_row`** (default):

```
Period,Document,Account,Starting Balance,Deposits,...,True Revenue,DTI %
Jul 2025,July_2025.pdf,MAIN ACCOUNT,8149.06,101019.45,...,51587.45,10.2%
Jul 2025,July_2025.pdf,EXPENSE ACCOUNT,414.90,14216.36,...,0.0,
...
NET,,1 Account,8149.06,427709.57,...,174227.90,15.9%
Average,,1 Account,2037.27,106927.39,...,43556.98,15.9%
```

**`month_as_col`** (pivoted):

```
Metric,Jul 2025,Aug 2025,Sep 2025,Oct 2025
Starting Balance,13320.22,51667.09,50352.87,28034.39
Deposits,115936.01,116600.20,88212.78,106960.58
True Revenue,51587.45,0.0,46764.70,75875.75
DTI %,10.5%,,16.7%,19.2%
```

The pivoted format uses the combined account (`account_id=0`) per statement, which is the aggregate of all accounts in that month. Good for pivot tables in Excel or Google Sheets.

Exclusions are applied before the CSV is generated. Excluded documents and accounts are omitted.

**In Python:**

```python theme={null}
r = requests.get(
    f"{API_BASE}/books/{book_id}/csv-export",
    headers=headers,
    params={"table_format": "month_as_col"}
)
with open("uw.csv", "wb") as f:
    f.write(r.content)
```

***

## Excel export

Returns an `.xlsx` file using your org's spreadsheet template.

```bash theme={null}
# Bank statements
curl "https://api.lendpathway.com/api/books/{book_id}/spreadsheet-export?sheet_type=mca" \
  -H "Authorization: Bearer pat_xxx" \
  -o statements.xlsx

# Credit report
curl "https://api.lendpathway.com/api/books/{book_id}/spreadsheet-export?sheet_type=vlad" \
  -H "Authorization: Bearer pat_xxx" \
  -o credit.xlsx

# Tax forms
curl "https://api.lendpathway.com/api/books/{book_id}/spreadsheet-export?sheet_type=tax" \
  -H "Authorization: Bearer pat_xxx" \
  -o tax.xlsx
```

***

## JSON analytics

Use `/analytics` for the full structured data. See [working with analytics](/cookbook/working-with-analytics) for field details.

```python theme={null}
analytics = requests.get(f"{API_BASE}/books/{book_id}/analytics", headers=headers).json()
```

Credit report data:

```python theme={null}
book = requests.get(f"{API_BASE}/books/{book_id}", headers=headers).json()
cr = book["book_meta"]["parser_v2_credit_report"]
```

Tax data:

```python theme={null}
tax = requests.get(f"{API_BASE}/books/{book_id}/tax-analytics", headers=headers).json()
```

***

## Build a complete deal package

```python theme={null}
import requests, json

def export_deal(book_id, output_dir="."):
    headers = {"Authorization": f"Bearer {TOKEN}"}

    # CSV
    r = requests.get(f"{API_BASE}/books/{book_id}/csv-export", headers=headers)
    with open(f"{output_dir}/{book_id}_uw.csv", "wb") as f:
        f.write(r.content)

    # Excel
    r = requests.get(
        f"{API_BASE}/books/{book_id}/spreadsheet-export",
        headers=headers,
        params={"sheet_type": "mca"}
    )
    with open(f"{output_dir}/{book_id}_statements.xlsx", "wb") as f:
        f.write(r.content)

    # Analytics JSON
    analytics = requests.get(f"{API_BASE}/books/{book_id}/analytics", headers=headers).json()
    with open(f"{output_dir}/{book_id}_analytics.json", "w") as f:
        json.dump(analytics, f, indent=2)

    print(f"Exported: {book_id}")
    print(f"  Revenue: ${analytics['true_revenue']:,.2f}")
    print(f"  Positions: {len(analytics['positions'])}")
```