Export Python Code Sample - Checks

import requests
import pandas as pd
from datetime import datetime

HEADERS = {
    'Accept': 'application/json',
    'CLIENT-ID': ,
    'AUTHORIZATION': 
}

def fetch_data(base_url, start_date, end_date):
    page = 1
    max_pages = 999
    first_request_made = False
    while page <= max_pages:
        print(f"page: {page} , max_pages {max_pages}")
        request_url = f"{base_url}?page={page}&created_date__gt={start_date}&created_date__lt={end_date}"
        try:
            response = requests.get(request_url, headers=HEADERS, timeout=300)
            if response.status_code != 200:
                print(f"Failed to fetch data: {response.status_code}")
                continue
            checks = response.json()["results"]
            total_checks = response.json()["count"]
            print(f"total checks: {total_checks}")
            if not checks:
                break
            total_pages = total_checks/50
            if max_pages == 999:
                max_pages = total_pages
            # Flatten nested fields
            flattened_checks = []
            for check in checks:
                flat_check = check.copy()
                # Handle endorsement nested field
                if 'endorsement' in flat_check and flat_check['endorsement']:
                    for key, value in flat_check['endorsement'].items():
                        flat_check[f'endorsement_{key}'] = value
                    del flat_check['endorsement']
                
                # Handle micr nested field
                if 'micr' in flat_check and flat_check['micr']:
                    for key, value in flat_check['micr'].items():
                        flat_check[f'micr_{key}'] = value
                    del flat_check['micr']
                
                # Handle meta nested field
                if 'meta' in flat_check and flat_check['meta']:
                    for key, value in flat_check['meta'].items():
                        if not isinstance(value, (list, dict)):
                            flat_check[f'meta_{key}'] = value
                        elif key == 'tags' and isinstance(value, list):
                            flat_check[f'meta_{key}'] = ','.join(str(tag) for tag in value) if value else ''
                        elif key == 'pages' and isinstance(value, list):
                            # Just store the count of pages
                            flat_check['meta_page_count'] = len(value)
                            # Store if front page exists
                            flat_check['meta_has_front_page'] = any(page.get('is_front', False) for page in value if isinstance(page, dict))
                        elif key == 'duplicates' and isinstance(value, list):
                            flat_check['meta_duplicate_count'] = len(value)
                    del flat_check['meta']
                
                flattened_checks.append(flat_check)
            
            # Create DataFrame with flattened data
            df = pd.DataFrame(flattened_checks)
            
            # Define the columns to keep and their order
            columns_order = [
                'id', 'external_id', 'pdf_url', 'created_date', 'updated_date', 'date', 'memo',
                'amount', 'currency_code', 'amount_text', 'payer_name', 'payer_address',
                'receiver_name', 'receiver_address', 'bank_name', 'bank_address', 'check_number',
                'micr_routing_number', 'micr_account_number', 'micr_serial_number', 'micr_raw',
                'fractional_routing_number', 'routing_from_fractional', 'meta_external_id', 'meta_tags',
                'meta_page_count', 'meta_has_front_page', 'meta_duplicate_count', 'meta_amount_text_value',
                'is_signed', 'is_endorsed', 'endorsement_is_mobile_or_remote_deposit_only',
                'endorsement_is_signed', 'text', 'custom_fields'
            ]
            
            # Filter to only include specified columns that exist in the DataFrame
            available_columns = [col for col in columns_order if col in df.columns]
            df = df[available_columns]
            
            if not first_request_made:
                df.to_csv("checks.csv", mode='w', index=False)
                first_request_made = True
            else:
                df.to_csv("checks.csv", mode='a', header=False, index=False)
            page += 1  # move to the next document_id only if the request is successful
        except Exception as e:
            print(f"Failed to retrieve documents from: {e}")
            # Do not increment the index, try again on the next iteration

def main():
    base_url = "https://api.veryfi.com/api/v8/partner/checks/"
    start_date = '2025-00-00 00:00:00'
    end_date = '2025-00-00 00:00:00'
    fetch_data(base_url, start_date, end_date)

if __name__ == "__main__":
    main()