Failed to execute 'json' on 'Response': Unexpected token 'I', "Internal s"... is not valid JSON Great question! Converting a PDF bank statement into Excel (or CSV) involves several steps because PDFs can vary widely in structure and formatting. Here’s a clear, high-level idea of how to approach PDF to Excel conversion, especially for bank statements:

---

1. Understand the PDF Format

• Text-based PDFs: These contain selectable text and tables embedded as text.
• Scanned PDFs (images): These contain images of pages, requiring OCR (Optical Character Recognition) to extract text.

Bank statements often have tabular transaction data, but layouts vary by bank.

---

2. Extraction Process Overview

Step 1: Load PDF and Extract Tables

• Use Python libraries specialized for PDF table extraction, such as:

  • pdfplumber: Great for extracting tables from text PDFs.
  • Camelot: Can extract tables from PDFs by analyzing the layout.
  • PyMuPDF (fitz): Useful for text extraction and page navigation.

Example with pdfplumber:

import pdfplumber

with pdfplumber.open("statement.pdf") as pdf:
    all_tables = []
    for page in pdf.pages:
        tables = page.extract_tables()
        all_tables.extend(tables)

---

Step 2: Clean and Normalize Data

• Convert extracted tables (lists of lists) into a structured format, e.g., Pandas DataFrame.
• Identify relevant columns: Date, Description, Amount, Balance.
• Normalize dates into consistent formats (e.g., YYYY-MM-DD).
• Clean up text fields (remove extra whitespace, line breaks).
• Handle multi-line descriptions by merging rows if needed.

---

Step 3: Handle Scanned PDFs (if applicable)

• Use OCR tools like Tesseract (via pytesseract) to convert images into text.
• Apply image preprocessing (grayscale, thresholding) to improve OCR accuracy.
• Post-process OCR text to extract tabular data using regex or heuristic rules.

---

Step 4: Export to Excel or CSV

• Use Pandas to export DataFrame to Excel or CSV:

import pandas as pd

# Assuming df is your cleaned DataFrame
df.to_excel("output.xlsx", index=False)  # Excel output
df.to_csv("output.csv", index=False)     # CSV output

---

Tips for Better Conversion

• Template-based parsing: If bank statements have consistent formats, write custom parsers for each template.
• Heuristics and rules: Use regex patterns to detect dates, amounts, and descriptions.
• Error handling: Detect and log rows that can’t be parsed correctly.
• Testing: Use a variety of bank statement samples to refine parsing logic.

---

Summary Flow

PDF file → Open PDF → Extract tables/text → Clean & normalize data → Convert to DataFrame → Export to Excel/CSV

---

If you want, I can help you write a sample Python script that uses pdfplumber to extract tables from a bank statement PDF and export them to Excel. Would you like me to do that?

I have bank statement PDFs that contain transaction tables with columns like Date, Description, Amount, and Balance. I want to extract this tabular data accurately and convert it into a clean Excel (.xlsx) or CSV file.

Please generate a Python script that:

• Uses pdfplumber (or similar libraries) to open and extract tables from PDF bank statements.
• Handles multi-page PDFs.
• Cleans and normalizes extracted data (e.g., trims whitespace, normalizes dates to YYYY-MM-DD, converts amounts to numbers).
• Deals with common PDF table extraction issues, like merged cells or multi-line text.
• Outputs the final clean data to Excel (.xlsx) using pandas.
• Optionally includes error handling and logging.

If the PDF is scanned (image-based), please suggest how to integrate OCR (e.g., pytesseract) to extract text before parsing.

Please write clean, well-commented code that I can run locally.

Thank you!