Understanding the Limitations of Converting PDF to CSV with Tabula-py in Python

Understanding the Issue with Converting PDF to CSV using Tabula-py in Python

In this article, we will delve into the process of converting a PDF file to a CSV format using the Tabula-py library in Python. We’ll explore the reasons behind the issue where column names are not being retrieved from the PDF file and provide step-by-step solutions to achieve the desired output.

Introduction to Tabula-py

Tabula-py is a powerful library that uses OCR (Optical Character Recognition) technology to extract data from scanned documents, including PDF files. It provides an easy-to-use interface for converting PDF files into CSV, Excel, or JSON formats.

The main advantage of using Tabula-py is its ability to automatically detect the column headers in the PDF file and map them to corresponding columns in the output CSV file.

The Code

Let’s take a look at the provided Python code snippet that attempts to convert a PDF file into a CSV format:

#!/usr/bin/env python3
import tabula
import pandas as pd
import csv

pdf_file='document-page1.pdf'
column_names=['Product','Batch No','Machin No','Time','Date','Drum/Bag No','Tare Wt.kg','Gross Wt.kg',
              'Net Wt.kg','Blender','Remarks','Operator']

# Page 1 processing
df1 = tabula.read_pdf(pdf_file, pages=1,area=(95,20, 800, 840),columns=[93,180,220,252,310,315,333,367,
                                                                      410,450,480,520]
                     ,pandas_options={'header': None}) #(top,left,bottom,right)

df1[0]=df1[0].drop(columns=5)
df1[0].columns=column_names
#df1[0].head(2)

df1[0].to_csv('result.csv')

In this code snippet, we first import the necessary libraries: tabula for PDF-to-CVS conversion and pandas for data manipulation. We then specify the input PDF file path, column names, and page numbers.

We use the read_pdf() function to extract data from the specified pages of the PDF file, specifying the area where the data is located using the area() parameter. The columns() parameter specifies which columns in the output CSV file correspond to which columns in the input PDF file.

Finally, we drop the unwanted column (index 5) and rename the remaining columns using the column_names list. We then save the resulting DataFrame to a CSV file named “result.csv”.

Understanding the Issue

The issue with this code snippet is that it does not correctly map the column headers from the PDF file to the corresponding columns in the output CSV file.

By default, Tabula-py attempts to identify the column headers by looking for consecutive columns with similar data types (e.g., numbers or text). However, in this case, we are providing our own column_names list, which may not match the actual column headers in the PDF file.

This discrepancy can be resolved by using the pandas_options={'header': None} parameter when calling the read_pdf() function. This tells Tabula-py to ignore the default column headers and instead use the provided column_names list as the column names for the output CSV file.

However, even with this adjustment, the code snippet still fails to correctly retrieve the data from the PDF file. To resolve this issue, we need to provide more accurate information about the layout of the PDF file.

Solution

To accurately extract data from the PDF file and create a correct CSV file, we can use the following steps:

1. Extract Page Information

Before attempting to extract data from the PDF file, it’s essential to understand the layout and structure of the page. This includes identifying the location of the column headers, data fields, and any other relevant features.

To achieve this, you can use a tool like Adobe Acrobat Reader or another PDF viewer with OCR capabilities to inspect the PDF file.

2. Determine Column Headers

Once we have a clear understanding of the layout and structure of the page, we can identify the column headers using our knowledge of the PDF file’s metadata.

In the case of this example, we assume that the column headers are located at specific columns (93, 180, 220, etc.) in the PDF file. However, without access to the actual PDF file or more information about its layout, we cannot accurately determine the column headers.

3. Adjust Column Mapping

If we have accurate information about the column headers and their corresponding values, we can adjust the columns parameter when calling the read_pdf() function accordingly.

For example:

df1 = tabula.read_pdf(pdf_file, pages=1,area=(95,20, 800, 840),columns=[93,180,220,252,310,315,333,367,
                                                                      410,450,480,520],
                     pandas_options={'header': None, 'merge_headers': False}) #(top,left,bottom,right)

In this adjusted code snippet, we’ve added the merge_headers parameter to prevent Tabula-py from merging adjacent columns and instead treated them as separate column headers.

By making these adjustments and providing more accurate information about the layout of the PDF file, we can improve our chances of successfully extracting data from the PDF file and creating a correct CSV file.

Last modified on 2024-12-05