Extracting tables from unstructured sources, such as images and PDFs, can be a hard task without the right tools. Manual extraction is time-consuming, error-prone, and delays productivity. Fortunately, Python combined with the PDF.co Web API, offers an efficient and reliable solution to automate this process.
In this tutorial, we will explore the process of easily extracting tables from various sources, including images and PDF documents. By the end of this tutorial, you will gain knowledge and learn to automate the extraction of valuable tabular data and simplify your workflow.
Convert Image to PDF in Python
- Install Request Module
- Open Python Editor
- Setup Python Code
- Run Program Result
- Image to PDF Output
- Demo
Extract Table from PDF in Python (Table Auto Detector)
We will use a sample image document as our starting point. Our goal is to convert this image into a PDF format and extract the table containing valuable data.

Convert Image to PDF in Python Workflow
Step 1: Install Request Module
- To begin, let’s install the requests module, which is used for managing new packages in Python. Open your command line interface and
type the following command:python -m pip install requests
. Then, press the Enter key to execute the command and install the requests module.
Step 2: Open Python Editor
- After installing the request module, open your Python code editor, such as Visual Studio Code or any other Python code editor of your choice.
- Next, input the Python code for Image to PDF conversion. You can access the sample Python code at this link.
Step 3: Setup Python Code
Let’s set up the Python code for image to PDF conversion.
- First, locate line 6 in your code and add your API Key. You can obtain the API Key by signing up at this link or accessing your PDF.co dashboard.
- Next, move to line 12 in your code and enter the name of your image file. Make sure the image file is present in the same directory as your Python script.
- Then, proceed to line 14 and input the desired name for your generated PDF file.
After configuring the code and applying any required modifications, save the file. Next, simply click on the Run button to execute the program.
Step 4: Run Program Result
- Once the program has been executed successfully, it will generate a converted PDF file. To view the output, navigate to your Python program folder and locate the file.
Step 5: Image to PDF Output
- Here’s the converted PDF document from Image file.

Step 6: Demo
- Kindly check out this Image to PDF Conversion in Python demo.

Now that we have successfully converted the image file to PDF format, we can proceed to extract the table from the converted PDF document.
Extract Table from PDF in Python (Table Auto Detector)
Step 1: Python Code Editor
- Now that we have already installed the request module in the initial workflow, we can proceed directly to inputting the Python code for table extraction from the PDF in the Visual Studio Code editor. You can access the Python sample code for this process by following this link.
Step 2: Setup Python Code
Let’s configure the Python code for table extraction from the PDF document.
- In line 6, insert your PDF.co API Key.
- In line 9, provide the direct URL of the generated PDF file. You can utilize the PDF.co File Storage to store your file and convert it into a URL specifically for PDF.co processes.
Once you have set up the code and made any necessary adjustments, proceed to save the file. Then, simply click on the Run button to execute the program.
Step 3: Run Program Result
- After successfully executing the program, it will extract the table from the converted PDF file. To view the output, go to the folder where your Python program is located and click on the JSON files.
Step 4: Demo
- Here’s the Table Extraction from the PDF document in Python demo.

In this tutorial, you gained knowledge on extracting tables from images using Python and PDF.co Web API. Additionally, you learned the process of converting images to PDFs in Python. You also discovered how to extract tables from PDF documents using Python and PDF.co Web API.