To get the number of pages from a PDF file based on a keyword, you can use the PyPDF2 library in Python. Here's an example of how to do this:

with open('example.pdf', 'rb') as pdf_file:

    # Create a PdfFileReader object to read the file

    pdf_reader = PyPDF2.PdfFileReader(pdf_file)

    # Get the total number of pages in the PDF file

    total_pages = pdf_reader.getNumPages()

    # Initialize a counter for the number of pages containing the keyword

    # Loop through each page in the PDF file

        # Get the text content of the page

        page = pdf_reader.getPage(page_num)

        # Check if the keyword is present in the page text

    # Print the total number of pages and the number of pages containing the keyword

    print(f'Keyword pages: {keyword_pages}')

In this example, we first open the PDF file in binary mode and create a PdfFileReader object to read the file. We then get the total number of pages in the PDF file using the getNumPages() method.

Next, we loop through each page in the PDF file using a for loop. For each page, we extract the text content using the extractText() method, and check if the keyword is present in the page text using the in operator. If the keyword is present, we increment a counter for the number of pages containing the keyword.

Finally, we print the total number of pages and the number of pages containing the keyword.

Note that this method may not be very accurate, as the keyword may appear multiple times on a single page, or may be split across multiple pages. Additionally, the extractText() method may not work well for PDF files with complex layouts or non-standard fonts. However, it should work well for simple PDF files with standard fonts.

Today we will know how to get the number of pages from a pdf file based on a keyword.

How to get the number of pages from a pdf file based on a keyword?

Python decorators are a powerful feature that allows modifying the behavior of functions or classes. While decorators are typically static, there are cases when it's necessary to edit their parameters dynamically during runtime. In this blog post, we will explore techniques to dynamically edit decorator parameters in Python, enabling flexible runtime customization.

Python decorators are functions that modify the functionality of other functions or classes. They are applied using the "@" symbol followed by the decorator name above the function or class definition. Decorators provide a way to add additional behavior, modify input or output, or implement cross-cutting concerns without modifying the original code.

In certain scenarios, the decorator's behavior may need to be modified dynamically during runtime. For example, when dealing with different environments, configurations, or runtime conditions, being able to change the decorator's parameters allows for adaptable and customizable behavior.

Techniques for Dynamic Parameter Editing:

Using a Higher-Order Decorator: Create a higher-order decorator function that accepts additional parameters and returns the original decorator function. This allows modifying the parameters before applying the decorator to the target function or class. By dynamically setting the parameters before applying the decorator, you can achieve runtime customization.

Using Function Attributes: Decorators can access and modify function attributes. You can add custom attributes to the target function and use them to store dynamic parameters. Within the decorator, retrieve the function attributes and use them to influence the decorator's behavior at runtime.

Using Global Variables or Configurations: Define global variables or configuration settings that can be modified during runtime. These variables can be accessed within the decorator to customize its behavior. By modifying the global variables before calling the decorated function, you can dynamically adjust the decorator's parameters.

Implementing Decorator Factories: Decorator factories are functions that generate decorators dynamically based on provided arguments. By creating a decorator factory, you can pass parameters during runtime and generate a new decorator with customized behavior.

Leveraging Function Wrappers: A function wrapper is a technique where a decorator is applied to a function that wraps the original function. Within the wrapper, you have access to both the decorator parameters and the original function's arguments. This allows for dynamic parameter editing by modifying the wrapper's behavior based on runtime conditions.

Ensure Clarity and Readability: Dynamic parameter editing in decorators can make the code more complex. Use clear variable names and comments to enhance readability and maintainability.

Carefully Manage Side Effects: Be mindful of the potential side effects of dynamically editing decorator parameters. Consider the impact on other parts of the codebase and ensure that the modifications do not introduce unexpected behaviors or conflicts.

Use Dynamic Editing Sparingly: While dynamic parameter editing can be powerful, it should be used judiciously. Excessive runtime modifications can make code harder to understand and debug. Only apply dynamic parameter editing when truly necessary.

Being able to edit decorator parameters dynamically during runtime empowers Python developers to customize behavior based on changing conditions. By employing techniques like higher-order decorators, function attributes, and decorator factories, developers can achieve flexible and adaptable code structures.

In this blog, we'll discuss dynamic parameter editing in Python decorators, allowing runtime customization for flexible behavior modification of functions or classes.

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