Python Script for Find and Replace

Python Script for Find and Replace

Overview

This page demonstrates a Python script that automates the process of finding and replacing text in .txt files based on a spreadsheet. The script includes features such as error handling, logging, configuration, and backup.

Python Script for Find and Replace

# Import necessary libraries
import pandas as pd
import os
import logging
import shutil
import configparser
from pathlib import Path

# Setup logging to track the script's progress and issues
def setup_logging():
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(levelname)s - %(message)s',
        handlers=[
            logging.FileHandler("find_and_replace.log"),
            logging.StreamHandler()
        ]
    )

# Load configuration from a config file
def load_configuration(config_file='config.ini'):
    config = configparser.ConfigParser()
    config.read(config_file)
    return config

# Create a backup of the original file
def backup_file(file_path):
    backup_path = f"{file_path}.bak"
    shutil.copyfile(file_path, backup_path)
    logging.info(f"Backup created: {backup_path}")

# Perform find and replace operations on a file
def find_and_replace_in_file(file_path, find_replace_dict):
    try:
        backup_file(file_path)
        
        with open(file_path, 'r', encoding='utf-8') as file:
            file_contents = file.read()

        for find_text, replace_text in find_replace_dict.items():
            file_contents = file_contents.replace(find_text, replace_text)

        with open(file_path, 'w', encoding='utf-8') as file:
            file.write(file_contents)

        logging.info(f"Processed file: {file_path}")

    except Exception as e:
        logging.error(f"Error processing file {file_path}: {e}")

# Main function to execute the script
def main():
    setup_logging()

    config = load_configuration()
    spreadsheet_path = config['DEFAULT']['SpreadsheetPath']
    txt_files_directory = config['DEFAULT']['TxtFilesDirectory']

    try:
        df = pd.read_excel(spreadsheet_path)
        find_replace_dict = dict(zip(df['Find'], df['Replace']))

        processed_files = 0
        for filename in os.listdir(txt_files_directory):
            if filename.endswith('.txt'):
                file_path = os.path.join(txt_files_directory, filename)
                find_and_replace_in_file(file_path, find_replace_dict)
                processed_files += 1

        logging.info(f"Processing completed. Total files processed: {processed_files}")

    except FileNotFoundError:
        logging.error("Spreadsheet not found. Please check the path in the configuration file.")
    except Exception as e:
        logging.error(f"Unexpected error: {e}")

# Entry point of the script
if __name__ == "__main__":
    main()

Extensions and Improvements

  • Logging: Provides a detailed log of what the script is doing, which is crucial for professional environments.
  • Backup: Creates a backup of the .txt files before any modifications.
  • Configuration File (config.ini): Allows the user to specify file paths through a configuration file, enhancing flexibility.
[DEFAULT]
SpreadsheetPath = path/to/your/spreadsheet.xlsx
TxtFilesDirectory = path/to/your/txt/files

Considerations

  • Environment: Ensure all necessary libraries (like pandas and openpyxl for spreadsheet reading) are installed.
  • Testing: It’s important to test thoroughly with different spreadsheets and .txt files to ensure the script behaves as expected in all scenarios.

Comments

Post a Comment

Popular posts from this blog

CIFAR-10 Dataset Classification Using Convolutional Neural Networks (CNNs) With PyTorch

CIFAR-10 Image Classification with CNN (PyTorch) - Complete Guide CIFAR-10 Image Classification Using CNN with PyTorch This comprehensive guide demonstrates how to classify images from the CIFAR-10 dataset using Convolutional Neural Networks (CNNs) built with PyTorch. The project covers the entire machine learning pipeline from data loading to model deployment, including advanced techniques for improving performance. 🚀 Key Features Complete PyTorch implementation Data augmentation techniques Multiple CNN architectures Training visualization Model evaluation metrics Hyperparameter tuning Model saving/loading Deployment options 📊 Performance Baseline model: ~70% accuracy Improved mod...
Read more

Radial Basis Function Networks with PyTorch

Advanced Radial Basis Function Networks with PyTorch Advanced Radial Basis Function Networks (RBFNs) with PyTorch Introduction to RBF Networks Radial Basis Function Networks are a type of artificial neural network that uses radial basis functions as activation functions. They are particularly effective for pattern recognition and function approximation problems. Key Characteristics Three-layer architecture : Input layer, hidden RBF layer, and linear output layer Localized activation : Each neuron in the hidden layer responds only to inputs near its center Fast training : Often requires fewer iterations than multilayer perceptrons Universal approximation : Can approximate any continuous function given enough hidden units Mathematical Foundation : The RBF network implements a function of the form: f(x) = Σ w_i * φ(||x - c_i||) ...
Read more

Long-short-term-memory (LSTM) Word Prediction With PyTorch

LSTM Text Generation with PyTorch - Complete Guide LSTM Text Generation with PyTorch A comprehensive guide to building word-level language models with Long Short-Term Memory networks Introduction to LSTM Text Generation Long Short-Term Memory (LSTM) networks are a special kind of recurrent neural network (RNN) capable of learning long-term dependencies. They are particularly useful for sequence prediction problems like text generation, where the context from previous words is crucial for predicting the next word. Key Concepts Word-level modeling : Predicts the next word given previous words Embeddings : Dense vector representations of words ...
Read more