Developed text processing and data manipulation functions

📅 2023-11-11 — Session: Developed text processing and data manipulation functions

🕒 03:00–04:40
🏷️ Labels: Python, Text Processing, Data Manipulation, NLP, Automation
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to develop and refine functions for text processing and data manipulation using Python, focusing on cleaning, formatting, and analyzing text data.

Key Activities

• Implemented a Python function to merge invalid text sections into valid ones, enhancing data integrity.
• Utilized Python’s random.sample for random sampling from tuples, addressing common errors with np.random.choice.
• Outlined a structured approach for semantic analysis, covering objectives, data preparation, and analysis techniques.
• Developed Python rules for fixing parsing errors using str.replace() and regular expressions.
• Created functions for text cleaning and standardization, focusing on punctuation, spacing, and spelling corrections.
• Applied cleaning functions to merged sections and regenerated them from original data after a disconnection.
• Converted cleaned text data into a Pandas DataFrame for further analysis.
• Counted word frequencies in Spanish text using NLTK, excluding stopwords.

Achievements

• Successfully developed and tested multiple text processing functions, improving data quality and consistency.
• Enhanced data manipulation capabilities with Pandas, facilitating structured data analysis.
• Established a framework for semantic analysis, setting the stage for future NLP tasks.

Pending Tasks

• Re-upload the original text file or raw text to regenerate lost merged_sections due to a disconnection.
• Further refine text cleaning functions to handle more complex inconsistencies.
• Explore additional NLP techniques for deeper semantic analysis.