10 Steps to Clean and Preprocess Text Data in Pandas for NLP Tasks

Use Pandas to read the text data into a DataFrame

Replace missing values with appropriate values or remove rows with missing data.

Remove common words like "the," "and," "a" that don't add much meaning.

Break down the text into individual words or tokens.

Reduce words to their root form to handle variations like "run," "running," and "ran."

Remove punctuation marks that might interfere with the analysis.

Convert numbers to a specific format or remove them if not relevant.

Use tools like spellcheckers or grammar checkers to improve the data quality.

Expand contractions like "don't" to "do not" for better analysis.

Convert text to a consistent format, such as lowercase or uppercase.