The article serves as a comprehensive guide for performing exploratory data analysis (EDA) on text data, specifically within the realm of Natural Language Processing (NLP), using various Python tools. It explores different techniques and libraries for understanding and visualizing text data, using a dataset of news headlines to demonstrate these methods. Key techniques discussed include text statistics analysis, such as word and sentence length frequency, stopword analysis, n-gram exploration, and topic modeling using Latent Dirichlet Allocation (LDA) with pyLDAvis for visualization. The guide also delves into sentiment analysis using TextBlob and VADER, named entity recognition (NER) with spaCy, parts of speech tagging, and exploring text complexity using readability indices like the Flesch Reading Ease. Throughout the article, practical code snippets are provided to help readers implement these analyses and visualize the results, offering a rounded toolkit for NLP data exploration.