EDA in Public (Part 1): Cleaning and Exploring Sales Data with Pandas

A tutorial demonstrates how to perform exploratory data analysis (EDA) on a raw e-commerce sales dataset using the Pandas library in Python. The process begins with loading the data, taking a random sample for manageability, and conducting an initial inspection to identify issues like missing values, incorrect data types, and outliers. Key data cleaning steps include handling missing descriptions, converting the invoice date column to a proper datetime format, and removing duplicate entries. The analysis also involves filtering out invalid transactions, such as returns indicated by negative quantities and data errors represented by zero unit prices, to prepare the data for revenue calculation.