Exploratory data analysis (EDA) uses statistics, domain knowledge, and visualizations to understand data characteristics, trends, and outliers. Like tasting ingredients while cooking, it helps shape the analysis before relying on its results. section 2.4.2
A useful first pass asks:
- How are variables distributed? Check characteristics such as mean, standard deviation, and range. section 2.4.2
- What patterns appear in visualizations? section 2.4.2
- Do the variables mean what you think they mean? section 2.4.2
- Are units consistent and headings standardized? section 2.4.2
EDA is not a one-time task or separate from deeper business analysis. Return to it as questions emerge, and investigate unusual patterns in their business context. section 2.6.2
Exploring order reviews
Inspect review-score distributions and missing values, then chart monthly review counts or compare score proportions over time. A change may reflect business operations or review collection, so check the context before drawing conclusions. section 2.6 section 2.6.2
DataFrame name: df_order_reviews Columns: - review_id (object): Unique identifier of each review. - order_id (object): Identifier of the order being reviewed. - review_score (int64): Note ranging from 1 to 5 given by the customer on a satisfaction survey. - review_comment_title (object): Comment title from the review left by the customer in Portuguese. - review_comment_message (object): Comment message from the review left by the customer in Portuguese. - review_creation_date (datetime64[ns]): The timestamp of the review creation. - review_answer_timestamp (datetime64[ns]): I am not sure what this one actually is.
Use the order-review schema to ground checks in the actual fields, including 1–5 scores, Portuguese comments, dates, and an uncertain response timestamp. section 2.6
