data-analysis

To analyze tabular data from CSV and Excel files, this Skill uses pandas to perform an iterative, multi-turn data probing workflow, ensuring you explore the dataset thoroughly before generating step-by-step Python code for aggregation and visualization.

Yes, you can generate data visualizations directly from an Excel file. This Skill uses matplotlib to create various chart types like bar, line, and scatter plots with proper labeling, while pandas handles the underlying data manipulation and cleaning.

The best way to clean and aggregate raw JSON or TSV data is through a structured, step-by-step approach. This Skill enforces a human-like analyst workflow, preventing large code blocks at once to ensure missing values and type conversions are handled accurately.

Yes, this data analysis approach strictly supports iterative coding for step-by-step exploration. It enforces rules against skipping data exploration and writing large code blocks at once, ensuring all conclusions are evidence-based through multi-turn data probing.

You should not skip data exploration because this Skill enforces a human-like data analyst workflow that requires multi-turn data probing before coding. Skipping exploration leads to uninformed scripts, whereas this method ensures high-quality, evidence-based outputs.