python_data_analyst_skill

Overview

This skill provides ready-to-run Python scripts for data cleaning, analysis, and visualization using Pandas and Matplotlib/Seaborn. It emphasizes vectorized operations, type hints, and reproducible plotting with Chinese font support when needed. Scripts are written for performance and clarity, with pragmatic comments and minimal looping.

How this skill works

The skill inspects tabular inputs (CSV, Excel, DataFrame-like structures) and produces cleaned DataFrames, aggregated summaries, and publication-ready plots. All functions include type hints and rely on Pandas vectorized APIs; plotting routines configure Matplotlib/Seaborn and font settings to render Chinese labels correctly. The code returns DataFrames, Series, or Matplotlib figures so results can be further processed or embedded.

When to use it

• Cleaning messy CSV or Excel exports with missing values, inconsistent types, or duplicate rows.
• Aggregating time series, grouping by categories, or computing cohort metrics efficiently.
• Building reproducible visualizations that require Chinese label rendering and publication-ready styling.
• Converting exploratory logic into fast, vectorized scripts ready for production pipelines.
• Embedding data validation and type-hinted functions into larger Python projects.

Best practices

• Prefer Pandas built-ins (groupby, transform, merge, vectorized ops) and avoid explicit Python loops.
• Annotate function signatures with type hints for DataFrame/Series return types for clarity and tooling.
• Configure Matplotlib/Seaborn fonts to support Chinese early in the script to avoid rendering issues.
• Write concise in-line comments explaining non-obvious transformations; keep functions single-responsibility.
• Use reproducible plotting defaults (figure size, DPI, color palette) and return figures for testing.

Example use cases

• Load a monthly sales CSV, convert date columns, aggregate by month, and plot a monthly trend line.
• Clean customer records: deduplicate, normalize text fields, impute missing values, and export a validated CSV.
• Compute product-level KPIs (revenue, conversion rate) with vectorized calculations and groupby aggregations.
• Create a bilingual dashboard plot with Chinese labels and English annotations for reports.
• Build a preprocessing function with type hints to prepare datasets for machine learning pipelines.