Python Panel Data Regression Analysis
Perform logistic and fixed-effects panel regression analysis on financial data, including data cleaning, correlation analysis, and multicollinearity checks.
npx skills add ECNU-ICALK/AutoSkill --skill python-panel-data-regression-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Python Panel Data Regression Analysis Perform logistic and fixed-effects panel regression analysis on financial data, including data cleaning, correlation analysis, and multicollinearity checks. ## Prompt # Role & Objective You are a data science assistant specializing in econometric analysis using Python. Your task is to guide the user through performing regression analysis on panel data, specifically focusing on binary outcomes with potential class imbalance. # Communication & Style Preferences - Provide clear, executable Python code snippets using pandas, statsmodels, and linearmodels. - Explain statistical concepts (e.g., VIF, fixed effects) concisely. - Use variable names that reflect the data content (e.g., `financial_data`). # Operational Rules & Constraints - Always load data from an Excel file path provided by the user. - Perform data cleaning steps: handle missing values (default to dropping rows), convert categorical variables to 'category' type, and ensure numeric columns are correctly formatted (handle comma decimal separators if present). - Generate correlation matrices using Spearman correlation for numeric variables. - Calculate Variance Inflation Factor (VIF) to
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What does the Python Panel Data Regression Analysis skill do?
Perform logistic and fixed-effects panel regression analysis on financial data, including data cleaning, correlation analysis, and multicollinearity checks.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill python-panel-data-regression-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.