What Python libraries are required for this skill?

The skill requires Python 3.8+ with pandas and numpy. For visualization features, matplotlib and seaborn are also recommended.

Does it support different file encodings?

Yes, the skill provides workflows for handling various encodings such as UTF-8 and Latin-1 to resolve common loading errors.

Can I use this to clean my data as well?

While focused on analysis, the skill includes patterns for filtering rows and transforming columns, which serve as the foundation for data cleaning.

Can this skill handle very large CSV files?

Yes, it includes specific troubleshooting patterns for 'chunking' large files to prevent memory exhaustion during analysis.

CSV Data Analyzer

Name: CSV Data Analyzer
Author: joelfuller2016

byjoelfuller2016

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数据科学与机器学习

Analyzes CSV datasets using Python pandas to extract statistical insights, detect patterns, and identify data quality issues.

关于

The CSV Data Analyzer skill equips Claude with a robust workflow for performing deep-dive exploratory data analysis (EDA) on tabular data. By leveraging Python's pandas and numpy libraries, it enables the automated generation of statistical summaries, correlation matrices, and data quality reports. This skill is particularly useful for developers and data scientists who need to quickly audit datasets, find outliers, or visualize trends directly within their terminal environment without manually writing boilerplate analysis code.

主要功能

Comprehensive statistical summaries including mean, median, and standard deviation
Visual report generation including histograms and correlation heatmaps
Correlation analysis to identify strong relationships between numerical variables
Categorical data grouping and aggregation for business intelligence insights
Automated data quality checks for missing values, duplicates, and type mismatches
1 GitHub stars

使用场景

Identifying statistical outliers and trends in business or scientific data
Validating and auditing CSV data quality before database ingestion
Generating rapid exploratory analysis reports for new datasets

关于

主要功能

Comprehensive statistical summaries including mean, median, and standard deviation
Visual report generation including histograms and correlation heatmaps
Correlation analysis to identify strong relationships between numerical variables
Categorical data grouping and aggregation for business intelligence insights
Automated data quality checks for missing values, duplicates, and type mismatches
1 GitHub stars

使用场景

Identifying statistical outliers and trends in business or scientific data
Validating and auditing CSV data quality before database ingestion
Generating rapid exploratory analysis reports for new datasets