CSV Data Summary Skill

This skill helps you analyze CSV files and generate comprehensive summaries with statistical insights and visualizations. It automatically detects the type of data you're working with and adapts the analysis accordingly.

Use Cases

Quick data exploration and understanding
Identifying data quality issues (missing values, outliers)
Discovering patterns and correlations in datasets
Creating visual summaries for reports and presentations
Time-series analysis when date columns are present
Categorical data distribution analysis

Prerequisites

You'll need Python with the following libraries:

pip install pandas>=2.0.0 matplotlib>=3.7.0 seaborn>=0.12.0

When to Use This Skill

Use this skill whenever you need to:

Understand the structure and content of a CSV file
Get summary statistics for numeric columns
Identify missing data and data quality issues
Visualize distributions and correlations
Analyze time-series trends
Get a comprehensive overview of categorical variables

How It Works

The skill automatically:

Loads and inspects the CSV file
Identifies data structure - column types, date columns, numeric columns, categories
Adapts analysis based on data type:
- Sales/E-commerce data: Time-series trends, revenue analysis, product performance
- Customer data: Distribution analysis, segmentation, geographic patterns
- Financial data: Trend analysis, statistical summaries, correlations
- Operational data: Time-series, performance metrics, distributions
- Survey data: Frequency analysis, cross-tabulations, distributions
Generates visualizations relevant to the specific dataset:
- Time-series plots (if date/timestamp columns exist)
- Correlation heatmaps (if multiple numeric columns exist)
- Category distributions (if categorical columns exist)
- Histograms for numeric distributions
Provides comprehensive output including:
- Data overview (rows, columns, types)
- Key statistics and metrics
- Missing data analysis
- Multiple relevant visualizations
- Actionable insights

Python Implementation

Basic Usage

from analyze import summarize_csv

# Analyze any CSV file
summary = summarize_csv('your_data.csv')
print(summary)

The script will automatically generate:

A comprehensive text summary
Multiple visualization files (PNG format)

Example Output

============================================================
📊 DATA OVERVIEW
============================================================
Rows: 5,000 | Columns: 8

📋 DATA TYPES:
  • order_date: object
  • total_revenue: float64
  • customer_segment: object
  ...

🔍 DATA QUALITY:
✓ No missing values - dataset is complete!

📈 NUMERICAL ANALYSIS:
[Summary statistics for all numeric columns]

🔗 CORRELATIONS:
[Correlation matrix showing relationships]

📅 TIME SERIES ANALYSIS:
Date range: 2024-01-05 to 2024-04-11
Span: 97 days

📊 VISUALIZATIONS CREATED:
  ✓ correlation_heatmap.png
  ✓ time_series_analysis.png
  ✓ distributions.png
  ✓ categorical_distributions.png

Command Line Usage

You can run the analysis from the command line:

# Analyze a specific CSV file
python scripts/analyze.py path/to/your/data.csv

# Use the sample data
python scripts/analyze.py resources/sample.csv

Understanding the Output

Data Overview

Shows the dimensions of your dataset (rows × columns)
Lists all column names
Shows data type for each column

Data Quality

Reports missing values by column
Shows percentage of missing data
Helps identify data cleaning needs

Numerical Analysis

Provides descriptive statistics (mean, std, min, max, quartiles)
Shows correlations between numeric columns
Creates correlation heatmap visualization

Categorical Analysis

Shows frequency distribution for each categorical variable
Displays top 10 values per category
Creates bar charts for categorical distributions

Time Series Analysis

Automatically detected when date/time columns are present
Shows date range and span
Creates trend plots for numeric metrics over time
Calculates daily/periodic aggregations

Visualizations Generated

The skill automatically creates relevant visualizations:

Correlation Heatmap (correlation_heatmap.png)
- Shows relationships between numeric variables
- Color-coded for easy interpretation
- Only generated when 2+ numeric columns exist
Time Series Analysis (time_series_analysis.png)
- Trend lines for numeric metrics over time
- Only generated when date/time columns exist
- Shows up to 3 key metrics
Distributions (distributions.png)
- Histograms for numeric columns
- Shows up to 4 numeric variables
- Helps identify outliers and data shape
Categorical Distributions (categorical_distributions.png)
- Bar charts for categorical variables
- Shows top 10 values per category
- Up to 4 categorical variables

Tips and Best Practices

Clean column names: Use lowercase and underscores for better readability
Date formats: Ensure date columns contain 'date' or 'time' in the name
Numeric data: Ensure numeric columns are properly typed (not strings)
Large files: The skill handles large files efficiently with pandas
Missing data: Review the data quality section carefully before analysis

Troubleshooting

Issue: Date columns not detected

Ensure column names contain 'date' or 'time'
Check date format is recognizable (YYYY-MM-DD, MM/DD/YYYY, etc.)

Issue: Numeric columns treated as text

Check for non-numeric characters in the data
Clean data or use pandas type conversion

Issue: Too many visualizations

The script automatically limits visualizations to the most relevant ones
Focus on the first few metrics of each type

Issue: Import errors

Ensure all dependencies are installed: pip install -r requirements.txt
Check Python version (3.8+ recommended)

Advanced Usage

Customizing the Analysis

You can modify analyze.py to:

Add custom metrics specific to your domain
Change visualization styles and colors
Adjust the number of categories shown
Add domain-specific insights

Integration with Other Tools

The script outputs:

Plain text summary (easy to parse)
PNG images (ready for reports)
Can be extended to output JSON, HTML, or PDF reports

Additional Resources

Differences from Excel Sheet Reference Skill

This skill focuses on:

Data analysis and visualization (not Excel formula creation)
CSV file format (not Excel workbooks)
Statistical insights (not cross-sheet references)
Python pandas (not openpyxl)

Use the excel-sheet-reference skill when you need to:

Create Excel files with multiple sheets
Use cross-sheet formulas (VLOOKUP, COUNTIFS, etc.)
Maintain data in Excel format with formulas

csv-data-summary