Exploratory Data Analysis

Discover patterns, anomalies, and relationships in tabular data through statistical analysis and visualization.

Supported formats: CSV, Excel (.xlsx, .xls), JSON, Parquet, TSV, Feather, HDF5, Pickle

Standard Workflow

•Run statistical analysis:

bash

python scripts/eda_analyzer.py <data_file> -o <output_dir>

•Generate visualizations:

bash

python scripts/visualizer.py <data_file> -o <output_dir>

•
Read analysis results from <output_dir>/eda_analysis.json
•
Create report using assets/report_template.md structure
•
Present findings with key insights and visualizations

Analysis Capabilities

Statistical Analysis

Run scripts/eda_analyzer.py to generate comprehensive analysis:

bash

python scripts/eda_analyzer.py sales_data.csv -o ./output

Produces output/eda_analysis.json containing:

•Dataset shape, types, memory usage
•Missing data patterns and percentages
•Summary statistics (numeric and categorical)
•Outlier detection (IQR and Z-score methods)
•Distribution analysis with normality tests
•Correlation matrices (Pearson and Spearman)
•Data quality metrics (completeness, duplicates)
•Automated insights

Visualizations

Run scripts/visualizer.py to generate plots:

bash

python scripts/visualizer.py sales_data.csv -o ./output

Creates high-resolution (300 DPI) PNG files in output/eda_visualizations/:

•Missing data heatmaps and bar charts
•Distribution plots (histograms with KDE)
•Box plots and violin plots for outliers
•Correlation heatmaps
•Scatter matrices for numeric relationships
•Categorical bar charts
•Time series plots (if datetime columns detected)

Automated Insights

Access generated insights from the "insights" key in the analysis JSON:

•Dataset size considerations
•Missing data warnings (when exceeding thresholds)
•Strong correlations for feature engineering
•High outlier rate flags
•Skewness requiring transformations
•Duplicate detection
•Categorical imbalance warnings

Reference Materials

Statistical Interpretation

See references/statistical_tests_guide.md for detailed guidance on:

•Normality tests (Shapiro-Wilk, Anderson-Darling, Kolmogorov-Smirnov)
•Distribution characteristics (skewness, kurtosis)
•Correlation methods (Pearson, Spearman)
•Outlier detection (IQR, Z-score)
•Hypothesis testing and data transformations

Use when interpreting statistical results or explaining findings.

Methodology

See references/eda_best_practices.md for comprehensive guidance on:

•6-step EDA process framework
•Univariate, bivariate, multivariate analysis approaches
•Visualization and statistical analysis guidelines
•Common pitfalls and domain-specific considerations
•Communication strategies for different audiences

Use when planning analysis or handling specific scenarios.

Report Template

Use assets/report_template.md to structure findings. Template includes:

•Executive summary
•Dataset overview
•Data quality assessment
•Univariate, bivariate, and multivariate analysis
•Outlier analysis
•Key insights and recommendations
•Limitations and appendices

Fill sections with analysis JSON results and embed visualizations using markdown image syntax.

Example: Complete Analysis

User request: "Explore this sales_data.csv file"

bash

# 1. Run analysis
python scripts/eda_analyzer.py sales_data.csv -o ./output

# 2. Generate visualizations
python scripts/visualizer.py sales_data.csv -o ./output

python

# 3. Read results
import json
with open('./output/eda_analysis.json') as f:
    results = json.load(f)

# 4. Build report from assets/report_template.md
# - Fill sections with results
# - Embed images: ![Missing Data](./output/eda_visualizations/missing_data.png)
# - Include insights from results['insights']
# - Add recommendations

Special Cases

Dataset Size Strategy

If < 100 rows: Note sample size limitations, use non-parametric methods

If 100-1M rows: Standard workflow applies

If > 1M rows: Sample first for quick exploration, note sample size in report, recommend distributed computing for full analysis

Data Characteristics

High-dimensional (>50 columns): Focus on key variables first, use correlation analysis to identify groups, consider PCA or feature selection. See references/eda_best_practices.md for guidance.

Time series: Datetime columns auto-detected, temporal visualizations generated automatically. Consider trends, seasonality, patterns.

Imbalanced: Categorical analysis flags imbalances automatically. Report distributions prominently, recommend stratified sampling if needed.

Output Guidelines

Format findings as markdown:

•Use headers, tables, and lists for structure
•Embed visualizations: ![Description](path/to/image.png)
•Include code blocks for suggested transformations
•Highlight key insights

Make reports actionable:

•Provide clear recommendations
•Flag data quality issues requiring attention
•Suggest next steps (modeling, feature engineering, further analysis)
•Tailor communication to user's technical level

Error Handling

Unsupported formats: Request conversion to supported format (CSV, Excel, JSON, Parquet)

Files too large: Recommend sampling or chunked processing

Corrupted data: Report specific errors, suggest cleaning steps, attempt partial analysis

Empty columns: Flag in data quality section, recommend removal or investigation

Resources

Scripts (handle all formats automatically):

•scripts/eda_analyzer.py - Statistical analysis engine
•scripts/visualizer.py - Visualization generator

References (load as needed):

•references/statistical_tests_guide.md - Test interpretation and methodology
•references/eda_best_practices.md - EDA process and best practices

Template:

•assets/report_template.md - Professional report structure

Key Points

•Run both scripts for complete analysis
•Structure reports using the template
•Provide actionable insights, not just statistics
•Use reference guides for detailed interpretations
•Document data quality issues and limitations
•Make clear recommendations for next steps