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name: data-storyteller description: Transform CSV/Excel data into narrative reports with auto-generated insights, visualizations, and PDF export. Auto-detects patterns and creates plain-English summaries.

Data Storyteller

Automatically transform raw data into compelling, insight-rich reports. Upload any CSV or Excel file and get back a complete analysis with visualizations, statistical summaries, and narrative explanations - all without writing code.

Core Workflow

1. Load and Analyze Data

from scripts.data_storyteller import DataStoryteller

# Initialize with your data file
storyteller = DataStoryteller("your_data.csv")

# Or from a pandas DataFrame
import pandas as pd
df = pd.read_csv("your_data.csv")
storyteller = DataStoryteller(df)

2. Generate Full Report

# Generate comprehensive report
report = storyteller.generate_report()

# Access components
print(report['summary'])           # Executive summary
print(report['insights'])          # Key findings
print(report['statistics'])        # Statistical analysis
print(report['visualizations'])    # Generated chart info

3. Export Options

# Export to PDF
storyteller.export_pdf("analysis_report.pdf")

# Export to HTML (interactive charts)
storyteller.export_html("analysis_report.html")

# Export charts only
storyteller.export_charts("charts/", format="png")

Quick Start Examples

Basic Analysis

from scripts.data_storyteller import DataStoryteller

# One-liner full analysis
DataStoryteller("sales_data.csv").generate_report().export_pdf("report.pdf")

Custom Analysis

storyteller = DataStoryteller("data.csv")

# Focus on specific columns
storyteller.analyze_columns(['revenue', 'customers', 'date'])

# Set analysis parameters
report = storyteller.generate_report(
    include_correlations=True,
    include_outliers=True,
    include_trends=True,
    time_column='date',
    chart_style='business'
)

Features

Auto-Detection

• Column Types: Numeric, categorical, datetime, text, boolean
• Data Quality: Missing values, duplicates, outliers
• Relationships: Correlations, dependencies, groupings
• Time Series: Trends, seasonality, anomalies

Generated Visualizations

Data Type	Charts Generated
Numeric	Histogram, box plot, trend line
Categorical	Bar chart, pie chart, frequency table
Time Series	Line chart, decomposition, forecast
Correlations	Heatmap, scatter matrix
Comparisons	Grouped bar, stacked area

Narrative Insights

The storyteller generates plain-English insights including:

• Executive summary of key findings
• Notable patterns and anomalies
• Statistical significance notes
• Actionable recommendations
• Data quality warnings

Output Sections

1. Executive Summary

High-level overview of the dataset and key findings in 2-3 paragraphs.

2. Data Profile

• Row/column counts
• Memory usage
• Missing value analysis
• Duplicate detection
• Data type distribution

3. Statistical Analysis

For each numeric column:

• Central tendency (mean, median, mode)
• Dispersion (std dev, IQR, range)
• Distribution shape (skewness, kurtosis)
• Outlier count

4. Categorical Analysis

For each categorical column:

• Unique values count
• Top/bottom categories
• Frequency distribution
• Category balance assessment

5. Correlation Analysis

• Correlation matrix with significance
• Strongest relationships highlighted
• Multicollinearity warnings

6. Time-Based Analysis

If datetime column detected:

• Trend direction and strength
• Seasonality patterns
• Year-over-year comparisons
• Growth rate calculations

7. Visualizations

Auto-generated charts saved to report:

• Distribution plots
• Trend charts
• Comparison charts
• Correlation heatmaps

8. Recommendations

Data-driven suggestions:

• Columns needing attention
• Potential data quality fixes
• Analysis suggestions
• Business implications

Chart Styles

# Available styles
styles = ['business', 'scientific', 'minimal', 'dark', 'colorful']

storyteller.generate_report(chart_style='business')

Configuration

storyteller = DataStoryteller(df)

# Configure analysis
storyteller.config.update({
    'max_categories': 20,       # Max categories to show
    'outlier_method': 'iqr',    # 'iqr', 'zscore', 'isolation'
    'correlation_threshold': 0.5,
    'significance_level': 0.05,
    'date_format': 'auto',      # Or specify like '%Y-%m-%d'
    'language': 'en',           # Narrative language
})

Supported File Formats

Format	Extension	Notes
CSV	.csv	Auto-detect delimiter
Excel	.xlsx, .xls	Multi-sheet support
JSON	.json	Records or columnar
Parquet	.parquet	For large datasets
TSV	.tsv	Tab-separated

Example Output

Sample Executive Summary

"This dataset contains 10,847 records across 15 columns, covering sales transactions from January 2023 to December 2024. Revenue shows a strong upward trend (+23% YoY) with clear seasonal peaks in Q4. The top 3 product categories account for 67% of total revenue. Notable finding: Customer acquisition cost has increased 15% while retention rate dropped 8%, suggesting potential profitability concerns worth investigating."

Sample Insight

"Strong correlation detected between marketing_spend and new_customers (r=0.78, p<0.001). However, this relationship weakens significantly after $50K monthly spend, suggesting diminishing returns beyond this threshold."

Best Practices

1. Clean data first: Remove obvious errors before analysis
2. Name columns clearly: Helps auto-detection and narratives
3. Include dates: Enables time-series analysis
4. Provide context: Tell the storyteller what the data represents

Limitations

• Maximum recommended: 1M rows, 100 columns
• Complex nested data may need flattening
• Images/binary data not supported
• PDF export requires reportlab package

Dependencies

pandas>=2.0.0
numpy>=1.24.0
matplotlib>=3.7.0
seaborn>=0.12.0
scipy>=1.10.0
reportlab>=4.0.0
openpyxl>=3.1.0