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

1. 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

Clean data first

Remove obvious errors before analysis

Name columns clearly

Helps auto-detection and narratives

Include dates

Enables time-series analysis

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