uziswiftedax 0.1.0

One-line automated EDA with interactive visual reports

⚡ swift-eda

One line. Full picture.

swift is a zero-config Python EDA library that turns any pandas DataFrame into a beautiful, interactive HTML report — complete with smart profiling, auto-selected charts, correlation analysis, and data quality alerts.

import swift
swift.analyse(df)

That's it.

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Install

pip install swift-eda

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Usage

import swift

# Minimal — opens report in browser automatically
swift.analyse(df)

# Custom title
swift.analyse(df, title="Q3 Sales Data")

# Dark theme
swift.analyse(df, theme="dark")

# Save to a file path instead of a temp file
swift.analyse(df, export="reports/my_report.html")

# Don't auto-open browser
swift.analyse(df, open_browser=False)

# Skip columns (IDs, UUIDs, irrelevant fields)
swift.analyse(df, exclude=["id", "uuid", "row_number"])

# Tune categorical detection threshold
swift.analyse(df, max_cat_unique=30)

# Combine options
swift.analyse(
    df,
    title="Customer Churn Analysis",
    theme="dark",
    exclude=["customer_id"],
    export="churn_report.html",
)

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What you get

Every swift report contains four sections:

Overview

• Summary stat cards: rows, columns, missing values, duplicate rows
• Null heatmap — visualize where data is missing across rows and columns
• Null % bar chart — ranked by missingness

Columns

• Grouped by type: Numeric, Categorical, Datetime, Boolean, Text, Constant
• Per-column: dtype badge, key stat chips, and an auto-selected interactive chart
• Data quality alerts shown inline (high nulls, skew, outliers, imbalance, etc.)

Correlations

• Pearson correlation heatmap with annotation
• Spearman correlation matrix
• Top correlated pairs ranked by |r|
• Cramér's V for categorical associations
• Point-biserial for numeric × categorical pairs

Alerts

• Color-coded, icon-tagged, filterable by severity (critical / warning / info)
• Covers: high nulls, constant columns, skewed distributions, outliers, imbalanced classes, mixed types, possible ID columns, and more

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How it works

swift runs a 5-step pipeline under the hood:

1. Ingest — smart dtype inference beyond raw pandas dtypes (detects constants, high-cardinality text, low-cardinality integer categoricals, parseable datetimes)
2. Profile — computes full stats per column: mean/median/IQR/skewness for numerics, top values/entropy for categoricals, date range for datetimes
3. Correlate — Pearson & Spearman matrices, Cramér's V for cat pairs, point-biserial for mixed pairs
4. Visualize — auto-selects the right Plotly chart per dtype (histogram+box, horizontal bar, donut, time series, length distribution)
5. Report — assembles everything into a self-contained HTML file with a fixed sidebar, smooth scroll-spy, and full responsive layout — no external dependencies except Plotly CDN

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Comparison

Feature	df.describe()	ydata-profiling	sweetviz	swift
One-line API	✅	✅	✅	✅
Interactive charts	❌	partial	❌	✅
Auto chart selection	❌	❌	partial	✅
Correlation types	Pearson only	Pearson + Spearman	Pearson	Pearson + Spearman + Cramér's V + point-biserial
Alert system	❌	✅	❌	✅
Dark theme	❌	❌	❌	✅
Self-contained HTML	❌	✅	✅	✅
Report size	—	Large (often 5–50 MB)	Medium	Small
Speed (1k rows)	Instant	Slow	Medium	Fast

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Contributing

Contributions are welcome! Please open an issue first to discuss what you'd like to change.

# Clone and install in dev mode
git clone https://github.com/yourusername/swift-eda
cd swift-eda
pip install -e ".[dev]"

# Run tests
pytest

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License

See LICENSE for details.