Skyulf Profiling vs. YData Profiling vs. Sweetviz
Choosing the right EDA (Exploratory Data Analysis) tool matters. This guide offers a practical, honest comparison between Skyulf's profiling engine and two popular alternatives: YData Profiling (formerly pandas-profiling) and Sweetviz.
TL;DR
| Feature | Skyulf | YData Profiling | Sweetviz |
|---|---|---|---|
| Backend | Polars (Rust) | Pandas (optional Spark support) | Pandas |
| Outputs | JSON profile object; optional terminal/plots | HTML report; JSON export; notebook widgets | HTML report; notebook embedding |
| Scales to larger data | Often better (Polars-based) | Can scale via Spark; Pandas mode can be heavy | Can be heavy (Pandas-based) |
| Target-Aware Analysis | Yes | Yes | Yes |
| Dataset comparison report | No | Yes | Yes |
| Time series analysis | Yes | Yes | Not advertised |
| Causal discovery (PC algorithm) | Yes | Not advertised | Not advertised |
| Rule extraction (surrogate decision tree) | Yes | Not advertised | Not advertised |
| Model-based outlier detection (Isolation Forest) | Yes | Not advertised | Not advertised |
| PCA projection | Yes | Not advertised | Not advertised |
| Geospatial analysis (lat/lon detection) | Yes | Not advertised | Not advertised |
| Subset profiling via filters | Yes | No | No |
| Normality / stationarity tests | Yes | No | No |
| ANOVA p-values (target interactions) | Yes (optional SciPy) | Not advertised | Not advertised |
| Feature importance (from surrogate tree) | Yes (scikit-learn) | Not advertised | Not advertised |
| Recommendations (drop/impute/encode hints) | Yes | Not advertised | Not advertised |
| PII heuristics (email/phone) | Yes | Not advertised | Not advertised |
| Leakage warnings (high corr to target) | Yes | Not advertised | Not advertised |
| Correlation Matrix | Yes | Yes | Yes |
| Missing Value Analysis | Yes | Yes | Yes |
| Duplicate Detection | Yes | Yes | Yes |
The Honest Truth
Where Skyulf Excels
1. Performance on Large Datasets
Skyulf is built on Polars, a DataFrame library written in Rust. This makes a real difference when you're working with datasets over 100K rows. YData Profiling and Sweetviz both rely on Pandas, which can become painfully slow and memory-hungry on larger datasets.
If you regularly work with datasets that push RAM limits, Polars-based workflows tend to be more resilient than Pandas-based ones. (Exact timing depends heavily on data types, cardinality, and what options you enable.)
2. ML-Focused Analysis
Skyulf was designed with machine learning workflows in mind, not just descriptive statistics. This means:
-
Causal Discovery: Using the PC algorithm from
causal-learn, Skyulf can infer potential causal relationships between variables. This helps you understand not just correlations, but which features might actually drive your target. Neither YData nor Sweetviz offers this. -
Rule Extraction: Skyulf trains a surrogate Decision Tree on your data and extracts human-readable rules like "If Age > 50 AND Income < 30k → High Risk". This is invaluable for fraud detection, churn analysis, or any use case where you need to explain segments to stakeholders.
-
Feature Importance (from the surrogate tree): Alongside rules, Skyulf exposes feature importances from the surrogate Decision Tree. This is not a replacement for model explainability, but it’s a fast way to see which columns dominate the tree’s decisions.
-
Outlier Detection: Built-in Isolation Forest identifies anomalous rows and explains why they're outliers (which features deviate most from the median). YData shows distribution plots but doesn't flag specific outlier rows.
-
PCA Projection: Skyulf computes 2D/3D PCA projections colored by target class, helping you visually assess class separability before training a model.
-
Target Interactions with ANOVA (p-values): For categorical targets, Skyulf can compute ANOVA p-values for numeric features (when SciPy is available) and rank associations accordingly. This helps you quickly find features that differ meaningfully across target classes.
3. Specialized Domain Analysis
-
Geospatial: If your data contains latitude/longitude columns, Skyulf automatically detects them and provides bounding box statistics plus sample points for map visualization.
-
Time Series: Skyulf detects datetime columns and analyzes trends, seasonality (day-of-week, month-of-year patterns), and stationarity. This context is critical before building forecasting models.
4. API-First Design
Skyulf returns structured JSON (Pydantic models) rather than HTML. This makes it easy to: - Integrate profiling into automated pipelines - Build custom dashboards - Store profiles in databases for tracking data drift over time - Apply dynamic filters and re-analyze subsets
If you want an HTML artifact you can email around, Skyulf is not trying to replace YData/Sweetviz today. Skyulf focuses on programmatic profiling that you can embed into products.
When to Use What
| Scenario | Recommended Tool |
|---|---|
| Large dataset (500K+ rows) | Skyulf |
| Need causal inference or rule extraction | Skyulf |
| Building ML pipelines (need JSON output) | Skyulf |
| Geospatial or time series data | Skyulf |
| Sharing HTML reports with business users | YData Profiling |
| Quick one-off HTML EDA on small datasets | Sweetviz or YData Profiling |
| Comparing train/test splits visually | Sweetviz |
| Spark/distributed environment | YData Profiling |
Quick Start with Skyulf
import polars as pl
from skyulf.profiling.analyzer import EDAAnalyzer
from skyulf.profiling.visualizer import EDAVisualizer
# 1. Load Data
df = pl.read_csv("your_dataset.csv")
# 2. Run Analysis
analyzer = EDAAnalyzer(df)
profile = analyzer.analyze(target_col="target")
# 3. Visualize Results (The Easy Way)
# This single class handles all the rich terminal output and matplotlib plots
viz = EDAVisualizer(profile, df)
# Print the dashboard
viz.summary()
# Show the plots
viz.plot()
Conclusion
There's no universally "best" profiling tool. Choose based on your needs:
- If you want a polished HTML report: YData Profiling or Sweetviz.
- If you want ML-oriented signals (rules, outliers, causal hypotheses) and an API-first profile object: Skyulf.