# Ydata Profiling 6 Comparison Reports

> Sub-skill of ydata-profiling: 6. Comparison Reports.

- Skill: `vamseeachanta/ydata-profiling-6-comparison-reports` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vamseeachanta/ydata-profiling-6-comparison-reports`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/ydata-profiling-6-comparison-reports/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: vamseeachanta (https://skillmd.com/u/vamseeachanta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vamseeachanta/ydata-profiling-6-comparison-reports

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# 6. Comparison Reports

## 6. Comparison Reports


**Comparing Two Datasets:**
```python
from ydata_profiling import ProfileReport, compare
import pandas as pd
import numpy as np

# Create two related datasets
np.random.seed(42)

# Training data
df_train = pd.DataFrame({
    "feature_1": np.random.randn(5000),
    "feature_2": np.random.exponential(100, 5000),
    "feature_3": np.random.choice(["A", "B", "C"], 5000),
    "target": np.random.randint(0, 2, 5000)
})

# Test data (slightly different distribution)
df_test = pd.DataFrame({
    "feature_1": np.random.randn(2000) * 1.2,  # Different variance
    "feature_2": np.random.exponential(120, 2000),  # Different mean
    "feature_3": np.random.choice(["A", "B", "C", "D"], 2000),  # New category
    "target": np.random.randint(0, 2, 2000)
})

# Generate individual profiles
profile_train = ProfileReport(df_train, title="Training Data")
profile_test = ProfileReport(df_test, title="Test Data")

# Compare profiles
comparison = compare([profile_train, profile_test])

# Save comparison report
comparison.to_file("train_test_comparison.html")
```

**Before/After Comparison:**
```python
from ydata_profiling import ProfileReport, compare
import pandas as pd
import numpy as np

# Original data
df_before = pd.DataFrame({
    "value": np.concatenate([
        np.random.randn(900),
        np.array([100, -50, 200, 150, -100])  # Outliers
    ]),
    "category": np.random.choice(["A", "B", "C"], 905),
    "score": np.random.uniform(0, 100, 905)
})

# Add missing values
df_before.loc[np.random.choice(905, 50), "value"] = np.nan

# Cleaned data (after preprocessing)
df_after = df_before.copy()

# Remove outliers
Q1 = df_after["value"].quantile(0.25)
Q3 = df_after["value"].quantile(0.75)
IQR = Q3 - Q1
df_after = df_after[
    (df_after["value"] >= Q1 - 1.5 * IQR) &
    (df_after["value"] <= Q3 + 1.5 * IQR)
]

# Fill missing values
df_after["value"] = df_after["value"].fillna(df_after["value"].median())

# Compare before and after
profile_before = ProfileReport(df_before, title="Before Cleaning")
profile_after = ProfileReport(df_after, title="After Cleaning")

comparison = compare([profile_before, profile_after])
comparison.to_file("cleaning_comparison.html")
```