df-autoprofiler 0.1.1

Automated data profiling and exploratory analysis for tabular datasets.

df-autoprofiler

df-autoprofiler is a lightweight, heuristic-based data profiling tool for pandas DataFrames.
It generates a self-contained HTML report with summaries, correlations, outlier detection, visualizations, and actionable suggestions — designed to scale to large datasets via sampling.

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1. Dataset Overview

Provides a high-level summary of your dataset:

• Number of rows and columns
• Numeric vs categorical feature detection
• Missing data indicators
• High-cardinality detection
• Machine Learning suitability hints

Once you create a profile with from df_autoprofiler.profiler import profile, you can do:

report = profile(df)

You can then access:

• report.numerics — numeric feature summaries

• report.categoricals — categorical feature summaries

• report.correlations — correlation tables

• report.outliers — outlier detection results

• report.missing — missing data summaries

• report.suggestions — actionable suggestions

2. Statistical Summaries

Numeric features: count, mean, std, quantiles, missing %

Categorical features: unique values, top category, frequency

All summaries are available via the report object:

numeric_summary = report.numerics
categorical_summary = report.categoricals

3. Correlations

Numeric ↔ Numeric: Pearson & Spearman

Categorical ↔ Categorical: Cramér’s V (with safety heuristics)

Categorical ↔ Numeric: Correlation Ratio (η²)

Automatic skipping of unstable or misleading correlations

Warnings when sampling or heuristics are applied

Access correlations:

report.correlations["numeric"]["pearson"]
report.correlations["numeric"]["spearman"]
report.correlations["mixed"]

4. Outlier Detection

IQR-based detection

Reports row indices per column

Skips constant or invalid columns

Access outliers:

outliers = report.outliers

5. Missing Data Analysis

Detects missing values across columns

Generates summary tables with counts and percentages

Access missing data:

missing_data = report.missing

6. Heuristic Suggestions

Prioritized recommendations with three levels:

• 🚨 High priority

• ⚠️ Medium priority

• ℹ️ Low priority

Examples:

High skew → consider log/Box-Cox transform

High missingness → consider imputation

Constant columns → consider dropping

Access suggestions:

suggestions = report.suggestions

7. Visualizations

Histograms, distributions, and correlation plots

Embedded directly into the HTML report

Handles special characters safely in filenames

Generate plots:

report.plots['hist_{NAME_OF_COL}']

Quickstart Example

import pandas as pd
from df_autoprofiler.profiler import profile

df = pd.read_csv("data.csv")

# Create the profile
report = profile(df)

# Access summaries
print(report.numerics.head())
print(report.categoricals.head())
print(report.missing.head())

# Generate HTML report
report.to_html("report.html")

Handling Large Datasets

• Automatic sampling to maintain performance

• Warnings included in report when sampling is applied

Example with a large synthetic dataset:

import pandas as pd
import numpy as np
from df_autoprofiler.profiler import profile

n = 1_000_000
df_large = pd.DataFrame({
    f"num_{i}": np.random.randn(n) for i in range(10)
})
for j in range(5):
    df_large[f"cat_{j}"] = np.random.choice(["A","B","C","D"], size=n)

report = profile(df_large)
report.to_html("large_report.html")

Installation

pip install df-autoprofiler

Known Limitations

• Correlation measures are heuristic-based

• ML readiness scoring is qualitative

• Sampling may hide rare patterns

• Bugs

📄 License

MIT License

🙌 Acknowledgments

Built with:

• pandas

• numpy

• scipy

• matplotlib