autoeda-plus 1.0.0

Automated EDA reports with quality scores, human-readable insights, and actionable recommendations

AutoEDA+

Automated Exploratory Data Analysis with quality scores, human-readable insights, and actionable preprocessing recommendations.

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What is AutoEDA+?

AutoEDA+ is a Python library for exploring Pandas DataFrames and CSV files. It goes beyond raw statistics to provide:

• Dataset Quality Score (0-100) with weighted breakdown
• Per-Column Health Scores with specific checks and flags
• Human-readable insights (e.g., "Salary is heavily right-skewed")
• Actionable recommendations (e.g., "Impute Age using Median")

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Installation

pip install autoeda-plus

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Quick Start

import autoeda as ae
import pandas as pd

# Load your dataset
df = pd.read_csv("titanic.csv")

# Run the complete analysis pipeline
result = ae.analyze(df)

# View the overall quality score
print(result.quality.score)

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Features Overview

1. Dataset Overview

Provides rows, columns, memory usage, missing cells, duplicate rows, and column type breakdown.

2. Dataset Quality Score

An overall health score from 0-100 computed from 5 weighted dimensions:

• Completeness (30%): Fraction of non-missing cells
• Duplicates (20%): Fraction of non-duplicate rows
• Outliers (20%): Inverse average outlier rate
• Consistency (15%): No mismatched data types
• Balance (15%): Category frequency balance

3. Column Health Score

Every column receives a score (0-100) with individual checks:

• Correct data type
• Missing value evaluation
• Outlier detection
• Cardinality checks
• Skewness detection

4. Human-Readable Insights

Transforms statistical metrics into accessible natural language.

Instead of: Skewness = 2.4
AutoEDA+ says: 'Salary' is highly right-skewed (skewness = 2.40). Most values are concentrated at the lower end with a long upper tail.

5. Smart Recommendations

Provides prioritized rule-based suggestions for data preprocessing.

[HIGH]   Investigate outliers in 'Income'
[MEDIUM] Impute 'Age' using Median
[MEDIUM] Convert 'JoinDate' to datetime
[LOW]    One-Hot Encode 'Gender'

6. Dataset Comparison (Train vs Test)

Compare two datasets to detect Data Drift and Schema Mismatches:

• Detects missing columns (e.g., target missing from test set).
• Calculates numeric distribution drift using the Kolmogorov-Smirnov (KS) test.
• Calculates categorical frequency shifts.
• Visualizes differences with overlaid histograms and grouped bar charts.

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Complete Public API Reference

Below are all the available API methods. You can easily copy and paste these into your project.

import autoeda as ae

# Full Pipeline Analysis
result = ae.analyze(dataframe)

# Dataset Comparison (Train vs Test)
comp = ae.compare(train_dataframe, test_dataframe)

# Dataset Profiling & Quality
ae.overview(dataframe)
ae.quality(dataframe)
ae.column_health(dataframe)[column_name]

# Statistical DataFrames & Metrics
ae.missing(dataframe)
ae.duplicates(dataframe)
ae.dtypes(dataframe)
ae.statistics(dataframe)
ae.outliers(dataframe)
ae.correlation(dataframe)
ae.distribution(dataframe)

# Insights & Recommendations
ae.insights(dataframe)
ae.recommend(dataframe)

# Visualizations (Standard EDA Plots)
ae.histogram(dataframe, column_name)
ae.boxplot(dataframe, column_name)
ae.countplot(dataframe, column_name)
ae.heatmap(dataframe)
ae.missing_heatmap(dataframe)
ae.scatter(dataframe, x_column_name, y_column_name)

# Visualizations (Dataset Comparison Plots)
ae.compare_histogram(train_dataframe, test_dataframe, column_name)
ae.compare_countplot(train_dataframe, test_dataframe, column_name)

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API Examples & Outputs

Dataset Profiling & Quality

ae.overview()
Returns a high-level summary profile of the dataset including shape and memory footprint.

In [1]: ae.overview(df)
Out[1]: DatasetProfile(n_rows=891, n_cols=12, memory='83.7 KB', ...)

ae.quality()
Calculates an overall 0-100 quality score based on completeness, duplicates, and outliers.

In [2]: ae.quality(df)
Out[2]: QualityResult(score=78.5, grade='C+', sub_scores=[...])

ae.column_health()
Returns specific health metrics (0-100 score) and flagged issues for a single column.

In [3]: ae.column_health(df)['Age']
Out[3]: ColumnHealth(score=80.0, missing_pct=19.87, outlier_pct=0.0)

Statistical DataFrames

ae.missing()
Generates a pandas DataFrame listing the exact count and percentage of missing values per column.

In [4]: ae.missing(df).head(2)
Out[4]: 
       missing_count  missing_pct
Cabin            687        77.10
Age              177        19.87

ae.duplicates()
Evaluates the dataset for completely identical rows and returns the count and percentage.

In [5]: ae.duplicates(df)
Out[5]: {'count': 0, 'percentage': 0.0}

ae.dtypes()
Returns a pandas DataFrame of the pandas internal data types of each column.

In [6]: ae.dtypes(df).head(2)
Out[6]: 
             type
PassengerId  int64
Survived     int64

ae.statistics()
Computes core descriptive statistics (mean, standard deviation, min, max) for all numeric columns.

In [7]: ae.statistics(df).head(2)
Out[7]: 
             mean        std   min   max
PassengerId  446.00   257.35     1   891
Survived       0.38     0.48     0     1

ae.outliers()
Detects statistical outliers in numeric columns using the IQR method and returns their frequencies.

In [8]: ae.outliers(df).head(2)
Out[8]: 
       outlier_count  outlier_pct
Fare             116        13.02
SibSp             46         5.16

ae.correlation()
Constructs a Pearson correlation matrix DataFrame mapping relationships between numeric variables.

In [9]: ae.correlation(df).iloc[:2, :2]
Out[9]: 
             PassengerId  Survived
PassengerId     1.000000 -0.005007
Survived       -0.005007  1.000000

Insights & Recommendations

ae.insights()
Analyzes statistical anomalies and formulates human-readable text insights regarding the dataset.

In [10]: ae.insights(df)[0]
Out[10]: "Dataset has 891 rows and 12 columns."

ae.recommend()
Generates a prioritized list of actionable data cleaning steps (e.g. dropping columns, imputing values).

In [11]: ae.recommend(df)[0]
Out[11]: "[HIGH] Drop 'Cabin' due to excessive missing values (77.10%)."

Dataset Comparison (Data Drift)

ae.compare()
Cross-references two datasets to detect schema mismatches and distribution drift (Kolmogorov-Smirnov test).

In [12]: comp = ae.compare(train_df, test_df)
         print(comp.insights[0])
         print([c.column for c in comp.drifted_columns])
Out[12]: 
Drift detected in 'Fare' (KS p-value = 0.0031).
['Fare', 'Age']

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Visualizations

When called in a Jupyter Notebook, these visualization functions natively render interactive Plotly graph_objects.Figure charts.

Standard EDA Plots

ae.histogram()
Displays the distribution of a numeric column with a Kernel Density Estimate (KDE) overlay.

In [13]: ae.histogram(df, 'Age')
Out[13]:

ae.boxplot()
Visualizes data dispersion and isolates statistical outliers for a specified numeric column.

In [14]: ae.boxplot(df, 'Fare')
Out[14]:

ae.heatmap()
Renders an interactive correlation matrix heatmap connecting all numeric variables.

In [15]: ae.heatmap(df)
Out[15]:

ae.missing_heatmap()
Generates a visual nullity matrix indicating precisely where missing values occur across rows.

In [16]: ae.missing_heatmap(df)
Out[16]:

Dataset Comparison Plots

ae.compare_histogram()
Overlays two distributions to visually compare a numeric column between a training and testing set.

In [17]: ae.compare_histogram(train_df, test_df, 'Fare')
Out[17]:

ae.compare_countplot()
Aligns categorical frequencies side-by-side to visually compare classifications across two datasets.

In [18]: ae.compare_countplot(train_df, test_df, 'Pclass')
Out[18]:

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Dependencies

Package	Version	Purpose
pandas	≥ 1.3	DataFrame operations
numpy	≥ 1.21	Numerical computations
scipy	≥ 1.7	KDE, skewness, kurtosis
plotly	≥ 5.0	Interactive Visualizations

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License

MIT License — see LICENSE.