Pydata-visualizer 0.2.2

A Python library for Exploratory Data Analysis and Profiling.

Pydata-visualizer

A powerful and intuitive Python library for exploratory data analysis and data profiling. Pydata-visualizer automatically analyzes your dataset, generates interactive visualizations, and provides detailed statistical insights with minimal code.

Features

• 📊 Comprehensive Data Profiling: Analyze numerical, categorical, boolean, and string data types
• 🔍 Automated Data Quality Checks: Detect missing values, outliers, skewed distributions, and more
• 📈 Interactive Visualizations: Generate distribution plots, correlations heatmaps, and statistical charts
• 📝 Rich HTML Reports: Export analysis to visually appealing and shareable HTML reports
• ⚡ Performance Optimized: Fast analysis even on large datasets
• 🔄 Correlation Analysis: Calculate Pearson, Spearman, and Cramér's V correlations between variables

Installation

pip install pydata-visualizer

Quick Start

import pandas as pd
from data_visualizer.profiler import AnalysisReport, Settings

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

# Create a report with default settings
report = AnalysisReport(df)
report.to_html("report.html")

Advanced Usage

Customizing Analysis Settings

from data_visualizer.profiler import AnalysisReport, Settings

# Configure analysis settings
report_settings = Settings(
    minimal=False,          # Set to True for faster, minimal analysis
    top_n_values=5,         # Show top 5 values in categorical columns
    skewness_threshold=2.0  # Tolerance for skewness alerts
)

# Create report with custom settings
report = AnalysisReport(df, settings=report_settings)

# Perform analysis and get results dictionary
results = report.analyse()

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

Report Structure

The generated report includes:

• Overview: Dataset dimensions, missing values, duplicate rows
• Variable Analysis: Detailed per-column statistics and visualizations
• Sample Data: Head and tail samples of the dataset
• Correlations: Correlation matrices and heatmaps

API Reference

AnalysisReport Class

class AnalysisReport:
    def __init__(self, data, settings=None):
        """
        Initialize the analysis report object.
        
        Parameters:
        -----------
        data : pandas.DataFrame
            The dataset to analyze
        settings : Settings, optional
            Configuration settings for the analysis
        """
        
    def analyse(self):
        """
        Perform the data analysis.
        
        Returns:
        --------
        dict
            A dictionary containing all analysis results
        """
        
    def to_html(self, filename="report.html"):
        """
        Generate an HTML report from the analysis.
        
        Parameters:
        -----------
        filename : str, optional
            Path to save the HTML report (default: "report.html")
        """

Settings Class

class Settings(pydantic.BaseModel):
    """
    Settings for the analysis report.
    
    Attributes:
    -----------
    minimal : bool, default=False
        Whether to perform minimal analysis
    top_n_values : int, default=10
        Number of top values to show for categorical columns
    skewness_threshold : float, default=1.0
        Threshold for skewness alerts
    """

Type Analyzers

The library automatically detects and applies the appropriate analysis for different data types:

• Numeric: Statistical measures, distribution plots, skewness, kurtosis
• Categorical/String: Value counts, cardinality, frequency distributions
• Boolean: Value counts and proportions
• Generic: Basic analysis for unrecognized types

Correlation Analysis

Three correlation methods are calculated when possible:

• Pearson: Linear correlation between numerical variables
• Spearman: Rank correlation capturing monotonic relationships
• Cramér's V: Measure of association between categorical variables

Data Quality Alerts

The library automatically detects potential issues in your data:

• High Missing Values: Columns with significant missing data
• Skewness: Highly skewed distributions

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Credits

Created by Aditya Deshmukh ([e-mail])

GitHub: https://github.com/Adi-Deshmukh/Data_Profiler