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# Sweetviz
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A pandas-based library that generates beautiful, high-density visualizations for exploratory data analysis (EDA) with minimal code. Sweetviz specializes in target analysis, dataset comparison, and feature analysis, offering unified mixed-type associations that integrate numerical correlations, categorical associations, and categorical-numerical relationships seamlessly.
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## Package Information
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- **Package Name**: sweetviz
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- **Language**: Python
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- **Installation**: `pip install sweetviz`
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## Core Imports
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```python
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import sweetviz as sv
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```
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## Basic Usage
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```python
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import sweetviz as sv
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import pandas as pd
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# Load your dataset
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df = pd.read_csv('your_dataset.csv')
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# Create a report analyzing the entire dataset
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my_report = sv.analyze(df)
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my_report.show_html()  # Opens in browser
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# Analyze with a target feature
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my_report = sv.analyze(df, target_feat='target_column')
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my_report.show_html()
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# Compare two datasets (e.g., training vs test)
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train_report = sv.compare([train_df, "Training"], [test_df, "Test"])
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train_report.show_html()
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# Compare subsets within the same dataset
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my_report = sv.compare_intra(df, df["gender"] == "male", ["Male", "Female"])
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my_report.show_html()
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```
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## Architecture
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Sweetviz operates through a three-step process:
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1. **Analysis Functions**: `analyze()`, `compare()`, or `compare_intra()` create `DataframeReport` objects
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2. **Report Processing**: The library analyzes feature types, calculates statistics, and generates associations
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3. **Output Generation**: Reports are rendered as self-contained HTML files or embedded in notebooks
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Key components:
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- **Analysis Engine**: Automatic type detection and statistical analysis
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- **Association Matrix**: Unified correlation analysis across numerical, categorical, and mixed data types
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- **Visualization Generator**: High-density charts and interactive HTML reports
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- **Configuration System**: Customizable settings via INI files and FeatureConfig objects
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## Capabilities
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### Core Analysis Functions
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Primary functions for creating exploratory data analysis reports. These functions analyze dataframes and return DataframeReport objects containing comprehensive statistics, visualizations, and association matrices.
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```python { .api }
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def analyze(source, target_feat=None, feat_cfg=None, pairwise_analysis='auto'): ...
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def compare(source, compare, target_feat=None, feat_cfg=None, pairwise_analysis='auto'): ...
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def compare_intra(source_df, condition_series, names, target_feat=None, feat_cfg=None, pairwise_analysis='auto'): ...
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```
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[Core Analysis](./core-analysis.md)
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### Report Generation and Display
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Methods for rendering and outputting analysis reports in various formats. DataframeReport objects provide multiple output options including HTML files, notebook embedding, and experiment tracking integration.
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```python { .api }
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class DataframeReport:
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    def show_html(filepath='SWEETVIZ_REPORT.html', open_browser=True, layout='widescreen', scale=None): ...
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    def show_notebook(w=None, h=None, scale=None, layout=None, filepath=None, file_layout=None, file_scale=None): ...
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    def log_comet(experiment): ...
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```
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[Report Display](./report-display.md)
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### Feature Configuration
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Configuration system for controlling feature type detection, analysis parameters, and report customization. Enables fine-tuned control over which features to analyze and how they should be interpreted.
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```python { .api }
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class FeatureConfig:
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    def __init__(skip=None, force_cat=None, force_text=None, force_num=None): ...
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    def get_predetermined_type(feature_name): ...
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    def get_all_mentioned_features(): ...
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```
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[Configuration](./configuration.md)
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## Types
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```python { .api }
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from typing import Union, Tuple, List
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import pandas as pd
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from enum import Enum
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# Core type aliases
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DataFrameInput = Union[pd.DataFrame, Tuple[pd.DataFrame, str]]
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class FeatureType(Enum):
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    TYPE_CAT = "CATEGORICAL"
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    TYPE_BOOL = "BOOL"
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    TYPE_NUM = "NUMERIC"
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    TYPE_TEXT = "TEXT"
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    TYPE_UNSUPPORTED = "UNSUPPORTED"
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    TYPE_ALL_NAN = "ALL_NAN"
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    TYPE_UNKNOWN = "UNKNOWN"
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    TYPE_SKIPPED = "SKIPPED"
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    def __str__(self): ...
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class NumWithPercent:
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    def __init__(self, number, total_for_percentage): ...
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    def __int__(self): ...
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    def __float__(self): ...
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    def __repr__(self): ...
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```