Pydata-visualizer Documentation
Welcome to the official documentation for Pydata-visualizer, a powerful Python library for exploratory data analysis and data profiling.
Overview
Pydata-visualizer is designed to help data scientists and analysts quickly explore, understand, and visualize their datasets. With minimal code, you can generate comprehensive reports and gain insights into your data’s structure, distribution, and quality.
Key Features
Comprehensive Data Profiling: Analyze numerical, categorical, boolean, and string data types
Automated Data Quality Checks: Detect missing values, outliers, skewed distributions, duplicate rows, and more
Interactive Visualizations: Generate distribution plots, correlation heatmaps, word clouds, and statistical charts using Plotly or Seaborn
Dual Rendering Modes: Choose between interactive Plotly charts or static Seaborn/Matplotlib visualizations
Text Analysis: Automatic word frequency analysis and word cloud generation for text columns
Rich HTML Reports: Export analysis to visually appealing and shareable HTML reports with interactive or static charts
Performance Optimized: Fast analysis even on large datasets
Correlation Analysis: Calculate Pearson, Spearman, and Cramér’s V correlations between variables
Flexible Configuration: Customize analysis thresholds and options via the Settings class
Quick Example
import pandas as pd
from data_visualizer.profiler import AnalysisReport
# Load your dataset
df = pd.read_csv("your_dataset.csv")
# Create a report with default settings
report = AnalysisReport(df)
report.to_html("report.html")