LazyAnalyst

Automated data analysis for Python — one line of code, complete insights.

LazyAnalyst automates everything you'd manually do with Pandas and NumPy. Load a dataset, run one line of code, and get a complete analysis with insights, visualizations, statistical tests, and a professional HTML report.

Quick Start

import lazyanalyst as la

# Analyze any CSV or Excel file
result = la.analyze("sales_data.csv")

# Open the interactive dashboard
result.dashboard()

# Or view the professional report
result.report()

That's it! LazyAnalyst handles data loading, quality auditing, cleaning, exploratory analysis, statistical testing, feature engineering, visualizations, and report generation — automatically.

Installation

pip install lazyanalyst

Requirements: Python 3.8+

Install from source:

git clone https://github.com/Tenali-Rama/lazyanalyst.git
cd lazyanalyst
pip install -e .

What You Get

After running analyze(), LazyAnalyst creates an outputs/ folder with:

File	Description
dashboard.html	Interactive Plotly dashboard
report.html	Professional HTML report
cleaned_data.csv	Your cleaned dataset
insights.txt	Plain-English insights
plots/	Generated charts (PNG)

Features

Automated Pipeline (11 Modules)

1. Data Loading — CSV/Excel with auto type inference
2. Schema Detection — Classifies columns (numerical, categorical, datetime, boolean, identifier)
3. Quality Audit — Detects missing values, duplicates, outliers
4. Data Cleaning — Auto-fixes common issues
5. Exploratory Analysis — Summary statistics and distributions
6. Visualizations — Histograms, bar charts, heatmaps
7. Feature Engineering — Ratios, interactions, transforms
8. Statistical Tests — Pearson/Spearman, T-tests, ANOVA, Chi-Square
9. Insights — Natural language findings
10. Dashboard — Interactive HTML dashboard
11. Report — Professional HTML report

Key Capabilities

• Smart Column Detection — Automatically identifies data types
• Quality Scoring — Rates your data quality (0-100)
• Resilient Processing — Continues even if individual steps fail
• No Configuration — Works out of the box
• Browser-Based Results — No server needed

Example Usage

Basic Analysis

import lazyanalyst as la

result = la.analyze("data.csv")
result.dashboard()  # Opens in browser
result.report()     # Opens in browser

With Options

# Generate only the report (skip dashboard)
result = la.analyze("data.xlsx", dashboard=False)

# Access cleaned data
cleaned_df = result.cleaned_data()
print(cleaned_df.describe())

Batch Processing

import lazyanalyst as la
import os

for filename in os.listdir("data/"):
    if filename.endswith(".csv"):
        result = la.analyze(f"data/{filename}")
        print(f"✓ Analyzed {filename}")

Supported Data

Format	Extensions	Notes
CSV	.csv	Auto-detects delimiter and encoding
Excel	.xlsx	Reads first sheet

Column Types Detected:

• Numerical — integers, floats
• Categorical — text or <20 unique values
• Datetime — date formats or date-related names
• Boolean — True/False, Yes/No, 0/1
• Identifier — IDs, codes, keys

API Reference

analyze(filepath, dashboard=True, report=True)

Runs the complete analysis pipeline.

Parameters:

• filepath (str): Path to CSV or Excel file
• dashboard (bool): Generate dashboard (default: True)
• report (bool): Generate report (default: True)

Returns: LazyAnalystResult object

Result Object Methods

Method	Description
dashboard()	Opens interactive dashboard in browser
report()	Opens HTML report in browser
cleaned_data()	Returns cleaned pandas DataFrame

Troubleshooting

Issue	Solution
FileNotFoundError	Check file path; use absolute path if needed
ValueError: unsupported file type	Ensure file is .csv or .xlsx
Dashboard won't open	Install plotly/dash; open dashboard.html directly
Slow analysis	Filter to relevant columns/rows before analysis

Development

Run Tests

pip install pytest
pytest tests/ -v

Contribute

git clone https://github.com/Tenali-Rama/lazyanalyst.git
cd lazyanalyst
pip install -e .
pip install -r requirements-dev.txt

License

MIT License — See LICENSE file for details.

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Transform your data analysis workflow with one line of code.

For detailed examples and advanced usage, see the Examples section below.

Examples

Sales Analysis

import lazyanalyst as la

result = la.analyze("sales.csv")
result.dashboard()

Finds: Top regions, revenue trends, product performance, correlations

Customer Analytics

result = la.analyze("customers.csv")
result.report()

Finds: Segment characteristics, churn factors, purchase patterns

Scientific Data

result = la.analyze("experiment_results.csv")
result.dashboard()

Finds: Measurement distributions, statistical significance, outliers

Financial Analysis

result = la.analyze("financial_data.csv")
result.report()

Finds: Revenue trends, profit margins, cost correlations, anomalies