📊 dva-lab — Data Visualization & Analytics Laboratory Library

dva-lab is an installable, self-contained Python library engineered for university students and educators. It enables students to retrieve, display, study, and execute all 12 Data Visualization and Analytics (DVA) laboratory experiments directly inside Jupyter Notebook, JupyterLab, or Google Colab.

The package stores the complete, authoritative source code, schemas, and datasets locally within the library. It does not depend on fetching code from the internet during runtime.

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📑 Table of Contents

• Features
• Installation
  • Google Colab & Jupyter Notebook
  • Local Development & Pip
  • Optional Dependency Sets
• Quick Start
• Complete Experiment Catalog
• Public API Reference
• Experiment Execution Guidelines
  • SQL & OLAP Experiments (Exp 1 & 2)
  • Python Plotting Experiments (Exp 3 to 9)
  • Power BI & DAX Experiments (Exp 10 to 12)
• Updating & Uninstalling
• Contributing
• License

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🚀 Features

• Jupyter-Native Experience: Seamless inline rendering of Matplotlib/Seaborn plots and rich HTML/Markdown experiment guides.
• Self-Contained & Deterministic: Bundles all source code, SQL definitions, Power BI DAX expressions, and sample datasets (sales_data.csv).
• Reproducible Seeds: Synthetic distributions use fixed random seeds (seed=0) matching laboratory manual benchmarks.
• Safe & Non-Destructive: Never connects to an unknown database without explicit authorization and never attempts mock Power BI execution.
• Convenient Shortcuts: Direct function handles (dva_lab.exp1() through dva_lab.exp12()) for fast classroom access.
• Lightweight Core: Base install contains zero compulsory heavy dependencies with lazy imports for plotting engines.

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📦 Installation

Google Colab & Jupyter Notebook

Paste and execute directly in a notebook cell:

# Install directly from GitHub with visualization dependencies
%pip install "dva-lab[viz] @ git+https://github.com/AnandChowdari/dva-lab.git"

Note: Replace AnandChowdari with your GitHub username or organization repository URL.

Local Development & Pip

Windows (PowerShell):

git clone https://github.com/AnandChowdari/dva-lab.git
cd dva-lab
pip install -e .[viz]

macOS / Linux:

git clone https://github.com/AnandChowdari/dva-lab.git
cd dva-lab
pip install -e ".[viz]"

Optional Dependency Sets

Group	Install Command	Included Packages	Purpose
Base	pip install git+https://github.com/AnandChowdari/dva-lab.git	Pure Python core	Code retrieval, schema inspection, CLI list
viz	pip install "dva-lab[viz]"	matplotlib, numpy, seaborn, scipy, statsmodels	Plotting experiments (Exp 3–9)
full	pip install "dva-lab[full]"	viz + pandas + ipython	Full workbook with simulations & dataset tools
test	pip install "dva-lab[test]"	viz + pandas + pytest	Running the test suite locally

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⚡ Quick Start

import dva_lab

# 1. Discover all registered experiments
dva_lab.list_experiments()

# 2. Display formatted Markdown manual for Experiment 3
dva_lab.show_experiment(3)

# 3. Retrieve raw source code as a string
code = dva_lab.get_code(3)
print(code)

# 4. Execute experiment and render plots inline
dva_lab.exp3()

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📚 Complete Experiment Catalog

#	Title	Category	Methodology / Tools	Focus
1	OLAP Operations Using Materialized Views	OLAP & SQL	PostgreSQL 12+, CUBE	Materialized view, Roll Up, Slice, Dice, Pivot
2	OLAP Operations Based on Star Schema	OLAP & SQL	PostgreSQL 12+, CUBE	Fact & dimension joins, Drill Down, Hierarchy
3	Vertical and Horizontal Bar Charts	Python Visualization	Matplotlib, NumPy	plt.bar, plt.barh, categorical comparison
4	Grouped and Stacked Bar Charts	Python Visualization	Matplotlib, NumPy	Positional offsets, stacked bottom vectors
5	Single & Multiple Variable Histograms	Python Visualization	Matplotlib, NumPy	Bin frequency, alpha transparency, normal distributions
6	Single & Multiple Variable Density Plots	Python Visualization	Seaborn, Matplotlib	Kernel Density Estimation (sns.kdeplot), area fills
7	Empirical Cumulative Distribution Function	Python Visualization	Statsmodels, Matplotlib	Non-parametric ECDF step function (sm.distributions.ECDF)
8	Q-Q Plot to Compare Datasets	Python Visualization	SciPy, Matplotlib	Normal probability quantiles (stats.probplot)
9	Scatter Plot Using Sample Data	Python Visualization	Matplotlib, NumPy	Bivariate scatter points, alpha blending, correlation
10	Power BI Sales Data Visualization	Power BI	Power BI Desktop, CSV	Column, Pie, Line, Card, Slicer dashboard
11	Power BI Calculations & DAX Measures	Power BI	DAX, Power BI Desktop	Total Cost, Profit, CALCULATE, ALL, percentages
12	Power BI Conditional & Logical Analysis	Power BI	DAX, Power BI Desktop	IF, AND, OR, COUNTROWS, dynamic sales tiers

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🛠 Public API Reference

dva_lab.list_experiments()

Returns a rich ExperimentList containing all 12 experiments.

• In Jupyter: Automatically renders a styled HTML table with badges.
• In Terminal: Displays an aligned text summary.
• Convert to DataFrame: dva_lab.list_experiments().to_dataframe()

dva_lab.show_experiment(number: int)

Displays the complete laboratory manual for the experiment:

• Experiment Aim
• Theoretical Concept & Pedagogical Context
• Complete, Corrected Input Code
• Expected Visual Output or Result Table
• Result Statement
• Execution Notes & Errata

dva_lab.get_code(number: int) -> str

Returns the exact, clean source code string for inspection or programmatic export:

with open("exp03.py", "w") as f:
    f.write(dva_lab.get_code(3))

dva_lab.run_experiment(number: int, **kwargs)

Executes the specified experiment:

• For Python experiments (3–9): Renders plots inline.
• Optional arguments:
  • show=True: Call plt.show() inline.
  • side_by_side=True: For multi-plot experiments (3, 4, 5, 6), combines subplots horizontally.

Convenience Shortcuts (dva_lab.exp1() ... dva_lab.exp12())

One-line helper functions to immediately access any experiment:

dva_lab.exp1()   # Shows PostgreSQL SQL queries and setup guide
dva_lab.exp3()   # Plots vertical and horizontal bar charts
dva_lab.exp10()  # Shows Power BI visual setup guide and previews dataset

dva_lab.load_sales_data() -> pd.DataFrame

Loads and returns the authoritative 10-record sales_data.csv used in Experiments 10–12 as a pandas DataFrame:

df = dva_lab.load_sales_data()
print(df.head())

dva_lab.about() / dva_lab.help()

Prints library version, repository information, and API guide.

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🔬 Experiment Execution Guidelines

1. SQL & OLAP Experiments (Experiments 1 and 2)

Experiments 1 and 2 define complete PostgreSQL scripts including DDL (CREATE TABLE), sample INSERT records, materialized views with GROUP BY CUBE, and OLAP queries (Roll Up, Drill Down, Slice, Dice, Pivot).

• Safety Guarantee: Calling dva_lab.run_experiment(1) or dva_lab.exp1() never executes SQL against an unconfigured server.
• Live Execution: If you have an active PostgreSQL connection (e.g. psycopg2 or SQLAlchemy), pass it explicitly:

  dva_lab.run_experiment(1, connection=your_db_connection)
  

2. Python Plotting Experiments (Experiments 3 to 9)

All Python experiments run locally using Matplotlib, Seaborn, Statsmodels, and SciPy.

• Fully compatible with Jupyter %matplotlib inline backend.
• Deterministic synthetic data generated with np.random.seed(0).
• Support individual sequential plots or combined views (side_by_side=True).

3. Power BI & DAX Experiments (Experiments 10 to 12)

Power BI Desktop reports are interactive Windows visual applications. dva-lab provides:

• Complete step-by-step instructions for fields, axes, and visual types.
• Exact DAX measures and calculated column formulas.
• Bundled CSV data via dva_lab.load_sales_data().
• Built-in Python validation simulation reproducing the DAX calculations directly in pandas:

  from dva_lab.experiments.exp11_powerbi_dax import simulate_in_python
  print(simulate_in_python())
  

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🔄 Updating & Uninstalling

Update to Latest Version

%pip install --upgrade --force-reinstall git+https://github.com/AnandChowdari/dva-lab.git

Uninstall

%pip uninstall dva-lab -y

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🧪 Testing

The library comes with a complete test suite covering 100% of registry lookups, code snippets, headless plotting rendering, and DAX simulations:

pytest -v

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🤝 Contributing

1. Fork the repository on GitHub.
2. Clone your fork and create a feature branch (git checkout -b feature/new-experiment).
3. Add or update experiment files in src/dva_lab/experiments/.
4. Ensure all tests pass: pytest.
5. Submit a Pull Request.

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📄 License

This project is licensed under the MIT License.