Python Lab Toolkit (python-lab-toolkit)

A beginner-friendly, production-quality Python package and CLI named pylab designed to generate complete, working, runnable solutions for all 52 Python laboratory questions with automatic dataset downloading and verified offline support.

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Table of Contents

1. Key Features
2. Installation
3. Quick Start
4. Complete Command Reference
5. The 52 Lab Questions Catalog
6. Automated Dataset Management
7. Offline Installation & Laboratory Bundle
8. Database Configuration (SQLite & MySQL)
9. CGI Web Handlers and Local Test Servers
10. Troubleshooting & FAQ
11. Development & Automated Testing
12. PyPI Publication Guide
13. License & Dataset Attribution

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1. Key Features

• Short, Intuitive Commands: Generate any project with simple commands like pylab q1, pylab q12, pylab q49.
• Zero Comments in Generated Code: In accordance with clean code standards, generated Python source code contains no explanatory inline comments, decorative comments, or header comments, remaining self-explanatory through readable structure and expressive naming.
• Real, Runnable Python Files: Generates real files on disk (not hidden functions or stubs) that students and instructors can inspect, run, and modify.
• Automated Dataset Handling: Automatically downloads datasets when missing or instantly extracts verified bundled datasets when offline.
• 100% Offline Capable: Small verified copies of all required datasets (winequality-red.csv, iris.csv, breast-cancer.csv, groceries.csv, Data.csv, dirtydata.csv, alphabet_stock_data.csv, rainfall.csv, students.csv) are bundled inside the package wheel.
• Zero External Dependencies for Generator: The pylab CLI and generator depend strictly on the Python standard library. It installs anywhere instantly without dependency conflicts.
• Accidental Overwrite Protection: Existing user files are never silently overwritten. Use --force to deliberately replace files.
• Built-in System Doctor: Run pylab doctor to check Python versions, directory permissions, and optional scientific libraries.

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2. Installation

Standard Installation via pip

python -m pip install python-lab-toolkit

Optional Scientific Libraries

The core toolkit runs anywhere on standard Python. If you want to execute machine learning, plotting, or numerical labs (Q23, Q31-Q52), install the scientific packages:

python -m pip install -r requirements-labs.txt

Or install specific dependencies per question:

pylab install-deps q49

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3. Quick Start

1. View Available Questions

pylab list

2. Generate a Single Question

Generate the modular text analysis lab (Question 12):

pylab q12

Output:

Question 12 generated successfully.

Project: q12_text_analysis/

Files:
  text_preprocessor.py
  text_analyzer.py
  main_analyzer.py
  README.md

Run:
  python q12_text_analysis/main_analyzer.py

3. Run the Generated Program

python q12_text_analysis/main_analyzer.py

4. Generate Machine Learning Labs with Automatic Datasets

Generate the 22-step Wine Quality regression pipeline:

pylab q49

This automatically obtains winequality-red.csv and winequality-white.csv, creates results/, and generates wine_regression.py and README.md.

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4. Complete Command Reference

Command	Description
pylab list	Display all 52 registered questions with numbers, titles, and categories.
pylab list --category <cat>	Filter questions by category (fundamentals, oop, pandas, etc.).
pylab q<N>	Generate Question <N> (e.g. pylab q1, pylab q25, pylab q49).
pylab q<N> --force	Overwrite existing project files.
pylab q<N> --offline	Force usage of bundled offline datasets.
pylab q<N> --output-dir <dir>	Generate the project inside a specified directory.
pylab category <cat>	Generate all questions belonging to a specific category.
pylab all	Generate all 52 questions (prompts for confirmation).
pylab all --yes	Generate all 52 questions non-interactively.
pylab doctor	Inspect environment, Python version, directory write permissions, and libraries.
pylab datasets	Display status and metadata for all bundled and downloadable datasets.
pylab install-deps <q>	Check and install missing optional pip packages for a specific question.
pylab version	Show the installed toolkit version.
pylab --help	Display CLI help and options.

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5. The 52 Lab Questions Catalog

Python Fundamentals (Questions 1–11)

• Q1 (q01_variable_name_validator): Validate identifier rules and check reserved keywords using keyword.iskeyword().
• Q2 (q02_single_number): Find the unique integer in an array where all other integers appear twice using XOR ($O(n)$ time, $O(1)$ space).
• Q3 (q03_string_operations): Implement process_string() to display uppercase, lowercase, length, slicing reversal [::-1], and palindrome status.
• Q4 (q04_menu_string_processor): Menu-driven program for substring checking, character occurrence counting, substring replacement, and uppercase conversion.
• Q5 (q05_phonebook): Dictionary-based contact management supporting Add, Update, Search, Delete, and Display.
• Q6 (q06_simple_calculator): Arithmetic calculator supporting +, -, *, /, //, %, ** with zero-division handling.
• Q7 (q07_session_attendees): Set operations on conference attendees: Union, Intersection, AI-only, and Cybersecurity-only.
• Q8 (q08_number_properties): Functions verifying Even/Odd, Palindrome, Armstrong numbers, Prime numbers, and Perfect numbers.
• Q9 (q09_greet_people): Variable argument function greet_people(*names) handling zero, one, or multiple parameters.
• Q10 (q10_password_strength): Password validator testing length, digits, uppercase, lowercase, and special characters with detailed deficiency reporting.
• Q11 (q11_recursion): Recursive sum and recursive factorial evaluated on $n = 5, 10, 18$.

Modular Programming (Question 12)

• Q12 (q12_text_analysis): Multi-module architecture with text_preprocessor.py, text_analyzer.py, and main_analyzer.py displaying required function docstrings via __doc__.

Object-Oriented Programming (Questions 13–18)

• Q13 (q13_student_class): Student class with name, roll_number, course, and display().
• Q14 (q14_circle_class): Circle class with radius, area(), and circumference() methods using math.pi.
• Q15 (q15_employee_salary): Employee gross salary calculator with basic pay, HRA (20%), and DA (10%).
• Q16 (q16_smart_calculator): SmartCalculator demonstrating default arguments, *args multiplication, and polymorphic type dispatch.
• Q17 (q17_complex_number): ComplexNumber class with + (__add__) and == (__eq__) operator overloading.
• Q18 (q18_vehicle_inheritance): Vehicle base class and Car derived class with method overriding and super().

Files, Exceptions, and Dates (Questions 19–22)

• Q19 (q19_file_word_counter): Read text file, count lines, words, characters, find top 5 frequent words, and handle file exceptions. Includes sample.txt.
• Q20 (q20_custom_exception_age): Custom exception InvalidAgeException validating ages between 18 and 60.
• Q21 (q21_transaction_handling): Robust banking transaction demonstrating try, except, else, and finally blocks.
• Q22 (q22_date_formatter): Date parser converting dd-mm-yyyy to MonthName dd, yyyy with invalid calendar date detection.

CSV Processing (Questions 23–24)

• Q23 (q23_rainfall_analysis): Read rainfall CSV data, calculate station averages, maximum rainfall record, standard deviation, and export results.
• Q24 (q24_students_csv): Manage students.csv, find highest-scoring student, and compute class average marks.

Database Systems (Questions 25–26)

• Q25 (q25_sqlite_library): SQLite Library Management system with parameterized single inserts, executemany() batch inserts, category queries, and menu CLI.
• Q26 (q26_mysql_employee): MySQL Employee Management system with parameterized queries, salary filtering (> ₹50,000), and secure environment credentials.

REST API & Networking (Question 27)

• Q27 (q27_crudcrud_api): Functional requests-based REST client performing Create, Read, Update, and Delete on students. Includes built-in mock_server.py for offline testing!

CGI & Web Applications (Questions 28–30)

• Q28 (q28_cgi_login): HTML login form and Python CGI handler displaying welcome response, with built-in server.py.
• Q29 (q29_cgi_registration): MSc registration form and Python CGI script returning formatted admission receipt.
• Q30 (q30_cgi_crud): Web-based database CRUD portal backed by SQLite, HTML frontend, and Python CGI HTTP server.

NumPy and Pandas Numerical Analysis (Questions 31–43)

• Q31 (q31_numpy_matrix_operations): Array 1–20 reshaped to 4×5, transpose, total/row/column sums, and even number extraction.
• Q32 (q32_numpy_slicing_reversal): 5×5 array (1–25) submatrix slicing, row slicing, column slicing, and axis reversals.
• Q33 (q33_pandas_student_pass_fail): Convert student marks Series to DataFrame, classify Pass/Fail, and filter passing students.
• Q34 (q34_pandas_employee_salary): Departmental salary analysis computing average salary per department and top earner.
• Q35 (q35_pandas_sales_barchart): 6 months sales table for 3 products, detecting top month, and plotting bar chart to results/.
• Q36 (q36_pandas_weather_plot): 7-day weather table calculating average temperature and total rainfall, plotting temperature trend line.
• Q37 (q37_pandas_age_categorization): Continuous age categorization using pd.cut() with mismatch detection.
• Q38 (q38_pandas_concat_rows): Concatenate two student DataFrames along rows (axis=0).
• Q39 (q39_pandas_concat_cols): Combine student DataFrames along columns (axis=1).
• Q40 (q40_pandas_concat_merge): Concatenate student batches and merge with exam scores DataFrame on student_id.
• Q41 (q41_pandas_join_operations): Relational join operations: Inner, Left, Right, and Outer joins with detailed semantics explanation.
• Q42 (q42_pandas_groupby_school): Group school records by school code, compute mean/min/max age, and inspect GroupBy object type.
• Q43 (q43_pandas_groupby_multi): Multi-column GroupBy (['school_code', 'class']) computing group counts with size().

Visualization and Preprocessing (Questions 44–48)

• Q44 (q44_alphabet_stock_visualization): Alphabet stock historical data filtering, line plots, volume bar plot, stacked OHLC histogram, and scatter correlation.
• Q45 (q45_multi_plot_demonstration): 2×2 multi-plot demonstration grid (waveform, categories, scatter, histogram).
• Q46 (q46_dataframe_barchart): Grouped categorical bar plot with value annotations and legends.
• Q47 (q47_data_preprocessing): Impute missing numerical values (SimpleImputer), One-Hot encode categorical features, and standard scale features (StandardScaler).
• Q48 (q48_dirtydata_cleaning): Clean dirty dataset: fix inconsistent date formats, impute null calories, correct outlier duration, and drop duplicates.

Machine Learning & Data Mining Pipelines (Questions 49–52)

• Q49 (q49_wine_quality_regression): Full 22-step regression pipeline on red and white wine: data integration, correlation heatmap, feature scaling, Linear Regression, Decision Tree Regressor, Random Forest Regressor, and $R^2$, MAE, MSE, RMSE evaluation.
• Q50 (q50_breast_cancer_classification): Breast cancer diagnosis classification, Random Forest feature ranking, top-feature reduction, and classifier comparison (Decision Tree, Random Forest, Logistic Regression) with confusion matrices and false-negative analysis.
• Q51 (q51_iris_clustering): Unsupervised clustering on Iris features: standardization, K-Means ($k = 2, 3, 4$), elbow method, and 2D PCA cluster visualization.
• Q52 (q52_apriori_association_rules): Market basket transaction processing with Apriori, frequent itemset generation, and association rules sorted by lift.

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6. Automated Dataset Management

When generating questions requiring datasets, pylab automatically acquires them:

q49_wine_quality_regression/
├── wine_regression.py
├── winequality-red.csv          <-- Auto-acquired
├── winequality-white.csv        <-- Auto-acquired
├── README.md
└── results/

Dataset Registry

Dataset Filename	Used In	Official Source	Bundled Offline
winequality-red.csv	Q49	UCI Machine Learning Repository	Yes
winequality-white.csv	Q49	UCI Machine Learning Repository	Yes
breast-cancer.csv	Q50	UCI Machine Learning Repository	Yes
iris.csv	Q51	UCI Machine Learning Repository	Yes
groceries.csv	Q52	Association Mining Market Baskets	Yes
Data.csv	Q47	ML Preprocessing Reference Data	Yes
dirtydata.csv	Q48	Data Cleaning Reference Data	Yes
alphabet_stock_data.csv	Q44	Financial Time Series Reference	Yes
rainfall.csv	Q23	Weather Station Records	Yes
students.csv	Q24	Student Grades Records	Yes

To view dataset status at any time, run:

pylab datasets

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7. Offline Installation & Laboratory Bundle

In lab environments where internet access is unavailable or restricted, you can prepare an offline installation bundle.

Step 1: Prepare the Offline Wheels Folder (on an internet-connected machine)

mkdir -p offline_bundle/wheels
python -m pip download -d offline_bundle/wheels python-lab-toolkit
python -m pip download -d offline_bundle/wheels -r requirements-labs.txt

Step 2: Install Completely Offline in the Laboratory

Transfer the offline_bundle folder to your offline laboratory computers and install:

python -m pip install --no-index --find-links ./offline_bundle/wheels python-lab-toolkit

Step 3: Run Generation Offline

pylab q49 --offline

The toolkit will immediately use the verified bundled datasets stored inside the package wheel.

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8. Database Configuration (SQLite & MySQL)

SQLite (Q25 & Q30)

SQLite is built into Python. No installation, server configuration, or credentials are required. The database files (library.db and cgi_records.db) are created automatically in the project folder.

MySQL (Q26)

Question 26 integrates with a MySQL database. To run Question 26 against your MySQL server:

1. Start your local MySQL service (e.g. sudo systemctl start mysqld or through XAMPP / MySQL Workbench).
2. Configure credentials using environment variables:

   export MYSQL_HOST="localhost"
   export MYSQL_USER="root"
   export MYSQL_PASSWORD="your_password"
   export MYSQL_DATABASE="company_db"
   

3. Run the manager:

   python q26_mysql_employee/mysql_employee_manager.py
   

   Or run the automated demonstration:

   python q26_mysql_employee/mysql_employee_manager.py --demo
   

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9. CGI Web Handlers and Local Test Servers

Questions 28, 29, and 30 demonstrate CGI forms and web applications. Each generated project contains a self-contained local Python HTTP server (server.py).

To test in your browser:

cd q28_cgi_login
python server.py

Open http://localhost:8080 in your web browser. You can enter credentials, submit forms, and inspect the CGI responses immediately!

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10. Troubleshooting & FAQ

"No module named 'numpy'" or other scientific library error

The code generator does not require scientific libraries, but running the generated script might. Run:

pylab install-deps <question_number>
# Example: pylab install-deps q49

Or install all lab dependencies at once:

pip install -r requirements-labs.txt

"The directory already contains existing files"

To prevent accidental loss of code, pylab will not overwrite existing directories. Pass --force:

pylab q12 --force

How can I check if my Python environment is ready?

Run the built-in system diagnostics tool:

pylab doctor

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11. Development & Automated Testing

Running Tests

The toolkit includes a 56-test automated test suite verifying registry integrity, file generation, syntax correctness, comment absence (Rule A), CLI subcommands, database queries, and program execution.

Run the test suite using standard Python:

python -m unittest discover -s tests -v

Building Distribution Packages

Build modern wheel and source distributions into dist/:

python setup.py sdist bdist_wheel

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12. PyPI Publication Guide

To publish python-lab-toolkit to PyPI:

Step 1: Install Build and Twine Tools

python -m pip install --upgrade build twine

Step 2: Build Clean Artifacts

rm -rf dist build src/*.egg-info
python -m build

Step 3: Check Package Metadata with Twine

python -m twine check dist/*

Step 4: Securely Upload to PyPI using an API Token

python -m twine upload --username __token__ dist/*

When prompted for your password, enter your PyPI API token (pypi-AgEI...).

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13. License & Dataset Attribution

Package License

This software is distributed under the MIT License. See LICENSE for full details.

Dataset Attributions

• Wine Quality Datasets (winequality-red.csv, winequality-white.csv): P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis (2009). Modeling wine preferences by data mining from physicochemical properties. Decision Support Systems, Elsevier, 47(4):547-553.
• Breast Cancer Wisconsin (Diagnostic) Dataset (breast-cancer.csv): Dr. William H. Wolberg, W. Nick Street, and Olvi L. Mangasarian. UCI Machine Learning Repository.
• Iris Dataset (iris.csv): R.A. Fisher (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179-188.
• Groceries Dataset (groceries.csv): Michael Hahsler, Kurt Hornik, and Christian Buchta (2005). arules - Mining Association Rules and Frequent Itemsets.