A comprehensive collection of practice problems covering essential Python libraries and frameworks for data science, machine learning, and web development.

This repository contains Jupyter notebooks with hands-on practice problems for mastering core Python libraries. Each notebook focuses on specific concepts and includes TODO exercises to help you build practical skills.

HTTP requests and API interactions using the requests library.

Topics covered:

• HTTP basics: GET/POST, query/body/header, status codes, timeout/retry
• Sessions and authentication: Session, cookies, tokens
• Data formats: JSON encoding/decoding, file upload/download
• Reliability: timeout, retry, idempotency, exception handling
• Performance and security: connection reuse, proxy, SSL certificate verification

Essential NumPy operations for numerical computing.

Topics covered:

• ndarray concepts: shape/dtype/axis, view vs copy
• Broadcasting mechanism
• Vectorization and performance: avoiding Python loops
• Sorting and indexing: boolean indexing, advanced indexing, np.where
• Statistics and linear algebra: sum/mean/std, dot product, norm

Data manipulation and analysis with Pandas.

Topics covered:

• Data types and missing values: dtype, NA/NaN, missing value handling strategies
• Index system: index/columns, multi-level index (concept)
• Transformation pipeline: select/filter/assign, apply/map vs vectorized operations
• Aggregation: groupby aggregations, rolling windows (concept)
• Merging: merge/join/concat, join keys and duplicate row risks
• Time series: datetime operations, resample (concept)
• Performance and memory: categorical data types, chunk processing, avoiding apply abuse

Data visualization with matplotlib.

Topics covered:

• Chart selection: line plots, bar charts, histograms, box plots, scatter plots
• Multiple axes and subplots (concept)
• Annotations: title/label/legend/annotation
• Reading charts: verifying distributions, identifying outliers, showing trends, comparing groups
• Output capabilities: saving figures, resolution, report-ready visualizations

Machine learning with scikit-learn.

Topics covered:

• Pipeline thinking: preprocess + model + evaluation
• Data splitting: train/valid/test, cross-validation (concept)
• Feature processing: encoding, standardization, missing values, data leakage risks
• Common models: linear models, tree models, ensemble models
• Evaluation metrics: classification/regression metrics, threshold and imbalanced data handling
• Overfitting and hyperparameter tuning: regularization, early stopping (concept), grid/random search
• Interpretability and diagnostics: feature importance, error analysis

Building REST APIs with FastAPI.

Topics covered:

• API design: REST resources, status codes, error response formats
• Pydantic validation: schema definition, type constraints, error messages
• Dependency injection: Depends (concept), authentication, configuration injection
• Asynchronous programming: async/await, I/O bound operations, concurrency model
• Middleware: logging, trace ID, CORS, rate limiting (concept)
• Deployment: Uvicorn/Gunicorn (concept), health checks, monitoring metrics (concept)
• Testing: API testing, contract stability

Deep learning with PyTorch.

Topics covered:

• Tensor basics: device/dtype/shape, broadcasting, in-place operation risks
• Autograd: computational graph, requires_grad, detach (concept)
• Training loop: forward/backward/step/zero_grad
• DataLoader: batching, shuffle, num_workers (concept)
• Common modules: nn.Module, loss functions, optimizers, schedulers (concept)
• Training stability: random seed, gradient explosion/vanishing, gradient clipping (concept)
• Performance and deployment: mixed precision (concept), TorchScript/ONNX (concept)

• Python 3.7+
• Jupyter Notebook or JupyterLab
• Required packages:

  pip install requests numpy pandas matplotlib scikit-learn fastapi uvicorn torch

1. Clone this repository:

   git clone [email protected]:tianzq13184/python-core-practice.git
   cd python-core-practice

2. Open any notebook in Jupyter:

   jupyter notebook

3. Complete the exercises marked with # TODO in each cell.

4. Run the cells to verify your solutions match the expected output.

python-core-practice/
├── README.md
├── python-algo-practice-problems.ipynb
├── requests-practice-problems.ipynb
├── numpy-practice-problems.ipynb
├── pandas-practice-problems.ipynb
├── matplotlib-practice-problems.ipynb
├── scikit-learn-practice-problems.ipynb
├── fastapi-practice-problems.ipynb
└── pytorch-practice-problems.ipynb

1. Beginner: Start with Requests and NumPy to understand HTTP requests and numerical computing basics.
2. Intermediate: Move to Pandas and Matplotlib for data analysis and visualization.
3. Advanced: Explore Scikit-learn for machine learning, FastAPI for web development, and PyTorch for deep learning.

Contributions are welcome! Please feel free to submit a Pull Request.

This project is open source and available for educational purposes.

Created for Python core practice and skill development.