# codebasics/py

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7,262 stars · 16,889 forks · Jupyter Notebook

## Links

- GitHub: https://github.com/codebasics/py
- awesome-repositories: https://awesome-repositories.com/repository/codebasics-py.md

## Topics

`jupyter` `jupyter-notebook` `jupyter-notebooks` `jupyter-tutorial` `numpy` `numpy-arrays` `numpy-tutorial` `pandas` `pandas-dataframe` `pandas-tutorial` `python` `python-pandas` `python-tutorial` `python-tutorials`

## Description

This project is a Python data science curriculum and programming tutorial collection. It provides a structured set of educational notebooks and scripts designed to teach data analysis, machine learning, and deep learning.

The repository serves as a learning path for building and tuning predictive models, including regression, decision trees, and neural networks. It includes a data visualization guide for creating financial time-series plots and a multiprocessing reference for implementing parallel task execution and shared memory synchronization.

The curriculum covers broader capability areas including tabular data manipulation, dimensionality reduction, and hyperparameter optimization. It also provides instruction on core programming fundamentals, algorithm study, and the development of specific applications such as face recognition and home price prediction.

The content is delivered through notebook-based interactive learning, combining executable code with rich text and inline visualizations.

## Tags

### Education & Learning Resources

- [Data Science Learning Materials](https://awesome-repositories.com/f/education-learning-resources/data-science-learning-materials.md) — Provides a complete collection of educational notebooks and scripts for learning data analysis, machine learning, and deep learning. ([source](https://github.com/codebasics/py/tree/master/jupyter))
- [Interactive Notebook Curricula](https://awesome-repositories.com/f/education-learning-resources/interactive-notebook-curricula.md) — Delivers a structured educational series using interactive notebooks for data science and machine learning skill development.
- [Programming Fundamentals](https://awesome-repositories.com/f/education-learning-resources/programming-fundamentals.md) — Provides a structured curriculum for learning programming fundamentals and the foundations of data science. ([source](https://github.com/codebasics/py/tree/master/TechTopics))
- [Algorithm Implementations](https://awesome-repositories.com/f/education-learning-resources/educational-resources/algorithms-theory-academics/cs-theory-foundations/algorithms/general-collections-and-study/algorithm-implementations.md) — Provides practical code implementations of various algorithms and programming patterns for study. ([source](https://github.com/codebasics/py/blob/master/README.md))
- [Python Tutorials](https://awesome-repositories.com/f/education-learning-resources/educational-resources/languages-and-programming-concepts/programming-language-mastery-guides/python-tutorials.md) — Offers a set of sample programs and exercises covering Python fundamentals from file I/O to object-oriented programming.
- [Machine Learning Study Paths](https://awesome-repositories.com/f/education-learning-resources/machine-learning-study-paths.md) — Provides a structured sequence of learning activities to build proficiency in regression, decision trees, and neural networks.
- [Python Programming Guides](https://awesome-repositories.com/f/education-learning-resources/python-programming-guides.md) — Provides structured roadmaps and sample code for learning core Python programming concepts and algorithms.
- [Data Visualization Tutorials](https://awesome-repositories.com/f/education-learning-resources/data-visualization-tutorials.md) — Ships a series of tutorials for creating and exporting financial time-series plots and charts.

### Artificial Intelligence & ML

- [Machine Learning Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-implementations.md) — Provides code-based implementations of core machine learning algorithms such as regression and decision trees. ([source](https://github.com/codebasics/py/tree/master/ML))
- [Data Preparation Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/data-ingestion-preparation/data-preparation-tools.md) — Guides the cleaning, encoding, and splitting of raw data to prepare it for machine learning models. ([source](https://github.com/codebasics/py/tree/master/ML))
- [Machine Learning Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training.md) — Offers a structured learning path for building, training, and tuning predictive models and neural networks.
- [Clustering Algorithms](https://awesome-repositories.com/f/artificial-intelligence-ml/clustering-algorithms.md) — Implements algorithms for grouping similar unlabeled data points to discover hidden patterns. ([source](https://github.com/codebasics/py/tree/master/ML))
- [Dimensionality Reduction](https://awesome-repositories.com/f/artificial-intelligence-ml/dimensionality-reduction.md) — Includes lessons on simplifying complex datasets by extracting essential structures for improved model training.
- [Dimensionality Reduction Techniques](https://awesome-repositories.com/f/artificial-intelligence-ml/dimensionality-reduction-techniques.md) — Demonstrates how to simplify complex datasets by reducing input variables while preserving essential information. ([source](https://github.com/codebasics/py/tree/master/ML))
- [Face Recognition](https://awesome-repositories.com/f/artificial-intelligence-ml/face-recognition.md) — Provides a guide for identifying and distinguishing faces using image processing and pattern recognition. ([source](https://github.com/codebasics/py/tree/master/DataScience))
- [Deep Learning Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/architectures/neural-network-components/deep-learning-implementations.md) — Includes educational codebases for implementing neural network architectures for image classification and prediction. ([source](https://github.com/codebasics/py/tree/master/DeepLearningML))
- [Neural Network Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/neural-network-layers.md) — Provides examples of using pre-defined architectural building blocks to construct deep learning models.
- [Layered Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/neural-network-layers/convolution-layers/layered-architectures.md) — Explains organizational patterns that structure neural networks as sequences of independent operational layers.
- [Training Progress Monitoring](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/utilities/training-progress-monitoring.md) — Provides tools for monitoring training progress, including loss tracking and hardware utilization efficiency. ([source](https://github.com/codebasics/py/tree/master/DeepLearningML))
- [Model Evaluation Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-and-validation/model-evaluation-metrics.md) — Provides tutorials on calculating precision, recall, and loss to evaluate the effectiveness of machine learning models. ([source](https://github.com/codebasics/py/tree/master/DeepLearningML))
- [Hyperparameter Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/training-algorithms/machine-learning-optimization/hyperparameter-tuning.md) — Implements iterative processes for optimizing model configurations to improve predictive accuracy.
- [Generalization Techniques](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/profiling-and-benchmarking/model-performance-optimization/generalization-techniques.md) — Teaches how to use dropout and data augmentation to improve model accuracy and generalization. ([source](https://github.com/codebasics/py/tree/master/DeepLearningML))
- [Hyperparameter Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/training-efficiency/hyperparameter-optimization.md) — Teaches automated methods for searching and selecting the best configuration parameters for machine learning models.
- [Real Estate Price Prediction](https://awesome-repositories.com/f/artificial-intelligence-ml/model-predictions/real-estate-price-prediction.md) — Implements a machine learning model to estimate residential property values based on regional housing data. ([source](https://github.com/codebasics/py/tree/master/DataScience))

### Data & Databases

- [Dataframe Processing](https://awesome-repositories.com/f/data-databases/data-processing-pipelines/data-processing/dataframe-processing.md) — Provides instruction and scripts for programmatic manipulation of tabular datasets using the dataframe abstraction.
- [Data Visualization Charts](https://awesome-repositories.com/f/data-databases/data-visualization-charts.md) — Provides tutorials for creating diverse chart types like bar charts and histograms to visualize data patterns. ([source](https://github.com/codebasics/py/tree/master/matpltlib))
- [Tabular Data Frames](https://awesome-repositories.com/f/data-databases/tabular-data-frames.md) — Teaches data cleaning and transformation using structured data frames for analysis. ([source](https://github.com/codebasics/py/tree/master/Modules))
- [Financial Time-Series Analysis](https://awesome-repositories.com/f/data-databases/financial-time-series-analysis.md) — Demonstrates how to manipulate and visualize financial time-series data to identify stock price patterns. ([source](https://github.com/codebasics/py/tree/master/jupyter))

### Development Tools & Productivity

- [Interactive Notebooks](https://awesome-repositories.com/f/development-tools-productivity/interactive-notebooks.md) — Uses computational documents combining live code, narrative text, and visual outputs for data analysis.
- [Parallel Execution](https://awesome-repositories.com/f/development-tools-productivity/parallel-execution.md) — Demonstrates running multiple functions concurrently across CPU cores to improve execution performance. ([source](https://github.com/codebasics/py/tree/master/Multiprocessing))

### Programming Languages & Runtimes

- [Programming Concepts](https://awesome-repositories.com/f/programming-languages-runtimes/language-features-paradigms/language-features/core-conceptual-frameworks/programming-concepts.md) — Explores fundamental programming concepts including file I/O and object-oriented programming through sample programs. ([source](https://github.com/codebasics/py/tree/master/Basics))
- [Python](https://awesome-repositories.com/f/programming-languages-runtimes/language-features-paradigms/concurrency-models/concurrency/python.md) — Provides practical implementations and reference materials for Python's multiprocessing and shared memory synchronization.

### Scientific & Mathematical Computing

- [Data Science](https://awesome-repositories.com/f/scientific-mathematical-computing/research-analysis-workflows/research-and-data-analysis-tools/data-science.md) — Provides a comprehensive curriculum for cleaning, transforming, and visualizing tabular data to extract insights.
- [Vectorized Array Operations](https://awesome-repositories.com/f/scientific-mathematical-computing/high-performance-execution-environments/scientific-computing-platforms/scientific-computing/vectorized-array-operations.md) — Teaches calculations performed on entire arrays at once to optimize performance during data analysis.

### Part of an Awesome List

- [Financial Data Analysis](https://awesome-repositories.com/f/awesome-lists/data/financial-data-analysis.md) — Includes guides for processing and visualizing stock market time-series data to identify financial trends.
- [Visualization Guides](https://awesome-repositories.com/f/awesome-lists/devtools/visualization-guides.md) — Includes detailed instructions and examples for creating and exporting financial time-series charts.

### Operating Systems & Systems Programming

- [Process Synchronization Locks](https://awesome-repositories.com/f/operating-systems-systems-programming/process-synchronization-locks.md) — Implements synchronization locks to prevent data corruption when multiple processes access shared memory. ([source](https://github.com/codebasics/py/tree/master/Multiprocessing))
- [Shared Memory Arrays](https://awesome-repositories.com/f/operating-systems-systems-programming/shared-memory-arrays.md) — Provides implementation examples for using shared memory arrays to share state across concurrent Python processes. ([source](https://github.com/codebasics/py/tree/master/Multiprocessing))

### Software Engineering & Architecture

- [Local Multiprocessing](https://awesome-repositories.com/f/software-engineering-architecture/distributed-task-queues/local-multiprocessing.md) — Provides a reference for distributing compute-intensive operations across multiple CPU cores using worker pools.
- [Parallel Processing](https://awesome-repositories.com/f/software-engineering-architecture/performance-reliability/performance-optimization/computational-efficiency/parallel-processing.md) — Implements parallel task execution using process pools and shared memory synchronization in Python.