This repository is written in English to reach a wider audience.
Welcome to the Data Science Libraries in Python repository! This project is designed as a didactic resource to explore and understand essential Python libraries to help with your Data Science studies. Here, you'll find useful informations that'll help you master these tools.
- Learn the purpose of each library: Understand what each library is used for and its importance in the data science workflow.
- Practice with code examples: Explore clear, didactic examples using fictitious datasets.
- Access curated resources: Find links to documentation, books, and courses for deeper learning.
The repository is organized into folders by theme to make navigation intuitive:
├── README.md
├── Data Science Workflow/
├── Data Manipulation/
│ ├── numpy_basics.ipynb
│ ├── pandas_data_cleaning.ipynb
├── Data Visualization/
│ ├── matplotlib_basics.ipynb
│ ├── seaborn_heatmaps.ipynb
│ ├── plotly_interactive_charts.ipynb
├── Machine Learning/
├── sklearn_regression.ipynb
├── sklearn_classification.ipynb
├── xgboost_basics.ipynb
Each folder contains Jupyter notebooks that:
- Introduce the library: Highlight its main features and applications.
- Provide code examples: Demonstrate common tasks and workflows.
- Include comments: Explain each step of the code for better understanding.
-
Data Manipulation
numpy: Numerical computing with multi-dimensional arrays.pandas: Data manipulation and analysis with DataFrames.
-
Data Visualization
matplotlib: Creating static, animated, and interactive plots.seaborn: Statistical data visualization built on Matplotlib.plotly: Interactive visualizations and dashboards.
-
Machine Learning
scikit-learn: Essential tools for machine learning (classification, regression, clustering, etc.).xgboost: Gradient boosting for structured data.
-
Official Documentation:
-
Books:
- "Python for Data Analysis" by Wes McKinney
- "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
-
Courses:
This is a dynamic project with ongoing updates. Here's the plan:
-
Initial Setup
- ✅ Create folders and templates for each theme.
- ✅ Add basic examples for NumPy and Pandas.
-
Expand Visualization Examples
- ✅ Create interactive dashboards with Plotly.
- 🔄 Add advanced plots in Seaborn. (In Progress ⬅️)
-
Machine Learning Use Cases
- ✅ Include examples with Scikit-learn (regression and classification).
- ✅ Add examples with XGBoost.
-
Polishing and Documentation
- 🔄 Refine code comments and add detailed explanations. (In Progress ⬅️)
- 🔄 Add Markdown explanations for workflows. (In Progress ⬅️)
Happy learning! 🚀