🚀 PyML Snippets
Supercharge your Python, Data Science & Machine Learning Workflow
Write cleaner code, save time, and boost productivity with 54 ready-to-use Visual Studio Code snippets for Python, Data Science, Machine Learning, EDA, Data Preprocessing, Visualization, Model Evaluation, and NLP.
⭐ If this extension helps you, don't forget to Star the repository.
📖 Overview
PyML Snippets is a lightweight VS Code extension designed for Python developers, Data Scientists, Machine Learning Engineers, and students.
Instead of writing repetitive boilerplate code, simply type a short prefix and press Tab to generate complete code instantly.
✨ Features
- 🚀 54 production-ready snippets
- 🐍 Python development snippets
- 📊 Data Science utilities
- 🤖 Machine Learning models
- 📈 Complete EDA templates
- 🧹 Data preprocessing
- 📉 Visualization snippets
- 🎯 Model evaluation
- 📝 NLP and text processing workflows
- 🔤 Text vectorization and Word2Vec
- 📌 Outlier detection with IQR
- ⚡ Train/Test Split
- 🔍 GridSearchCV
- 🔄 ML Pipelines
- 🧠 Classification & Regression snippets
🎥 Demo
Example:
Type
tts
↓
Press TAB
↓
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(...)
🚀 Quick Start
1. Open a Python file
Create or open any .py file.
2. Type a snippet prefix
Example
tts
3. Press
TAB
Done ✅
📚 Snippet Library
| Prefix |
Description |
| tts |
Train Test Split |
| scaler |
Standard Scaler |
| minmax |
Min Max Scaler |
| one |
One Hot Encoding |
| label |
Label Encoding |
| npm |
Import NumPy, Pandas, Matplotlib and Seaborn |
| metrics |
Import and print classification metrics |
| makereg |
Create Regression Dataset |
| clfevl |
Complete Classification Evaluation |
| pipeline |
Pipeline with StandardScaler |
| regeval |
Regression Evaluation Metrics |
| fitpred |
Fit model and predict |
| makeclf |
Create Classification Dataset |
| binaryclf |
Binary Classification Dataset |
| imbclf |
Imbalanced Classification Dataset |
| grid |
GridSearchCV Template |
| compareclf |
Compare Multiple Classification Models |
| ctonehot |
ColumnTransformer with OneHotEncoder |
| gaussian |
Import GaussianNB |
| logistic |
Import Logistic Regression |
| knn |
Import KNN |
| svc |
Import SVC |
| rfc |
Import Random Forest |
| linear |
Linear Regression Import |
| ridge |
Ridge Import |
| lasso |
Lasso Import |
| elastic |
ElasticNet Import |
| dt |
Decision Tree Classifier |
| dtr |
Decision Tree Regressor |
| extra |
Extra Trees |
| ada |
AdaBoost |
| gb |
Gradient Boosting |
| histgb |
Hist Gradient Boosting |
| eda |
Professional EDA Summary |
| edaplot |
EDA Correlation Heatmap |
| edashort |
Quick EDA |
| csv |
Read CSV File |
| heat |
Correlation Heatmap |
| hist |
Histogram with KDE |
| scatter |
Scatter Plot |
| count |
Count Plot |
| comparereg |
Compare Multiple Regression Models |
| nlp-remove |
Complete NLP text cleaning with stopword, URL, HTML tag, and extra space removal |
| lemmatize |
Apply lemmatization to a text column using WordNetLemmatizer |
| bagof |
Convert text data into Bag of Words representation |
| countvector |
Convert text into a numerical matrix using CountVectorizer |
| nlp-corpus |
Clean, lowercase, tokenize, remove stopwords, and lemmatize text into a corpus |
| nlp-word2vec |
Train and use a Word2Vec model with vocabulary, word vectors, similarity, and similar words |
| nlp-word2vec-google |
Load Google's pretrained Word2Vec model and perform word vectors, similarity, and vocabulary operations |
| column-transformer |
Create a ColumnTransformer for numerical and categorical features |
| tfidf |
Convert text into TF-IDF features with configurable n-grams and frequency thresholds |
| remove-outlier |
Detect and remove outliers from a numerical column using the IQR method |
| outlier |
Detect outliers in a numerical column using the IQR method |
| nlp-text-classification |
Complete NLP text classification workflow using TF-IDF and Logistic Regression |
💡 Why PyML Snippets?
✅ Save hours of repetitive coding
✅ Write cleaner code
✅ Learn faster
✅ Improve productivity
✅ Beginner Friendly
✅ Perfect for Data Science projects
🎯 Who is this for?
- Python Developers
- Data Scientists
- Machine Learning Engineers
- AI Enthusiasts
- College Students
- Kaggle Users
- Researchers
📦 Installation
👉 Install Now:
https://marketplace.visualstudio.com/items?itemName=DevManish007.pyml-snippets
From VS Code Marketplace
- Open Extensions
- Search PyML Snippets
- Click Install
Manual Installation
Install the generated .vsix file using:
Extensions
↓
...
↓
Install from VSIX...
🛣️ Roadmap
Upcoming updates:
- Pandas snippets
- NumPy snippets
- Seaborn snippets
- Matplotlib snippets
- XGBoost
- LightGBM
- CatBoost
- TensorFlow
- PyTorch
- SHAP
- Optuna
- SQL snippets
🤝 Contributing
Contributions are welcome!
Feel free to open an Issue or submit a Pull Request.
👨💻 Developer
Dev_Manish
Python Developer • Data Science • Machine Learning • AI
GitHub
https://github.com/Developer-Manish007
📄 License
This project is licensed under the MIT License.
Copyright (c) 2026 Dev_Manish
See the LICENSE file for the complete license terms.
⭐ Star this repository if you find it useful!
Made with ❤️ by Dev_Manish
