Core Libraries & Frameworks
NumPy
The fundamental package for numerical computation with Python. Provides support for arrays, matrices, and high-level mathematical functions.
Learn MorePandas
A powerful library for data manipulation and analysis. Offers data structures like DataFrames for easy handling of tabular data.
Learn MoreMatplotlib
A comprehensive library for creating static, animated, and interactive visualizations in Python.
Learn MoreSeaborn
A data visualization library based on Matplotlib. Provides a high-level interface for drawing attractive and informative statistical graphics.
Learn MoreMachine Learning Frameworks
Scikit-learn
An open-source machine learning library that features various classification, regression, and clustering algorithms including support vector machines, random forests, and gradient boosting.
Learn MoreTensorFlow
An end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources.
Learn MorePyTorch
An open source machine learning framework that accelerates the path from research prototyping to production deployment of ML.
Learn MoreMicrosoft & Python Integration
Azure Machine Learning
Build, train, and deploy machine learning models on a scalable cloud platform.
Azure ML SDK for Python
Interact with Azure Machine Learning services from your Python environment.
SDK DocsMLflow Integration
Track experiments, package code, and deploy models with MLflow on Azure ML.
MLflow GuideMicrosoft Cognitive Services
Leverage pre-trained AI models for vision, speech, language, and decision services.
Cognitive Services Python SDK
Easy integration of AI capabilities into your Python applications.
OverviewLearning & Tutorials
Microsoft Learn: Python Data Science
Structured learning paths and modules for data science with Python.
Start LearningMicrosoft Learn: Machine Learning
Fundamentals and advanced concepts in machine learning.
Start LearningVS Code for Python
Tips and extensions for a productive Python development experience.
Developer ToolsExample Usage Snippet
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Create a simple DataFrame
data = {'col1': [1, 2, 3, 4, 5],
'col2': [10, 20, 25, 30, 15]}
df = pd.DataFrame(data)
print("DataFrame Head:")
print(df.head())
# Calculate mean of col2
mean_col2 = df['col2'].mean()
print(f"\nMean of col2: {mean_col2:.2f}")
# Plotting
plt.figure(figsize=(8, 5))
plt.plot(df['col1'], df['col2'], marker='o', linestyle='-', color='#0078d4')
plt.title('Sample Data Visualization')
plt.xlabel('Column 1')
plt.ylabel('Column 2')
plt.grid(True, linestyle='--', alpha=0.6)
plt.show()
This snippet demonstrates basic data manipulation with Pandas and plotting with Matplotlib.