Welcome to the Libraries Lab section of our Python Tutorials. In this lab, we will explore various libraries that enhance the capabilities of Python. These libraries can help you achieve a wide range of tasks from data analysis to web development.
Common Python Libraries
Here are some of the most commonly used Python libraries:
- Pandas: For data manipulation and analysis.
- NumPy: For numerical computations.
- Matplotlib: For data visualization.
- Scikit-learn: For machine learning.
- Flask: For web development.
Pandas
Pandas is a powerful data analysis library in Python. It provides data structures and data analysis tools for manipulating numerical tables and time series data.
- Installation: Use
pip install pandas - Usage:
- Load data into a DataFrame
- Manipulate and analyze data
- Export data to various formats
NumPy
NumPy is a fundamental package for scientific computing with Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
- Installation: Use
pip install numpy - Usage:
- Create and manipulate arrays
- Perform mathematical operations on arrays
Matplotlib
Matplotlib is a plotting library for Python. It provides an object-oriented API for embedding plots into applications and web pages.
- Installation: Use
pip install matplotlib - Usage:
- Create various types of plots
- Customize plot appearance
- Save plots to various formats
Scikit-learn
Scikit-learn is a machine learning library in Python. It provides simple and efficient tools for data analysis and modeling.
- Installation: Use
pip install scikit-learn - Usage:
- Preprocess data
- Train machine learning models
- Evaluate model performance
Flask
Flask is a micro web framework for Python. It is used for web development and is highly extensible.
- Installation: Use
pip install flask - Usage:
- Create web applications
- Define routes
- Handle requests and responses