What is a Python Library?

When we use Python for data analysis, machine learning, or any programming, we do not write each and every code.

Why Should we?

Some experts already wrote code for us and packed it as a Library.

This reusable collection of code is called a Python Library.

A library in Python is like a ready-made toolbox.

Instead of building every tool yourself, you take the tools from the toolbox and start using it in your code.

Why do we need Python Libraries?

Save time – We do not write the same code again and again.

Easy to use – Just install, import, and start using.

Powerful features – Some libraries can do complex tasks in one line of code.

Community support – Big communities maintain them, so they keep improving.

Examples of Python Data Science Libraries:

Pandas – Works with data tables like Excel, filtering and summarizing data

NumPy – Works with numbers, arrays, mathematical calculations

Matplotlib – Creates graphs and charts

Scikit-learn – For machine learning models like regression, classification, clustering

Seaborn - Better Graphs with Less coding

SciPy - Advanced Scientific Calculations and Statistics

Statsmodels - Statistical Analysis and Detailed Reports

TensorFlow - Deep Learning and Neural Networks

PyTorch - Deep Learning and Neural Networks

XGBoost - Boosting Algorithms and fast machine learning models

LightGBM - Boosting Algorithms and fast machine learning models

Keras - High level Library for Deep Learning Models