Python-essentials-for-Data-Science

Important python libraries tutorials

This repository contains tutorials, examples, and exercises on essential Python libraries widely used in Data Science and Machine Learning.
It is designed as a quick reference and learning resource for beginners and intermediate practitioners.

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📚 Libraries Covered

• NumPy → Numerical computations, arrays, linear algebra, random numbers
• Pandas → Data manipulation, cleaning, merging, grouping, and analysis

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📖 Topics Covered

🔢 NumPy

Array creation and indexing

Mathematical operations

Broadcasting

Linear algebra basics

Random number generation

🐼 Pandas

Series and DataFrames

Reading and writing data (CSV, Excel, etc.)

Data selection, filtering, and indexing

Handling missing data

GroupBy, merge, and join operations

Data cleaning and preprocessing

Holidays

Time Series Data

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🛠️ Installation & Setup

Clone the repository:

git clone https://github.com/<your-username>/Python-essentials-for-Data-Science.git
cd Python-essentials-for-Data-Science

Create and activate a virtual environment (recommended):

python -m venv venv
source venv/bin/activate    # Mac/Linux
venv\Scripts\activate       # Windows

Install dependencies:

pip install -r requirements.txt

## 📓 **Usage**

Open Jupyter Notebook to explore tutorials:

jupyter notebook

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