Pandas-Tutorial-Data-Cleaning-

This repository contains Jupyter Notebooks that demonstrate hands-on Pandas operations and data cleaning techniques for exploratory data analysis (EDA).

📊 Pandas Tutorial & Data Cleaning

This repository contains Jupyter Notebooks that cover Pandas basics, tutorials, and real-world data cleaning workflows.
It is designed for learners and practitioners who want to strengthen their data wrangling and preprocessing skills using Python.

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📌 Project Overview

The project consists of two key notebooks:

1. pandas-tutorial.ipynb

   • Covers Pandas fundamentals including:
     • Series & DataFrames
     • Data selection and indexing
     • Filtering and grouping
     • Merging, joining, and concatenation
     • Handling missing values
     • Basic statistical functions

2. data-analysis-and-data-cleaning.ipynb

   • Focuses on real-world data cleaning and preparation:
     • Handling null values and duplicates
     • Feature engineering
     • String operations and categorical encoding
     • Outlier detection and treatment
     • Exploratory data analysis (EDA)

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🛠️ Technologies Used

• Python 3
• Jupyter Notebook
• Pandas – Data manipulation and analysis
• NumPy – Numerical computing
• Matplotlib, Seaborn – Visualization

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🚀 How to Run the Project

1. Clone this repository:

   git clone https://github.com/your-username/pandas-data-cleaning.git
   cd pandas-data-cleaning
   

pip install -r requirements.txt jupyter notebook

📈 Learning Outcomes Learn how to use Pandas effectively for data wrangling. Gain experience with data cleaning techniques for preparing datasets. Understand how to handle messy, real-world data before modeling.

📌 Future Work Add tutorials on time series analysis using Pandas. Include performance optimization tips with large datasets. Extend project with case studies from Kaggle datasets.