🧠 NumPy & 🐼 Pandas Python Libraries – Beginner to Moderate Level Tutorials

Welcome to the NumPy and Pandas Python Library Tutorial Repository!
This project provides two beginner-to-moderate level Jupyter Notebooks that demonstrate how to use NumPy and Pandas for numerical computation and data analysis in Python.
It’s perfect for students, developers, and data-science enthusiasts who want to build a strong foundation in Python’s data ecosystem.

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📘 Project Overview

Library	Description
🧠 NumPy	A library for fast numerical operations, linear algebra, and multi-dimensional arrays.
🐼 Pandas	A library for handling, analyzing, and visualizing structured data easily and efficiently.

Both notebooks are designed to be beginner-friendly yet insightful enough for moderate-level learners.

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📂 Folder Structure

data-science-libraries/
│
├── NumPy                    # NumPy hands-on notebook
├── pandas                   # Pandas practical guide
└── README.md                # Documentation (this file)

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🧠 NumPy Python Library – Moderate Level Tutorial

Welcome to the NumPy Python Library Tutorial — a structured Jupyter Notebook designed for anyone who wants to strengthen their understanding of numerical computing with Python.

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📘 Topics Covered

Section	Topic	Description
1️⃣	Importing & Basics	Understanding what NumPy is and its version
2️⃣	Array Creation	Using lists, ranges, zeros, ones, random, etc.
3️⃣	Array Properties	Learn shape, dtype, dimension, and size
4️⃣	Indexing & Slicing	Accessing and modifying array elements
5️⃣	Mathematical Operations	Element-wise arithmetic and universal functions
6️⃣	Statistical Functions	Mean, median, std, min, max, sum
7️⃣	Reshaping & Stacking	Reshape and merge multiple arrays
8️⃣	Broadcasting	Perform operations on arrays of different shapes
9️⃣	Mini Project – Student Marks Analysis	Real-world example using NumPy

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🚀 How to Run NumPy Notebook

pip install numpy jupyter
jupyter notebook NumPy_Tutorial.ipynb

💡 Example Outputs

Addition: [ 7 14 21]
Mean: 52.857142857142854
Topper: Student 5 with Average Marks: 94.33
Best Subject Index: 3 with Average: 86.8
✅ NumPy Tutorial Completed Successfully!

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🎯 Learning Outcomes

• Create and manipulate NumPy arrays effectively
• Perform fast vectorized numerical computations
• Reshape, stack, and broadcast arrays
• Apply NumPy in real-world data analysis

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🐼 Pandas Python Library - Beginner to Moderate Level

Welcome to the Pandas Python Library Tutorial Repository!
This notebook demonstrates how to use Pandas for data analysis and data manipulation in Python — from beginner to moderate level.

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📚 Topics Covered

1. 📘 Introduction to Pandas
2. 🧾 Creating and Reading DataFrames
3. 📂 Reading & Writing CSV Files
4. 🔍 Data Selection and Filtering
5. 🧩 Adding & Removing Columns
6. 🧹 Handling Missing Data
7. 📊 Grouping and Aggregation
8. 🪄 Sorting Data
9. 📈 Visualization using Matplotlib

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🧠 What You’ll Learn

• Create and explore DataFrames
• Clean, filter, and modify data
• Perform statistical operations
• Handle missing or duplicate values
• Visualize data using Matplotlib
• Save and load datasets (CSV format)

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🚀 How to Run Pandas Notebook

pip install pandas numpy matplotlib
jupyter notebook pandas_basics.ipynb

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📊 Example Outputs

• Filtering students by city or marks
• Filling missing values with fillna()
• Grouping by city to find average marks
• Sorting and visualizing student marks

Example code:

grouped = df.groupby('City')['Marks'].mean()
print(grouped)

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📈 Visualization Example

Name     Marks
Amit     88
Sneha    92
Ravi     79
Priya    85
Vikram   95

📊 The bar chart visualizes marks comparison among students.

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🧩 Technologies Used

• 🐍 Python 3.x
• 🧠 NumPy
• 🐼 Pandas
• 📈 Matplotlib
• 📘 Jupyter Notebook

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🧑‍💻 Author

👤 Krushnal Patil
🎓 Final Year B.Tech (IT) Student | 💻 Aspiring Data Scientist

📫 Connect with me:

• 🌐 GitHub
• 💼 LinkedIn
• ✉️ Email: Krushnalpatil056@gmail.com

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🏷️ Keywords

Python NumPy Pandas Data Science Machine Learning DataFrame
Data Analysis Jupyter Notebook Python Projects Moderate Level CSV Handling

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⭐ How You Can Contribute

If you find this project helpful:

• ⭐ Star this repository
• 🐛 Report issues or suggest improvements
• 🧠 Fork and extend with new topics
• 🔁 Share it with your learning community

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📜 License

This project is open source under the MIT License.
Feel free to use, modify, or share for educational purposes.

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🧠 “Data is the new oil — and Pandas & NumPy are your refinery tools.”
– Krushnal Patil