🐍 Python Projects Repository

Welcome to the Python Projects Repository! This repository hosts multiple beginner-to-intermediate level Python-based projects with well-documented code and practical demonstrations. Explore two main projects included here:

📘 Project 1: Python Tutorials with Pandas

A comprehensive, beginner-to-intermediate Python tutorial series using Jupyter Notebook. This project introduces Python fundamentals and transitions smoothly into powerful data manipulation using the Pandas library.

📚 Table of Contents

• Introduction
• Python Data Types
• Control Structures
• Functions & Lambda Expressions
• File Input/Output
• Pandas Library
  • Series
  • DataFrames
  • Data Viewing & Manipulation

📌 Introduction

Python is a high-level, interpreted programming language known for its readability, simplicity, and vibrant community. It’s ideal for:

• Data analysis
• Web development
• Automation
• Scientific computing

🔤 Python Data Types

• Variables: Dynamically typed
• Numeric Types: int, float, complex
• Strings: Immutable sequences
• Lists: Mutable ordered collections
• Dictionaries: Key-value mappings
• Tuples: Immutable sequences
• Sets: Unordered collections of unique items

⚖️ Comparison & Logical Operators

• Relational: ==, !=, <, <=, >, >=
• Logical: and, or, not, in, not in

🔁 Control Structures

• Conditional Statements: if, elif, else
• Loops: for, while, nested loops supported

🧠 Functions & Lambda Expressions

• Defined using def
• Anonymous functions using lambda
• Usage with map(), filter(), reduce()

Example:

square = lambda x: x ** 2
print(square(4))  # Output: 16

🗂️ File Input/Output

• input() for keyboard input
• File modes: "r", "w", "a", "r+", "a+"
• Operations: read(), write(), close(), seek(), tell()
• File management: os.rename(), os.remove()

🐼 Pandas Library

A high-performance library for data analysis and manipulation.

📌 Series

• 1D labeled arrays
• Supports slicing, indexing, vectorized operations
• Constructed from dict, ndarray, or scalar

📊 DataFrames

• 2D labeled data structure
• Constructed from lists of dicts, dict of Series, structured arrays, etc.

⚙️ Key Operations

• Column/row selection
• Arithmetic operations
• Transposing with .T
• Sorting: .sort_values(), .sort_index()
• Boolean indexing and filtering

👁️ Data Viewing & Manipulation

• .head(), .tail(n), .describe()
• Indexing with .loc[], .iloc[]
• Boolean filtering using .isin() and conditions

💻 How to Run

1. 📥 Install Anaconda
   👉 Download Anaconda

2. 🚀 Launch Jupyter Notebook

   • Start via Anaconda Navigator
   • Or run in terminal:

     jupyter notebook

3. 📂 Open the Notebook

   • Navigate to Python-Tutorials.ipynb

4. ▶️ Run Cells

   • Use Shift + Enter to execute cells

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📝 Project 2: Sentence Splitting using NLTK

A simple yet effective demonstration of using NLTK (Natural Language Toolkit) to split a paragraph into individual sentences.

🧪 Steps Overview

📦 Step 1: Import the Library

import nltk

🔧 Step 2: Import Tokenizer

from nltk.tokenize import sent_tokenize

📝 Step 3: Define Your Text

text = "All the great players and great people have one thing in common. They work even harder when no one is watching them."

✂️ Step 4: Tokenize into Sentences

sentences = sent_tokenize(text)

📤 Step 5: Print the Output

print(sentences)

Output:

['All the great players and great people have one thing in common.', 'They work even harder when no one is watching them.']

📚 Additional Information

• sent_tokenize() uses a pre-trained Punkt tokenizer.
• To use it for the first time:

nltk.download('punkt')

✅ Summary

This program:

• Loads a paragraph of text.
• Splits the paragraph into individual sentences.
• Prints the result as a list of strings.

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🏷️ Project 3: Label Encoding with Python

This project demonstrates how to perform label encoding using Python in a Jupyter Notebook. Label encoding is a data preprocessing technique used in machine learning to convert categorical labels into numeric form so that algorithms can work effectively with the data.

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📁 File Description

• Label_Encoding.ipynb: Jupyter notebook that contains Python code for performing label encoding on a sample dataset using pandas.

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📚 Objectives

• Understand the concept of label encoding
• Create a sample dataset with categorical features
• Apply label encoding using different methods
• Analyze the effects of encoding on the dataset

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🔧 Technologies and Libraries Used

• Python 3
• pandas
• numpy
• Jupyter Notebook

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🚀 Workflow Overview

1. Import Libraries

The notebook starts by importing necessary libraries:

import pandas as pd
import numpy as np

2. Create a Dataset

A sample dataset is created with one or more categorical columns that require encoding. These columns represent categories like Country, Color, Gender, etc.

3. Perform Label Encoding

Label encoding is applied using:

• pandas.factorize()
• pandas.Categorical().codes

Example:

df['encoded'] = pd.factorize(df['category_column'])[0]

4. Compare Encoded Results

The encoded results are compared with the original data to understand the transformation.

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📈 Use Cases of Label Encoding

• Preparing categorical data for machine learning models
• Converting textual class labels to integers
• Encoding target labels in supervised learning

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✅ Best Practices

• Ensure that the categorical variables are ordinal if using Label Encoding.
• For nominal (non-ordered) categories, consider using One-Hot Encoding to prevent unintended ordinal relationships.

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📌 Note

Label Encoding assigns each unique category value an integer, which can introduce unintended order relationships. Be cautious when using it with nominal data.

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📂 Folder Structure

Python-Projects-Repo/
├── Python-Basic/
│   └── Python_Assignment_1.ipynb
│   └── README.md
├── Python_NLP/
│   └── Python_NLP.ipynb
│   └── README.md
├── Label-Encoding-Tutorial/
│   └── Label_Encoding.ipynb
│   └── README.md
└── README.md  <-- [You are here]

🧑‍💻 Author

Muhammad Saeed
BSc Electrical Engineering
Expert in Python, Embedded Systems, and AI Applications

Happy Coding! 🚀