📊 Cleaning, Exploring, and Analyzing Data — The Pandas Way 🧠

A hands-on journey through Pandas, diving deep into data cleaning, manipulation, transformation, and analysis — the core of data science with Python.

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🧠 Tech Stack Badges

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🧩 Mission Statement

This repository serves as my personal Pandas Lab 🧪 — where I explore, clean, and transform data using the Pandas library.

Each notebook represents a step in mastering data manipulation, aggregation, indexing, and visualization, laying a strong foundation for advanced analytics and machine learning.

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📂 Folder Structure

💡 Each notebook inside the Pandas folder explores a unique concept of Pandas — from fundamental data structures to real-world data analysis.

pandas-lab/
│
└── Pandas/
    ├── Series/
    │   ├── Pandas_Series-checkpoint.ipynb
    │   ├── Series_Maths_Methods_and_Indexing-checkpoint.ipynb
    │   ├── Series_Methods-checkpoint.ipynb
    │   ├── Boolean_indexing_on_series-checkpoint.ipynb
    │   ├── Series_with_Python_Functionalities-checkpoint.ipynb
    │   ├── Editing_Series-checkpoint.ipynb
    │   ├── Series_Using_read_CSV-checkpoint.ipynb
    │   ├── Plotting_graphs_on_series-checkpoint.ipynb
    │   ├── bollywood-checkpoint.csv
    │   └── subs-checkpoint.csv
    ├── DataFrame/
    │   ├── DataFrame_Creation.ipynb
    │   ├── DataFrame_Functions.ipynb
    │   ├── DataFrame_Attributes_And_Methods.ipynb
    │   ├── Filtering_a_DataFrame.ipynb
    │   ├── Adding_New_Cols.ipynb
    │   ├── Selecting_rows_&_columns_from_a_dataFrame.ipynb
    │   ├── batsman_runs_ipl.csv
    │   ├── diabetes.csv
    │   ├── ipl-matches.csv
    │   └── movies.csv

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🧮 Topics Covered

🔹 Series

Notebook	Description
Pandas_Series	Introduction to Pandas Series and its core structure
Series_Maths_Methods_and_Indexing	Performing mathematical operations and exploring indexing
Series_Methods	Exploring built-in Series methods for data manipulation
Boolean_indexing_on_series	Filtering data with conditional selections
Series_with_Python_Functionalities	Integrating Series with Python’s native functions
Editing_Series	Modifying Series values and structure efficiently
Series_Using_read_CSV	Creating Series directly from CSV files
Plotting_graphs_on_series	Visualizing Series data using Pandas’ built-in plotting
bollywood.csv / subs.csv	Datasets used for hands-on analysis and visualization

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🔹 DataFrame

Notebook	Description
DataFrame_Creation	Creating DataFrames from dictionaries, lists, and CSV files
DataFrame_Functions	Applying essential DataFrame functions for data transformation
DataFrame_Attributes_And_Methods	Understanding DataFrame properties, info, and key methods
Filtering_a_DataFrame	Selecting data using conditional filtering and logical operations
Adding_New_Cols	Creating and modifying columns dynamically
Selecting_rows_&_columns_from_a_dataFrame	Accessing rows and columns using loc, iloc, and label-based indexing
batsman_runs_ipl.csv / diabetes.csv / ipl-matches.csv / movies.csv	Real-world datasets for hands-on practice and exploration

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📚 Learning Resources

• 🔹 Pandas Official Docs
• 🔹 Pandas Series Lecture by CampusX
• 🔹 Important Series Methods Lecture by CampusX

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🧰 Tools & Environment

• Python 3.x
• Pandas
• NumPy
• Jupyter Notebook

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✨ Author

Shafaq Aslam
📍 Passionate learner exploring Data Analytics, Machine Learning, and AI through consistent hands-on practice.

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🔖 Tags for SEO

pandas python data-analysis data-cleaning data-visualization dataframe series machine-learning data-science jupyter-notebooks learning-lab

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“Turning raw data into meaningful insights — one DataFrame at a time.”