📊 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 folder inside the Pandas directory explores a specific concept of Pandas — from Series and DataFrames to advanced topics like GroupBy, Merging, and Time Handling.

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
    │
    ├── GroupBy/
    │   ├── GroupBy_object.ipynb
    │   ├── GroupBy_attributes_and_methods.ipynb
    │   ├── GroupBy_on_multiple_cols.ipynb
    │   ├── GroupBy_aggregate_method.ipynb
    │   ├── Looping_and_built-in_functions.ipynb
    │   ├── deliveries.csv
    │   └── imdb-top-100.csv
    │
    ├── Merging_Joining_and_Concatenating/
    │   ├── Joining_and_concatenating.ipynb
    │   ├── Merging.ipynb
    │   ├── Practice_questions.ipynb
    │   ├── courses.csv
    │   ├── deliveries.csv
    │   ├── matches.csv
    │   ├── students.csv
    │   ├── reg-month1.csv
    │   └── reg-month2.csv
    │
    ├── MultiIndexing_and_Melt/
    │   ├── MultiIndex_Series.ipynb
    │   ├── MultiIndex_DataFrame.ipynb
    │   ├── Long_Vs_Wide_Data.ipynb
    │   ├── time_series_covid19_confirmed_global.csv
    │   ├── time_series_covid19_death_global.csv
    │   └── wideLong.png
    │
    ├── Pivot_Table/
    │   ├── Pivot_table.ipynb
    │   └── expense_data.csv
    │
    ├── Vectorized_String_Operations/
    │   ├── Pandas_string.ipynb
    │   └── titanic.csv
    │
    └── Date_and_Time_in_Pandas/
        ├── date_and_time_in_pandas.ipynb
        ├── DatetimeIndex_object.ipynb
        ├── functions_and_accessors.ipynb
        └── expense_data.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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🔹 GroupBy

Notebook	Description
GroupBy_object	Creating and exploring GroupBy objects
GroupBy_attributes_and_methods	Understanding key attributes and aggregation methods
GroupBy_on_multiple_cols	Applying grouping on multiple columns
GroupBy_aggregate_method	Using the .agg() method for complex aggregations
Looping_and_built-in_functions	Iterating over groups and applying built-in functions
deliveries.csv / imdb-top-100.csv	Practice datasets for aggregation and grouping

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🔹 Merging, Joining, and Concatenating

Notebook	Description
Joining_and_concatenating	Combining data vertically and horizontally
Merging	Merging datasets using keys and relationships
Practice_questions	Exercises to apply merging and joining concepts
courses.csv / deliveries.csv / matches.csv / students.csv / reg-month1.csv / reg-month2.csv	Practice datasets for combining and joining operations

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🔹 MultiIndexing and Melt

Notebook	Description
MultiIndex_Series	Creating and managing hierarchical Series
MultiIndex_DataFrame	Working with multi-level DataFrames
Long_Vs_Wide_Data	Converting data between long and wide formats using melt() and pivot()
time_series_covid19_confirmed_global.csv / time_series_covid19_death_global.csv / wideLong.png	Real datasets for reshaping and reformatting exercises

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🔹 Pivot Table

Notebook	Description
Pivot_table	Creating pivot tables for summarizing and analyzing data
expense_data.csv	Dataset for pivot table practice and visualization

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🔹 Vectorized String Operations

Notebook	Description
Pandas_string	Working with vectorized string operations for data cleaning
titanic.csv	Dataset for applying string manipulation techniques

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🔹 Date and Time in Pandas

Notebook	Description
date_and_time_in_pandas	Introduction to date and time operations in Pandas
DatetimeIndex_object	Understanding and working with DatetimeIndex
functions_and_accessors	Using datetime-specific functions and accessors
expense_data.csv	Dataset for datetime manipulation and analysis

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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.”