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shafaq-aslam / repository
A comprehensive collection of Jupyter notebooks exploring Pandas, from Series and DataFrames to data cleaning, aggregation, merging, and visualization. A complete hands-on guide for mastering data manipulation and analysis with Python.
A hands-on journey through Pandas, diving deep into data cleaning, manipulation, transformation, and analysis — the core of data science with Python.
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.
💡 Each folder inside the
Pandasdirectory explores a specific concept of Pandas — from Series and DataFrames to advanced topics like GroupBy, Merging, and Time Handling.
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| Notebook | Description |
|---|---|
| Pivot_table | Creating pivot tables for summarizing and analyzing data |
| expense_data.csv | Dataset for pivot table practice and visualization |
| Notebook | Description |
|---|---|
| Pandas_string | Working with vectorized string operations for data cleaning |
| titanic.csv | Dataset for applying string manipulation techniques |
| 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 |
Shafaq Aslam
📍 Passionate learner exploring Data Analytics, Machine Learning, and AI through consistent hands-on practice.
pandas python data-analysis data-cleaning data-visualization dataframe series machine-learning data-science jupyter-notebooks learning-lab
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