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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://capsule-render.vercel.app/api?type=waving\u0026color=0:22C55E,100:2563EB\u0026height=200\u0026section=header\u0026text=Pandas%20Lab%20🐼\u0026fontSize=40\u0026fontColor=ffffff\" alt=\"Pandas Lab Banner\"/\u003e\n\u003c/p\u003e\n\n\u003ch2 align=\"center\"\u003e📊 Cleaning, Exploring, and Analyzing Data — The Pandas Way 🧠\u003c/h2\u003e\n\n\u003e A hands-on journey through **Pandas**, diving deep into data cleaning, manipulation, transformation, and analysis — the core of data science with Python.\n\n---\n\n## 🧠 Tech Stack Badges\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Python-3776AB?style=for-the-badge\u0026logo=python\u0026logoColor=white\"/\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Jupyter_Notebook-F37626?style=for-the-badge\u0026logo=jupyter\u0026logoColor=white\"/\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Pandas-150458?style=for-the-badge\u0026logo=pandas\u0026logoColor=white\"/\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Matplotlib-005C97?style=for-the-badge\u0026logo=plotly\u0026logoColor=white\"/\u003e\n  \u003cimg src=\"https://img.shields.io/badge/NumPy-013243?style=for-the-badge\u0026logo=numpy\u0026logoColor=white\"/\u003e\n\u003c/p\u003e\n\n---\n\n## 🧩 Mission Statement\n\nThis repository serves as **my personal Pandas Lab** 🧪 where I explore, clean, and transform data using the Pandas library.  \n\nEach notebook represents a step in mastering **data manipulation**, **aggregation**, **indexing**, and **visualization**, laying a strong foundation for advanced analytics and machine learning.\n\n---\n\n## 📂 Folder Structure\n\n\u003e 💡 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.\n\n\u003cpre\u003e\npandas-lab/\n│\n└── Pandas/\n    ├── Series/\n    │   ├── Pandas_Series-checkpoint.ipynb\n    │   ├── Series_Maths_Methods_and_Indexing-checkpoint.ipynb\n    │   ├── Series_Methods-checkpoint.ipynb\n    │   ├── Boolean_indexing_on_series-checkpoint.ipynb\n    │   ├── Series_with_Python_Functionalities-checkpoint.ipynb\n    │   ├── Editing_Series-checkpoint.ipynb\n    │   ├── Series_Using_read_CSV-checkpoint.ipynb\n    │   ├── Plotting_graphs_on_series-checkpoint.ipynb\n    │   ├── bollywood-checkpoint.csv\n    │   └── subs-checkpoint.csv\n    │\n    ├── DataFrame/\n    │   ├── DataFrame_Creation.ipynb\n    │   ├── DataFrame_Functions.ipynb\n    │   ├── DataFrame_Attributes_And_Methods.ipynb\n    │   ├── Filtering_a_DataFrame.ipynb\n    │   ├── Adding_New_Cols.ipynb\n    │   ├── Selecting_rows_\u0026_columns_from_a_dataFrame.ipynb\n    │   ├── batsman_runs_ipl.csv\n    │   ├── diabetes.csv\n    │   ├── ipl-matches.csv\n    │   └── movies.csv\n    │\n    ├── GroupBy/\n    │   ├── GroupBy_object.ipynb\n    │   ├── GroupBy_attributes_and_methods.ipynb\n    │   ├── GroupBy_on_multiple_cols.ipynb\n    │   ├── GroupBy_aggregate_method.ipynb\n    │   ├── Looping_and_built-in_functions.ipynb\n    │   ├── deliveries.csv\n    │   └── imdb-top-100.csv\n    │\n    ├── Merging_Joining_and_Concatenating/\n    │   ├── Joining_and_concatenating.ipynb\n    │   ├── Merging.ipynb\n    │   ├── Practice_questions.ipynb\n    │   ├── courses.csv\n    │   ├── deliveries.csv\n    │   ├── matches.csv\n    │   ├── students.csv\n    │   ├── reg-month1.csv\n    │   └── reg-month2.csv\n    │\n    ├── MultiIndexing_and_Melt/\n    │   ├── MultiIndex_Series.ipynb\n    │   ├── MultiIndex_DataFrame.ipynb\n    │   ├── Long_Vs_Wide_Data.ipynb\n    │   ├── time_series_covid19_confirmed_global.csv\n    │   ├── time_series_covid19_death_global.csv\n    │   └── wideLong.png\n    │\n    ├── Pivot_Table/\n    │   ├── Pivot_table.ipynb\n    │   └── expense_data.csv\n    │\n    ├── Vectorized_String_Operations/\n    │   ├── Pandas_string.ipynb\n    │   └── titanic.csv\n    │\n    └── Date_and_Time_in_Pandas/\n        ├── date_and_time_in_pandas.ipynb\n        ├── DatetimeIndex_object.ipynb\n        ├── functions_and_accessors.ipynb\n        └── expense_data.csv\n\u003c/pre\u003e\n\n---\n\n## 🧮 Topics Covered\n\n### 🔹 **Series**\n| Notebook | Description |\n|-----------|--------------|\n| **Pandas_Series** | Introduction to Pandas Series and its core structure |\n| **Series_Maths_Methods_and_Indexing** | Performing mathematical operations and exploring indexing |\n| **Series_Methods** | Exploring built-in Series methods for data manipulation |\n| **Boolean_indexing_on_series** | Filtering data with conditional selections |\n| **Series_with_Python_Functionalities** | Integrating Series with Python’s native functions |\n| **Editing_Series** | Modifying Series values and structure efficiently |\n| **Series_Using_read_CSV** | Creating Series directly from CSV files |\n| **Plotting_graphs_on_series** | Visualizing Series data using Pandas’ built-in plotting |\n| **bollywood.csv / subs.csv** | Datasets used for hands-on analysis and visualization |\n\n---\n\n### 🔹 **DataFrame**\n| Notebook | Description |\n|-----------|--------------|\n| **DataFrame_Creation** | Creating DataFrames from dictionaries, lists, and CSV files |\n| **DataFrame_Functions** | Applying essential DataFrame functions for data transformation |\n| **DataFrame_Attributes_And_Methods** | Understanding DataFrame properties, info, and key methods |\n| **Filtering_a_DataFrame** | Selecting data using conditional filtering and logical operations |\n| **Adding_New_Cols** | Creating and modifying columns dynamically |\n| **Selecting_rows_\u0026_columns_from_a_dataFrame** | Accessing rows and columns using loc, iloc, and label-based indexing |\n| **batsman_runs_ipl.csv / diabetes.csv / ipl-matches.csv / movies.csv** | Real-world datasets for hands-on practice and exploration |\n\n---\n\n### 🔹 **GroupBy**\n| Notebook | Description |\n|-----------|--------------|\n| **GroupBy_object** | Creating and exploring GroupBy objects |\n| **GroupBy_attributes_and_methods** | Understanding key attributes and aggregation methods |\n| **GroupBy_on_multiple_cols** | Applying grouping on multiple columns |\n| **GroupBy_aggregate_method** | Using the `.agg()` method for complex aggregations |\n| **Looping_and_built-in_functions** | Iterating over groups and applying built-in functions |\n| **deliveries.csv / imdb-top-100.csv** | Practice datasets for aggregation and grouping |\n\n---\n\n### 🔹 **Merging, Joining, and Concatenating**\n| Notebook | Description |\n|-----------|--------------|\n| **Joining_and_concatenating** | Combining data vertically and horizontally |\n| **Merging** | Merging datasets using keys and relationships |\n| **Practice_questions** | Exercises to apply merging and joining concepts |\n| **courses.csv / deliveries.csv / matches.csv / students.csv / reg-month1.csv / reg-month2.csv** | Practice datasets for combining and joining operations |\n\n---\n\n### 🔹 **MultiIndexing and Melt**\n| Notebook | Description |\n|-----------|--------------|\n| **MultiIndex_Series** | Creating and managing hierarchical Series |\n| **MultiIndex_DataFrame** | Working with multi-level DataFrames |\n| **Long_Vs_Wide_Data** | Converting data between long and wide formats using `melt()` and `pivot()` |\n| **time_series_covid19_confirmed_global.csv / time_series_covid19_death_global.csv / wideLong.png** | Real datasets for reshaping and reformatting exercises |\n\n---\n\n### 🔹 **Pivot Table**\n| Notebook | Description |\n|-----------|--------------|\n| **Pivot_table** | Creating pivot tables for summarizing and analyzing data |\n| **expense_data.csv** | Dataset for pivot table practice and visualization |\n\n---\n\n### 🔹 **Vectorized String Operations**\n| Notebook | Description |\n|-----------|--------------|\n| **Pandas_string** | Working with vectorized string operations for data cleaning |\n| **titanic.csv** | Dataset for applying string manipulation techniques |\n\n---\n\n### 🔹 **Date and Time in Pandas**\n| Notebook | Description |\n|-----------|--------------|\n| **date_and_time_in_pandas** | Introduction to date and time operations in Pandas |\n| **DatetimeIndex_object** | Understanding and working with `DatetimeIndex` |\n| **functions_and_accessors** | Using datetime-specific functions and accessors |\n| **expense_data.csv** | Dataset for datetime manipulation and analysis |\n\n---\n\n## 📚 Learning Resources\n\n- 🔹 [Pandas Official Docs](https://pandas.pydata.org/docs/)\n- 🔹 [Pandas Series Lecture by CampusX](https://www.youtube.com/live/zCDVUyq8lkw?si=reHlZ3smFor4iFiJ)\n- 🔹 [Important Series Methods Lecture by CampusX](https://youtu.be/80QpbZA38HA?si=my4dFczhNvKMdUSx)\n\n---\n\n## 🧰 Tools \u0026 Environment\n\n- **Python 3.x**\n- **Pandas**\n- **NumPy**\n- **Jupyter Notebook**\n\n---\n\n## ✨ Author\n\n**Shafaq Aslam**  \n📍 Passionate learner exploring Data Analytics, Machine Learning, and AI through consistent hands-on practice.\n\n---\n\n## 🔖 Tags for SEO\n\n`pandas` `python` `data-analysis` `data-cleaning` `data-visualization` `dataframe` `series` `machine-learning` `data-science` `jupyter-notebooks` `learning-lab`\n\n---\n\n\u003cp align=\"center\"\u003e\u003ci\u003e“Turning raw data into meaningful insights — one DataFrame at a time.”\u003c/i\u003e\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshafaq-aslam%2Fpandas-lab","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshafaq-aslam%2Fpandas-lab","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshafaq-aslam%2Fpandas-lab/lists"}