https://github.com/shafaq-aslam/pandas-lab

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.
https://github.com/shafaq-aslam/pandas-lab

analytics data-analysis data-cleaning data-science data-visualization dataframe jupyter-notebook machine-learning pandas pandas-dataframe pandas-library pandas-series python python3 series

Last synced: 2 months ago
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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.

• Host: GitHub
• URL: https://github.com/shafaq-aslam/pandas-lab
• Owner: shafaq-aslam
• Created: 2025-10-14T08:16:49.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-10-21T17:17:50.000Z (8 months ago)
• Last Synced: 2025-10-21T18:11:26.840Z (8 months ago)
• Topics: analytics, data-analysis, data-cleaning, data-science, data-visualization, dataframe, jupyter-notebook, machine-learning, pandas, pandas-dataframe, pandas-library, pandas-series, python, python3, series
• Language: Jupyter Notebook
• Homepage:
• Size: 976 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          


  



📊 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.

---

## 📂 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



---

## 🧮 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 |

---

### 🔹 **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 |

---

### 🔹 **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 |

---

### 🔹 **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 |

---

### 🔹 **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 |

---

### 🔹 **Pivot Table**

| Notebook | Description |

|-----------|--------------|

| **Pivot_table** | Creating pivot tables for summarizing and analyzing data |

| **expense_data.csv** | Dataset for pivot table practice and visualization |

---

### 🔹 **Vectorized String Operations**

| Notebook | Description |

|-----------|--------------|

| **Pandas_string** | Working with vectorized string operations for data cleaning |

| **titanic.csv** | Dataset for applying string manipulation techniques |

---

### 🔹 **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 |

---

## 📚 Learning Resources

- 🔹 [Pandas Official Docs](https://pandas.pydata.org/docs/)

- 🔹 [Pandas Series Lecture by CampusX](https://www.youtube.com/live/zCDVUyq8lkw?si=reHlZ3smFor4iFiJ)

- 🔹 [Important Series Methods Lecture by CampusX](https://youtu.be/80QpbZA38HA?si=my4dFczhNvKMdUSx)

---

## 🧰 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.”