Excel vs Python: Data Analysis Comparison

This project compares Excel and Python workflows for analyzing the same dataset, with a focus on functionality, ease of use, and output quality.
It contains both an Excel dashboard and equivalent Python-based analysis so you can see how each tool handles the same tasks.

Project Structure

- `data/` — Raw and processed datasets  
- `excel_dashboard/` — Excel dashboards and sheets  
- `images/` — Plots and charts used in README and docs  
- `notebooks/` — Jupyter notebooks for exploration & analysis  
- `python_analysis/` — Standalone Python scripts  
- `.gitignore` — Files & folders to be ignored by Git  
- `LICENSE` — License for reuse  
- `README.md` — Project overview (this file)  
- `requirements.txt` — Python dependencies

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How to Reproduce the Code

1. Clone this repository

git clone https://github.com/sakshikharkwal/Excel-vs-Python-Analysis.git
cd Excel-vs-Python-Analysis

2. Create and activate a virtual environment

python -m venv .venv

Activate:

• Windows (PowerShell)

  .venv\Scripts\Activate.ps1

• Windows (Git Bash)

  source .venv/Scripts/activate

• macOS/Linux

  source .venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Explore the project

• Excel Dashboard → Open files in excel_dashboard/ using Excel

• Python Scripts → Run .py files inside python_analysis/

• Notebooks → Launch:

  jupyter notebook

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Observations & Findings

Excel-specific challenges:

1. YEAR() bug with text-formatted dates: If your date is stored as text (e.g., 08/11/2016), Excel’s YEAR() fails because it tries to interpret "2016" as a serial date number (2016 days after 1 Jan 1900 → 08 Jul 1905). TEXT([@[Order Date]],"yyyy") works because it just extracts the year from the string. Temporary fix used: Convert column type to Number instead of Date. Limitation: In slicers, there is no way to change the data type.

2. Excel Online limitations:

   • Cannot add slicers to regular tables (only available in Pivot Tables).
   • Reduced functionality in the free Excel Online version compared to desktop Excel.

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Python advantages:

1. Jupyter Notebook (and Python in general) has no feature restrictions in the free version.
2. More automation and reproducibility — the same analysis can run on any dataset without manual rework.
3. Libraries like pandas, matplotlib, and seaborn provide more control over transformations and visuals.