Welcome to this collection of Python and Excel resources! This repository contains hands-on examples of Excel formulas, Python libraries for data analysis, and visualization tools, along with sample code and output images for reference.
Whether you're a beginner or looking to brush up on practical skills, this repo is structured to make learning fast, intuitive, and applicable.
- Excel/ – Examples of Excel formulas & features
- Matplotlib/ – Python visualizations using Matplotlib
- NumPy/ – Numerical computing examples
- Pandas/ – Data manipulation & analysis examples
- Seaborn/ – Statistical data visualizations
- Plotly/ – Interactive plots & dashboards
Each folder contains .py code and corresponding .png outputs for visual reference.
Learn how to utilize Excel formulas and features for data analysis, automation, and reporting.
Key Topics Covered:
- Formulas:
SUM,IF,VLOOKUP,INDEX-MATCH - Data Features: Conditional Formatting, Pivot Tables, Charts
- Use Case: Quickly summarize sales data, highlight trends, or automate calculations.
Matplotlib is a fundamental plotting library in Python. Ideal for static, publication-quality plots.
Example Use Case:
- Visualize trends in sales, stock prices, or scientific measurements.
Sample Plot Types:
- Line plots, bar charts, scatter plots, histograms, pie charts
NumPy provides fast numerical computation with multi-dimensional arrays. It’s the backbone for most scientific computing in Python.
Example Use Case:
- Performing matrix operations, calculating statistics, or handling large datasets efficiently.
Pandas is a powerful data manipulation library. It allows you to clean, filter, and transform structured data with ease.
Example Use Case:
- Analyzing sales data: filter high-value customers, aggregate monthly revenue, detect missing values.
Key Functions:
DataFrame,read_csv,groupby,merge,pivot_table
Seaborn is a statistical visualization library built on Matplotlib. It makes it easy to create beautiful and informative graphics.
Example Use Case:
- Compare distributions of sales across regions using boxplots or histograms.
Popular Plots:
- Heatmaps, pairplots, boxplots, violin plots
Plotly is a library for interactive plotting in Python. Great for dashboards and web-based data exploration.
Example Use Case:
- Interactive sales dashboards with zoomable graphs, tooltips, and filters for decision-making.
Features:
- Scatter plots, line charts, 3D plots, choropleth maps, dashboards
- Clone the repository:
git clone https://github.com/Ramoware/ML-Learning.git- Navigate to the folder of interest:
cd Excel # or Matplotlib, NumPy, Pandas, Seaborn, Plotly- Run the Python scripts to generate plots:
python example.py- Refer to the
.pngimages to see expected output.
To run this project, you need to install the required Python libraries.
- Make sure you have
pipinstalled. - Run the following command in your project root:
pip install -r requirements.txtThis will install all necessary libraries to run the Python scripts in this repository.
- Hands-on examples with both code and output.
- Covers Excel & Python: perfect for data analysts and beginners.
- Ready-to-use scripts for visualization and analysis.
Feel free to fork, add examples, or suggest improvements!
Happy Learning! 🎉📊📈