This repository contains core Python skills essential for Data Analysis using:
- 🧮 NumPy – for numerical computing, array manipulation, and linear algebra
- 🐼 Pandas – for data wrangling, cleaning, and transformation
- 📊 Visualization & EDA (upcoming) – data exploration and visual analytics
Whether you're a beginner or brushing up your skills, these notebooks are designed to provide hands-on practice with real datasets.
| File | Description |
|---|---|
NumPy_Basics.ipynb |
Learn NumPy array creation, slicing, vectorized operations, and broadcasting |
data.txt |
Sample file used for NumPy exercises |
| File | Description |
|---|---|
Pandas_Basics.ipynb |
Learn how to work with Series and DataFrames |
Pandas_with_Olympics_data.ipynb |
Analysis on Olympic data using Pandas and real-world cleaning techniques |
| Dataset | Description |
|---|---|
bios.csv, coffee.csv |
Practice datasets for cleaning, grouping, and filtering |
noc_regions.csv |
Used in Olympic analysis |
olympics-data.xlsx |
Real-world dataset for multi-indexing and merging in Pandas |
- 📊 Add Matplotlib & Seaborn visualization notebooks
- 🧼 More real-world EDA tasks and challenges
- 🧠 ML-ready datasets with preprocessing steps
L Anunai Sai Goud
Passionate about Data Science, Python, and AI Automation.
📫 Connect with me on LinkedIn
This repository is open-source and available under the MIT License.