This repository contains Python notebooks and examples covering data analysis essentials — from basics to advanced topics — using popular libraries like Pandas, NumPy, Matplotlib, and Seaborn.
It is designed to help learners and recruiters quickly understand how Python is applied in real data analysis workflows. :contentReference[oaicite:0]{index=0}
The main goal of this project is to demonstrate:
- Data manipulation using Pandas & NumPy
- Exploratory Data Analysis (EDA)
- Visualizations with Matplotlib & Seaborn
- Practical Python usage for data tasks
| Library | Purpose |
|---|---|
| Python | Core language |
| NumPy | Numerical operations |
| Pandas | Data handling & manipulation |
| Matplotlib | Data visualization |
| Seaborn | Advanced statistical plotting |
Each folder or notebook focuses on a specific library or concept:
- Importing and Cleaning Data
- Aggregation & Grouping
- Filtering & Sorting
- Descriptive Statistics
- Visualizing Trends and Patterns
- Explored distributions using histograms and boxplots
- Identified correlations between variables
- Compared group-wise metrics (mean, median, etc.)
- Clone the repo
- Open the notebooks in Jupyter
- Run cells step-by-step to learn
- Modify with your own datasets
git clone https://github.com/kunalkumar2001/Data-Analyst-Python-and-Its-library.git