https://github.com/1sumer/python-repo
This repository provides a detailed collection of Python scripts and notebooks for Implementing various EDA Project.
https://github.com/1sumer/python-repo
eda matplotlib numpy pandas python seaborn
Last synced: 10 months ago
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This repository provides a detailed collection of Python scripts and notebooks for Implementing various EDA Project.
- Host: GitHub
- URL: https://github.com/1sumer/python-repo
- Owner: 1sumer
- Created: 2024-11-27T08:12:07.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-04T05:02:52.000Z (10 months ago)
- Last Synced: 2025-04-04T06:19:19.969Z (10 months ago)
- Topics: eda, matplotlib, numpy, pandas, python, seaborn
- Language: Jupyter Notebook
- Homepage: https://github.com/1sumer/Machine-Learning
- Size: 174 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Python for Data Analysis and Visualization
## 📌 Project Overview
This repository provides a comprehensive guide to data analysis and visualization using Python. It covers essential libraries like **NumPy, Pandas, Matplotlib, and Seaborn**, along with **Exploratory Data Analysis (EDA)** techniques. The goal is to equip users with practical skills for handling, processing, visualizing, and extracting insights from data.
---
## 🎯 Objectives
- 🔹 **Understand Python Data Libraries**: Learn the core functionalities of **NumPy, Pandas, Matplotlib, and Seaborn**.
- 🔹 **Perform Exploratory Data Analysis (EDA)**: Gain insights from data using statistical and visualization techniques.
- 🔹 **Data Manipulation**: Learn how to **clean, transform, and preprocess data** effectively.
- 🔹 **Data Visualization**: Create compelling **visualizations** to understand data trends and patterns.
---
## 📂 Content
### **1️⃣ NumPy: Numerical Computing**
- 📌 Creating and manipulating **arrays**
- 📌 Array operations and **broadcasting**
- 📌 **Statistical and mathematical** functions
- 📌 **Indexing, slicing, and reshaping**
- 📌 Handling **missing values and NaNs**
### **2️⃣ Pandas: Data Manipulation**
- 📌 Loading datasets from **CSV, Excel, and databases**
- 📌 **DataFrame and Series** objects
- 📌 Data **selection, filtering, and grouping**
- 📌 Handling **missing data**
- 📌 **Merging and joining** datasets
- 📌 **Pivot tables and multi-indexing**
### **3️⃣ Matplotlib: Data Visualization**
- 📌 Creating **basic plots** (line, bar, scatter, histogram, etc.)
- 📌 Customizing plots (**titles, labels, legends, colors**)
- 📌 **Subplots and multiple plots**
- 📌 **Saving and exporting** figures
### **4️⃣ Seaborn: Statistical Data Visualization**
- 📌 **Pair plots and correlation heatmaps**
- 📌 **Box plots and violin plots**
- 📌 **Distribution and density plots**
- 📌 **Categorical plots** (bar, count, strip, swarm plots)
- 📌 **Advanced visualizations** with themes and styles
### **5️⃣ Exploratory Data Analysis (EDA)**
- 📌 Understanding **data distributions**
- 📌 Detecting and handling **outliers**
- 📌 Identifying **correlations between variables**
- 📌 **Feature engineering and scaling**
- 📌 **Principal Component Analysis (PCA)**
- 📌 **Case study**: EDA on real-world datasets
---
## ⚙️ How to Use
### **🔧 Setup**
1️⃣ Install **Python 3.x**
2️⃣ Use `pip install -r requirements.txt` to install dependencies
3️⃣ Run Jupyter Notebook using `jupyter notebook`
### **▶️ Run Scripts**
- 📌 Navigate to individual folders for **NumPy, Pandas, Matplotlib, Seaborn, and EDA**.
- 📌 Open **Jupyter notebooks** for interactive exploration.
- 📌 Modify scripts and test on **your datasets**.
---
## 📌 Prerequisites
- ✅ Basic **Python programming** knowledge
- ✅ Understanding of **data structures**
- ✅ Familiarity with **Jupyter Notebook**
---
## 📊 Algorithms & Techniques in Repository
| **Topic** | **Description** |
|---------------|--------------------------------------------------|
| **NumPy** | Numerical computations and array manipulations. |
| **Pandas** | Data manipulation and preprocessing. |
| **Matplotlib** | Basic and advanced data visualizations. |
| **Seaborn** | Statistical visualization with aesthetic designs. |
| **EDA** | Extracting insights through data exploration. |
---
## 📢 Conclusion
This repository serves as a valuable **resource** for anyone looking to **master Python for data analysis and visualization**. By exploring various **examples and exercises**, users can build strong foundational skills for working with **data**.
---
## 📝 License
This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.
---
## 📚 Acknowledgements
- 🔹 [NumPy Documentation](https://numpy.org/doc/stable/)
- 🔹 [Pandas Documentation](https://pandas.pydata.org/docs/)
- 🔹 [Matplotlib Documentation](https://matplotlib.org/stable/contents.html)
- 🔹 [Seaborn Documentation](https://seaborn.pydata.org/)