# 数据分析知识

**Repository Path**: trident_python/data-analysis-knowledge

## Basic Information

- **Project Name**: 数据分析知识
- **Description**: 数据分析知识【numpy、pandas、matplotlib】
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-09-14
- **Last Updated**: 2026-09-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Data Analysis Knowledge Base

This project is a data analysis learning resource focused on data visualization and analysis practices.

## Project Introduction

This repository provides basic knowledge and practical cases related to data analysis, with a focus on introducing the usage of the Seaborn visualization library.

## Data Files

The project includes the following datasets:

| Filename | Description |
|--------|------|
| `house_sales.csv` | House sales data |
| `mall_order_item.csv` | Mall order item data |
| `penguins.csv` | Penguin dataset |
| `tb_book.csv` | Book sales data |
| `tm_movie.csv` | Movie data |
| `weather.csv` | Weather data |

## Visualization Examples

The project provides the following visualization example images:

- `_hist_check.png` - Histogram check example
- `movie_rating.png` - Movie rating visualization
- `world_population.png` - World population data visualization

## Main Content

### Seaborn Visualization Tutorial

The `4.0-seaborn` directory contains a detailed tutorial on the Seaborn visualization library, covering:

- Basic chart plotting
- Data distribution display
- Relational data visualization
- Categorical data handling
- Chart style settings

## Usage Instructions

1. Ensure the Python environment and Jupyter Notebook are installed
2. Install necessary dependency libraries:
```bash
pip install pandas seaborn matplotlib
```
3. Open the `4.0-seaborn/main.ipynb` file and run the example code

## Dependencies

- Python 3.7+
- pandas
- seaborn
- matplotlib
- jupyter

## Target Audience

- Beginners in data analysis
- Developers needing to learn data visualization
- Scholars interested in Python data processing

## Contribution Guide

Welcome to submit Issues or Pull Requests to improve the content of this project.