# numpy-ex3

**Repository Path**: arsalabangash/numpy-ex3

## Basic Information

- **Project Name**: numpy-ex3
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 80
- **Created**: 2025-03-19
- **Last Updated**: 2025-04-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Exercise: Repository Metrics Analysis with NumPy

## **English Instructions**

### **Objective**  
Analyze repository metrics (**Stars**, **Forks**, and **Active Days**) using NumPy arrays.

### **Tasks**  
1. **Data Loading**  
   - Read your assigned country file (`pakistan-repos.csv` or `china-repos.csv`)

2. **Data Preparation**  
   - Create a 2D NumPy array containing:  
     - `Stars` count (integer values)  
     - `Forks` count (integer values)  
     - `Active Days` (days between `created_at` and `last_commit`)  

3. **Statistical Analysis**  
   For each metric (Stars/Forks/Active Days), calculate:  
   - Mean (1 decimal place)  
   - Median (1 decimal place)  
   - Variance (1 decimal place)  
   - Standard deviation (1 decimal place)  

4. **Output Formatting**  
   Print results with **exact indentation**:  
   ```
   Stars:
       Average: 854.2
       Median: 765.0
       Variance: 12345.6
       Standard Deviation: 111.1
   Forks:
       Average: 234.5
       Median: 210.0
       Variance: 2345.6
       Standard Deviation: 48.4
   Active Days:
       Average: 349.0
       Median: 456.0
       Variance: 5678.9
       Standard Deviation: 75.4
   ```

### **Requirements**  
- Use **only NumPy functions** (no Python loops)  
- Calculate dates using `np.datetime64` type  
- Round all values to **1 decimal place**  
- Maintain exact indentation with 4 spaces per level  

---

## **Chinese Instructions (中文说明)**  

### **目标**  
使用NumPy分析仓库的星标数、分支数和活跃天数

### **任务**  
1. **数据加载**  
   - 读取分配的国家数据文件  

2. **数据准备**  
   - 创建包含以下指标的二维数组：  
     - 星标数（整数值）  
     - 分支数（整数值）  
     - 活跃天数（创建时间到最后提交的天数差）  

3. **统计分析**  
   为每个指标计算：  
   - 平均值（保留1位小数）  
   - 中位数（保留1位小数）  
   - 方差（保留1位小数）  
   - 标准差（保留1位小数）  

4. **结果输出**  
   按严格缩进格式打印结果  

### **要求**  
- 仅使用NumPy函数（禁止循环）  
- 使用`np.datetime64`处理日期计算  
- 所有数值保留1位小数  
- 保持严格的4空格缩进格式  

---

**Key Learning Points**  
1. 时间序列数据处理  
2. 使用向量化操作进行统计计算  
3. 数据精度控制与格式化输出  
4. NumPy结构化数组操作  

**Sample Data Structure**  
```csv
repo_name,owner,stars,forks,language,created_at,last_commit,description
ai-framework,CodeMasters,1500,450,Python,2022-01-15,2024-03-20,"Advanced machine learning platform"
cloud-engine,DataWizards,890,230,Go,2023-02-28,2024-03-18,"Distributed cloud computing system"
```  

*Note: Active Days = last_commit date - created_at date*