import csv, json, pandas as pd
# 读CSV
with open('数据.csv', encoding='utf-8') as f:
reader = csv.DictReader(f)
rows = list(reader)
# 写JSON
with open('输出.json', 'w', encoding='utf-8') as f:
json.dump(rows, f, ensure_ascii=False, indent=2)
# 读Excel
df = pd.read_excel('报表.xlsx')
print(df.describe()) # 自动统计import requests
# GET请求
resp = requests.get('https://api.example.com/data',
headers={'Authorization': 'Bearer YOUR_KEY'})
data = resp.json()
# POST 调用AI
resp = requests.post('https://api.deepseek.com/chat/completions',
json={'model':'deepseek-chat','messages':[
{'role':'user','content':'你好'}'
]},
headers={'Authorization':'Bearer sk-xxx'})
print(resp.json()['choices'][0]['message']['content'])import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['font.sans-serif'] = ['SimHei','PingFang SC']
# 柱状图
years = ['2021','2022','2023','2024']
values = [168, 185, 203, 215]
plt.bar(years, values, color=['#7aa87a','#7a9fb5','#c8a96e','#c48a6e'])
plt.title('保康县GDP趋势（亿元）')
plt.savefig('gdp_trend.png', dpi=200) # 保存图片
plt.show()from openai import OpenAI
client = OpenAI(
base_url='https://api.deepseek.com',
api_key='sk-xxx'
)
# 文本分析
resp = client.chat.completions.create(
model='deepseek-chat',
messages=[{'role':'user',
'content':'分析这段文本的核心观点：' + text}]
)
print(resp.choices[0].message.content)import schedule, time
def 生成日报():
# 你的自动报表逻辑
print('✅ 日报已生成')
# 每个工作日9点执行
schedule.every().monday.at('09:00').do(生成日报)
schedule.every().tuesday.at('09:00').do(生成日报)
# 或每分钟执行
schedule.every(1).minutes.do(生成日报)
while True:
schedule.run_pending()
time.sleep(30)from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI(title='数据API')
class 数据(BaseModel):
name: str
value: float
@app.get('/api/reports')
def 获取报表():
return {'reports': [...]}
@app.post('/api/analyze')
def 分析数据(data: 数据):
return {'result': f'收到{data.name}，值{data.value}'}
# 运行: uvicorn main:app --reload
# 文档自动生成: http://localhost:8000/docspackage main
import (
"encoding/json"
"fmt"
"net/http"
)
type Report struct {
ID int `json:"id"`
Name string `json:"name"`
Value float64 `json:"value"`
}
func main() {
http.HandleFunc("/api/reports", func(w http.ResponseWriter, r *http.Request) {
reports := []Report{{1, "GDP", 215.47}}
json.NewEncoder(w).Encode(reports)
})
fmt.Println("Server on :8080")
http.ListenAndServe(":8080", nil)
}
// 编译：go build -o server main.go
// 运行：./server（单文件，无需Python环境）package main
import (
"fmt"
"sync"
"time"
)
func fetch(url string, wg *sync.WaitGroup) {
defer wg.Done()
fmt.Println("请求:", url)
time.Sleep(100 * time.Millisecond)
}
func main() {
urls := []string{"api1", "api2", "api3", "api4"}
var wg sync.WaitGroup
for _, url := range urls {
wg.Add(1)
go fetch(url, &wg) // 并发执行
}
wg.Wait() // 等待所有完成
fmt.Println("✅ 全部完成")
}
// 4个请求同时执行，总耗时≈100ms
// Python串行版≈400ms+package main
import (
"flag"
"fmt"
)
func main() {
name := flag.String("name", "保康", "报表名称")
year := flag.Int("year", 2026, "年份")
flag.Parse()
fmt.Printf("📊 %s %d年统计报表\n", *name, *year)
fmt.Println("使用方式：")
fmt.Println(" ./report --name=GDP --year=2025")
}
// 编译：GOOS=windows GOARCH=amd64 go build
// → 发给同事的Windows电脑直接运行，无需装Pythonimport (
"database/sql"
_ "github.com/mattn/go-sqlite3"
)
type Report struct {
ID int `json:"id"`
Name string `json:"name"`
Val float64 `json:"val"`
}
func main() {
db, _ := sql.Open("sqlite3", "reports.db")
rows, _ := db.Query("SELECT id, name, val FROM reports")
for rows.Next() {
var r Report
rows.Scan(&r.ID, &r.Name, &r.Val)
fmt.Printf("%d: %s = %.2f\n", r.ID, r.Name, r.Val)
}
}学习路线：
推荐把玩顺序：
💡 先看动画理解过程 → 再看代码实现 → 自己写一遍
入门练习：
💡 用眼睛看懂 → 自己写一遍 → 记忆最深
特色框架教学：
📌 适合已看过Hello算法、有基础后刷题时对照
Python 1-2个月上手做项目 → Go 2-3个月补性能短板。Python负责AI/脚本/原型，Go负责高性能API/微服务，双剑合璧。