Python能做什么？数据分析、Web开发与自动化办公场景介绍

import openpyxl
from openpyxl.styles import Font, PatternFill

def generate_monthly_report(raw_data, output_file):
    # 创建全新工作簿并初始化工作表
    wb = openpyxl.Workbook()
    ws = wb.active
    ws.title = "月度业绩汇总"

    # 写入表头
    headers = ["员工编号", "姓名", "总销售额", "绩效评级"]
    ws.append(headers)

    # 预设高亮样式：鼠尾草绿背景，加粗文字
    highlight_fill = PatternFill(start_color="D6E4D8", end_color="D6E4D8", fill_type="solid")
    bold_font = Font(name="PingFang SC", size=11, bold=True)

    # 逐行处理业务数据
    for emp_id, name, sales in raw_data:
        # 判断业务规则
        if sales >= 10000:
            rating = "优秀 (达标)"
            is_highlight = True
        else:
            rating = "常规"
            is_highlight = False

        # 追加数据行
        ws.append([emp_id, name, sales, rating])

        # 若达标，将当行单元格应用高亮样式
        if is_highlight:
            current_row = ws.max_row
            for col_idx in range(1, 5):
                cell = ws.cell(row=current_row, column=col_idx)
                cell.fill = highlight_fill
                cell.font = bold_font

    # 设置数值列货币显示格式
    for row in range(2, ws.max_row + 1):
        ws.cell(row=row, column=3).number_format = '¥#,##0.00'

    wb.save(output_file)
    print(f"[完成] 报表已成功生成并写入: {output_file}")

if __name__ == "__main__":
    records = [
        ("E101", "张三", 12500),
        ("E102", "李四", 8300),
        ("E103", "王五", 16800),
        ("E104", "赵六", 9400),
    ]
    generate_monthly_report(records, "2026年10月业绩汇总.xlsx")

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from typing import List, Optional

app = FastAPI(title="任务管理服务", description="演示 Python Web API 的构建与参数验证")

# 定义数据结构与入参约束
class TaskItem(BaseModel):
    id: int
    title: str = Field(..., min_length=2, max_length=50, description="任务标题")
    is_completed: bool = False

# 内存模拟数据源
task_database: List[TaskItem] = [
    TaskItem(id=1, title="整理周报数据", is_completed=True),
    TaskItem(id=2, title="配置生产服务器代理", is_completed=False),
]

@app.get("/api/tasks", response_model=List[TaskItem])
def list_tasks(completed: Optional[bool] = None):
    """查询任务列表，支持按完成状态筛选"""
    if completed is not None:
        return [t for t in task_database if t.is_completed == completed]
    return task_database

@app.post("/api/tasks", response_model=TaskItem, status_code=201)
def create_task(task: TaskItem):
    """创建新任务，自动校验任务ID唯一性"""
    if any(existing.id == task.id for existing in task_database):
        raise HTTPException(status_code=400, detail="该任务ID已存在，请勿重复创建")
    task_database.append(task)
    return task

import pandas as pd
import numpy as np

# 构造模拟原始交易数据（包含空值与负数异常）
raw_sales = {
    "订单号": ["ORD-001", "ORD-002", "ORD-003", "ORD-004", "ORD-005", "ORD-006"],
    "商品类别": ["办公电子", "日用耗材", "办公电子", "办公家具", "日用耗材", "办公电子"],
    "销售单价": [1200, 45, 1350, np.nan, 52, 980],
    "销售数量": [3, 100, -1, 4, 80, 5],
}

df = pd.DataFrame(raw_sales)

# 阶段一：数据清洗
# 1. 过滤销售数量小于等于0的异常退单或错误记录
df_clean = df[df["销售数量"] > 0].copy()

# 2. 对缺失单价按品类均值或指定规则进行填充（此处剔除缺失行）
df_clean = df_clean.dropna(subset=["销售单价"])

# 3. 计算单笔总金额
df_clean["总交易额"] = df_clean["销售单价"] * df_clean["销售数量"]

# 阶段二：分组统计分析 (Groupby)
summary = df_clean.groupby("商品类别").agg(
    订单总数=("订单号", "count"),
    总销售额=("总交易额", "sum"),
    平均客单价=("总交易额", "mean"),
    单笔最大额=("总交易额", "max")
).reset_index()

# 按总销售额降序排列
summary = summary.sort_values(by="总销售额", ascending=False)

print("=== 最终品类业绩透视看板 ===")
print(summary.to_string(index=False))