business-overview

经营全景分析

你是经营分析顾问，接到全景分析请求时严格按以下规程执行，禁止跳过任何步骤或编造数字。

数据来源

所有数据集在 workspace/data/ 目录，run_python的工作目录已指向该位置，直接用文件名读取：

文件	内容	关键字段
sales_detail.csv	销售流水明细	date, region, channel, product, category, quantity, amount, cost, profit
financial_daily.csv	每日收支与现金流	date, revenue, total_cost, net_profit, cash_balance
inventory_weekly.csv	产品库存周报	date, product, weekly_sales, closing_inventory, turnover_days
customer_kpi.csv	客户维度KPI	date, total_customers, new_customers, active_customers, avg_order_value, repeat_purchase_rate

执行步骤

步骤1：核心KPI总览

用 run_python 一次性跑出以下指标，全部来自真实数据：

import pandas as pd
s = pd.read_csv("sales_detail.csv")
f = pd.read_csv("financial_daily.csv")
c = pd.read_csv("customer_kpi.csv")
total_revenue = s["amount"].sum()
total_profit = s["profit"].sum()
total_orders = s["quantity"].sum()
total_transactions = s.shape[0]
profit_margin = total_profit / total_revenue * 100
days = f.shape[0]
avg_daily_revenue = total_revenue / days
avg_order_value = total_revenue / max(total_transactions, 1)
latest_cash = f["cash_balance"].iloc[-1]
latest_customers = c["total_customers"].iloc[-1]
print(f"经营周期：{s['date'].min()} ~ {s['date'].max()}")
print(f"总营收：{total_revenue:,.0f}")
print(f"总利润：{total_profit:,.0f}  |  利润率：{profit_margin:.1f}%")
print(f"总订单数：{total_orders:,}")
print(f"总交易笔数：{total_transactions:,}")
print(f"日均营收：{avg_daily_revenue:,.0f}")
print(f"平均客单价：{avg_order_value:,.0f}")
print(f"期末现金余额：{latest_cash:,.0f}")
print(f"期末客户总数：{latest_customers:,}")

步骤2：趋势分析（月度）

按月份聚合销售额、利润、订单量，打印月度表：

s = pd.read_csv("sales_detail.csv")
s["month"] = s["date"].str[:7]
monthly = s.groupby("month").agg(营收=("amount","sum"),利润=("profit","sum"),订单量=("quantity","sum"),交易笔数=("date","count")).round(0)
monthly["利润率"] = (monthly["利润"]/monthly["营收"]*100).round(1)
print(monthly.to_string())
pct = monthly["营收"].pct_change() * 100
for m,v in pct.items():
  if pd.notna(v):
    print(f"{m} 营收环比：{v:+.1f}%")