{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 对于每种作物的数据统计",
   "id": "493d1cd2ba436df4"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-09-06T08:09:09.618585Z",
     "start_time": "2024-09-06T08:09:09.599851Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "\n",
    "# 指定默认字体为支持中文的字体\n",
    "matplotlib.rcParams['font.family'] = 'Microsoft YaHei'  # 或者 'SimHei' 'SimSun', 'Microsoft YaHei'\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "LandType = {\"A\": \"平旱地\", \"B\": \"梯田\", \"C\": \"山坡地\", \"D\": \"水浇地\", \"E\": \"普通大棚\", \"F\": \"智慧大棚\"}\n"
   ],
   "outputs": [],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-06T06:38:36.915607Z",
     "start_time": "2024-09-06T06:38:36.482384Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_planting = pd.read_excel('./data/2.xlsx', sheet_name=0)\n",
    "df_crop_details = pd.read_excel('./data/2.xlsx', sheet_name=1)"
   ],
   "id": "9f4633f8e90ffe1a",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-06T06:38:36.931334Z",
     "start_time": "2024-09-06T06:38:36.917487Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_planting['landName'] = df_planting['landName'].ffill()\n",
    "df_crop_details['cropLandType'] = df_crop_details['cropLandType'].str.rstrip()"
   ],
   "id": "ef9302cf0dc64751",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-06T06:38:36.946802Z",
     "start_time": "2024-09-06T06:38:36.932693Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def get_crop_price(crop_num, crop_land_type, season):\n",
    "    if crop_land_type == \"智慧大棚\" and season == \"第一季\":\n",
    "        crop_land_type = \"普通大棚\"\n",
    "    s = \\\n",
    "    df_crop_details[(df_crop_details['cropNum'] == crop_num) & (df_crop_details['cropLandType'] == crop_land_type) & (\n",
    "            df_crop_details['season'] == season)].price.values[0].split('-')\n",
    "    return (float(s[0]) + float(s[1])) / 2\n",
    "\n",
    "\n",
    "def get_crop_yield(crop_num, crop_land_type, season):\n",
    "    if crop_land_type == \"智慧大棚\" and season == \"第一季\":\n",
    "        crop_land_type = \"普通大棚\"\n",
    "    return \\\n",
    "    df_crop_details[(df_crop_details['cropNum'] == crop_num) & (df_crop_details['cropLandType'] == crop_land_type) & (\n",
    "            df_crop_details['season'] == season)].unitYield.values[0]\n",
    "\n",
    "\n",
    "def get_crop_cost(crop_num, crop_land_type, season):\n",
    "    if crop_land_type == \"智慧大棚\" and season == \"第一季\":\n",
    "        crop_land_type = \"普通大棚\"\n",
    "    return \\\n",
    "    df_crop_details[(df_crop_details['cropNum'] == crop_num) & (df_crop_details['cropLandType'] == crop_land_type) & (\n",
    "            df_crop_details['season'] == season)].cost.values[0]\n",
    "\n",
    "def get_crop_profit(crop_num, crop_land_type, season):\n",
    "    return get_crop_yield(crop_num, crop_land_type, season) * get_crop_price(crop_num, crop_land_type, season) - get_crop_cost(crop_num, crop_land_type, season)"
   ],
   "id": "6571c03dd0a145d",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-06T06:38:37.380747Z",
     "start_time": "2024-09-06T06:38:37.293442Z"
    }
   },
   "cell_type": "code",
   "source": [
    "total_yield = {crop: 0 for crop in df_crop_details['cropNum'].unique()}\n",
    "total_cost = {crop: 0 for crop in df_crop_details['cropNum'].unique()}\n",
    "total_income = {crop: 0 for crop in df_crop_details['cropNum'].unique()}\n",
    "total_profit = {crop: 0 for crop in df_crop_details['cropNum'].unique()}\n",
    "for line in df_planting.values:\n",
    "    # print(line[1], LandType[line[0][0]], line[5])\n",
    "    yld = line[4] * get_crop_yield(line[1], LandType[line[0][0]], line[5])\n",
    "    cost = line[4] * get_crop_cost(line[1], LandType[line[0][0]], line[5])\n",
    "    income = yld * get_crop_price(line[1], LandType[line[0][0]], line[5])\n",
    "    profit = income - cost\n",
    "\n",
    "    total_yield[line[1]] += yld\n",
    "    total_cost[line[1]] += cost\n",
    "    total_income[line[1]] += income\n",
    "    total_profit[line[1]] += profit\n",
    "print(total_yield)  # 总产量\n",
    "print(total_cost)  # 总开销\n",
    "print(total_income)  # 总收入\n",
    "print(total_profit)  # 总利润"
   ],
   "id": "483a03b545d80063",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 57000.0, 2: 21850.0, 3: 22400.0, 4: 33040.0, 5: 9875.0, 6: 170840.0, 7: 132750.0, 8: 71400.0, 9: 30000.0, 10: 12500.0, 11: 1500.0, 12: 35100.0, 13: 36000.0, 14: 14000.0, 15: 10000.0, 16: 21000.0, 17: 36480.0, 18: 26880.0, 19: 6480.0, 20: 30000.0, 21: 36210.0, 22: 45360.0, 23: 900.0, 24: 2610.0, 25: 3600.0, 26: 4050.0, 27: 4500.0, 28: 35480.0, 29: 13050.0, 30: 2850.0, 31: 1200.0, 32: 3600.0, 33: 1800.0, 34: 1800.0, 35: 150000.0, 36: 100000.0, 37: 36000.0, 38: 9000.0, 39: 7200.0, 40: 18000.0, 41: 4200.0}\n",
      "{1: 58800.0, 2: 18400.0, 3: 21000.0, 4: 33600.0, 5: 8750.0, 6: 99900.0, 7: 67500.0, 8: 66600.0, 9: 20000.0, 10: 9000.0, 11: 5250.0, 12: 13000.0, 13: 36000.0, 14: 14000.0, 15: 7000.0, 16: 28560.0, 17: 24320.0, 18: 13440.0, 19: 4320.0, 20: 30000.0, 21: 30232.0, 22: 14232.0, 23: 900.0, 24: 1860.0, 25: 2700.0, 26: 3150.0, 27: 1800.0, 28: 17860.0, 29: 3255.0, 30: 1260.0, 31: 720.0, 32: 1500.0, 33: 750.0, 34: 360.0, 35: 60000.0, 36: 12500.0, 37: 6000.0, 38: 5400.0, 39: 3600.0, 40: 18000.0, 41: 42000.0}\n",
      "{1: 185250.0, 2: 163875.0, 3: 184800.0, 4: 231280.0, 5: 66656.25, 6: 597940.0, 7: 398250.0, 8: 481950.0, 9: 180000.0, 10: 93750.0, 11: 60000.0, 12: 52650.0, 13: 117000.0, 14: 77000.0, 15: 35000.0, 16: 147000.0, 17: 291840.0, 18: 181440.0, 19: 42120.0, 20: 112500.0, 21: 227325.0, 22: 251856.0, 23: 6210.0, 24: 14958.0, 25: 19800.0, 26: 26325.0, 27: 22500.0, 28: 205252.0, 29: 97020.0, 30: 16380.000000000002, 31: 8700.0, 32: 16200.0, 33: 8100.0, 34: 8640.0, 35: 375000.0, 36: 250000.0, 37: 117000.0, 38: 517500.0, 39: 136800.0, 40: 288000.0, 41: 420000.0}\n",
      "{1: 126450.0, 2: 145475.0, 3: 163800.0, 4: 197680.0, 5: 57906.25, 6: 498040.0, 7: 330750.0, 8: 415350.0, 9: 160000.0, 10: 84750.0, 11: 54750.0, 12: 39650.0, 13: 81000.0, 14: 63000.0, 15: 28000.0, 16: 118440.0, 17: 267520.0, 18: 168000.0, 19: 37800.0, 20: 82500.0, 21: 197093.0, 22: 237624.0, 23: 5310.0, 24: 13098.0, 25: 17100.0, 26: 23175.0, 27: 20700.0, 28: 187392.0, 29: 93765.0, 30: 15120.000000000002, 31: 7980.0, 32: 14700.0, 33: 7350.0, 34: 8280.0, 35: 315000.0, 36: 237500.0, 37: 111000.0, 38: 512100.0, 39: 133200.0, 40: 270000.0, 41: 378000.0}\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# 对每种类型的地块中的种植情况和盈利情况进行统计",
   "id": "aa7b13944d227aa3"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-06T06:38:38.680027Z",
     "start_time": "2024-09-06T06:38:38.640023Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_land = pd.read_excel('./data/1.xlsx', sheet_name=0)\n",
    "df_crop_land = pd.read_excel('./data/1.xlsx', sheet_name=1)\n"
   ],
   "id": "49e1827ce35f0e3b",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-06T06:39:45.321314Z",
     "start_time": "2024-09-06T06:39:45.200761Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"---------------------\")\n",
    "land_crop_stats = {land_type:{} for land_type in LandType.keys()}\n",
    "#  该变量存储的内容例如{\"A\":{\"1\":[field, profit] }}表示在A类型的地块中种植的作物编号为1的作物的总产量和总利润\n",
    "\n",
    "for land in df_land.values:\n",
    "    print(land[0], land[1], land[2])\n",
    "    for crop in df_planting.values:\n",
    "        if crop[0] == land[0]:\n",
    "            print(crop[1], crop[2], crop[3], crop[4], crop[5])\n",
    "            land_crop_stats[land[0][0]][crop[1]] = [get_crop_yield(crop[1], LandType[land[0][0]], crop[5]), 0]\n",
    "            land_crop_stats[land[0][0]][crop[1]][1] += get_crop_profit(crop[1], LandType[land[0][0]], crop[5])\n",
    "    print(\"---------------------\")\n",
    "print(land_crop_stats)"
   ],
   "id": "54504f6c133b1508",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------\n",
      "A1 平旱地 80.0\n",
      "6 小麦 粮食 80.0 单季\n",
      "---------------------\n",
      "A2 平旱地 55.0\n",
      "7 玉米 粮食 55.0 单季\n",
      "---------------------\n",
      "A3 平旱地 35.0\n",
      "7 玉米 粮食 35.0 单季\n",
      "---------------------\n",
      "A4 平旱地 72.0\n",
      "1 黄豆 粮食（豆类） 72.0 单季\n",
      "---------------------\n",
      "A5 平旱地 68.0\n",
      "4 绿豆 粮食（豆类） 68.0 单季\n",
      "---------------------\n",
      "A6 平旱地 55.0\n",
      "8 谷子 粮食 55.0 单季\n",
      "---------------------\n",
      "B1 梯田 60.0\n",
      "6 小麦 粮食 60.0 单季\n",
      "---------------------\n",
      "B2 梯田 46.0\n",
      "2 黑豆 粮食（豆类） 46.0 单季\n",
      "---------------------\n",
      "B3 梯田 40.0\n",
      "3 红豆 粮食（豆类） 40.0 单季\n",
      "---------------------\n",
      "B4 梯田 28.0\n",
      "4 绿豆 粮食（豆类） 28.0 单季\n",
      "---------------------\n",
      "B5 梯田 25.0\n",
      "5 爬豆 粮食（豆类） 25.0 单季\n",
      "---------------------\n",
      "B6 梯田 86.0\n",
      "8 谷子 粮食 86.0 单季\n",
      "---------------------\n",
      "B7 梯田 55.0\n",
      "6 小麦 粮食 55.0 单季\n",
      "---------------------\n",
      "B8 梯田 44.0\n",
      "8 谷子 粮食 44.0 单季\n",
      "---------------------\n",
      "B9 梯田 50.0\n",
      "9 高粱 粮食 50.0 单季\n",
      "---------------------\n",
      "B10 梯田 25.0\n",
      "10 黍子 粮食 25.0 单季\n",
      "---------------------\n",
      "B11 梯田 60.0\n",
      "1 黄豆 粮食（豆类） 60.0 单季\n",
      "---------------------\n",
      "B12 梯田 45.0\n",
      "7 玉米 粮食 45.0 单季\n",
      "---------------------\n",
      "B13 梯田 35.0\n",
      "14 莜麦 粮食 35.0 单季\n",
      "---------------------\n",
      "B14 梯田 20.0\n",
      "15 大麦 粮食 20.0 单季\n",
      "---------------------\n",
      "C1 山坡地 15.0\n",
      "11 荞麦 粮食 15.0 单季\n",
      "---------------------\n",
      "C2 山坡地 13.0\n",
      "12 南瓜 粮食 13.0 单季\n",
      "---------------------\n",
      "C3 山坡地 15.0\n",
      "1 黄豆 粮食（豆类） 15.0 单季\n",
      "---------------------\n",
      "C4 山坡地 18.0\n",
      "13 红薯 粮食 18.0 单季\n",
      "---------------------\n",
      "C5 山坡地 27.0\n",
      "6 小麦 粮食 27.0 单季\n",
      "---------------------\n",
      "C6 山坡地 20.0\n",
      "3 红豆 粮食（豆类） 20.0 单季\n",
      "---------------------\n",
      "D1 水浇地 15.0\n",
      "20 土豆 蔬菜  15.0 第一季\n",
      "36 白萝卜 蔬菜  15.0 第二季\n",
      "---------------------\n",
      "D2 水浇地 10.0\n",
      "28 小青菜 蔬菜  10.0 第一季\n",
      "35 大白菜 蔬菜  10.0 第二季\n",
      "---------------------\n",
      "D3 水浇地 14.0\n",
      "21 西红柿 蔬菜  14.0 第一季\n",
      "35 大白菜 蔬菜  14.0 第二季\n",
      "---------------------\n",
      "D4 水浇地 6.0\n",
      "22 茄子 蔬菜  6.0 第一季\n",
      "35 大白菜 蔬菜  6.0 第二季\n",
      "---------------------\n",
      "D5 水浇地 10.0\n",
      "17 豇豆 蔬菜（豆类） 10.0 第一季\n",
      "36 白萝卜 蔬菜  10.0 第二季\n",
      "---------------------\n",
      "D6 水浇地 12.0\n",
      "18 刀豆 蔬菜（豆类） 12.0 第一季\n",
      "37 红萝卜 蔬菜  12.0 第二季\n",
      "---------------------\n",
      "D7 水浇地 22.0\n",
      "16 水稻 粮食 22.0 单季\n",
      "---------------------\n",
      "D8 水浇地 20.0\n",
      "16 水稻 粮食 20.0 单季\n",
      "---------------------\n",
      "E1 普通大棚  0.6\n",
      "18 刀豆 蔬菜（豆类） 0.6 第一季\n",
      "38 榆黄菇 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E2 普通大棚  0.6\n",
      "24 青椒 蔬菜  0.6 第一季\n",
      "38 榆黄菇 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E3 普通大棚  0.6\n",
      "25 菜花 蔬菜  0.6 第一季\n",
      "38 榆黄菇 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E4 普通大棚  0.6\n",
      "26 包菜 蔬菜  0.6 第一季\n",
      "39 香菇 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E5 普通大棚  0.6\n",
      "28 小青菜 蔬菜  0.6 第一季\n",
      "39 香菇 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E6 普通大棚  0.6\n",
      "27 油麦菜 蔬菜  0.6 第一季\n",
      "39 香菇 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E7 普通大棚  0.6\n",
      "19 芸豆 蔬菜（豆类） 0.6 第一季\n",
      "40 白灵菇 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E8 普通大棚  0.6\n",
      "19 芸豆 蔬菜（豆类） 0.6 第一季\n",
      "40 白灵菇 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E9 普通大棚  0.6\n",
      "18 刀豆 蔬菜（豆类） 0.6 第一季\n",
      "40 白灵菇 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E10 普通大棚  0.6\n",
      "17 豇豆 蔬菜（豆类） 0.6 第一季\n",
      "41 羊肚菌 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E11 普通大棚  0.6\n",
      "17 豇豆 蔬菜（豆类） 0.6 第一季\n",
      "41 羊肚菌 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E12 普通大棚  0.6\n",
      "22 茄子 蔬菜  0.6 第一季\n",
      "41 羊肚菌 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E13 普通大棚  0.6\n",
      "21 西红柿 蔬菜  0.6 第一季\n",
      "41 羊肚菌 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E14 普通大棚  0.6\n",
      "29 黄瓜 蔬菜  0.6 第一季\n",
      "41 羊肚菌 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E15 普通大棚  0.6\n",
      "30 生菜 蔬菜  0.3 第一季\n",
      "27 油麦菜 蔬菜  0.3 第一季\n",
      "41 羊肚菌 食用菌 0.6 第二季\n",
      "---------------------\n",
      "E16 普通大棚  0.6\n",
      "31 辣椒 蔬菜  0.6 第一季\n",
      "41 羊肚菌 食用菌 0.6 第二季\n",
      "---------------------\n",
      "F1 智慧大棚 0.6\n",
      "32 空心菜 蔬菜  0.3 第一季\n",
      "33 黄心菜 蔬菜  0.3 第一季\n",
      "24 青椒 蔬菜  0.3 第二季\n",
      "21 西红柿 蔬菜  0.3 第二季\n",
      "---------------------\n",
      "F2 智慧大棚 0.6\n",
      "25 菜花 蔬菜  0.3 第一季\n",
      "26 包菜 蔬菜  0.3 第一季\n",
      "22 茄子 蔬菜  0.3 第二季\n",
      "29 黄瓜 蔬菜  0.3 第二季\n",
      "---------------------\n",
      "F3 智慧大棚 0.6\n",
      "17 豇豆 蔬菜（豆类） 0.6 第一季\n",
      "28 小青菜 蔬菜  0.3 第二季\n",
      "30 生菜  蔬菜  0.3 第二季\n",
      "---------------------\n",
      "F4 智慧大棚 0.6\n",
      "19 芸豆 蔬菜（豆类） 0.6 第一季\n",
      "34 芹菜 蔬菜  0.3 第二季\n",
      "23 菠菜  蔬菜  0.3 第二季\n",
      "---------------------\n",
      "{'A': {6: [800, 2350.0], 7: [1000, 2500.0], 1: [400, 900.0], 4: [350, 2100.0], 8: [400, 2340.0]}, 'B': {6: [760, 2210.0], 2: [475, 3162.5], 3: [380, 2785.0], 4: [330, 1960.0], 5: [395, 2316.25], 8: [380, 2205.0], 9: [600, 3200.0], 10: [500, 3390.0], 1: [380, 835.0], 7: [950, 2350.0], 14: [400, 1800.0], 15: [500, 1400.0]}, 'C': {11: [100, 3650.0], 12: [2700, 3050.0], 1: [360, 770.0], 13: [2000, 4500.0], 6: [720, 2070.0], 3: [360, 2620.0]}, 'D': {20: [2000, 5500.0], 36: [4000, 9500.0], 28: [3200, 16800.0], 35: [5000, 10500.0], 21: [2400, 13000.0], 22: [6400, 33200.0], 17: [3000, 22000.0], 18: [2000, 12500.0], 37: [3000, 9250.0], 16: [500, 2820.0]}, 'E': {18: [2400, 15000.0], 38: [5000, 284500.0], 24: [3000, 13750.0], 25: [4000, 19000.0], 26: [4500, 25750.0], 39: [4000, 74000.0], 28: [4000, 21000.0], 27: [5000, 23000.0], 19: [3600, 21000.0], 40: [10000, 150000.0], 17: [3600, 26400.0], 41: [1000, 90000.0], 22: [8000, 41600.0], 21: [3000, 16350.0], 29: [15000, 101500.0], 30: [5000, 24250.0], 31: [2000, 13300.0]}, 'F': {32: [12000, 49000.0], 33: [6000, 24500.0], 24: [2700, 16160.0], 21: [2700, 17610.0], 25: [4000, 19000.0], 26: [4500, 25750.0], 22: [7200, 44880.0], 29: [13500, 109550.0], 17: [3600, 26400.0], 28: [3600, 22640.0], 30: [4500, 26150.000000000004], 19: [3600, 21000.0], 34: [6000, 27600.0], 23: [3000, 17700.0]}}\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-06T08:09:14.856806Z",
     "start_time": "2024-09-06T08:09:14.771032Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 提取作物编号和对应的利润\n",
    "crop_ids = list(land_crop_stats['A'].keys())\n",
    "profits = [info[1] for info in land_crop_stats['A'].values()]\n",
    "\n",
    "# 创建饼图\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(profits, labels=crop_ids, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Profit Distribution for Land Type A啊啊啊')\n",
    "plt.show()"
   ],
   "id": "d0ab1b80cf38a2d1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-06T07:56:00.789409Z",
     "start_time": "2024-09-06T07:56:00.779094Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 数据导出\n",
    "def get_crop_name(crop_num):\n",
    "    return df_crop_details[df_crop_details['cropNum'] == crop_num]['cropName'].values[0]\n",
    "\n",
    "land_crop_stats_A = {get_crop_name(crop_num):v for crop_num, v in land_crop_stats['A'].items()}\n",
    "land_crop_stats_A"
   ],
   "id": "c072743a8ac6f911",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'小麦': [800, 2350.0],\n",
       " '玉米': [1000, 2500.0],\n",
       " '黄豆': [400, 900.0],\n",
       " '绿豆': [350, 2100.0],\n",
       " '谷子': [400, 2340.0]}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-06T08:00:34.699835Z",
     "start_time": "2024-09-06T08:00:34.632874Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 创建一个Excel写入器\n",
    "with pd.ExcelWriter('land_crop_stats.xlsx') as writer:\n",
    "    for land_type in ['A', 'B', 'C', 'D', 'E', 'F']:\n",
    "        # 转换数据格式\n",
    "        land_crop_stats_land = {get_crop_name(crop_num): v for crop_num, v in land_crop_stats[land_type].items()}\n",
    "        df = pd.DataFrame.from_dict(land_crop_stats_land, orient='index', columns=['产量', '利润'])\n",
    "        df.reset_index(inplace=True)\n",
    "        df.rename(columns={'index': '作物'}, inplace=True)\n",
    "        \n",
    "        # 写入对应的sheet\n",
    "        df.to_excel(writer, sheet_name=land_type, index=False)\n",
    "\n",
    "print(\"Excel文件已成功导出为 'land_crop_stats.xlsx'\")\n"
   ],
   "id": "2f358e6cfe30db3d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excel文件已成功导出为 'land_crop_stats.xlsx'\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "fd23d207b8344fac"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}