{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "6668a6f1", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import datetime\n", "\n", "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 2, "id": "60a9ce96", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('農地土地覆蓋_2025_僅受益地_水利小組.csv', \n", " usecols=['年份', '期作', '作物類別', '水利小組編號', '面積'],\n", " #dtype={'水利小': str}\n", " )\n", "df_crop = pd.read_excel('作物類別整併.xlsx')" ] }, { "cell_type": "code", "execution_count": 3, "id": "24192ff2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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面積年份期作作物類別水利小組編號
02031.33814920250小麥I0902504
13450.45025320250小麥I0902422
22492.82000020250小麥I0902403
31295.72066520250小麥I0902403
42897.30745620250小麥I0902419
..................
10729031858.47568720253鳳梨I1040509
1072904357.81797920253鳳梨I1040509
10729052292.64650920253鳳梨I1040904
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1072908 rows × 5 columns

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" ], "text/plain": [ " 面積 年份 期作 作物類別 水利小組編號\n", "0 2031.338149 2025 0 小麥 I0902504\n", "1 3450.450253 2025 0 小麥 I0902422\n", "2 2492.820000 2025 0 小麥 I0902403\n", "3 1295.720665 2025 0 小麥 I0902403\n", "4 2897.307456 2025 0 小麥 I0902419\n", "... ... ... .. ... ...\n", "1072903 1858.475687 2025 3 鳳梨 I1040509\n", "1072904 357.817979 2025 3 鳳梨 I1040509\n", "1072905 2292.646509 2025 3 鳳梨 I1040904\n", "1072906 511.447036 2025 3 鳳梨 I1040803\n", "1072907 1878.389632 2025 3 鳳梨 I1040509\n", "\n", "[1072908 rows x 5 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 4, "id": "270403d4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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作物類別作物別(整併)作物編碼
0大心芥菜芥菜類AR01
1大芥菜芥菜類AR01
2大芥菜(芥菜)芥菜類AR01
3包心芥菜芥菜類AR01
4芥菜芥菜類AR01
............
95豌豆豌豆AR57
96蕎麥蕎麥AR58
97薑薑AR59
98蘿蔔蘿蔔AR60
99蘿蔔類蘿蔔AR60
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100 rows × 3 columns

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" ], "text/plain": [ " 作物類別 作物別(整併) 作物編碼\n", "0 大心芥菜 芥菜類 AR01\n", "1 大芥菜 芥菜類 AR01\n", "2 大芥菜(芥菜) 芥菜類 AR01\n", "3 包心芥菜 芥菜類 AR01\n", "4 芥菜 芥菜類 AR01\n", ".. ... ... ...\n", "95 豌豆 豌豆 AR57\n", "96 蕎麥 蕎麥 AR58\n", "97 薑 薑 AR59\n", "98 蘿蔔 蘿蔔 AR60\n", "99 蘿蔔類 蘿蔔 AR60\n", "\n", "[100 rows x 3 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_crop" ] }, { "cell_type": "code", "execution_count": 5, "id": "5ef5b17b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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面積年份期作作物類別水利小組編號作物別(整併)作物編碼
02031.33814920250小麥I0902504小麥AR02
13450.45025320250小麥I0902422小麥AR02
22492.82000020250小麥I0902403小麥AR02
31295.72066520250小麥I0902403小麥AR02
42897.30745620250小麥I0902419小麥AR02
........................
10729031858.47568720253鳳梨I1040509鳳梨AR52
1072904357.81797920253鳳梨I1040509鳳梨AR52
10729052292.64650920253鳳梨I1040904鳳梨AR52
1072906511.44703620253鳳梨I1040803鳳梨AR52
10729071878.38963220253鳳梨I1040509鳳梨AR52
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1072908 rows × 7 columns

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" ], "text/plain": [ " 面積 年份 期作 作物類別 水利小組編號 作物別(整併) 作物編碼\n", "0 2031.338149 2025 0 小麥 I0902504 小麥 AR02\n", "1 3450.450253 2025 0 小麥 I0902422 小麥 AR02\n", "2 2492.820000 2025 0 小麥 I0902403 小麥 AR02\n", "3 1295.720665 2025 0 小麥 I0902403 小麥 AR02\n", "4 2897.307456 2025 0 小麥 I0902419 小麥 AR02\n", "... ... ... .. ... ... ... ...\n", "1072903 1858.475687 2025 3 鳳梨 I1040509 鳳梨 AR52\n", "1072904 357.817979 2025 3 鳳梨 I1040509 鳳梨 AR52\n", "1072905 2292.646509 2025 3 鳳梨 I1040904 鳳梨 AR52\n", "1072906 511.447036 2025 3 鳳梨 I1040803 鳳梨 AR52\n", "1072907 1878.389632 2025 3 鳳梨 I1040509 鳳梨 AR52\n", "\n", "[1072908 rows x 7 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.merge(df_crop, on='作物類別', how='left')\n", "df" ] }, { "cell_type": "code", "execution_count": 6, "id": "5a40a7ff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "# 查看作物類別整併缺漏的資料\n", "print(df[df['作物別(整併)'].isna()]['作物類別'].unique())\n", "#df[df['作物別(整併)'].isna()].to_excel('作物類別整併_缺漏.xlsx', index=False)" ] }, { "cell_type": "code", "execution_count": 6, "id": "fa17f2dc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Counter({'I1040209': 2581,\n", " 'I1500715': 2417,\n", " 'I0700817': 2159,\n", " 'I1040109': 2147,\n", " 'I1040201': 2119,\n", " 'I1500801': 2109,\n", " 'I1400220': 2057,\n", " 'I1040311': 2027,\n", " 'I0902501': 1969,\n", " 'I1500806': 1924,\n", " 'I1400210': 1898,\n", " 'I0900706': 1823,\n", " 'I1040401': 1800,\n", " 'I1040404': 1788,\n", " 'I0900909': 1769,\n", " 'I0900202': 1743,\n", " 'I1040214': 1709,\n", " 'I0700803': 1697,\n", " 'I1500713': 1671,\n", " 'I0902203': 1668,\n", " 'I0900102': 1658,\n", " 'I0800408': 1636,\n", " 'I1500802': 1634,\n", " 'I1040208': 1617,\n", " 'I1040405': 1606,\n", " 'I1040309': 1581,\n", " 'I0701816': 1581,\n", " 'I1170113': 1542,\n", " 'I0700214': 1525,\n", " 'I1040403': 1493,\n", " 'I1400216': 1490,\n", " 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'I0100916': 416,\n", " 'I0400415': 416,\n", " 'I1030103': 416,\n", " 'I0300951': 415,\n", " 'I0100215': 415,\n", " 'I0800603': 415,\n", " 'I1130108': 415,\n", " 'I1020806': 415,\n", " 'I1011007': 415,\n", " 'I0100802': 414,\n", " 'I1300108': 414,\n", " 'I1160407': 413,\n", " 'I1500505': 413,\n", " 'I0100613': 413,\n", " 'I0300812': 413,\n", " 'I1010201': 413,\n", " 'I1030603': 413,\n", " 'I1010918': 413,\n", " 'I1020508': 412,\n", " 'I1010818': 412,\n", " 'I1150901': 411,\n", " 'I0601504': 411,\n", " 'I1120404': 411,\n", " 'I1120210': 411,\n", " 'I0400509': 411,\n", " 'I0100513': 410,\n", " 'I0701608': 410,\n", " 'I1400604': 410,\n", " 'I0500408': 409,\n", " 'I0400518': 409,\n", " 'I0100103': 409,\n", " 'I0200307': 409,\n", " 'I1130206': 409,\n", " 'I0701506': 408,\n", " 'I1300515': 407,\n", " 'I1030611': 407,\n", " 'I0800219': 406,\n", " 'I1130401': 406,\n", " 'I1020101': 406,\n", " 'I1020607': 406,\n", " 'I1161012': 405,\n", " 'I1150603': 405,\n", " 'I0701409': 405,\n", " 'I0700903': 405,\n", " 'I1140508': 405,\n", " 'I0400318': 405,\n", " 'I1140509': 404,\n", " 'I0901009': 403,\n", " 'I0100502': 403,\n", " 'I0701622': 403,\n", " 'I1150703': 402,\n", " 'I0300928': 402,\n", " 'I0700410': 401,\n", " 'I1020804': 401,\n", " 'I0902805': 400,\n", " 'I0902102': 400,\n", " 'I1160208': 400,\n", " 'I0100701': 400,\n", " 'I0601105': 400,\n", " 'I0701404': 400,\n", " ...})" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "count_class = Counter(df[\"水利小組編號\"])\n", "count_class" ] }, { "cell_type": "code", "execution_count": 7, "id": "8cdbe411", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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面積年份期作作物類別水利小組編號作物別(整併)作物編碼工作站編號
02031.33814920250小麥0902504小麥AR0209025
13450.45025320250小麥0902422小麥AR0209024
22492.82000020250小麥0902403小麥AR0209024
31295.72066520250小麥0902403小麥AR0209024
42897.30745620250小麥0902419小麥AR0209024
...........................
10729031858.47568720253鳳梨1040509鳳梨AR5210405
1072904357.81797920253鳳梨1040509鳳梨AR5210405
10729052292.64650920253鳳梨1040904鳳梨AR5210409
1072906511.44703620253鳳梨1040803鳳梨AR5210408
10729071878.38963220253鳳梨1040509鳳梨AR5210405
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1072908 rows × 8 columns

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" ], "text/plain": [ " 面積 年份 期作 作物類別 水利小組編號 作物別(整併) 作物編碼 工作站編號\n", "0 2031.338149 2025 0 小麥 0902504 小麥 AR02 09025\n", "1 3450.450253 2025 0 小麥 0902422 小麥 AR02 09024\n", "2 2492.820000 2025 0 小麥 0902403 小麥 AR02 09024\n", "3 1295.720665 2025 0 小麥 0902403 小麥 AR02 09024\n", "4 2897.307456 2025 0 小麥 0902419 小麥 AR02 09024\n", "... ... ... .. ... ... ... ... ...\n", "1072903 1858.475687 2025 3 鳳梨 1040509 鳳梨 AR52 10405\n", "1072904 357.817979 2025 3 鳳梨 1040509 鳳梨 AR52 10405\n", "1072905 2292.646509 2025 3 鳳梨 1040904 鳳梨 AR52 10409\n", "1072906 511.447036 2025 3 鳳梨 1040803 鳳梨 AR52 10408\n", "1072907 1878.389632 2025 3 鳳梨 1040509 鳳梨 AR52 10405\n", "\n", "[1072908 rows x 8 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['工作站編號'] = df['水利小組編號'].str[1:6]\n", "df['水利小組編號'] = df['水利小組編號'].str[1:]\n", "df" ] }, { "cell_type": "code", "execution_count": 8, "id": "9212f470", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WorkStationIdWorkStationGrpIdDataYearPeriodNoCropTypeArea
009025090250420250AR022031.338149
109024090242220250AR023450.450253
209024090240320250AR022492.820000
309024090240320250AR021295.720665
409024090241920250AR022897.307456
.....................
107290310405104050920253AR521858.475687
107290410405104050920253AR52357.817979
107290510409104090420253AR522292.646509
107290610408104080320253AR52511.447036
107290710405104050920253AR521878.389632
\n", "

1072908 rows × 6 columns

\n", "
" ], "text/plain": [ " WorkStationId WorkStationGrpId DataYear PeriodNo CropType \\\n", "0 09025 0902504 2025 0 AR02 \n", "1 09024 0902422 2025 0 AR02 \n", "2 09024 0902403 2025 0 AR02 \n", "3 09024 0902403 2025 0 AR02 \n", "4 09024 0902419 2025 0 AR02 \n", "... ... ... ... ... ... \n", "1072903 10405 1040509 2025 3 AR52 \n", "1072904 10405 1040509 2025 3 AR52 \n", "1072905 10409 1040904 2025 3 AR52 \n", "1072906 10408 1040803 2025 3 AR52 \n", "1072907 10405 1040509 2025 3 AR52 \n", "\n", " Area \n", "0 2031.338149 \n", "1 3450.450253 \n", "2 2492.820000 \n", "3 1295.720665 \n", "4 2897.307456 \n", "... ... \n", "1072903 1858.475687 \n", "1072904 357.817979 \n", "1072905 2292.646509 \n", "1072906 511.447036 \n", "1072907 1878.389632 \n", "\n", "[1072908 rows x 6 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reorder & rename columns\n", "df_sql = df[['工作站編號','水利小組編號','年份','期作','作物編碼','面積']] \n", "df_sql = df_sql.rename(columns={\n", " '工作站編號': 'WorkStationId',\n", " '水利小組編號': 'WorkStationGrpId',\n", " '年份': 'DataYear',\n", " '期作': 'PeriodNo',\n", " '作物編碼':'CropType',\n", " '面積':'Area',\n", " })\n", "df_sql" ] }, { "cell_type": "code", "execution_count": 41, "id": "7a36c958", "metadata": {}, "outputs": [], "source": [ "# 剔除嘉南一般區範圍\n", "#stn_chianan = [\"11101\",\"11501\",\"11502\",\"11503\",\"11504\",\"11505\",\"11510\",\"11701\",\"11702\"]\n", "#df = df[~df['WorkStationId'].isin(stn_chianan)]\n", "#df" ] }, { "cell_type": "code", "execution_count": 9, "id": "9c80309f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WorkStationIdWorkStationGrpIdDataYearPeriodNoCropTypeArea
009025090250420250AR022031.338149
109024090242220250AR023450.450253
209024090240320250AR022492.820000
309024090240320250AR021295.720665
409024090241920250AR022897.307456
.....................
107290310405104050920253AR521858.475687
107290410405104050920253AR52357.817979
107290510409104090420253AR522292.646509
107290610408104080320253AR52511.447036
107290710405104050920253AR521878.389632
\n", "

1072908 rows × 6 columns

\n", "
" ], "text/plain": [ " WorkStationId WorkStationGrpId DataYear PeriodNo CropType \\\n", "0 09025 0902504 2025 0 AR02 \n", "1 09024 0902422 2025 0 AR02 \n", "2 09024 0902403 2025 0 AR02 \n", "3 09024 0902403 2025 0 AR02 \n", "4 09024 0902419 2025 0 AR02 \n", "... ... ... ... ... ... \n", "1072903 10405 1040509 2025 3 AR52 \n", "1072904 10405 1040509 2025 3 AR52 \n", "1072905 10409 1040904 2025 3 AR52 \n", "1072906 10408 1040803 2025 3 AR52 \n", "1072907 10405 1040509 2025 3 AR52 \n", "\n", " Area \n", "0 2031.338149 \n", "1 3450.450253 \n", "2 2492.820000 \n", "3 1295.720665 \n", "4 2897.307456 \n", "... ... \n", "1072903 1858.475687 \n", "1072904 357.817979 \n", "1072905 2292.646509 \n", "1072906 511.447036 \n", "1072907 1878.389632 \n", "\n", "[1072908 rows x 6 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 剔除重複資料(check,非必須)\n", "df_sql = df_sql.drop_duplicates()\n", "df_sql" ] }, { "cell_type": "markdown", "id": "fc3b92d4", "metadata": {}, "source": [ "# 以工作站、水利小組統計" ] }, { "cell_type": "code", "execution_count": 10, "id": "69f68284", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WorkStationIdWorkStationGrpIdDataYearPeriodNoCropTypeArea
001001010010120251AR06484977.719205
101001010010320251AR06838879.309128
201001010010420251AR062176.787995
301001010010520251AR06591105.989396
401001010010620251AR06232180.216356
.....................
1103716000160001020251AR06167098.493037
1103816000160001020252AR06129312.533291
1103916000160001220251AR06163857.170950
1104016000160001220252AR06163857.076999
1104116000160001420251AR0612784.162065
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11042 rows × 6 columns

\n", "
" ], "text/plain": [ " WorkStationId WorkStationGrpId DataYear PeriodNo CropType \\\n", "0 01001 0100101 2025 1 AR06 \n", "1 01001 0100103 2025 1 AR06 \n", "2 01001 0100104 2025 1 AR06 \n", "3 01001 0100105 2025 1 AR06 \n", "4 01001 0100106 2025 1 AR06 \n", "... ... ... ... ... ... \n", "11037 16000 1600010 2025 1 AR06 \n", "11038 16000 1600010 2025 2 AR06 \n", "11039 16000 1600012 2025 1 AR06 \n", "11040 16000 1600012 2025 2 AR06 \n", "11041 16000 1600014 2025 1 AR06 \n", "\n", " Area \n", "0 484977.719205 \n", "1 838879.309128 \n", "2 2176.787995 \n", "3 591105.989396 \n", "4 232180.216356 \n", "... ... \n", "11037 167098.493037 \n", "11038 129312.533291 \n", "11039 163857.170950 \n", "11040 163857.076999 \n", "11041 12784.162065 \n", "\n", "[11042 rows x 6 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_summary = df_sql.groupby(['WorkStationId','WorkStationGrpId','DataYear','PeriodNo','CropType'])['Area'].sum().reset_index()\n", "df_summary" ] }, { "cell_type": "code", "execution_count": 11, "id": "3cdf124c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WorkStationIdWorkStationGrpIdDataYearPeriodNoCropTypeAreaChiananNormalDistrict
001001010010120251AR06484977.7192050
101001010010320251AR06838879.3091280
201001010010420251AR062176.7879950
301001010010520251AR06591105.9893960
401001010010620251AR06232180.2163560
........................
1103716000160001020251AR06167098.4930370
1103816000160001020252AR06129312.5332910
1103916000160001220251AR06163857.1709500
1104016000160001220252AR06163857.0769990
1104116000160001420251AR0612784.1620650
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11042 rows × 7 columns

\n", "
" ], "text/plain": [ " WorkStationId WorkStationGrpId DataYear PeriodNo CropType \\\n", "0 01001 0100101 2025 1 AR06 \n", "1 01001 0100103 2025 1 AR06 \n", "2 01001 0100104 2025 1 AR06 \n", "3 01001 0100105 2025 1 AR06 \n", "4 01001 0100106 2025 1 AR06 \n", "... ... ... ... ... ... \n", "11037 16000 1600010 2025 1 AR06 \n", "11038 16000 1600010 2025 2 AR06 \n", "11039 16000 1600012 2025 1 AR06 \n", "11040 16000 1600012 2025 2 AR06 \n", "11041 16000 1600014 2025 1 AR06 \n", "\n", " Area ChiananNormalDistrict \n", "0 484977.719205 0 \n", "1 838879.309128 0 \n", "2 2176.787995 0 \n", "3 591105.989396 0 \n", "4 232180.216356 0 \n", "... ... ... \n", "11037 167098.493037 0 \n", "11038 129312.533291 0 \n", "11039 163857.170950 0 \n", "11040 163857.076999 0 \n", "11041 12784.162065 0 \n", "\n", "[11042 rows x 7 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stn_chianan = [\"11101\",\"11501\",\"11502\",\"11503\",\"11504\",\"11505\",\"11510\",\"11701\",\"11702\"]\n", "df_summary['ChiananNormalDistrict'] = df_summary['WorkStationId'].apply(lambda x: 1 if (x in stn_chianan) else 0)\n", "df_summary" ] }, { "cell_type": "code", "execution_count": null, "id": "3aee39cd", "metadata": {}, "outputs": [], "source": [ "#df_summary.to_excel(f'tbl_CropAreaData_History2025_{datetime.date.today().strftime('%Y%m%d')}.xlsx', index=False)" ] }, { "cell_type": "markdown", "id": "e9123260", "metadata": {}, "source": [ "## 資料庫格式" ] }, { "cell_type": "code", "execution_count": 12, "id": "05d2cef5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WorkStationIdWorkStationGrpIdDataYearPeriodNoCropTypeRealCropTypeCalcAreaChiananNormalDistrict
001001010010120251TRAR06484977.7192050
101001010010320251TRAR06838879.3091280
201001010010420251TRAR062176.7879950
301001010010520251TRAR06591105.9893960
401001010010620251TRAR06232180.2163560
...........................
1103716000160001020251TRAR06167098.4930370
1103816000160001020252TRAR06129312.5332910
1103916000160001220251TRAR06163857.1709500
1104016000160001220252TRAR06163857.0769990
1104116000160001420251TRAR0612784.1620650
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11042 rows × 8 columns

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" ], "text/plain": [ " WorkStationId WorkStationGrpId DataYear PeriodNo CropType \\\n", "0 01001 0100101 2025 1 TR \n", "1 01001 0100103 2025 1 TR \n", "2 01001 0100104 2025 1 TR \n", "3 01001 0100105 2025 1 TR \n", "4 01001 0100106 2025 1 TR \n", "... ... ... ... ... ... \n", "11037 16000 1600010 2025 1 TR \n", "11038 16000 1600010 2025 2 TR \n", "11039 16000 1600012 2025 1 TR \n", "11040 16000 1600012 2025 2 TR \n", "11041 16000 1600014 2025 1 TR \n", "\n", " RealCropType CalcArea ChiananNormalDistrict \n", "0 AR06 484977.719205 0 \n", "1 AR06 838879.309128 0 \n", "2 AR06 2176.787995 0 \n", "3 AR06 591105.989396 0 \n", "4 AR06 232180.216356 0 \n", "... ... ... ... \n", "11037 AR06 167098.493037 0 \n", "11038 AR06 129312.533291 0 \n", "11039 AR06 163857.170950 0 \n", "11040 AR06 163857.076999 0 \n", "11041 AR06 12784.162065 0 \n", "\n", "[11042 rows x 8 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_summary_SQL = df_summary.rename(columns={'CropType':'RealCropType', 'Area':'CalcArea'})\n", "df_summary_SQL['CropType'] = df_summary_SQL['RealCropType'].apply(lambda x: 'TR' if x == 'AR06' else 'TM')\n", "df_summary_SQL = df_summary_SQL[['WorkStationId','WorkStationGrpId','DataYear','PeriodNo','CropType','RealCropType','CalcArea','ChiananNormalDistrict']]\n", "df_summary_SQL" ] }, { "cell_type": "code", "execution_count": null, "id": "9dd937de", "metadata": {}, "outputs": [], "source": [ "#df_summary_SQL.to_excel('tbl_CropAreaData_History2025_資料庫格式.xlsx', index=False)" ] }, { "cell_type": "markdown", "id": "cca6f05d", "metadata": {}, "source": [ "### 匯入資料庫" ] }, { "cell_type": "code", "execution_count": 13, "id": "1cd9d9da", "metadata": {}, "outputs": [], "source": [ "from sqlalchemy import create_engine\n", "\n", "SERVER = 'labdb'\n", "DATABASE = 'A304_WRWSR'\n", "USERNAME = ''\n", "PASSWORD = ''\n", "\n", "engine = create_engine(\n", " f\"mssql+pyodbc://@{SERVER}/{DATABASE}?driver=ODBC+Driver+17+for+SQL+Server&trusted_connection=yes\"\n", ")" ] }, { "cell_type": "code", "execution_count": 15, "id": "ffbfc9a3", "metadata": {}, "outputs": [], "source": [ "df_summary_SQL['DataDate'] = datetime.datetime.now().strftime('%Y-%m-%d')" ] }, { "cell_type": "code", "execution_count": 16, "id": "4989c5c7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "91" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_summary_SQL.to_sql(\n", " name='tbl_CropAreaData_History',\n", " con=engine,\n", " index=False, # 等同 rownames=FALSE\n", " if_exists='append' # 等同 append=TRUE\n", ")" ] }, { "cell_type": "markdown", "id": "c851ce5f", "metadata": {}, "source": [ "# 以管理處統計" ] }, { "cell_type": "code", "execution_count": 30, "id": "e69621d5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IADataYearPeriodNoChiananNormalDistrictArea
00120251010919.929181
10220251068.166411
2032025109223.722207
3032025201055.978329
4042025103062.345771
504202520921.468054
6052025102473.501630
7052025202045.412497
8062025103147.870593
9062025202132.731321
100720251010201.296152
11072025207493.318440
12082025103146.912366
13082025203149.539875
140920251021644.138841
150920252013797.869427
161020251020409.700141
17102025209554.534600
181120251017425.570253
19112025115210.514160
20112025206631.179751
21112025214664.195882
22122025103387.729844
2312202520163.314164
24132025103046.032451
2513202520266.568294
26142025105364.884350
27142025205084.870747
28152025106424.955882
29152025205734.288477
3016202510202.034033
3116202520138.344075
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" ], "text/plain": [ " IA DataYear PeriodNo ChiananNormalDistrict Area\n", "0 01 2025 1 0 10919.929181\n", "1 02 2025 1 0 68.166411\n", "2 03 2025 1 0 9223.722207\n", "3 03 2025 2 0 1055.978329\n", "4 04 2025 1 0 3062.345771\n", "5 04 2025 2 0 921.468054\n", "6 05 2025 1 0 2473.501630\n", "7 05 2025 2 0 2045.412497\n", "8 06 2025 1 0 3147.870593\n", "9 06 2025 2 0 2132.731321\n", "10 07 2025 1 0 10201.296152\n", "11 07 2025 2 0 7493.318440\n", "12 08 2025 1 0 3146.912366\n", "13 08 2025 2 0 3149.539875\n", "14 09 2025 1 0 21644.138841\n", "15 09 2025 2 0 13797.869427\n", "16 10 2025 1 0 20409.700141\n", "17 10 2025 2 0 9554.534600\n", "18 11 2025 1 0 17425.570253\n", "19 11 2025 1 1 5210.514160\n", "20 11 2025 2 0 6631.179751\n", "21 11 2025 2 1 4664.195882\n", "22 12 2025 1 0 3387.729844\n", "23 12 2025 2 0 163.314164\n", "24 13 2025 1 0 3046.032451\n", "25 13 2025 2 0 266.568294\n", "26 14 2025 1 0 5364.884350\n", "27 14 2025 2 0 5084.870747\n", "28 15 2025 1 0 6424.955882\n", "29 15 2025 2 0 5734.288477\n", "30 16 2025 1 0 202.034033\n", "31 16 2025 2 0 138.344075" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_summary['IA'] = df_summary['WorkStationId'].str[:2]\n", "df_summary_IA = df_summary[df_summary['CropType'] == 'AR06'].groupby(['IA','DataYear','PeriodNo','ChiananNormalDistrict'])['Area'].sum().reset_index()\n", "df_summary_IA['Area'] = df_summary_IA['Area']/10000\n", "\n", "df_summary_IA" ] }, { "cell_type": "code", "execution_count": 17, "id": "82fcfdc4", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\johnnychan\\AppData\\Local\\Temp\\ipykernel_5616\\452614714.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " d['normal Area'] = df_summary_IA[(df_summary_IA['IA']=='11') & (df_summary_IA['PeriodNo']==1) & (df_summary_IA['ChiananNormalDistrict']==1)]['Area'].to_list()\n", "C:\\Users\\johnnychan\\AppData\\Local\\Temp\\ipykernel_5616\\452614714.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " d['total Area'] = d['Area'] + d['normal Area']\n" ] }, { "data": { "text/html": [ "
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IADataYearPeriodNoChiananNormalDistrictAreanormal Areatotal Area
181120251017425.5702535210.5141622636.084413
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" ], "text/plain": [ " IA DataYear PeriodNo ChiananNormalDistrict Area normal Area \\\n", "18 11 2025 1 0 17425.570253 5210.51416 \n", "\n", " total Area \n", "18 22636.084413 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = df_summary_IA[(df_summary_IA['IA']=='11') & (df_summary_IA['PeriodNo']==1) & (df_summary_IA['ChiananNormalDistrict']==0)]\n", "d['normal Area'] = df_summary_IA[(df_summary_IA['IA']=='11') & (df_summary_IA['PeriodNo']==1) & (df_summary_IA['ChiananNormalDistrict']==1)]['Area'].to_list()\n", "d['total Area'] = d['Area'] + d['normal Area']\n", "d" ] }, { "cell_type": "markdown", "id": "d1275748", "metadata": {}, "source": [ "# 以作物類別統計" ] }, { "cell_type": "markdown", "id": "27ad82b0", "metadata": {}, "source": [ "### 僅受益地" ] }, { "cell_type": "code", "execution_count": 36, "id": "6cf9a1b1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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作物編碼作物類別面積
0AR01大芥菜589.772255
1AR02小麥192.665264
2AR03山藥16.142249
3AR04毛豆6320.264662
4AR05水芋1292.635272
5AR06水稻188192.918198
6AR07水蕹菜476.873414
7AR08牛蒡39.108112
8AR10玉米15645.339168
9AR12甘藷3610.330469
10AR13甘藍2316.912326
11AR14百香果334.833191
12AR15西瓜686.876532
13AR16龍鬚菜128.901091
14AR22花椰菜38.152194
15AR25青花菜210.134377
16AR26洋蔥664.593105
17AR27紅豆1913.996833
18AR28胡麻(芝麻)317.753386
19AR29胡蘿蔔1160.988870
20AR33茭白筍616.388362
21AR35草莓23.854508
22AR36荖藤515.277975
23AR37馬鈴薯1921.810718
24AR39甜瓜868.741048
25AR41荷361.833262
26AR45菱角296.530002
27AR49落花生11895.151558
28AR51蒜3950.259663
29AR52鳳梨923.791801
30AR54蔥63.468879
31AR56蔥頭(紅蔥頭)440.032601
32AR58蕎麥100.048218
33AR59薑26.753046
34AR60蘿蔔296.436034
\n", "
" ], "text/plain": [ " 作物編碼 作物類別 面積\n", "0 AR01 大芥菜 589.772255\n", "1 AR02 小麥 192.665264\n", "2 AR03 山藥 16.142249\n", "3 AR04 毛豆 6320.264662\n", "4 AR05 水芋 1292.635272\n", "5 AR06 水稻 188192.918198\n", "6 AR07 水蕹菜 476.873414\n", "7 AR08 牛蒡 39.108112\n", "8 AR10 玉米 15645.339168\n", "9 AR12 甘藷 3610.330469\n", "10 AR13 甘藍 2316.912326\n", "11 AR14 百香果 334.833191\n", "12 AR15 西瓜 686.876532\n", "13 AR16 龍鬚菜 128.901091\n", "14 AR22 花椰菜 38.152194\n", "15 AR25 青花菜 210.134377\n", "16 AR26 洋蔥 664.593105\n", "17 AR27 紅豆 1913.996833\n", "18 AR28 胡麻(芝麻) 317.753386\n", "19 AR29 胡蘿蔔 1160.988870\n", "20 AR33 茭白筍 616.388362\n", "21 AR35 草莓 23.854508\n", "22 AR36 荖藤 515.277975\n", "23 AR37 馬鈴薯 1921.810718\n", "24 AR39 甜瓜 868.741048\n", "25 AR41 荷 361.833262\n", "26 AR45 菱角 296.530002\n", "27 AR49 落花生 11895.151558\n", "28 AR51 蒜 3950.259663\n", "29 AR52 鳳梨 923.791801\n", "30 AR54 蔥 63.468879\n", "31 AR56 蔥頭(紅蔥頭) 440.032601\n", "32 AR58 蕎麥 100.048218\n", "33 AR59 薑 26.753046\n", "34 AR60 蘿蔔 296.436034" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(df.groupby(['作物編碼','作物類別'])['面積'].sum()/10000).reset_index()" ] }, { "cell_type": "markdown", "id": "0392905f", "metadata": {}, "source": [ "### 全部圖資範圍" ] }, { "cell_type": "code", "execution_count": null, "id": "b488c580", "metadata": {}, "outputs": [], "source": [ "df_all = pd.read_csv('農地土地覆蓋_2025.csv', \n", " usecols=['年份', '期作', '作物類別', '面積'],\n", " #dtype={'水利小': str}\n", " )\n", "\n", "df = df_all.merge(df_crop, on='作物類別', how='left')" ] }, { "cell_type": "code", "execution_count": null, "id": "27cda698", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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作物編碼作物類別面積
5AR06水稻233272.690649
8AR10玉米20620.542762
29AR49落花生14444.869525
3AR04毛豆9370.340891
31AR52鳳梨6706.458688
10AR13甘藍6384.751342
30AR51蒜4889.779143
9AR12甘藷4614.143444
12AR15西瓜4598.823583
18AR27紅豆3930.407361
25AR37馬鈴薯2788.673095
4AR05水芋1536.923627
20AR29胡蘿蔔1517.486730
26AR39甜瓜1061.145025
17AR26洋蔥985.181931
22AR33茭白筍756.817487
33AR56蔥頭(紅蔥頭)688.477700
0AR01大芥菜681.304724
24AR36荖藤657.034811
36AR60蘿蔔573.444294
6AR07水蕹菜532.988815
11AR14百香果416.310510
27AR41荷410.487597
19AR28胡麻(芝麻)367.850263
23AR35草莓361.962592
35AR59薑339.223733
28AR45菱角335.653173
1AR02小麥239.718723
16AR25青花菜233.747140
15AR24金針菜211.160165
14AR22花椰菜164.178378
2AR03山藥162.529088
13AR16龍鬚菜148.786254
7AR08牛蒡116.053533
34AR58蕎麥104.839222
21AR30苦瓜83.294255
32AR54蔥68.570467
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" ], "text/plain": [ " 作物編碼 作物類別 面積\n", "5 AR06 水稻 233272.690649\n", "8 AR10 玉米 20620.542762\n", "29 AR49 落花生 14444.869525\n", "3 AR04 毛豆 9370.340891\n", "31 AR52 鳳梨 6706.458688\n", "10 AR13 甘藍 6384.751342\n", "30 AR51 蒜 4889.779143\n", "9 AR12 甘藷 4614.143444\n", "12 AR15 西瓜 4598.823583\n", "18 AR27 紅豆 3930.407361\n", "25 AR37 馬鈴薯 2788.673095\n", "4 AR05 水芋 1536.923627\n", "20 AR29 胡蘿蔔 1517.486730\n", "26 AR39 甜瓜 1061.145025\n", "17 AR26 洋蔥 985.181931\n", "22 AR33 茭白筍 756.817487\n", "33 AR56 蔥頭(紅蔥頭) 688.477700\n", "0 AR01 大芥菜 681.304724\n", "24 AR36 荖藤 657.034811\n", "36 AR60 蘿蔔 573.444294\n", "6 AR07 水蕹菜 532.988815\n", "11 AR14 百香果 416.310510\n", "27 AR41 荷 410.487597\n", "19 AR28 胡麻(芝麻) 367.850263\n", "23 AR35 草莓 361.962592\n", "35 AR59 薑 339.223733\n", "28 AR45 菱角 335.653173\n", "1 AR02 小麥 239.718723\n", "16 AR25 青花菜 233.747140\n", "15 AR24 金針菜 211.160165\n", "14 AR22 花椰菜 164.178378\n", "2 AR03 山藥 162.529088\n", "13 AR16 龍鬚菜 148.786254\n", "7 AR08 牛蒡 116.053533\n", "34 AR58 蕎麥 104.839222\n", "21 AR30 苦瓜 83.294255\n", "32 AR54 蔥 68.570467" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(df_all.groupby(['作物編碼','作物類別'])['面積'].sum()/10000).reset_index().sort_values(by=['面積'], ascending=[False])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 5 }