{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "08971b87-edf9-4d12-81a2-8185f8812fb1", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ mode_0 = 2,684,921\n", "✅ mode_1 = 804,489\n", "✅ mode_2 = 258,189\n", "📊 合計 = 3,747,599\n" ] } ], "source": [ "import re\n", "from pathlib import Path\n", "\n", "raw = \"\"\"\n", "7108162.csv: mode_0=135 (56.49%) | mode_1=0 (0.00%) | mode_2=104 (43.51%)\n", "7408338.csv: mode_0=1432 (100.00%) | mode_1=0 (0.00%) | mode_2=0 (0.00%)\n", "7657698.csv: mode_0=1 (0.07%) | mode_1=1286 (91.01%) | mode_2=126 (8.92%)\n", "7721164.csv: mode_0=0 (0.00%) | mode_1=483 (100.00%) | mode_2=0 (0.00%)\n", "PatNo_ID_1559466135.csv: mode_0=515 (6.26%) | mode_1=796 (9.67%) | mode_2=6919 (84.06%)\n", "PatNo_ID_1559513931.csv: mode_0=27229 (100.00%) | mode_1=0 (0.00%) | mode_2=0 (0.00%)\n", "PatNo_ID_1560013303.csv: mode_0=2368 (93.12%) | mode_1=0 (0.00%) | mode_2=147 (5.78%)\n", 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| mode_2=733 (3.03%)\n", "PatNo_ID_1593203352.csv: mode_0=9357 (98.54%) | mode_1=0 (0.00%) | mode_2=0 (0.00%)\n", "PatNo_ID_1593386732.csv: mode_0=1210 (100.00%) | mode_1=0 (0.00%) | mode_2=0 (0.00%)\n", "PatNo_ID_1593416100.csv: mode_0=1866 (56.07%) | mode_1=1233 (37.05%) | mode_2=229 (6.88%)\n", "PatNo_ID_1593472048.csv: mode_0=5340 (64.88%) | mode_1=903 (10.97%) | mode_2=4 (0.05%)\n", "PatNo_ID_1593562809.csv: mode_0=79008 (90.36%) | mode_1=7616 (8.71%) | mode_2=8 (0.01%)\n", "PatNo_ID_1593593586.csv: mode_0=17012 (90.10%) | mode_1=1868 (9.89%) | mode_2=2 (0.01%)\n", "PatNo_ID_1593691014.csv: mode_0=55 (3.68%) | mode_1=1263 (84.48%) | mode_2=177 (11.84%)\n", "PatNo_ID_1593720818.csv: mode_0=3622 (92.61%) | mode_1=0 (0.00%) | mode_2=288 (7.36%)\n", "PatNo_ID_1593838524.csv: mode_0=2162 (99.27%) | mode_1=0 (0.00%) | mode_2=0 (0.00%)\n", "PatNo_ID_1593907744.csv: mode_0=21537 (99.76%) | mode_1=0 (0.00%) | mode_2=0 (0.00%)\n", "PatNo_ID_1594027022.csv: mode_0=21952 (100.00%) | mode_1=0 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"PatNo_ID_1594464829.csv: mode_0=957 (23.93%) | mode_1=307 (7.67%) | mode_2=2675 (66.88%)\n", "PatNo_ID_1594465597.csv: mode_0=4793 (73.77%) | mode_1=1233 (18.98%) | mode_2=309 (4.76%)\n", "PatNo_ID_1594467719.csv: mode_0=1018 (39.77%) | mode_1=294 (11.48%) | mode_2=1 (0.04%)\n", "PatNo_ID_1594471407.csv: mode_0=11282 (98.68%) | mode_1=0 (0.00%) | mode_2=0 (0.00%)\n", "PatNo_ID_1594473688.csv: mode_0=19325 (99.60%) | mode_1=78 (0.40%) | mode_2=0 (0.00%)\n", "PatNo_ID_1594479330.csv: mode_0=3727 (100.00%) | mode_1=0 (0.00%) | mode_2=0 (0.00%)\n", "PatNo_ID_1594511911.csv: mode_0=2553 (46.52%) | mode_1=0 (0.00%) | mode_2=2902 (52.88%)\n", "PatNo_ID_1594511914.csv: mode_0=1161 (11.95%) | mode_1=7752 (79.78%) | mode_2=763 (7.85%)\n", "PatNo_ID_1594528842.csv: mode_0=2282 (91.61%) | mode_1=133 (5.34%) | mode_2=0 (0.00%)\n", "PatNo_ID_1594533379.csv: mode_0=781 (75.83%) | mode_1=0 (0.00%) | mode_2=237 (23.01%)\n", "PatNo_ID_1594735845.csv: mode_0=32802 (61.69%) | mode_1=6258 (11.77%) | mode_2=14112 (26.54%)\n", "PatNo_ID_1594796566.csv: mode_0=49 (3.74%) | mode_1=0 (0.00%) | mode_2=1261 (96.26%)\n", "PatNo_ID_1594802957.csv: mode_0=2560 (46.70%) | mode_1=249 (4.54%) | mode_2=2673 (48.76%)\n", "PatNo_ID_1594845806.csv: mode_0=2630 (62.46%) | mode_1=1320 (31.35%) | mode_2=261 (6.20%)\n", "PatNo_ID_1594857190.csv: mode_0=23252 (99.93%) | mode_1=0 (0.00%) | mode_2=0 (0.00%)\n", "PatNo_ID_1594862135.csv: mode_0=2543 (18.52%) | mode_1=2 (0.01%) | mode_2=9707 (70.71%)\n", "PatNo_ID_1594862572.csv: mode_0=2835 (35.90%) | mode_1=5048 (63.93%) | mode_2=13 (0.16%)\n", "PatNo_ID_1594862905.csv: mode_0=59708 (92.88%) | mode_1=3487 (5.42%) | mode_2=1093 (1.70%)\n", "PatNo_ID_1594879195.csv: mode_0=2403 (28.65%) | mode_1=4650 (55.44%) | mode_2=1334 (15.91%)\n", "PatNo_ID_1594886956.csv: mode_0=7280 (42.31%) | mode_1=8692 (50.51%) | mode_2=931 (5.41%)\n", "PatNo_ID_1594888142.csv: mode_0=39090 (99.63%) | mode_1=0 (0.00%) | mode_2=0 (0.00%)\n", "PatNo_ID_1594900319.csv: mode_0=1181 (90.08%) | mode_1=130 (9.92%) | mode_2=0 (0.00%)\n", "PatNo_ID_1594904742.csv: mode_0=24503 (84.70%) | mode_1=3168 (10.95%) | mode_2=1039 (3.59%)\n", "PatNo_ID_1594912862.csv: mode_0=12745 (100.00%) | mode_1=0 (0.00%) | mode_2=0 (0.00%)\n", "PatNo_ID_1594926122.csv: mode_0=18085 (100.00%) | mode_1=0 (0.00%) | mode_2=0 (0.00%)\n", "PatNo_ID_1594959115.csv: mode_0=702 (70.84%) | mode_1=95 (9.59%) | mode_2=157 (15.84%)\n", "PatNo_ID_1594969804.csv: mode_0=3522 (97.35%) | mode_1=96 (2.65%) | mode_2=0 (0.00%)\n", "PatNo_ID_1594970427.csv: mode_0=458 (21.82%) | mode_1=1171 (55.79%) | mode_2=468 (22.30%)\n", "PatNo_ID_1594978835.csv: mode_0=1606 (100.00%) | mode_1=0 (0.00%) | mode_2=0 (0.00%)\n", "PatNo_ID_1595012967.csv: mode_0=14126 (62.23%) | mode_1=7732 (34.06%) | mode_2=796 (3.51%)\n", "PatNo_ID_1595023326.csv: mode_0=919 (72.31%) | mode_1=0 (0.00%) | mode_2=352 (27.69%)\n", "PatNo_ID_1595051455.csv: mode_0=2419 (62.11%) | mode_1=1215 (31.19%) | mode_2=247 (6.34%)\"\"\"\n", "\n", "# 抓 mode 數字\n", "pairs = re.findall(r\"mode_0=(\\d+).+?mode_1=(\\d+).+?mode_2=(\\d+)\", raw)\n", "\n", "total_0 = sum(int(a) for a,b,c in pairs)\n", "total_1 = sum(int(b) for a,b,c in pairs)\n", "total_2 = sum(int(c) for a,b,c in pairs)\n", "\n", "print(f\"✅ mode_0 = {total_0:,}\")\n", "print(f\"✅ mode_1 = {total_1:,}\")\n", "print(f\"✅ mode_2 = {total_2:,}\")\n", "print(f\"📊 合計 = {total_0 + total_1 + total_2:,}\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "e103c3ce-2089-4d00-82f9-3f80866f72ce", "metadata": { "jupyter": { "source_hidden": true }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🚀 啟動 SVV 統計任務 (useable==1) | 20251106_204844\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "掃描檔案: 3%|▎ | 7/264 [00:00<00:05, 45.69file/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "4216007.csv: 三欄>0=0 (0.0%) | 兩欄>0=0 (0.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=0\n", "7108162.csv: 三欄>0=0 (0.0%) | 兩欄>0=135 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=135\n", "7408338.csv: 三欄>0=0 (0.0%) | 兩欄>0=1,431 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=1,431\n", "7657698.csv: 三欄>0=0 (0.0%) | 兩欄>0=1,282 (99.8%) | 一欄>0=2 (0.2%) | 都不>0或空=0 (0.0%) | 總筆數=1,284\n", "7721164.csv: 三欄>0=483 (100.0%) | 兩欄>0=0 (0.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=483\n", "PatNo_ID_1559466135.csv: 三欄>0=0 (0.0%) | 兩欄>0=0 (0.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=0\n", "PatNo_ID_1559513931.csv: 三欄>0=0 (0.0%) | 兩欄>0=27,199 (100.0%) | 一欄>0=6 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=27,205\n", "PatNo_ID_1560013303.csv: 三欄>0=55 (2.3%) | 兩欄>0=2,310 (97.6%) | 一欄>0=2 (0.1%) | 都不>0或空=0 (0.0%) | 總筆數=2,367\n", "PatNo_ID_1560672902.csv: 三欄>0=2,834 (57.9%) | 兩欄>0=2,064 (42.1%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=4,898\n", "PatNo_ID_1562733396.csv: 三欄>0=4,695 (70.1%) | 兩欄>0=1,999 (29.9%) | 一欄>0=2 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=6,696\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "掃描檔案: 5%|▍ | 12/264 [00:00<00:10, 23.65file/s]/tmp/ipykernel_621010/304996257.py:55: DtypeWarning: Columns (4,15) have mixed types. 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Specify dtype option on import or set low_memory=False.\n", " df = pd.read_csv(path)\n", "掃描檔案: 90%|████████▉ | 237/264 [00:11<00:01, 25.32file/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "PatNo_ID_1594461482.csv: 三欄>0=0 (0.0%) | 兩欄>0=33,190 (100.0%) | 一欄>0=7 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=33,197\n", "PatNo_ID_1594464829.csv: 三欄>0=307 (24.3%) | 兩欄>0=956 (75.6%) | 一欄>0=1 (0.1%) | 都不>0或空=0 (0.0%) | 總筆數=1,264\n", "PatNo_ID_1594465597.csv: 三欄>0=662 (11.2%) | 兩欄>0=5,242 (88.8%) | 一欄>0=1 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=5,905\n", "PatNo_ID_1594467719.csv: 三欄>0=288 (22.0%) | 兩欄>0=1,023 (78.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=1,311\n", "PatNo_ID_1594471407.csv: 三欄>0=0 (0.0%) | 兩欄>0=11,276 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=11,276\n", "PatNo_ID_1594473688.csv: 三欄>0=0 (0.0%) | 兩欄>0=19,304 (99.5%) | 一欄>0=99 (0.5%) | 都不>0或空=0 (0.0%) | 總筆數=19,403\n", "PatNo_ID_1594479330.csv: 三欄>0=0 (0.0%) | 兩欄>0=3,714 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=3,714\n", "PatNo_ID_1594511911.csv: 三欄>0=0 (0.0%) | 兩欄>0=2,550 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=2,550\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "掃描檔案: 92%|█████████▏| 243/264 [00:12<00:00, 28.97file/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "PatNo_ID_1594511914.csv: 三欄>0=5,087 (57.2%) | 兩欄>0=3,807 (42.8%) | 一欄>0=3 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=8,897\n", "PatNo_ID_1594528842.csv: 三欄>0=60 (2.5%) | 兩欄>0=2,329 (97.5%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=2,389\n", "PatNo_ID_1594533379.csv: 三欄>0=0 (0.0%) | 兩欄>0=778 (99.6%) | 一欄>0=3 (0.4%) | 都不>0或空=0 (0.0%) | 總筆數=781\n", "PatNo_ID_1594735845.csv: 三欄>0=0 (0.0%) | 兩欄>0=9,823 (50.3%) | 一欄>0=9,702 (49.7%) | 都不>0或空=4 (0.0%) | 總筆數=19,529\n", "PatNo_ID_1594796566.csv: 三欄>0=0 (0.0%) | 兩欄>0=47 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=47\n", "PatNo_ID_1594802957.csv: 三欄>0=0 (0.0%) | 兩欄>0=2,742 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=2,742\n", "PatNo_ID_1594845806.csv: 三欄>0=0 (0.0%) | 兩欄>0=3,949 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=3,949\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "掃描檔案: 94%|█████████▎| 247/264 [00:12<00:00, 30.32file/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "PatNo_ID_1594857190.csv: 三欄>0=0 (0.0%) | 兩欄>0=23,247 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=23,247\n", "PatNo_ID_1594862135.csv: 三欄>0=2 (0.1%) | 兩欄>0=2,541 (99.8%) | 一欄>0=2 (0.1%) | 都不>0或空=0 (0.0%) | 總筆數=2,545\n", "PatNo_ID_1594862572.csv: 三欄>0=0 (0.0%) | 兩欄>0=2,822 (35.8%) | 一欄>0=5,056 (64.1%) | 都不>0或空=4 (0.1%) | 總筆數=7,882\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_621010/304996257.py:55: DtypeWarning: Columns (4,14,15,16) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df = pd.read_csv(path)\n", "掃描檔案: 95%|█████████▌| 251/264 [00:12<00:00, 22.68file/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "PatNo_ID_1594862905.csv: 三欄>0=0 (0.0%) | 兩欄>0=56,429 (94.1%) | 一欄>0=3,510 (5.9%) | 都不>0或空=0 (0.0%) | 總筆數=59,939\n", "PatNo_ID_1594879195.csv: 三欄>0=0 (0.0%) | 兩欄>0=6,798 (99.9%) | 一欄>0=7 (0.1%) | 都不>0或空=0 (0.0%) | 總筆數=6,805\n", "PatNo_ID_1594886956.csv: 三欄>0=3,309 (20.7%) | 兩欄>0=12,663 (79.3%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=15,972\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_621010/304996257.py:55: DtypeWarning: Columns (2,3,4,8,9,10,11,12,13,14,15,16) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df = pd.read_csv(path)\n", "掃描檔案: 97%|█████████▋| 257/264 [00:12<00:00, 19.03file/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "PatNo_ID_1594888142.csv: 三欄>0=0 (0.0%) | 兩欄>0=38,073 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=1 (0.0%) | 總筆數=38,074\n", "PatNo_ID_1594900319.csv: 三欄>0=0 (0.0%) | 兩欄>0=1,181 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=1,181\n", "PatNo_ID_1594904742.csv: 三欄>0=1,570 (6.2%) | 兩欄>0=23,864 (93.6%) | 一欄>0=58 (0.2%) | 都不>0或空=0 (0.0%) | 總筆數=25,492\n", "PatNo_ID_1594912862.csv: 三欄>0=0 (0.0%) | 兩欄>0=12,722 (99.8%) | 一欄>0=23 (0.2%) | 都不>0或空=0 (0.0%) | 總筆數=12,745\n", "PatNo_ID_1594926122.csv: 三欄>0=0 (0.0%) | 兩欄>0=18,034 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=18,034\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "掃描檔案: 100%|██████████| 264/264 [00:12<00:00, 20.39file/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "PatNo_ID_1594959115.csv: 三欄>0=0 (0.0%) | 兩欄>0=90 (12.9%) | 一欄>0=605 (87.1%) | 都不>0或空=0 (0.0%) | 總筆數=695\n", "PatNo_ID_1594969804.csv: 三欄>0=0 (0.0%) | 兩欄>0=3,615 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=3,615\n", "PatNo_ID_1594970427.csv: 三欄>0=1,167 (71.7%) | 兩欄>0=460 (28.3%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=1,627\n", "PatNo_ID_1594978835.csv: 三欄>0=0 (0.0%) | 兩欄>0=1,600 (99.7%) | 一欄>0=5 (0.3%) | 都不>0或空=0 (0.0%) | 總筆數=1,605\n", "PatNo_ID_1595012967.csv: 三欄>0=0 (0.0%) | 兩欄>0=15,746 (72.8%) | 一欄>0=5,880 (27.2%) | 都不>0或空=0 (0.0%) | 總筆數=21,626\n", "PatNo_ID_1595023326.csv: 三欄>0=0 (0.0%) | 兩欄>0=908 (100.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=908\n", "PatNo_ID_1595051455.csv: 三欄>0=837 (23.0%) | 兩欄>0=2,795 (76.9%) | 一欄>0=0 (0.0%) | 都不>0或空=2 (0.1%) | 總筆數=3,634\n", "\n", "=== 全域統計 (useable==1) ===\n", "三欄>0=262,215 (8.2%)\n", "兩欄>0=2,766,826 (86.9%)\n", "一欄>0=153,591 (4.8%)\n", "都不>0或空=48 (0.0%)\n", "總筆數=3,182,680\n", "📄 詳細記錄：/home/jovyan/phase_1/v1/data_1/logs/run_svv_valid_useable1_20251106_204844.txt\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# /home/jovyan/phase_1/v1/code/stat_svv_valid_useable1_pct.py\n", "# -*- coding: utf-8 -*-\n", "\"\"\"\n", "目標：\n", "- 掃描 /home/jovyan/phase_1/v1/data_1/v1.1.4/ 內所有 CSV\n", "- 只針對 useable == 1 的列，統計 sponvt / vti / vte > 0 的四類筆數：\n", " ① 三欄皆 > 0 ② 任兩欄 > 0 ③ 只有一欄 > 0 ④ 全部 ≤ 0 或 (null)\n", "- 每檔與全域：同時輸出「筆數」與「佔比 (%)」\n", "- 所有 print()/tqdm.write() 輸出同步寫入 LOG\n", "\n", "Log：/home/jovyan/phase_1/v1/data_1/logs/run_svv_valid_useable1_YYYYMMDD_HHMMSS.txt\n", "\"\"\"\n", "\n", "import os, sys, time, pandas as pd\n", "from tqdm import tqdm\n", "\n", "# ===== 路徑設定 =====\n", "DATA_DIR = \"/home/jovyan/phase_1/v1/data_1/v1.1.4/\"\n", "LOG_DIR = \"/home/jovyan/phase_1/v1/data_1/logs/\"\n", "os.makedirs(LOG_DIR, exist_ok=True)\n", "run_ts = time.strftime(\"%Y%m%d_%H%M%S\")\n", "LOG_PATH = os.path.join(LOG_DIR, f\"run_svv_valid_useable1_{run_ts}.txt\")\n", "\n", "# ===== Tee：把 print 同步寫到 Console 與 Log =====\n", "class TeeLogger:\n", " def __init__(self, logfile):\n", " self.log = open(logfile, \"a\", encoding=\"utf-8\")\n", " self.stdout = sys.stdout\n", " def write(self, message):\n", " self.stdout.write(message)\n", " self.log.write(message)\n", " self.log.flush()\n", " def flush(self):\n", " self.stdout.flush()\n", " self.log.flush()\n", "sys.stdout = TeeLogger(LOG_PATH) # 攔截 print\n", "# (備註) tqdm 進度條仍寫 stderr；但 tqdm.write() 走 stdout，會被 TeeLogger 同步進 log\n", "\n", "def pct(n, d):\n", " \"\"\"回傳 0~100 的百分比（小數點 1 位）；d<=0 時回傳 0.0\"\"\"\n", " if d and d > 0:\n", " return round((n / d) * 100.0, 1)\n", " return 0.0\n", "\n", "def main():\n", " print(f\"🚀 啟動 SVV 統計任務 (useable==1) | {run_ts}\")\n", " files = sorted([f for f in os.listdir(DATA_DIR) if f.lower().endswith(\".csv\")])\n", "\n", " # 全域加總\n", " G3 = G2 = G1 = G0 = GT = 0\n", "\n", " for f in tqdm(files, desc=\"掃描檔案\", unit=\"file\"):\n", " path = os.path.join(DATA_DIR, f)\n", " try:\n", " df = pd.read_csv(path)\n", "\n", " # 欄位檢查\n", " if \"useable\" not in df.columns:\n", " print(f\"⚠️ 缺少 useable 欄位：{f}\")\n", " continue\n", " cols = [\"sponvt\", \"vti\", \"vte\"]\n", " if not all(c in df.columns for c in cols):\n", " print(f\"⚠️ 缺少必要欄位 {cols}：{f}\")\n", " continue\n", "\n", " # 僅統計 useable==1\n", " df1 = df[df[\"useable\"] == 1]\n", " if df1.empty:\n", " # 仍回報 0，讓 log 可見\n", " print(f\"{f}: 三欄>0=0 (0.0%) | 兩欄>0=0 (0.0%) | 一欄>0=0 (0.0%) | 都不>0或空=0 (0.0%) | 總筆數=0\")\n", " continue\n", "\n", " sub = df1[cols].apply(pd.to_numeric, errors=\"coerce\") # (null)→NaN，自動略過非數值\n", " cond = (sub > 0).sum(axis=1) # 每列 >0 的欄數\n", " c3 = int((cond == 3).sum())\n", " c2 = int((cond == 2).sum())\n", " c1 = int((cond == 1).sum())\n", " c0 = int((cond == 0).sum())\n", " total = int(len(cond))\n", "\n", " # 全域彙總\n", " G3 += c3; G2 += c2; G1 += c1; G0 += c0; GT += total\n", "\n", " # 檔案層級輸出（含佔比）\n", " print(\n", " f\"{f}: \"\n", " f\"三欄>0={c3:,} ({pct(c3,total)}%) | \"\n", " f\"兩欄>0={c2:,} ({pct(c2,total)}%) | \"\n", " f\"一欄>0={c1:,} ({pct(c1,total)}%) | \"\n", " f\"都不>0或空={c0:,} ({pct(c0,total)}%) | \"\n", " f\"總筆數={total:,}\"\n", " )\n", "\n", " except Exception as e:\n", " print(f\"❌ 讀取失敗: {f} | {e}\")\n", "\n", " # 全域統計（含佔比）\n", " print(\"\\n=== 全域統計 (useable==1) ===\")\n", " print(f\"三欄>0={G3:,} ({pct(G3,GT)}%)\")\n", " print(f\"兩欄>0={G2:,} ({pct(G2,GT)}%)\")\n", " print(f\"一欄>0={G1:,} ({pct(G1,GT)}%)\")\n", " print(f\"都不>0或空={G0:,} ({pct(G0,GT)}%)\")\n", " print(f\"總筆數={GT:,}\")\n", " print(f\"📄 詳細記錄：{LOG_PATH}\")\n", "\n", "if __name__ == \"__main__\":\n", " main()" ] }, { "cell_type": "code", "execution_count": null, "id": "fd1c5811-c808-4bfd-802f-3cc8602648d7", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }