신호생성 repo (24. 1. 5 ~).
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['TL_IF_SIGL_.csv', 'TL_IF_SIGL_CYCL_.csv', 'view_data.ipynb']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.listdir()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"TL_IF_SIGL = pd.read_csv(os.listdir()[0])\n",
"TL_IF_SIGL_CYCL = pd.read_csv(os.listdir()[1])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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" PHASE_DT CRSRD_ID RINGA_PHASE RINGA_FLOW \\\n",
"0 2024-06-24 17:37:45.000 403 4 1 \n",
"1 2024-06-24 17:37:45.000 404 1 1 \n",
"2 2024-06-24 17:37:45.000 405 1 1 \n",
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"... ... ... ... ... \n",
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"119590 2024-06-26 15:39:41.000 610 1 1 \n",
"\n",
" RINGB_PHASE RINGB_FLOW FRST_REG_DT \n",
"0 4 1 2024-06-24 17:37:52.000 \n",
"1 1 1 2024-06-24 17:37:52.000 \n",
"2 1 1 2024-06-24 17:37:52.000 \n",
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"... ... ... ... \n",
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"119590 1 1 2024-06-26 15:39:41.000 \n",
"\n",
"[119591 rows x 7 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"현시 시각\n",
"교차로 ID\n",
"Ring A 현시 번호\n",
"Ring A 이동류 번호\n",
"Ring B 현시 번호\n",
"Ring B 이동류 번호\n",
"저장 시각\n",
"'''\n",
"TL_IF_SIGL # 신호 운영 이력"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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" OCRN_DT CRSRD_ID RINGA_PHASE1 RINGA_PHASE2 \\\n",
"0 2024-06-26 15:48:06.000 8 30 30 \n",
"1 2024-06-26 15:48:34.000 5 67 18 \n",
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"3 2024-06-26 15:49:49.000 7 118 22 \n",
"4 2024-06-26 15:50:06.000 8 30 30 \n",
".. ... ... ... ... \n",
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"172 2024-06-26 15:43:03.000 3 85 25 \n",
"173 2024-06-26 15:43:09.000 2 44 50 \n",
"174 2024-06-26 15:44:51.000 9 20 54 \n",
"175 2024-06-26 15:45:10.000 7 117 22 \n",
"\n",
" RINGA_PHASE3 RINGA_PHASE4 RINGA_PHASE5 RINGA_PHASE6 RINGA_PHASE7 \\\n",
"0 30 30 0 0 0 \n",
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" RINGA_PHASE8 RINGB_PHASE1 RINGB_PHASE2 RINGB_PHASE3 RINGB_PHASE4 \\\n",
"0 0 30 30 30 30 \n",
"1 0 67 18 18 37 \n",
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"3 0 118 22 0 0 \n",
"4 0 30 30 30 30 \n",
".. ... ... ... ... ... \n",
"171 0 45 73 17 35 \n",
"172 0 85 25 40 20 \n",
"173 0 44 50 12 30 \n",
"174 0 20 54 18 18 \n",
"175 0 117 22 0 0 \n",
"\n",
" RINGB_PHASE5 RINGB_PHASE6 RINGB_PHASE7 RINGB_PHASE8 \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 34 0 0 0 \n",
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"171 0 0 0 0 \n",
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"\n",
" FRST_REG_DT \n",
"0 2024-06-26 15:48:05.000 \n",
"1 2024-06-26 15:48:34.000 \n",
"2 2024-06-26 15:48:48.000 \n",
"3 2024-06-26 15:49:49.000 \n",
"4 2024-06-26 15:50:06.000 \n",
".. ... \n",
"171 2024-06-26 15:42:42.000 \n",
"172 2024-06-26 15:43:03.000 \n",
"173 2024-06-26 15:43:09.000 \n",
"174 2024-06-26 15:44:50.000 \n",
"175 2024-06-26 15:45:09.000 \n",
"\n",
"[176 rows x 19 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"발생시각\n",
"교차로 ID\n",
"Ring A 1현시 녹색 시간 (1~8)\n",
"Ring B 1현시 녹색 시간 (1~8)\n",
"저장 시각\n",
"'''\n",
"TL_IF_SIGL_CYCL # 신호 운영 주기 이력"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PHASE_DT</th>\n",
" <th>CRSRD_ID</th>\n",
" <th>RINGA_PHASE</th>\n",
" <th>RINGA_FLOW</th>\n",
" <th>RINGB_PHASE</th>\n",
" <th>RINGB_FLOW</th>\n",
" <th>FRST_REG_DT</th>\n",
" <th>UNIX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2622</th>\n",
" <td>2024-06-24 17:37:41</td>\n",
" <td>216</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2024-06-24 17:37:51.000</td>\n",
" <td>1719250661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12097</th>\n",
" <td>2024-06-24 17:37:41</td>\n",
" <td>676</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2024-06-24 17:37:53.000</td>\n",
" <td>1719250661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12096</th>\n",
" <td>2024-06-24 17:37:41</td>\n",
" <td>675</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2024-06-24 17:37:53.000</td>\n",
" <td>1719250661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12095</th>\n",
" <td>2024-06-24 17:37:41</td>\n",
" <td>674</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2024-06-24 17:37:53.000</td>\n",
" <td>1719250661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12094</th>\n",
" <td>2024-06-24 17:37:41</td>\n",
" <td>673</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2024-06-24 17:37:53.000</td>\n",
" <td>1719250661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115356</th>\n",
" <td>2024-06-26 15:39:42</td>\n",
" <td>398</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:39:42.000</td>\n",
" <td>1719416382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115357</th>\n",
" <td>2024-06-26 15:39:42</td>\n",
" <td>399</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:39:42.000</td>\n",
" <td>1719416382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115358</th>\n",
" <td>2024-06-26 15:39:42</td>\n",
" <td>400</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:39:42.000</td>\n",
" <td>1719416382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115350</th>\n",
" <td>2024-06-26 15:39:42</td>\n",
" <td>392</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:39:42.000</td>\n",
" <td>1719416382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115354</th>\n",
" <td>2024-06-26 15:39:42</td>\n",
" <td>396</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:39:42.000</td>\n",
" <td>1719416382</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>119591 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" PHASE_DT CRSRD_ID RINGA_PHASE RINGA_FLOW RINGB_PHASE \\\n",
"2622 2024-06-24 17:37:41 216 3 1 3 \n",
"12097 2024-06-24 17:37:41 676 3 1 3 \n",
"12096 2024-06-24 17:37:41 675 4 1 4 \n",
"12095 2024-06-24 17:37:41 674 5 1 5 \n",
"12094 2024-06-24 17:37:41 673 5 1 5 \n",
"... ... ... ... ... ... \n",
"115356 2024-06-26 15:39:42 398 1 1 1 \n",
"115357 2024-06-26 15:39:42 399 1 1 1 \n",
"115358 2024-06-26 15:39:42 400 1 1 1 \n",
"115350 2024-06-26 15:39:42 392 1 1 1 \n",
"115354 2024-06-26 15:39:42 396 1 1 1 \n",
"\n",
" RINGB_FLOW FRST_REG_DT UNIX \n",
"2622 1 2024-06-24 17:37:51.000 1719250661 \n",
"12097 1 2024-06-24 17:37:53.000 1719250661 \n",
"12096 1 2024-06-24 17:37:53.000 1719250661 \n",
"12095 1 2024-06-24 17:37:53.000 1719250661 \n",
"12094 1 2024-06-24 17:37:53.000 1719250661 \n",
"... ... ... ... \n",
"115356 1 2024-06-26 15:39:42.000 1719416382 \n",
"115357 1 2024-06-26 15:39:42.000 1719416382 \n",
"115358 1 2024-06-26 15:39:42.000 1719416382 \n",
"115350 1 2024-06-26 15:39:42.000 1719416382 \n",
"115354 1 2024-06-26 15:39:42.000 1719416382 \n",
"\n",
"[119591 rows x 8 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import datetime\n",
"secwise = TL_IF_SIGL\n",
"secwise['PHASE_DT'] = pd.to_datetime(secwise['PHASE_DT'])\n",
"secwise['UNIX'] = secwise['PHASE_DT'].apply(lambda x: int(x.timestamp()))\n",
"secwise = secwise.sort_values(by='UNIX')\n",
"display(secwise)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.hist(secwise['UNIX'], bins=100)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"19593\n",
"99998\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PHASE_DT</th>\n",
" <th>CRSRD_ID</th>\n",
" <th>RINGA_PHASE</th>\n",
" <th>RINGA_FLOW</th>\n",
" <th>RINGB_PHASE</th>\n",
" <th>RINGB_FLOW</th>\n",
" <th>FRST_REG_DT</th>\n",
" <th>UNIX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>21586</th>\n",
" <td>2024-06-26 15:37:22</td>\n",
" <td>150</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:22.000</td>\n",
" <td>1719416242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21589</th>\n",
" <td>2024-06-26 15:37:22</td>\n",
" <td>153</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:22.000</td>\n",
" <td>1719416242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21590</th>\n",
" <td>2024-06-26 15:37:22</td>\n",
" <td>154</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:22.000</td>\n",
" <td>1719416242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21591</th>\n",
" <td>2024-06-26 15:37:22</td>\n",
" <td>155</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:22.000</td>\n",
" <td>1719416242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21588</th>\n",
" <td>2024-06-26 15:37:22</td>\n",
" <td>152</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:22.000</td>\n",
" <td>1719416242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115356</th>\n",
" <td>2024-06-26 15:39:42</td>\n",
" <td>398</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:39:42.000</td>\n",
" <td>1719416382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115357</th>\n",
" <td>2024-06-26 15:39:42</td>\n",
" <td>399</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:39:42.000</td>\n",
" <td>1719416382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115358</th>\n",
" <td>2024-06-26 15:39:42</td>\n",
" <td>400</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:39:42.000</td>\n",
" <td>1719416382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115350</th>\n",
" <td>2024-06-26 15:39:42</td>\n",
" <td>392</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:39:42.000</td>\n",
" <td>1719416382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115354</th>\n",
" <td>2024-06-26 15:39:42</td>\n",
" <td>396</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:39:42.000</td>\n",
" <td>1719416382</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>99998 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" PHASE_DT CRSRD_ID RINGA_PHASE RINGA_FLOW RINGB_PHASE \\\n",
"21586 2024-06-26 15:37:22 150 1 1 1 \n",
"21589 2024-06-26 15:37:22 153 1 1 1 \n",
"21590 2024-06-26 15:37:22 154 2 1 2 \n",
"21591 2024-06-26 15:37:22 155 1 1 1 \n",
"21588 2024-06-26 15:37:22 152 3 1 3 \n",
"... ... ... ... ... ... \n",
"115356 2024-06-26 15:39:42 398 1 1 1 \n",
"115357 2024-06-26 15:39:42 399 1 1 1 \n",
"115358 2024-06-26 15:39:42 400 1 1 1 \n",
"115350 2024-06-26 15:39:42 392 1 1 1 \n",
"115354 2024-06-26 15:39:42 396 1 1 1 \n",
"\n",
" RINGB_FLOW FRST_REG_DT UNIX \n",
"21586 1 2024-06-26 15:37:22.000 1719416242 \n",
"21589 1 2024-06-26 15:37:22.000 1719416242 \n",
"21590 1 2024-06-26 15:37:22.000 1719416242 \n",
"21591 1 2024-06-26 15:37:22.000 1719416242 \n",
"21588 1 2024-06-26 15:37:22.000 1719416242 \n",
"... ... ... ... \n",
"115356 1 2024-06-26 15:39:42.000 1719416382 \n",
"115357 1 2024-06-26 15:39:42.000 1719416382 \n",
"115358 1 2024-06-26 15:39:42.000 1719416382 \n",
"115350 1 2024-06-26 15:39:42.000 1719416382 \n",
"115354 1 2024-06-26 15:39:42.000 1719416382 \n",
"\n",
"[99998 rows x 8 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"thres = (secwise['UNIX'].max() + secwise['UNIX'].min()) // 2\n",
"secwise_former = secwise[secwise['UNIX'] < thres]\n",
"secwise_latter = secwise[secwise['UNIX'] > thres]\n",
"print(len(secwise_former))\n",
"print(len(secwise_latter))\n",
"secwise = secwise_latter\n",
"secwise"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"# 누락된 교차로번호 없음\n",
"list(np.sort(secwise['CRSRD_ID'].unique())) == list(range(1, 755))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"8\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"flow_nos = secwise[['RINGA_PHASE', 'RINGB_PHASE']].values.flatten()\n",
"print(len(np.unique(flow_nos)))\n",
"plt.hist(flow_nos, bins=8)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PHASE_DT</th>\n",
" <th>CRSRD_ID</th>\n",
" <th>RINGA_PHASE</th>\n",
" <th>RINGA_FLOW</th>\n",
" <th>RINGB_PHASE</th>\n",
" <th>RINGB_FLOW</th>\n",
" <th>FRST_REG_DT</th>\n",
" <th>UNIX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2024-06-26 15:37:22</td>\n",
" <td>21</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:22.000</td>\n",
" <td>1719416242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2024-06-26 15:37:23</td>\n",
" <td>21</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:23.000</td>\n",
" <td>1719416243</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2024-06-26 15:37:24</td>\n",
" <td>21</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:23.000</td>\n",
" <td>1719416244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2024-06-26 15:37:25</td>\n",
" <td>21</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:24.000</td>\n",
" <td>1719416245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2024-06-26 15:37:26</td>\n",
" <td>21</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:25.000</td>\n",
" <td>1719416246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2024-06-26 15:37:27</td>\n",
" <td>21</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:26.000</td>\n",
" <td>1719416247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2024-06-26 15:37:28</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:27.000</td>\n",
" <td>1719416248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2024-06-26 15:37:29</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:28.000</td>\n",
" <td>1719416249</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2024-06-26 15:37:30</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:29.000</td>\n",
" <td>1719416250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2024-06-26 15:37:31</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:30.000</td>\n",
" <td>1719416251</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2024-06-26 15:37:32</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:31.000</td>\n",
" <td>1719416252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2024-06-26 15:37:33</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:32.000</td>\n",
" <td>1719416253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2024-06-26 15:37:34</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:33.000</td>\n",
" <td>1719416254</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2024-06-26 15:37:35</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:34.000</td>\n",
" <td>1719416255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2024-06-26 15:37:36</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:35.000</td>\n",
" <td>1719416256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2024-06-26 15:37:37</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:36.000</td>\n",
" <td>1719416257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2024-06-26 15:37:38</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:37.000</td>\n",
" <td>1719416258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2024-06-26 15:37:39</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:38.000</td>\n",
" <td>1719416259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2024-06-26 15:37:40</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:39.000</td>\n",
" <td>1719416260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2024-06-26 15:37:41</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:40.000</td>\n",
" <td>1719416261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2024-06-26 15:37:42</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:41.000</td>\n",
" <td>1719416262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2024-06-26 15:37:43</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:42.000</td>\n",
" <td>1719416263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2024-06-26 15:37:44</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:43.000</td>\n",
" <td>1719416264</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2024-06-26 15:37:45</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:44.000</td>\n",
" <td>1719416265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2024-06-26 15:37:46</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:45.000</td>\n",
" <td>1719416266</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2024-06-26 15:37:47</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:46.000</td>\n",
" <td>1719416267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2024-06-26 15:37:48</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:47.000</td>\n",
" <td>1719416268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2024-06-26 15:37:49</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:48.000</td>\n",
" <td>1719416269</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2024-06-26 15:37:50</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:49.000</td>\n",
" <td>1719416270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2024-06-26 15:37:51</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:50.000</td>\n",
" <td>1719416271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2024-06-26 15:37:52</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:51.000</td>\n",
" <td>1719416272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>2024-06-26 15:37:53</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:52.000</td>\n",
" <td>1719416273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>2024-06-26 15:37:54</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:53.000</td>\n",
" <td>1719416274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>2024-06-26 15:37:55</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:54.000</td>\n",
" <td>1719416275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>2024-06-26 15:37:56</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:55.000</td>\n",
" <td>1719416276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>2024-06-26 15:37:57</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:56.000</td>\n",
" <td>1719416277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>2024-06-26 15:37:58</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:57.000</td>\n",
" <td>1719416278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>2024-06-26 15:37:59</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:58.000</td>\n",
" <td>1719416279</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>2024-06-26 15:38:00</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:37:59.000</td>\n",
" <td>1719416280</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>2024-06-26 15:38:01</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2024-06-26 15:38:00.000</td>\n",
" <td>1719416281</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PHASE_DT CRSRD_ID RINGA_PHASE RINGA_FLOW RINGB_PHASE \\\n",
"0 2024-06-26 15:37:22 21 6 1 6 \n",
"1 2024-06-26 15:37:23 21 6 1 6 \n",
"2 2024-06-26 15:37:24 21 6 1 6 \n",
"3 2024-06-26 15:37:25 21 6 1 6 \n",
"4 2024-06-26 15:37:26 21 6 1 6 \n",
"5 2024-06-26 15:37:27 21 6 1 6 \n",
"6 2024-06-26 15:37:28 21 1 1 1 \n",
"7 2024-06-26 15:37:29 21 1 1 1 \n",
"8 2024-06-26 15:37:30 21 1 1 1 \n",
"9 2024-06-26 15:37:31 21 1 1 1 \n",
"10 2024-06-26 15:37:32 21 1 1 1 \n",
"11 2024-06-26 15:37:33 21 1 1 1 \n",
"12 2024-06-26 15:37:34 21 1 1 1 \n",
"13 2024-06-26 15:37:35 21 1 1 1 \n",
"14 2024-06-26 15:37:36 21 1 1 1 \n",
"15 2024-06-26 15:37:37 21 1 1 1 \n",
"16 2024-06-26 15:37:38 21 1 1 1 \n",
"17 2024-06-26 15:37:39 21 1 1 1 \n",
"18 2024-06-26 15:37:40 21 1 1 1 \n",
"19 2024-06-26 15:37:41 21 1 1 1 \n",
"20 2024-06-26 15:37:42 21 1 1 1 \n",
"21 2024-06-26 15:37:43 21 1 1 1 \n",
"22 2024-06-26 15:37:44 21 1 1 1 \n",
"23 2024-06-26 15:37:45 21 1 1 1 \n",
"24 2024-06-26 15:37:46 21 1 1 1 \n",
"25 2024-06-26 15:37:47 21 1 1 1 \n",
"26 2024-06-26 15:37:48 21 1 1 1 \n",
"27 2024-06-26 15:37:49 21 1 1 1 \n",
"28 2024-06-26 15:37:50 21 2 1 2 \n",
"29 2024-06-26 15:37:51 21 2 1 2 \n",
"30 2024-06-26 15:37:52 21 2 1 2 \n",
"31 2024-06-26 15:37:53 21 2 1 2 \n",
"32 2024-06-26 15:37:54 21 2 1 2 \n",
"33 2024-06-26 15:37:55 21 2 1 2 \n",
"34 2024-06-26 15:37:56 21 2 1 2 \n",
"35 2024-06-26 15:37:57 21 2 1 2 \n",
"36 2024-06-26 15:37:58 21 2 1 2 \n",
"37 2024-06-26 15:37:59 21 2 1 2 \n",
"38 2024-06-26 15:38:00 21 2 1 2 \n",
"39 2024-06-26 15:38:01 21 2 1 2 \n",
"\n",
" RINGB_FLOW FRST_REG_DT UNIX \n",
"0 1 2024-06-26 15:37:22.000 1719416242 \n",
"1 1 2024-06-26 15:37:23.000 1719416243 \n",
"2 1 2024-06-26 15:37:23.000 1719416244 \n",
"3 1 2024-06-26 15:37:24.000 1719416245 \n",
"4 1 2024-06-26 15:37:25.000 1719416246 \n",
"5 1 2024-06-26 15:37:26.000 1719416247 \n",
"6 1 2024-06-26 15:37:27.000 1719416248 \n",
"7 1 2024-06-26 15:37:28.000 1719416249 \n",
"8 1 2024-06-26 15:37:29.000 1719416250 \n",
"9 1 2024-06-26 15:37:30.000 1719416251 \n",
"10 1 2024-06-26 15:37:31.000 1719416252 \n",
"11 1 2024-06-26 15:37:32.000 1719416253 \n",
"12 1 2024-06-26 15:37:33.000 1719416254 \n",
"13 1 2024-06-26 15:37:34.000 1719416255 \n",
"14 1 2024-06-26 15:37:35.000 1719416256 \n",
"15 1 2024-06-26 15:37:36.000 1719416257 \n",
"16 1 2024-06-26 15:37:37.000 1719416258 \n",
"17 1 2024-06-26 15:37:38.000 1719416259 \n",
"18 1 2024-06-26 15:37:39.000 1719416260 \n",
"19 1 2024-06-26 15:37:40.000 1719416261 \n",
"20 1 2024-06-26 15:37:41.000 1719416262 \n",
"21 1 2024-06-26 15:37:42.000 1719416263 \n",
"22 1 2024-06-26 15:37:43.000 1719416264 \n",
"23 1 2024-06-26 15:37:44.000 1719416265 \n",
"24 1 2024-06-26 15:37:45.000 1719416266 \n",
"25 1 2024-06-26 15:37:46.000 1719416267 \n",
"26 1 2024-06-26 15:37:47.000 1719416268 \n",
"27 1 2024-06-26 15:37:48.000 1719416269 \n",
"28 1 2024-06-26 15:37:49.000 1719416270 \n",
"29 1 2024-06-26 15:37:50.000 1719416271 \n",
"30 1 2024-06-26 15:37:51.000 1719416272 \n",
"31 1 2024-06-26 15:37:52.000 1719416273 \n",
"32 1 2024-06-26 15:37:53.000 1719416274 \n",
"33 1 2024-06-26 15:37:54.000 1719416275 \n",
"34 1 2024-06-26 15:37:55.000 1719416276 \n",
"35 1 2024-06-26 15:37:56.000 1719416277 \n",
"36 1 2024-06-26 15:37:57.000 1719416278 \n",
"37 1 2024-06-26 15:37:58.000 1719416279 \n",
"38 1 2024-06-26 15:37:59.000 1719416280 \n",
"39 1 2024-06-26 15:38:00.000 1719416281 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"int_id = 21\n",
"secw = secwise[secwise.CRSRD_ID==int_id]\n",
"secw[:40].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: middle;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>OCRN_DT</th>\n",
" <th>CRSRD_ID</th>\n",
" <th>RINGA_PHASE1</th>\n",
" <th>RINGA_PHASE2</th>\n",
" <th>RINGA_PHASE3</th>\n",
" <th>RINGA_PHASE4</th>\n",
" <th>RINGA_PHASE5</th>\n",
" <th>RINGA_PHASE6</th>\n",
" <th>RINGA_PHASE7</th>\n",
" <th>RINGA_PHASE8</th>\n",
" <th>RINGB_PHASE1</th>\n",
" <th>RINGB_PHASE2</th>\n",
" <th>RINGB_PHASE3</th>\n",
" <th>RINGB_PHASE4</th>\n",
" <th>RINGB_PHASE5</th>\n",
" <th>RINGB_PHASE6</th>\n",
" <th>RINGB_PHASE7</th>\n",
" <th>RINGB_PHASE8</th>\n",
" <th>FRST_REG_DT</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
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" <td>2024-06-26 15:48:06.000</td>\n",
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" <td>0</td>\n",
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" <td>2024-06-26 15:48:05.000</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>2024-06-26 15:48:34.000</td>\n",
" <td>5</td>\n",
" <td>67</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
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" <td>18</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>2024-06-26 15:48:34.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2024-06-26 15:48:49.000</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
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" <td>0</td>\n",
" <td>2024-06-26 15:48:48.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2024-06-26 15:49:49.000</td>\n",
" <td>7</td>\n",
" <td>118</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>118</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2024-06-26 15:49:49.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2024-06-26 15:50:06.000</td>\n",
" <td>8</td>\n",
" <td>30</td>\n",
" <td>30</td>\n",
" <td>30</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>30</td>\n",
" <td>30</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2024-06-26 15:50:06.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>171</th>\n",
" <td>2024-06-26 15:42:42.000</td>\n",
" <td>4</td>\n",
" <td>45</td>\n",
" <td>73</td>\n",
" <td>17</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>45</td>\n",
" <td>73</td>\n",
" <td>17</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2024-06-26 15:42:42.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>172</th>\n",
" <td>2024-06-26 15:43:03.000</td>\n",
" <td>3</td>\n",
" <td>85</td>\n",
" <td>25</td>\n",
" <td>40</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>85</td>\n",
" <td>25</td>\n",
" <td>40</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2024-06-26 15:43:03.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>173</th>\n",
" <td>2024-06-26 15:43:09.000</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2024-06-26 15:43:09.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>2024-06-26 15:44:51.000</td>\n",
" <td>9</td>\n",
" <td>20</td>\n",
" <td>54</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>54</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2024-06-26 15:44:50.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175</th>\n",
" <td>2024-06-26 15:45:10.000</td>\n",
" <td>7</td>\n",
" <td>117</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>117</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2024-06-26 15:45:09.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>176 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" OCRN_DT CRSRD_ID RINGA_PHASE1 RINGA_PHASE2 \\\n",
"0 2024-06-26 15:48:06.000 8 30 30 \n",
"1 2024-06-26 15:48:34.000 5 67 18 \n",
"2 2024-06-26 15:48:49.000 2 44 50 \n",
"3 2024-06-26 15:49:49.000 7 118 22 \n",
"4 2024-06-26 15:50:06.000 8 30 30 \n",
".. ... ... ... ... \n",
"171 2024-06-26 15:42:42.000 4 45 73 \n",
"172 2024-06-26 15:43:03.000 3 85 25 \n",
"173 2024-06-26 15:43:09.000 2 44 50 \n",
"174 2024-06-26 15:44:51.000 9 20 54 \n",
"175 2024-06-26 15:45:10.000 7 117 22 \n",
"\n",
" RINGA_PHASE3 RINGA_PHASE4 RINGA_PHASE5 RINGA_PHASE6 RINGA_PHASE7 \\\n",
"0 30 30 0 0 0 \n",
"1 18 37 0 0 0 \n",
"2 12 30 34 0 0 \n",
"3 0 0 0 0 0 \n",
"4 30 30 0 0 0 \n",
".. ... ... ... ... ... \n",
"171 17 35 0 0 0 \n",
"172 40 20 0 0 0 \n",
"173 12 30 34 0 0 \n",
"174 18 18 30 0 0 \n",
"175 0 0 0 0 0 \n",
"\n",
" RINGA_PHASE8 RINGB_PHASE1 RINGB_PHASE2 RINGB_PHASE3 RINGB_PHASE4 \\\n",
"0 0 30 30 30 30 \n",
"1 0 67 18 18 37 \n",
"2 0 44 50 12 30 \n",
"3 0 118 22 0 0 \n",
"4 0 30 30 30 30 \n",
".. ... ... ... ... ... \n",
"171 0 45 73 17 35 \n",
"172 0 85 25 40 20 \n",
"173 0 44 50 12 30 \n",
"174 0 20 54 18 18 \n",
"175 0 117 22 0 0 \n",
"\n",
" RINGB_PHASE5 RINGB_PHASE6 RINGB_PHASE7 RINGB_PHASE8 \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 34 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
".. ... ... ... ... \n",
"171 0 0 0 0 \n",
"172 0 0 0 0 \n",
"173 34 0 0 0 \n",
"174 30 0 0 0 \n",
"175 0 0 0 0 \n",
"\n",
" FRST_REG_DT \n",
"0 2024-06-26 15:48:05.000 \n",
"1 2024-06-26 15:48:34.000 \n",
"2 2024-06-26 15:48:48.000 \n",
"3 2024-06-26 15:49:49.000 \n",
"4 2024-06-26 15:50:06.000 \n",
".. ... \n",
"171 2024-06-26 15:42:42.000 \n",
"172 2024-06-26 15:43:03.000 \n",
"173 2024-06-26 15:43:09.000 \n",
"174 2024-06-26 15:44:50.000 \n",
"175 2024-06-26 15:45:09.000 \n",
"\n",
"[176 rows x 19 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cclwise = TL_IF_SIGL_CYCL\n",
"cclwise"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>OCRN_DT</th>\n",
" <th>FRST_REG_DT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2024-06-26 15:48:06.000</td>\n",
" <td>2024-06-26 15:48:05.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2024-06-26 15:48:34.000</td>\n",
" <td>2024-06-26 15:48:34.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2024-06-26 15:48:49.000</td>\n",
" <td>2024-06-26 15:48:48.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2024-06-26 15:49:49.000</td>\n",
" <td>2024-06-26 15:49:49.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2024-06-26 15:50:06.000</td>\n",
" <td>2024-06-26 15:50:06.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>171</th>\n",
" <td>2024-06-26 15:42:42.000</td>\n",
" <td>2024-06-26 15:42:42.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>172</th>\n",
" <td>2024-06-26 15:43:03.000</td>\n",
" <td>2024-06-26 15:43:03.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>173</th>\n",
" <td>2024-06-26 15:43:09.000</td>\n",
" <td>2024-06-26 15:43:09.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>2024-06-26 15:44:51.000</td>\n",
" <td>2024-06-26 15:44:50.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175</th>\n",
" <td>2024-06-26 15:45:10.000</td>\n",
" <td>2024-06-26 15:45:09.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>176 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" OCRN_DT FRST_REG_DT\n",
"0 2024-06-26 15:48:06.000 2024-06-26 15:48:05.000\n",
"1 2024-06-26 15:48:34.000 2024-06-26 15:48:34.000\n",
"2 2024-06-26 15:48:49.000 2024-06-26 15:48:48.000\n",
"3 2024-06-26 15:49:49.000 2024-06-26 15:49:49.000\n",
"4 2024-06-26 15:50:06.000 2024-06-26 15:50:06.000\n",
".. ... ...\n",
"171 2024-06-26 15:42:42.000 2024-06-26 15:42:42.000\n",
"172 2024-06-26 15:43:03.000 2024-06-26 15:43:03.000\n",
"173 2024-06-26 15:43:09.000 2024-06-26 15:43:09.000\n",
"174 2024-06-26 15:44:51.000 2024-06-26 15:44:50.000\n",
"175 2024-06-26 15:45:10.000 2024-06-26 15:45:09.000\n",
"\n",
"[176 rows x 2 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cclwise[['OCRN_DT', 'FRST_REG_DT']] # 발생시각, 저장시각"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"82\n",
"94\n",
"0\n"
]
}
],
"source": [
"# 저장시각이 발생시각보다 빠르게 일어났다. -> 말이 안되는 것 같은데\n",
"print((cclwise['OCRN_DT'] == cclwise['FRST_REG_DT']).sum())\n",
"print((cclwise['OCRN_DT'] > cclwise['FRST_REG_DT']).sum())\n",
"print((cclwise['OCRN_DT'] < cclwise['FRST_REG_DT']).sum())"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>UNIX</th>\n",
" <th>OCRN_DT</th>\n",
" <th>CRSRD_ID</th>\n",
" <th>RINGA_PHASE1</th>\n",
" <th>RINGA_PHASE2</th>\n",
" <th>RINGA_PHASE3</th>\n",
" <th>RINGA_PHASE4</th>\n",
" <th>RINGA_PHASE5</th>\n",
" <th>RINGA_PHASE6</th>\n",
" <th>RINGA_PHASE7</th>\n",
" <th>RINGA_PHASE8</th>\n",
" <th>RINGB_PHASE1</th>\n",
" <th>RINGB_PHASE2</th>\n",
" <th>RINGB_PHASE3</th>\n",
" <th>RINGB_PHASE4</th>\n",
" <th>RINGB_PHASE5</th>\n",
" <th>RINGB_PHASE6</th>\n",
" <th>RINGB_PHASE7</th>\n",
" <th>RINGB_PHASE8</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1719416886</td>\n",
" <td>2024-06-26 15:48:06</td>\n",
" <td>8</td>\n",
" <td>30</td>\n",
" <td>30</td>\n",
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" <td>30</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1719416914</td>\n",
" <td>2024-06-26 15:48:34</td>\n",
" <td>5</td>\n",
" <td>67</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
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" <td>18</td>\n",
" <td>37</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1719416929</td>\n",
" <td>2024-06-26 15:48:49</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
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" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1719416989</td>\n",
" <td>2024-06-26 15:49:49</td>\n",
" <td>7</td>\n",
" <td>118</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>118</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1719417006</td>\n",
" <td>2024-06-26 15:50:06</td>\n",
" <td>8</td>\n",
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" <td>2024-06-26 15:42:42</td>\n",
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" <td>35</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>40</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>173</th>\n",
" <td>1719416589</td>\n",
" <td>2024-06-26 15:43:09</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
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" <td>0</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>1719416691</td>\n",
" <td>2024-06-26 15:44:51</td>\n",
" <td>9</td>\n",
" <td>20</td>\n",
" <td>54</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>54</td>\n",
" <td>18</td>\n",
" <td>18</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>175</th>\n",
" <td>1719416710</td>\n",
" <td>2024-06-26 15:45:10</td>\n",
" <td>7</td>\n",
" <td>117</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
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"</table>\n",
"<p>176 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" UNIX OCRN_DT CRSRD_ID RINGA_PHASE1 RINGA_PHASE2 \\\n",
"0 1719416886 2024-06-26 15:48:06 8 30 30 \n",
"1 1719416914 2024-06-26 15:48:34 5 67 18 \n",
"2 1719416929 2024-06-26 15:48:49 2 44 50 \n",
"3 1719416989 2024-06-26 15:49:49 7 118 22 \n",
"4 1719417006 2024-06-26 15:50:06 8 30 30 \n",
".. ... ... ... ... ... \n",
"171 1719416562 2024-06-26 15:42:42 4 45 73 \n",
"172 1719416583 2024-06-26 15:43:03 3 85 25 \n",
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"174 1719416691 2024-06-26 15:44:51 9 20 54 \n",
"175 1719416710 2024-06-26 15:45:10 7 117 22 \n",
"\n",
" RINGA_PHASE3 RINGA_PHASE4 RINGA_PHASE5 RINGA_PHASE6 RINGA_PHASE7 \\\n",
"0 30 30 0 0 0 \n",
"1 18 37 0 0 0 \n",
"2 12 30 34 0 0 \n",
"3 0 0 0 0 0 \n",
"4 30 30 0 0 0 \n",
".. ... ... ... ... ... \n",
"171 17 35 0 0 0 \n",
"172 40 20 0 0 0 \n",
"173 12 30 34 0 0 \n",
"174 18 18 30 0 0 \n",
"175 0 0 0 0 0 \n",
"\n",
" RINGA_PHASE8 RINGB_PHASE1 RINGB_PHASE2 RINGB_PHASE3 RINGB_PHASE4 \\\n",
"0 0 30 30 30 30 \n",
"1 0 67 18 18 37 \n",
"2 0 44 50 12 30 \n",
"3 0 118 22 0 0 \n",
"4 0 30 30 30 30 \n",
".. ... ... ... ... ... \n",
"171 0 45 73 17 35 \n",
"172 0 85 25 40 20 \n",
"173 0 44 50 12 30 \n",
"174 0 20 54 18 18 \n",
"175 0 117 22 0 0 \n",
"\n",
" RINGB_PHASE5 RINGB_PHASE6 RINGB_PHASE7 RINGB_PHASE8 \n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 34 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
".. ... ... ... ... \n",
"171 0 0 0 0 \n",
"172 0 0 0 0 \n",
"173 34 0 0 0 \n",
"174 30 0 0 0 \n",
"175 0 0 0 0 \n",
"\n",
"[176 rows x 19 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cclwise['OCRN_DT'] = pd.to_datetime(cclwise['OCRN_DT'])\n",
"cclwise['UNIX'] = cclwise['OCRN_DT'].apply(lambda x: int(x.timestamp()))\n",
"cclwise = cclwise[[cclwise.columns[-1]] + list(cclwise.columns[:-1])]\n",
"cclwise = cclwise.drop(columns=['FRST_REG_DT'])\n",
"cclwise"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"col_map = dict()\n",
"alphs = ['A', 'B']\n",
"integers = [str(i) for i in range(1, 9)]\n",
"for alph in alphs:\n",
" for i in integers:\n",
" col_map[f'RING{alph}_PHASE{i}'] = f'{alph}{i}'\n",
"# col_map['CRSRD_ID'] = 'int_id'\n",
"cclwise = cclwise.rename(columns=col_map)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>A2</th>\n",
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" <th>B4</th>\n",
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" <th>B6</th>\n",
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" <td>2024-06-26 15:43:03</td>\n",
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" <td>0</td>\n",
" <td>139</td>\n",
" <td>139</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>176 rows × 17 columns</p>\n",
"</div>"
],
"text/plain": [
" UNIX OCRN_DT CRSRD_ID A1 A2 A3 A4 A5 A6 B1 \\\n",
"0 1719416886 2024-06-26 15:48:06 8 30 30 30 30 0 0 30 \n",
"1 1719416914 2024-06-26 15:48:34 5 67 18 18 37 0 0 67 \n",
"2 1719416929 2024-06-26 15:48:49 2 44 50 12 30 34 0 44 \n",
"3 1719416989 2024-06-26 15:49:49 7 118 22 0 0 0 0 118 \n",
"4 1719417006 2024-06-26 15:50:06 8 30 30 30 30 0 0 30 \n",
".. ... ... ... ... .. .. .. .. .. ... \n",
"171 1719416562 2024-06-26 15:42:42 4 45 73 17 35 0 0 45 \n",
"172 1719416583 2024-06-26 15:43:03 3 85 25 40 20 0 0 85 \n",
"173 1719416589 2024-06-26 15:43:09 2 44 50 12 30 34 0 44 \n",
"174 1719416691 2024-06-26 15:44:51 9 20 54 18 18 30 0 20 \n",
"175 1719416710 2024-06-26 15:45:10 7 117 22 0 0 0 0 117 \n",
"\n",
" B2 B3 B4 B5 B6 cycle_A cycle_B \n",
"0 30 30 30 0 0 120 120 \n",
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"2 50 12 30 34 0 170 170 \n",
"3 22 0 0 0 0 140 140 \n",
"4 30 30 30 0 0 120 120 \n",
".. .. .. .. .. .. ... ... \n",
"171 73 17 35 0 0 170 170 \n",
"172 25 40 20 0 0 170 170 \n",
"173 50 12 30 34 0 170 170 \n",
"174 54 18 18 30 0 140 140 \n",
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"\n",
"[176 rows x 17 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cclwise['cycle_A'] = cclwise[[f'A{i}' for i in integers]].sum(axis=1)\n",
"cclwise['cycle_B'] = cclwise[[f'B{i}' for i in integers]].sum(axis=1)\n",
"cclwise = cclwise.drop(columns=['A7', 'A8', 'B7', 'B8'])\n",
"cclwise"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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" <td>52</td>\n",
" <td>20</td>\n",
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" <td>20</td>\n",
" <td>30</td>\n",
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" <td>2024-06-26 16:29:39</td>\n",
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" <td>35</td>\n",
" <td>39</td>\n",
" <td>0</td>\n",
" <td>62</td>\n",
" <td>62</td>\n",
" <td>24</td>\n",
" <td>35</td>\n",
" <td>39</td>\n",
" <td>0</td>\n",
" <td>222</td>\n",
" <td>222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1719418658</td>\n",
" <td>2024-06-26 16:17:38</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>52</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>52</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>180</td>\n",
" <td>180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1719418839</td>\n",
" <td>2024-06-26 16:20:39</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>52</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>52</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>180</td>\n",
" <td>180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1719419020</td>\n",
" <td>2024-06-26 16:23:40</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>52</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>52</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>180</td>\n",
" <td>180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1719419199</td>\n",
" <td>2024-06-26 16:26:39</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>52</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>52</td>\n",
" <td>20</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>180</td>\n",
" <td>180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1719416250</td>\n",
" <td>2024-06-26 15:37:30</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>170</td>\n",
" <td>170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1719416759</td>\n",
" <td>2024-06-26 15:45:59</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>170</td>\n",
" <td>170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1719417269</td>\n",
" <td>2024-06-26 15:54:29</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>170</td>\n",
" <td>170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1719417844</td>\n",
" <td>2024-06-26 16:04:04</td>\n",
" <td>2</td>\n",
" <td>65</td>\n",
" <td>65</td>\n",
" <td>25</td>\n",
" <td>37</td>\n",
" <td>42</td>\n",
" <td>0</td>\n",
" <td>65</td>\n",
" <td>65</td>\n",
" <td>25</td>\n",
" <td>37</td>\n",
" <td>42</td>\n",
" <td>0</td>\n",
" <td>234</td>\n",
" <td>234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1719416419</td>\n",
" <td>2024-06-26 15:40:19</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>170</td>\n",
" <td>170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>1719416589</td>\n",
" <td>2024-06-26 15:43:09</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>170</td>\n",
" <td>170</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" UNIX OCRN_DT CRSRD_ID A1 A2 A3 A4 A5 A6 B1 B2 \\\n",
"0 1719416929 2024-06-26 15:48:49 2 44 50 12 30 34 0 44 50 \n",
"1 1719417098 2024-06-26 15:51:38 2 44 50 12 30 34 0 44 50 \n",
"2 1719417439 2024-06-26 15:57:19 2 44 50 12 30 34 0 44 50 \n",
"3 1719417609 2024-06-26 16:00:09 2 44 50 12 30 34 0 44 50 \n",
"4 1719418480 2024-06-26 16:14:40 2 44 52 20 30 34 0 44 52 \n",
"5 1719419379 2024-06-26 16:29:39 2 44 52 20 30 34 0 44 52 \n",
"6 1719419560 2024-06-26 16:32:40 2 44 52 20 30 34 0 44 52 \n",
"7 1719418077 2024-06-26 16:07:57 2 65 65 25 37 42 0 65 65 \n",
"8 1719418299 2024-06-26 16:11:39 2 62 62 24 35 39 0 62 62 \n",
"9 1719418658 2024-06-26 16:17:38 2 44 52 20 30 34 0 44 52 \n",
"10 1719418839 2024-06-26 16:20:39 2 44 52 20 30 34 0 44 52 \n",
"11 1719419020 2024-06-26 16:23:40 2 44 52 20 30 34 0 44 52 \n",
"12 1719419199 2024-06-26 16:26:39 2 44 52 20 30 34 0 44 52 \n",
"13 1719416250 2024-06-26 15:37:30 2 44 50 12 30 34 0 44 50 \n",
"14 1719416759 2024-06-26 15:45:59 2 44 50 12 30 34 0 44 50 \n",
"15 1719417269 2024-06-26 15:54:29 2 44 50 12 30 34 0 44 50 \n",
"16 1719417844 2024-06-26 16:04:04 2 65 65 25 37 42 0 65 65 \n",
"17 1719416419 2024-06-26 15:40:19 2 44 50 12 30 34 0 44 50 \n",
"18 1719416589 2024-06-26 15:43:09 2 44 50 12 30 34 0 44 50 \n",
"\n",
" B3 B4 B5 B6 cycle_A cycle_B \n",
"0 12 30 34 0 170 170 \n",
"1 12 30 34 0 170 170 \n",
"2 12 30 34 0 170 170 \n",
"3 12 30 34 0 170 170 \n",
"4 20 30 34 0 180 180 \n",
"5 20 30 34 0 180 180 \n",
"6 20 30 34 0 180 180 \n",
"7 25 37 42 0 234 234 \n",
"8 24 35 39 0 222 222 \n",
"9 20 30 34 0 180 180 \n",
"10 20 30 34 0 180 180 \n",
"11 20 30 34 0 180 180 \n",
"12 20 30 34 0 180 180 \n",
"13 12 30 34 0 170 170 \n",
"14 12 30 34 0 170 170 \n",
"15 12 30 34 0 170 170 \n",
"16 25 37 42 0 234 234 \n",
"17 12 30 34 0 170 170 \n",
"18 12 30 34 0 170 170 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CRSRD_ID = 2\n",
"cclw = cclwise[cclwise.CRSRD_ID == CRSRD_ID].reset_index(drop=True)\n",
"cclw"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "ta",
"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": "3.8.10"
}
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"nbformat": 4,
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