One day Scheduler.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"BMSDETAILDAY1 - Sheet1 (1).csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>BookingNumber</th>\n",
       "      <th>BrandCode</th>\n",
       "      <th>ProgramCode</th>\n",
       "      <th>TapeCode</th>\n",
       "      <th>TapeDuration</th>\n",
       "      <th>ScheduleDate</th>\n",
       "      <th>SpotRate</th>\n",
       "      <th>StartTime</th>\n",
       "      <th>EndTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00015</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00016</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00017</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INPRA00027</td>\n",
       "      <td>INMOK00015</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INPRA00027</td>\n",
       "      <td>INMOK00016</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BookingNumber   BrandCode ProgramCode    TapeCode  TapeDuration  \\\n",
       "0     18101001S  INMOK00001  INBHA00046  INMOK00015            10   \n",
       "1     18101001S  INMOK00001  INBHA00046  INMOK00016            15   \n",
       "2     18101001S  INMOK00001  INBHA00046  INMOK00017            15   \n",
       "3     18101001S  INMOK00001  INPRA00027  INMOK00015            10   \n",
       "4     18101001S  INMOK00001  INPRA00027  INMOK00016            15   \n",
       "\n",
       "         ScheduleDate  SpotRate StartTime EndTime  \n",
       "0  2018-10-01 0:00:00     200.0       NaN     NaN  \n",
       "1  2018-10-01 0:00:00     200.0       NaN     NaN  \n",
       "2  2018-10-01 0:00:00     200.0       NaN     NaN  \n",
       "3  2018-10-01 0:00:00     200.0       NaN     NaN  \n",
       "4  2018-10-01 0:00:00     200.0       NaN     NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['IsActive']=int(1);\n",
    "df['Scheduled']=int(0);\n",
    "df['Priority']=int(1);\n",
    "df['Held_Back_Value']=0;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BookingNumber</th>\n",
       "      <th>BrandCode</th>\n",
       "      <th>ProgramCode</th>\n",
       "      <th>TapeCode</th>\n",
       "      <th>TapeDuration</th>\n",
       "      <th>ScheduleDate</th>\n",
       "      <th>SpotRate</th>\n",
       "      <th>StartTime</th>\n",
       "      <th>EndTime</th>\n",
       "      <th>IsActive</th>\n",
       "      <th>Scheduled</th>\n",
       "      <th>Priority</th>\n",
       "      <th>Held_Back_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00015</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00016</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00017</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INPRA00027</td>\n",
       "      <td>INMOK00015</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INPRA00027</td>\n",
       "      <td>INMOK00016</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BookingNumber   BrandCode ProgramCode    TapeCode  TapeDuration  \\\n",
       "0     18101001S  INMOK00001  INBHA00046  INMOK00015            10   \n",
       "1     18101001S  INMOK00001  INBHA00046  INMOK00016            15   \n",
       "2     18101001S  INMOK00001  INBHA00046  INMOK00017            15   \n",
       "3     18101001S  INMOK00001  INPRA00027  INMOK00015            10   \n",
       "4     18101001S  INMOK00001  INPRA00027  INMOK00016            15   \n",
       "\n",
       "         ScheduleDate  SpotRate StartTime EndTime  IsActive  Scheduled  \\\n",
       "0  2018-10-01 0:00:00     200.0       NaN     NaN         1          0   \n",
       "1  2018-10-01 0:00:00     200.0       NaN     NaN         1          0   \n",
       "2  2018-10-01 0:00:00     200.0       NaN     NaN         1          0   \n",
       "3  2018-10-01 0:00:00     200.0       NaN     NaN         1          0   \n",
       "4  2018-10-01 0:00:00     200.0       NaN     NaN         1          0   \n",
       "\n",
       "   Priority  Held_Back_Value  \n",
       "0         1                0  \n",
       "1         1                0  \n",
       "2         1                0  \n",
       "3         1                0  \n",
       "4         1                0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26565a2cf60>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(), cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['StartTime'][0]=='nan'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df=df.fillna('1899-12-03 00:00:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26566e214a8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(), cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>BookingNumber</th>\n",
       "      <th>BrandCode</th>\n",
       "      <th>ProgramCode</th>\n",
       "      <th>TapeCode</th>\n",
       "      <th>TapeDuration</th>\n",
       "      <th>ScheduleDate</th>\n",
       "      <th>SpotRate</th>\n",
       "      <th>StartTime</th>\n",
       "      <th>EndTime</th>\n",
       "      <th>IsActive</th>\n",
       "      <th>Scheduled</th>\n",
       "      <th>Priority</th>\n",
       "      <th>Held_Back_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00015</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00016</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00017</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INPRA00027</td>\n",
       "      <td>INMOK00015</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INPRA00027</td>\n",
       "      <td>INMOK00016</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BookingNumber   BrandCode ProgramCode    TapeCode  TapeDuration  \\\n",
       "0     18101001S  INMOK00001  INBHA00046  INMOK00015            10   \n",
       "1     18101001S  INMOK00001  INBHA00046  INMOK00016            15   \n",
       "2     18101001S  INMOK00001  INBHA00046  INMOK00017            15   \n",
       "3     18101001S  INMOK00001  INPRA00027  INMOK00015            10   \n",
       "4     18101001S  INMOK00001  INPRA00027  INMOK00016            15   \n",
       "\n",
       "         ScheduleDate  SpotRate            StartTime              EndTime  \\\n",
       "0  2018-10-01 0:00:00     200.0  1899-12-03 00:00:00  1899-12-03 00:00:00   \n",
       "1  2018-10-01 0:00:00     200.0  1899-12-03 00:00:00  1899-12-03 00:00:00   \n",
       "2  2018-10-01 0:00:00     200.0  1899-12-03 00:00:00  1899-12-03 00:00:00   \n",
       "3  2018-10-01 0:00:00     200.0  1899-12-03 00:00:00  1899-12-03 00:00:00   \n",
       "4  2018-10-01 0:00:00     200.0  1899-12-03 00:00:00  1899-12-03 00:00:00   \n",
       "\n",
       "   IsActive  Scheduled  Priority  Held_Back_Value  \n",
       "0         1          0         1                0  \n",
       "1         1          0         1                0  \n",
       "2         1          0         1                0  \n",
       "3         1          0         1                0  \n",
       "4         1          0         1                0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1899-12-30 7:00:00'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['StartTime'][6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "from datetime import time\n",
    "from datetime import datetime\n",
    "from datetime import timedelta"
   ]
  },
  {
   "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>BookingNumber</th>\n",
       "      <th>BrandCode</th>\n",
       "      <th>ProgramCode</th>\n",
       "      <th>TapeCode</th>\n",
       "      <th>TapeDuration</th>\n",
       "      <th>ScheduleDate</th>\n",
       "      <th>SpotRate</th>\n",
       "      <th>StartTime</th>\n",
       "      <th>EndTime</th>\n",
       "      <th>IsActive</th>\n",
       "      <th>Scheduled</th>\n",
       "      <th>Priority</th>\n",
       "      <th>Held_Back_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00015</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00016</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00017</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INPRA00027</td>\n",
       "      <td>INMOK00015</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INPRA00027</td>\n",
       "      <td>INMOK00016</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1899-12-03 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BookingNumber   BrandCode ProgramCode    TapeCode  TapeDuration  \\\n",
       "0     18101001S  INMOK00001  INBHA00046  INMOK00015            10   \n",
       "1     18101001S  INMOK00001  INBHA00046  INMOK00016            15   \n",
       "2     18101001S  INMOK00001  INBHA00046  INMOK00017            15   \n",
       "3     18101001S  INMOK00001  INPRA00027  INMOK00015            10   \n",
       "4     18101001S  INMOK00001  INPRA00027  INMOK00016            15   \n",
       "\n",
       "         ScheduleDate  SpotRate            StartTime              EndTime  \\\n",
       "0  2018-10-01 0:00:00     200.0  1899-12-03 00:00:00  1899-12-03 00:00:00   \n",
       "1  2018-10-01 0:00:00     200.0  1899-12-03 00:00:00  1899-12-03 00:00:00   \n",
       "2  2018-10-01 0:00:00     200.0  1899-12-03 00:00:00  1899-12-03 00:00:00   \n",
       "3  2018-10-01 0:00:00     200.0  1899-12-03 00:00:00  1899-12-03 00:00:00   \n",
       "4  2018-10-01 0:00:00     200.0  1899-12-03 00:00:00  1899-12-03 00:00:00   \n",
       "\n",
       "   IsActive  Scheduled  Priority  Held_Back_Value  \n",
       "0         1          0         1                0  \n",
       "1         1          0         1                0  \n",
       "2         1          0         1                0  \n",
       "3         1          0         1                0  \n",
       "4         1          0         1                0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['StartTime']=(df['StartTime'].astype('str')).str.split(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MDStartTime']=df['StartTime'].apply(lambda x : x[-1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['EndTime']=(df['EndTime'].astype('str')).str.split(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MDEndTime']=df['EndTime'].apply(lambda x : x[-1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df['MDEndTime']\n",
    "x=x.apply(lambda x : x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MDEndTime']=x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df['MDStartTime']\n",
    "x=x.apply(lambda x : x[0])\n",
    "df['MDStartTime']=x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(str(df['MDEndTime'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mdtime(x):\n",
    "    y=timedelta(seconds=0)\n",
    "    leng=len(str(x))\n",
    "    s1=int(x[leng-1])\n",
    "    s2=int(x[leng-2])\n",
    "    y=y+timedelta(seconds=s1+s2*10)\n",
    "    m1=int(x[leng-4])\n",
    "    m2=int(x[leng-5])\n",
    "    y=y+timedelta(minutes=m1+m2*10)\n",
    "    h1=int(x[leng-7])\n",
    "    if leng>7:\n",
    "        h2=int(x[leng-8])\n",
    "    else: \n",
    "        h2=0\n",
    "    y=y+timedelta(hours=h1+h2*10)\n",
    "    return y\n",
    "df['MDEndTime']=df['MDEndTime'].apply(mdtime)\n",
    "df['MDStartTime']=df['MDStartTime'].apply(mdtime)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "CurrentClock=timedelta(seconds=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "MaximumClock=timedelta(days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "global j\n",
    "def startup():\n",
    "    finallog=pd.DataFrame(columns=('CurrentTime','BookingNumber','Duration','BrandCode','TapeCode'))\n",
    "    j=0\n",
    "    print(\"Enter the time you want to start scheduling from in seconds, minutes and hours\")\n",
    "    s=int(input())\n",
    "    m=int(input())\n",
    "    h=int(input())\n",
    "    CurrentClock=timedelta(seconds=s,minutes=m,hours=h)\n",
    "    while (CurrentClock<MaximumClock):\n",
    "        print(\"Enter the program segment's program code and duration in mins and seconds\")\n",
    "        CurrentProgramCode=str(input())\n",
    "        m=int(input())\n",
    "        s=int(input())\n",
    "       # finallog.append(pd.Series([CurrentClock,timedelta(seconds=s,minutes=m),CurrentProgramCode,0,0],index=finallog.columns()), ignore_index=True)\n",
    "       # finallog.loc[j]=[CurrentClock,timedelta(seconds=s,minutes=m),CurrentProgramCode,0,0]\n",
    "       # j+=1\n",
    "        df_2=pd.DataFrame(data={'CurrentTime':[CurrentClock],'BookingNumber':[\"\"],'Duration':[timedelta(seconds=s, minutes=m)],'BrandCode':[\"\"],'TapeCode':[CurrentProgramCode]})\n",
    "        finallog=pd.concat([finallog,df_2],axis=0)\n",
    "        print(finallog)\n",
    "        CurrentClock=CurrentClock+timedelta(seconds=s,minutes=m)\n",
    "        print(\"Enter the break duration in seconds\")\n",
    "        break_time=int(input())\n",
    "        Newbreak(CurrentProgramCode)\n",
    "        df.head()\n",
    "        while(CurrentClock<CurrentClock+timedelta(seconds=break_time)):\n",
    "            Prioritize()\n",
    "            sort()\n",
    "            t=addadv(CurrentClock)\n",
    "            print(\"output:\",t)\n",
    "            if(a==1): finallog.append(t)\n",
    "            if(a==0): break #theres nothing happening inbetween to finallog\n",
    "        print(finallog)\n",
    "    print(\"Day scheduling is over\")        \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['TapeDuration']) # absolutely useless info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the time you want to start scheduling from in seconds, minutes and hours\n",
      "0\n",
      "0\n",
      "0\n",
      "Enter the program segment's program code and duration in mins and seconds\n",
      "INBHA00046\n",
      "10\n",
      "40\n",
      "  CurrentTime BookingNumber Duration BrandCode    TapeCode\n",
      "0      0 days               00:10:40            INBHA00046\n",
      "Enter the break duration in seconds\n",
      "600\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joel\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:10: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "string indices must be integers",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-66-6144a7142686>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mstartup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-54-3c25cd3c370d>\u001b[0m in \u001b[0;36mstartup\u001b[1;34m()\u001b[0m\n\u001b[0;32m     27\u001b[0m             \u001b[0mPrioritize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m             \u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maddadv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCurrentClock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"output:\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m             \u001b[1;32mif\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfinallog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-65-538ee813ff13>\u001b[0m in \u001b[0;36maddadv\u001b[1;34m(CurrentClock)\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[0mi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'BrandCode'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'BrandCode'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m                 \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m             \u001b[0mi\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: string indices must be integers"
     ]
    }
   ],
   "source": [
    "startup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Newbreak(CurrentProgramCode):\n",
    "    x=np.ones(len(df))\n",
    "    y=np.ones(len(df))*0\n",
    "    i=0\n",
    "    for index, row in df.iterrows():\n",
    "        if(row['IsActive']==0):\n",
    "            y=0\n",
    "        else: \n",
    "            y=((row['Held_Back_Value'])*6+1)/6\n",
    "        if(row['ProgramCode']!=CurrentProgramCode or row['Scheduled']==1):\n",
    "            x[i]=0\n",
    "        if(row['MDStartTime']!=row['MDEndTime']):\n",
    "            if(CurrentClock>=row['MDStartTime'] and CurrentClock<=row['MDEndTime'] ):\n",
    "                x[i]=1\n",
    "            else:\n",
    "                x[i]=0\n",
    "        i+=1\n",
    "    df['IsActive']=x\n",
    "    df['Held_Back_Value']=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxsp=df['SpotRate'].max()\n",
    "minsp=df['SpotRate'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalize = lambda x : (x-minsp)/(maxsp-minsp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127.5"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minsp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Joel\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26566ed6320>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['SpotRate'], 50, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "KMeanCentres=np.array([1/6,0.5,5/6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans=KMeans(3,'k-means++',15,300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "spotrate=np.array(normalize(df['SpotRate']))\n",
    "spotrate=spotrate.reshape(-1,1)\n",
    "pred=kmeans.fit(spotrate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "centspr=kmeans.cluster_centers_\n",
    "centpri=KMeanCentres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "CentCoord=np.ones((9,2),dtype=np.complex_)\n",
    "a=0\n",
    "for i in centpri:\n",
    "    for j in centspr:\n",
    "        CentCoord[a][0]=i\n",
    "        CentCoord[a][1]=j\n",
    "        a=a+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.16666667, 0.5       , 0.83333333])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KMeanCentres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=3, n_init=15, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01162246],\n",
       "       [0.86074848],\n",
       "       [0.43429069]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centspr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.16666667+0.j, 0.01162246+0.j],\n",
       "       [0.16666667+0.j, 0.86074848+0.j],\n",
       "       [0.16666667+0.j, 0.43429069+0.j],\n",
       "       [0.5       +0.j, 0.01162246+0.j],\n",
       "       [0.5       +0.j, 0.86074848+0.j],\n",
       "       [0.5       +0.j, 0.43429069+0.j],\n",
       "       [0.83333333+0.j, 0.01162246+0.j],\n",
       "       [0.83333333+0.j, 0.86074848+0.j],\n",
       "       [0.83333333+0.j, 0.43429069+0.j]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CentCoord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def Prioritize():\n",
    "    temp=np.zeros(9)\n",
    "    x=np.ones(len(df))\n",
    "    j=0\n",
    "    for index, rows in df.iterrows():\n",
    "        i=0\n",
    "        if (rows['Scheduled']==0 and rows['IsActive']==1):\n",
    "            while(i<9):\n",
    "                temp[i]=math.sqrt(((float(CentCoord[i][0])-float(rows['Held_Back_Value']))**2+(float(CentCoord[i][1])-float(normalize(rows['SpotRate']))**2)))\n",
    "                i+=1\n",
    "            tmp=temp.min()\n",
    "            for num,tup in enumerate(temp):\n",
    "                if tup==tmp: x[j]=num\n",
    "        j+=1\n",
    "    df['Priority']=x\n",
    "def sort():\n",
    "    df.sort_values(by=['Priority','SpotRate','TapeDuration'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>BookingNumber</th>\n",
       "      <th>BrandCode</th>\n",
       "      <th>ProgramCode</th>\n",
       "      <th>TapeCode</th>\n",
       "      <th>TapeDuration</th>\n",
       "      <th>ScheduleDate</th>\n",
       "      <th>SpotRate</th>\n",
       "      <th>StartTime</th>\n",
       "      <th>EndTime</th>\n",
       "      <th>IsActive</th>\n",
       "      <th>Scheduled</th>\n",
       "      <th>Priority</th>\n",
       "      <th>Held_Back_Value</th>\n",
       "      <th>MDStartTime</th>\n",
       "      <th>MDEndTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00015</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>[1899-12-03, 00:00:00]</td>\n",
       "      <td>[1899-12-03, 00:00:00]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00016</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>[1899-12-03, 00:00:00]</td>\n",
       "      <td>[1899-12-03, 00:00:00]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INBHA00046</td>\n",
       "      <td>INMOK00017</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>[1899-12-03, 00:00:00]</td>\n",
       "      <td>[1899-12-03, 00:00:00]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INPRA00027</td>\n",
       "      <td>INMOK00015</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>[1899-12-03, 00:00:00]</td>\n",
       "      <td>[1899-12-03, 00:00:00]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18101001S</td>\n",
       "      <td>INMOK00001</td>\n",
       "      <td>INPRA00027</td>\n",
       "      <td>INMOK00016</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-10-01 0:00:00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>[1899-12-03, 00:00:00]</td>\n",
       "      <td>[1899-12-03, 00:00:00]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BookingNumber   BrandCode ProgramCode    TapeCode  TapeDuration  \\\n",
       "0     18101001S  INMOK00001  INBHA00046  INMOK00015            10   \n",
       "1     18101001S  INMOK00001  INBHA00046  INMOK00016            15   \n",
       "2     18101001S  INMOK00001  INBHA00046  INMOK00017            15   \n",
       "3     18101001S  INMOK00001  INPRA00027  INMOK00015            10   \n",
       "4     18101001S  INMOK00001  INPRA00027  INMOK00016            15   \n",
       "\n",
       "         ScheduleDate  SpotRate               StartTime  \\\n",
       "0  2018-10-01 0:00:00     200.0  [1899-12-03, 00:00:00]   \n",
       "1  2018-10-01 0:00:00     200.0  [1899-12-03, 00:00:00]   \n",
       "2  2018-10-01 0:00:00     200.0  [1899-12-03, 00:00:00]   \n",
       "3  2018-10-01 0:00:00     200.0  [1899-12-03, 00:00:00]   \n",
       "4  2018-10-01 0:00:00     200.0  [1899-12-03, 00:00:00]   \n",
       "\n",
       "                  EndTime  IsActive  Scheduled  Priority  Held_Back_Value  \\\n",
       "0  [1899-12-03, 00:00:00]         1          0         1                0   \n",
       "1  [1899-12-03, 00:00:00]         1          0         1                0   \n",
       "2  [1899-12-03, 00:00:00]         1          0         1                0   \n",
       "3  [1899-12-03, 00:00:00]         1          0         1                0   \n",
       "4  [1899-12-03, 00:00:00]         1          0         1                0   \n",
       "\n",
       "  MDStartTime MDEndTime  \n",
       "0      0 days    0 days  \n",
       "1      0 days    0 days  \n",
       "2      0 days    0 days  \n",
       "3      0 days    0 days  \n",
       "4      0 days    0 days  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#startup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def addadv(CurrentClock):\n",
    "    x=np.ones(len(df))\n",
    "    if(df['IsActive'][0]==1):\n",
    "        #-finallog=finallog.append(Series([CurrentClock,timedelta(seconds=df['Duration'][0]),df['BookingNumber'][0],df['BrandCode'][0],df['SpotRate'][0]]))\n",
    "        finallog=pd.Series([CurrentClock,pd.Timedelta(seconds=df['TapeDuration'][0]),df['BookingNumber'][0],df['BrandCode'][0],df['SpotRate'][0]])\n",
    "        CurrentClock=CurrentClock+pd.Timedelta(seconds=df['TapeDuration'][0])\n",
    "        i=0\n",
    "        for row in df:\n",
    "            if row['BrandCode']==df['BrandCode'][0]:\n",
    "                x[i]=0\n",
    "            i+=1\n",
    "        df['IsActive']=df['IsActive'] & x[i]\n",
    "        df['Scheduled'][0]=1\n",
    "        update(df['BrandCode'])\n",
    "        return 1,finallog\n",
    "    \n",
    "    else:print('Ads exhausted for this break') \n",
    "    return 0, None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update(BrandCode):\n",
    "    x=np.ones(len(df))\n",
    "    i=0\n",
    "    for index, rows in df.iterrows():\n",
    "        if(rows['BrandCode']==BrandCode or rows['IsActive']==0):\n",
    "            x[i]=0;\n",
    "    df['IsActive']=x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['held_back_value']=int(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['Held_Back_Value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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