Cleaning and Extracting Data.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"543 Project: Predicting Waiting times.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"vxOkt4GdIXUx"},"source":["# Predicting ED waiting times and bottle-necks"]},{"cell_type":"markdown","metadata":{"id":"iiXx2v5vPs37"},"source":["### Imports"]},{"cell_type":"code","metadata":{"id":"Ts65bOJZHq6J"},"source":["import pandas as pd \n","import io\n","import numpy as np\n","from google.colab import files\n","from datetime import date, time, datetime\n","from google.colab import drive"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3YmRJldovUvU"},"source":["### File Upload"]},{"cell_type":"code","metadata":{"colab":{"resources":{"http://localhost:8080/nbextensions/google.colab/files.js":{"data":"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","headers":[["content-type","application/javascript"]],"ok":true,"status":200,"status_text":""}},"base_uri":"https://localhost:8080/","height":514},"id":"fqiLwz68vTVC","executionInfo":{"elapsed":181994,"status":"ok","timestamp":1619922512427,"user":{"displayName":"Sim Bhattarai","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhpaBPF0DuLN5KkInccG6Ttqk5D8haa72YxXR_6=s64","userId":"11084487050159240062"},"user_tz":360},"outputId":"e30959e3-3a74-40b7-87b3-4dd7dc8b6279"},"source":["uploaded = files.upload()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","     <input type=\"file\" id=\"files-54f51307-4ab7-4dae-8352-4071b78546cd\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-54f51307-4ab7-4dae-8352-4071b78546cd\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script src=\"/nbextensions/google.colab/files.js\"></script> "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Saving DI.xlsx to DI.xlsx\n","Saving DI(1).xlsx to DI(1).xlsx\n","Saving DI(2).xlsx to DI(2).xlsx\n","Saving Labs.xlsx to Labs.xlsx\n","Saving Labs(1).xlsx to Labs(1).xlsx\n","Saving Labs(2).xlsx to Labs(2).xlsx\n","Saving Labs(3).xlsx to Labs(3).xlsx\n","Saving Labs(4).xlsx to Labs(4).xlsx\n","Saving Labs(5).xlsx to Labs(5).xlsx\n","Saving Labs(6).xls to Labs(6).xls\n","Saving Sample-Data.xlsx to Sample-Data.xlsx\n","Saving Shifts-Physicians.csv to Shifts-Physicians.csv\n","Saving Visits.xlsx to Visits.xlsx\n","Saving Visits(1).xlsx to Visits(1).xlsx\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"vmUeQ-Ic3M1u"},"source":["df = pd.read_excel(io.BytesIO(uploaded['Sample-Data.xlsx']))\n","df1 = pd.read_excel(io.BytesIO(uploaded['Visits.xlsx']))\n","df2 = pd.read_excel(io.BytesIO(uploaded['Visits(1).xlsx']))\n","\n","df_phys = pd.read_csv(io.BytesIO(uploaded['Shifts-Physicians.csv']))\n","\n","df_labs = pd.read_excel(io.BytesIO(uploaded['Labs.xlsx']))\n","df_labs1 = pd.read_excel(io.BytesIO(uploaded['Labs(1).xlsx']))\n","df_labs2 = pd.read_excel(io.BytesIO(uploaded['Labs(2).xlsx']))\n","df_labs3 = pd.read_excel(io.BytesIO(uploaded['Labs(3).xlsx']))\n","df_labs4 = pd.read_excel(io.BytesIO(uploaded['Labs(4).xlsx']))\n","df_labs5 = pd.read_excel(io.BytesIO(uploaded['Labs(5).xlsx']))\n","df_labs6 = pd.read_excel(io.BytesIO(uploaded['Labs(6).xls']))\n","\n","\n","df_DI = pd.read_excel(io.BytesIO(uploaded['DI.xlsx']))\n","df_DI1 = pd.read_excel(io.BytesIO(uploaded['DI(1).xlsx']))\n","df_DI2 = pd.read_excel(io.BytesIO(uploaded['DI(2).xlsx']))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"EVtWDZYrHKgG"},"source":["### Data Transforming: Change to Date Time"]},{"cell_type":"code","metadata":{"id":"kF7nuN2gA_OM"},"source":["df.FIRST_CONTACT_DT = pd.to_datetime(df.FIRST_CONTACT_DT, format='%d-%m-%Y %H:%M:%S')\n","df.INIT_LOC_DT = pd.to_datetime(df.INIT_LOC_DT, format='%d-%m-%Y %H:%M:%S')\n","df.INIT_TREAT_LOC_DT = pd.to_datetime(df.INIT_TREAT_LOC_DT, format='%d-%m-%Y %H:%M:%S')\n","df.PHYS_ASSESSMENT_DT = pd.to_datetime(df.PHYS_ASSESSMENT_DT, format='%d-%m-%Y %H:%M:%S')\n","df.CONSULT_REQUESTED_DT = pd.to_datetime(df.CONSULT_REQUESTED_DT , format='%d-%m-%Y %H:%M:%S')\n","df.IP_BED_ASSIGNED_DT  = pd.to_datetime(df.IP_BED_ASSIGNED_DT , format='%d-%m-%Y %H:%M:%S')\n","df.LAST_CONTACT_DT = pd.to_datetime(df.LAST_CONTACT_DT, format='%d-%m-%Y %H:%M:%S')\n","df.DEC_TO_ADMIT_DT = pd.to_datetime(df.DEC_TO_ADMIT_DT, format='%d-%m-%Y %H:%M:%S')\n","\n","df1.FIRST_CONTACT_DT = pd.to_datetime(df1.FIRST_CONTACT_DT, format='%d-%m-%Y %H:%M:%S')\n","df1.INIT_LOC_DT = pd.to_datetime(df1.INIT_LOC_DT, format='%d-%m-%Y %H:%M:%S')\n","df1.INIT_TREAT_LOC_DT = pd.to_datetime(df1.INIT_TREAT_LOC_DT, format='%d-%m-%Y %H:%M:%S')\n","df1.PHYS_ASSESSMENT_DT = pd.to_datetime(df1.PHYS_ASSESSMENT_DT, format='%d-%m-%Y %H:%M:%S')\n","df1.CONSULT_REQUESTED_DT = pd.to_datetime(df1.CONSULT_REQUESTED_DT , format='%d-%m-%Y %H:%M:%S')\n","df1.IP_BED_ASSIGNED_DT  = pd.to_datetime(df1.IP_BED_ASSIGNED_DT , format='%d-%m-%Y %H:%M:%S')\n","df1.LAST_CONTACT_DT = pd.to_datetime(df1.LAST_CONTACT_DT, format='%d-%m-%Y %H:%M:%S')\n","df1.DEC_TO_ADMIT_DT = pd.to_datetime(df1.DEC_TO_ADMIT_DT, format='%d-%m-%Y %H:%M:%S')\n","\n","df2.FIRST_CONTACT_DT = pd.to_datetime(df2.FIRST_CONTACT_DT, format='%d-%m-%Y %H:%M:%S')\n","df2.INIT_LOC_DT = pd.to_datetime(df2.INIT_LOC_DT, format='%d-%m-%Y %H:%M:%S')\n","df2.INIT_TREAT_LOC_DT = pd.to_datetime(df2.INIT_TREAT_LOC_DT, format='%d-%m-%Y %H:%M:%S')\n","df2.PHYS_ASSESSMENT_DT = pd.to_datetime(df2.PHYS_ASSESSMENT_DT, format='%d-%m-%Y %H:%M:%S')\n","df2.CONSULT_REQUESTED_DT = pd.to_datetime(df2.CONSULT_REQUESTED_DT , format='%d-%m-%Y %H:%M:%S')\n","df2.IP_BED_ASSIGNED_DT  = pd.to_datetime(df2.IP_BED_ASSIGNED_DT , format='%d-%m-%Y %H:%M:%S')\n","df2.LAST_CONTACT_DT = pd.to_datetime(df2.LAST_CONTACT_DT, format='%d-%m-%Y %H:%M:%S')\n","df2.DEC_TO_ADMIT_DT = pd.to_datetime(df2.DEC_TO_ADMIT_DT, format='%d-%m-%Y %H:%M:%S')\n","\n","df_labs.MIN_RESULT_DT = pd.to_datetime(df_labs.MIN_RESULT_DT, format='%d/%m/%Y %H:%M:%S')\n","df_labs1.MIN_RESULT_DT = pd.to_datetime(df_labs1.MIN_RESULT_DT, format='%d/%m/%Y %H:%M:%S')\n","df_labs2.MIN_RESULT_DT = pd.to_datetime(df_labs2.MIN_RESULT_DT, format='%d/%m/%Y %H:%M:%S')\n","df_labs3.MIN_RESULT_DT = pd.to_datetime(df_labs3.MIN_RESULT_DT, format='%d/%m/%Y %H:%M:%S')\n","df_labs4.MIN_RESULT_DT = pd.to_datetime(df_labs4.MIN_RESULT_DT, format='%d/%m/%Y %H:%M:%S')\n","df_labs5.MIN_RESULT_DT = pd.to_datetime(df_labs5.MIN_RESULT_DT, format='%d/%m/%Y %H:%M:%S')\n","df_labs6.MIN_RESULT_DT = pd.to_datetime(df_labs6.MIN_RESULT_DT, format='%d/%m/%Y %H:%M:%S')\n","\n","\n","df_labs.ORD_REQ_DT = pd.to_datetime(df_labs.ORD_REQ_DT, format='%d/%m/%Y %H:%M:%S')\n","df_labs1.ORD_REQ_DT = pd.to_datetime(df_labs1.ORD_REQ_DT, format='%d/%m/%Y %H:%M:%S')\n","df_labs2.ORD_REQ_DT = pd.to_datetime(df_labs2.ORD_REQ_DT, format='%d/%m/%Y %H:%M:%S')\n","df_labs3.ORD_REQ_DT = pd.to_datetime(df_labs3.ORD_REQ_DT, format='%d/%m/%Y %H:%M:%S')\n","df_labs4.ORD_REQ_DT = pd.to_datetime(df_labs4.ORD_REQ_DT, format='%d/%m/%Y %H:%M:%S')\n","df_labs5.ORD_REQ_DT = pd.to_datetime(df_labs5.ORD_REQ_DT, format='%d/%m/%Y %H:%M:%S')\n","df_labs6.ORD_REQ_DT = pd.to_datetime(df_labs6.ORD_REQ_DT, format='%d/%m/%Y %H:%M:%S')\n","\n","df_DI.MIN_RESULT_DT = pd.to_datetime(df_DI.MIN_RESULT_DT, format='%d/%m/%Y %H:%M:%S')\n","df_DI1.MIN_RESULT_DT = pd.to_datetime(df_DI1.MIN_RESULT_DT, format='%d/%m/%Y %H:%M:%S')\n","df_DI2.MIN_RESULT_DT = pd.to_datetime(df_DI2.MIN_RESULT_DT, format='%d/%m/%Y %H:%M:%S')\n","\n","df_DI.ORD_REQ_DT = pd.to_datetime(df_DI.ORD_REQ_DT, format='%d/%m/%Y %H:%M:%S')\n","df_DI1.ORD_REQ_DT = pd.to_datetime(df_DI1.ORD_REQ_DT, format='%d/%m/%Y %H:%M:%S')\n","df_DI2.ORD_REQ_DT = pd.to_datetime(df_DI2.ORD_REQ_DT, format='%d/%m/%Y %H:%M:%S')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"aChliBYIHVRB"},"source":["### Append Data Sets"]},{"cell_type":"code","metadata":{"id":"vFTkfDdaWRL_"},"source":["df = pd.DataFrame().append([df, df1, df2], ignore_index=True)\n","df_labs = pd.DataFrame().append([df_labs, df_labs1, df_labs2, df_labs3, df_labs4, df_labs5, df_labs6], ignore_index=True)\n","df_DI = pd.DataFrame().append([df_DI, df_DI1, df_DI2], ignore_index=True)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"XYTotllkblPy"},"source":["### Preserving certain variables as global"]},{"cell_type":"code","metadata":{"id":"M8470YGobiY1"},"source":["ctas = df['CTAS']\n","disp_group = df['DISP_GROUP']\n","arrival =  df['LAST_CONTACT_DT']\n","sample_n = len(df)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Q3XtTfNYHYiB"},"source":["### Data Transforming: Change Categorical Variables to Binary"]},{"cell_type":"code","metadata":{"id":"pxSXFjz-HHT8"},"source":["df = pd.get_dummies(df, columns=['CTAS', 'ARRIVAL_MODE', 'INIT_TREAT_LOC_GRP', 'INIT_LOC_GRP', 'DISP_GROUP'])"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"JNF0eMFbm6Co"},"source":["### Mount Google Drive"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DHCjWTGmm4w_","executionInfo":{"elapsed":21502,"status":"ok","timestamp":1619922956422,"user":{"displayName":"Sim Bhattarai","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhpaBPF0DuLN5KkInccG6Ttqk5D8haa72YxXR_6=s64","userId":"11084487050159240062"},"user_tz":360},"outputId":"e03c8406-9504-4eb3-e32d-bf7c351a263b"},"source":["from google.colab import drive\n","drive.mount('/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /drive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"oEsGJyeEiMWQ"},"source":["### Finding number of people waiting in ED Waiting Room Variables"]},{"cell_type":"code","metadata":{"id":"TKtXsrQfbZxU"},"source":["def waiting_before(a_start, a_end, a_ctas, b_start, b_end, b_ctas, same_ctas):\n","  check = bool(b_start < a_start and b_end > a_start)\n","  if (same_ctas):\n","    return (a_ctas == b_ctas and check)\n","  else:\n","    return check\n","\n","\n","def waiting_after(a_start, a_end, a_ctas, b_start, b_end, b_ctas, same_ctas):\n","  check = bool(b_start > a_start and b_end > a_end)\n","  if (same_ctas):\n","    return (a_ctas == b_ctas and check)\n","  else:\n","    return check\n","\n","def in_between(a_start, a_end, a_ctas, b_start, b_end, b_ctas, same_ctas):\n","  check = bool(b_start > a_start and b_end < a_end)\n","  if (same_ctas):\n","    return (a_ctas == b_ctas and check)\n","  else:\n","    return check\n","\n","def date_list_within_range(i, cut):\n","  if ((i - cut) <= 0):\n","    start = 0\n","  else:\n","    start = i - cut\n","  \n","  \n","  if ((i+cut) >= sample_n):\n","    end = sample_n\n","  else:\n","    end = i + cut\n","  return start, end\n","\n","\n","# general function\n","def find_num_of_patients(patient_num, col_start, col_end, start_index, end_index, check_function, same_ctas=False):\n","  count = 0\n","  patient_a_ctas = ctas[patient_num]\n","  patient_a_start = col_start[patient_num]\n","  patient_a_end = col_end[patient_num]\n","  for i in range(start_index, end_index):\n","    patient_b_ctas = ctas[i]\n","    patient_b_start = col_start[i]\n","    patient_b_end = col_end[i]\n","    if (check_function(patient_a_start, patient_a_end, patient_a_ctas, \n","                       patient_b_start, patient_b_end, patient_b_ctas, \n","                       same_ctas)):\n","        count += 1\n","  return count"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yw78dZ7-aW1d"},"source":["Number_of_Patients_In_Waiting_Before = [0]*len(df)\n","Number_of_Patients_In_Waiting_Before_CTAS = [0]*len(df)\n","\n","for i in range(0,len(df)):\n","  start, end = date_list_within_range(i, 50)\n","  Number_of_Patients_In_Waiting_Before[i] = find_num_of_patients(i, df['FIRST_CONTACT_DT'][start:end], df['INIT_TREAT_LOC_DT'][start:end], start,end, waiting_before, False)\n","  Number_of_Patients_In_Waiting_Before_CTAS[i] = find_num_of_patients(i, df['FIRST_CONTACT_DT'][start:end], df['INIT_TREAT_LOC_DT'][start:end], start,end, waiting_before, True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":375},"id":"LxfAx1iBqYdC","executionInfo":{"elapsed":6256,"status":"error","timestamp":1619923827791,"user":{"displayName":"Sim Bhattarai","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhpaBPF0DuLN5KkInccG6Ttqk5D8haa72YxXR_6=s64","userId":"11084487050159240062"},"user_tz":360},"outputId":"897f6b24-edac-46d3-a25d-4acf80f213fc"},"source":["pd.DataFrame(Number_of_Patients_In_Waiting_Before).to_excel(\"/drive/My Drive/variables/Number_of_Patients_In_Waiting_Before.xlsx\")\n","pd.DataFrame(Number_of_Patients_In_Waiting_Before_CTAS).to_excel(\"/drive/My Drive/variables/Number_of_Patients_In_Waiting_Before_CTAS.xlsx\")"],"execution_count":null,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-13-1bfc8a7b31cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNumber_of_Patients_In_Waiting_Before\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/drive/My Drive/variables/Number_of_Patients_In_Waiting_Before.xlsx\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNumber_of_Patients_In_Waiting_Before_CTAS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/drive/My Drive/variables/Number_of_Patients_In_Waiting_Before_CTAS.xlsx\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mto_excel\u001b[0;34m(self, excel_writer, sheet_name, na_rep, float_format, columns, header, index, index_label, startrow, startcol, engine, merge_cells, encoding, inf_rep, verbose, freeze_panes)\u001b[0m\n\u001b[1;32m   2030\u001b[0m             \u001b[0mstartcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstartcol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2031\u001b[0m             \u001b[0mfreeze_panes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreeze_panes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2032\u001b[0;31m             \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2033\u001b[0m         )\n\u001b[1;32m   2034\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/formats/excel.py\u001b[0m in \u001b[0;36mwrite\u001b[0;34m(self, writer, sheet_name, startrow, startcol, freeze_panes, engine)\u001b[0m\n\u001b[1;32m    740\u001b[0m         )\n\u001b[1;32m    741\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mneed_save\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 742\u001b[0;31m             \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/excel/_openpyxl.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0mSave\u001b[0m \u001b[0mworkbook\u001b[0m \u001b[0mto\u001b[0m \u001b[0mdisk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \"\"\"\n\u001b[0;32m---> 43\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/openpyxl/workbook/workbook.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m    388\u001b[0m             \u001b[0msave_dump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m             \u001b[0msave_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/openpyxl/writer/excel.py\u001b[0m in \u001b[0;36msave_workbook\u001b[0;34m(workbook, filename)\u001b[0m\n\u001b[1;32m    280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m     \"\"\"\n\u001b[0;32m--> 282\u001b[0;31m     \u001b[0marchive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mZipFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mZIP_DEFLATED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallowZip64\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    283\u001b[0m     \u001b[0mwriter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExcelWriter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworkbook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marchive\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m     \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.7/zipfile.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, file, mode, compression, allowZip64, compresslevel)\u001b[0m\n\u001b[1;32m   1238\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1239\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1240\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilemode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1241\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1242\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mfilemode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodeDict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/drive/My Drive/variables/Number_of_Patients_In_Waiting_Before.xlsx'"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uMw0maPRrFN9","executionInfo":{"elapsed":537975,"status":"ok","timestamp":1619924378576,"user":{"displayName":"Sim Bhattarai","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhpaBPF0DuLN5KkInccG6Ttqk5D8haa72YxXR_6=s64","userId":"11084487050159240062"},"user_tz":360},"outputId":"fd3395ef-b08c-43e6-995a-f337ecbbf3da"},"source":["Number_of_Patients_In_Waiting_After = [0]*len(df)\n","Number_of_Patients_In_Waiting_After_CTAS = [0]*len(df)\n","\n","for i in range(0, len(df)):\n","  start, end = date_list_within_range(i, 50)\n","  Number_of_Patients_In_Waiting_After[i] = find_num_of_patients(i, df['FIRST_CONTACT_DT'][start:end], df['INIT_TREAT_LOC_DT'][start:end], start,end, waiting_after, False)\n","  Number_of_Patients_In_Waiting_After_CTAS[i] = find_num_of_patients(i, df['FIRST_CONTACT_DT'][start:end], df['INIT_TREAT_LOC_DT'][start:end], start,end, waiting_after, True)\n","  print(i)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 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Drive/variables/Number_of_Patients_In_Waiting_After.xlsx\")\n","pd.DataFrame(Number_of_Patients_In_Waiting_After_CTAS).to_excel(\"/drive/My Drive/variables/Number_of_Patients_In_Waiting_After_CTAS.xlsx\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qsFBplwHuz-q"},"source":["Number_of_Patient_Treated_After_Treatment_Before = [0]*len(df)\n","\n","for i in range(0, len(df)):\n","  start, end = date_list_within_range(i, 50)\n","  Number_of_Patient_Treated_After_Treatment_Before[i] = find_num_of_patients(i, df['FIRST_CONTACT_DT'][start:end], df['INIT_TREAT_LOC_DT'][start:end], start,end, in_between, False)\n","  Number_of_Patient_Treated_After_Treatment_Before_CTAS[i] = find_num_of_patients(i, df['FIRST_CONTACT_DT'][start:end], df['INIT_TREAT_LOC_DT'][start:end], start,end, in_between, True)\n","  print(i)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"sDT59IEYvPPU"},"source":["pd.DataFrame(Number_of_Patient_Treated_After_Treatment_Before).to_excel(\"/drive/My Drive/variables/Number_of_Patient_Treated_After_Treatment_Before.xlsx\")\n","pd.DataFrame(Number_of_Patient_Treated_After_Treatment_Before_CTAS).to_excel(\"/drive/My Drive/variables/Number_of_Patient_Treated_After_Treatment_Before_CTAS.xlsx\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"j6ezYUPLc1LG"},"source":["## Finding Number of Patients Waiting for Discharge Upon Arrival"]},{"cell_type":"code","metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"2pILoGaVdIw1","outputId":"f1a53551-0ff2-4408-ccfe-9e2a389ed9f8"},"source":["def waiting_for_dispatch(b_num, a_arrival, b_start, b_end,):\n","  status = disp_group[b_num]\n","  check = bool(status == 'Discharged' and \n","              b_start < a_arrival and b_end > a_arrival)\n","  return check  \n","\n","def find_num_of_patients_2(patient_num, col_start, col_end, start_index, end_index, check_function):\n","  count1 = 0\n","  count2 = 0\n","  patient_a_start = arrival[patient_num]\n","  patient_a_ctas = ctas[patient_num]\n","  for i in range(start_index, end_index):\n","    patient_b_ctas = ctas[i]\n","    patient_b_start = col_start[i]\n","    patient_b_end = col_end[i]\n","    if (pd.isnull(patient_b_start) == False and pd.isnull(patient_b_end) == False):\n","      if (check_function(i, patient_a_start, patient_b_start, patient_b_end)):\n","          count1 += 1\n","          if (patient_a_ctas == patient_b_ctas):\n","            count2 += 1\n","  return count1, count2\n","\n","Number_of_Patients_Waiting_For_Discharge = [0]*len(df)\n","Number_of_Patients_Waiting_For_Discharge_CTAS = [0]*len(df)\n","\n","for i in range(0, len(df)):\n","  start, end = date_list_within_range(i, 300)\n","  Number_of_Patients_Waiting_For_Discharge[i], Number_of_Patients_Waiting_For_Discharge_CTAS[i] = find_num_of_patients_2(i, df['PHYS_ASSESSMENT_DT'][start:end], df['LAST_CONTACT_DT'][start:end], start, end, waiting_for_dispatch)\n","  print(i)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 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Drive/variables/Number_of_Patients_Waiting_For_Discharge.xlsx\")\n","pd.DataFrame(Number_of_Patients_Waiting_For_Discharge_CTAS).to_excel(\"/drive/My Drive/variables/Number_of_Patients_Waiting_For_Discharge_CTAS.xlsx\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rbsmD0xziXId"},"source":["## Finding Number of Patients Waiting for Hospital Admission Upon Arrival"]},{"cell_type":"code","metadata":{"id":"BVie5twHiss8"},"source":["def waiting_for_admission(b_num, a_arrival, b_start, b_end):\n","  status = disp_group[b_num]\n","  check = bool(status == 'Admitted' and \n","               b_start < a_arrival and b_end > a_arrival)\n","  return check\n","\n","Number_of_Patients_Waiting_For_Admission = [0]*len(df)\n","Number_of_Patients_Waiting_For_Admission_CTAS = [0]*len(df)\n","\n","for i in range(0, len(df)):\n","  start, end = date_list_within_range(i, 300)\n","  Number_of_Patients_Waiting_For_Admission[i], Number_of_Patients_Waiting_For_Admission_CTAS[i] = find_num_of_patients_2(i, df['PHYS_ASSESSMENT_DT'][start:end], df['DEC_TO_ADMIT_DT'][start:end], start, end, waiting_for_admission)\n","  print(i)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"M4wQiJu-Y4h3"},"source":["pd.DataFrame(Number_of_Patients_Waiting_For_Admission).to_excel(\"/drive/My Drive/variables/Number_of_Patients_Waiting_For_Admission.xlsx\")\n","pd.DataFrame(Number_of_Patients_Waiting_For_Admission_CTAS).to_excel(\"/drive/My Drive/variables/Number_of_Patients_Waiting_For_Admission_CTAS.xlsx\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WDfXzE39AvQe"},"source":["## Calculating Total Time In Between Time Blocks and Cumulative"]},{"cell_type":"code","metadata":{"id":"CSBYx69ZjI_u"},"source":["def find_total_time(patient_num, col_start, col_end):\n","  return (col_end[patient_num] - col_start[patient_num]).total_seconds() /60\n","\n","def find_conditional_total_time(patient_num, col_start, col_end_1, col_end_2):\n","  if pd.isnull(col_end_1[patient_num]):\n","    return find_total_time(patient_num, col_start, col_end_2)\n","  else:\n","    return find_total_time(patient_num, col_start, col_end_1)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"fQPxBcUql_na"},"source":["tot_time_init_loc = [0]*len(df)\n","tot_time_init_treat_loc = [0]*len(df)\n","tot_time_phys = [0]*len(df)\n","tot_time_consult = [0]*len(df)\n","tot_time_bed = [0]*len(df)\n","tot_time_last = [0]*len(df)\n","\n","tot_time_init_treat_loc_c = [0]*len(df)\n","tot_time_phys_c = [0]*len(df)\n","tot_time_consult_c = [0]*len(df)\n","tot_time_bed_c = [0]*len(df)\n","tot_time_last_c = [0]*len(df)\n","tot_time_for_treatment_c = [0]*len(df)\n","\n","for i in range(0, len(df)):\n","  tot_time_init_loc[i] = find_total_time(i, df['FIRST_CONTACT_DT'], df['INIT_LOC_DT'])\n","  tot_time_init_treat_loc[i] = find_total_time(i, df['INIT_LOC_DT'], df['INIT_TREAT_LOC_DT'])\n","  tot_time_phys[i] = find_total_time(i, df['INIT_TREAT_LOC_DT'], df['PHYS_ASSESSMENT_DT'])\n","  tot_time_consult[i] = find_total_time(i, df['PHYS_ASSESSMENT_DT'], df['CONSULT_REQUESTED_DT'])\n","  tot_time_bed[i] = find_total_time(i, df['CONSULT_REQUESTED_DT'], df['IP_BED_ASSIGNED_DT'])\n","  tot_time_last[i] = find_total_time(i, df['IP_BED_ASSIGNED_DT'], df['LAST_CONTACT_DT'])\n","\n","  tot_time_init_treat_loc_c[i] = find_total_time(i, df['FIRST_CONTACT_DT'], df['INIT_TREAT_LOC_DT'])\n","  tot_time_phys_c[i] = find_total_time(i, df['FIRST_CONTACT_DT'], df['PHYS_ASSESSMENT_DT'])\n","  tot_time_consult_c[i] = find_total_time(i, df['FIRST_CONTACT_DT'], df['CONSULT_REQUESTED_DT'])\n","  tot_time_bed_c[i] = find_total_time(i, df['FIRST_CONTACT_DT'], df['IP_BED_ASSIGNED_DT'])\n","  tot_time_last_c[i] = find_total_time(i, df['FIRST_CONTACT_DT'], df['LAST_CONTACT_DT'])\n","  \n","  #This is total time of treatment using \"Decision to admit DT\" might replace. \n","  tot_time_for_treatment_c[i] = find_conditional_total_time(i, df['PHYS_ASSESSMENT_DT'], df['DEC_TO_ADMIT_DT'], df['LAST_CONTACT_DT'])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"92oBGLkFehEB"},"source":["time = pd.DataFrame()\n","time['tot_time_init_loc'] =  [abs(x) for x in tot_time_init_loc]\n","time['tot_time_init_treat_loc'] =  [abs(x) for x in tot_time_init_treat_loc]\n","time['tot_time_phys'] =  [abs(x) for x in tot_time_phys]\n","time['tot_time_consult'] =  [abs(x) for x in tot_time_consult]\n","time['tot_time_bed'] =  [abs(x) for x in tot_time_bed]\n","time['tot_time_last'] =  [abs(x) for x in tot_time_last]\n","\n","time['tot_time_init_treat_loc_c'] =  [abs(x) for x in tot_time_init_treat_loc_c]\n","time['tot_time_phys_c'] =  [abs(x) for x in tot_time_phys_c]\n","time['tot_time_consult_c'] =  [abs(x) for x in tot_time_consult_c]\n","time['tot_time_bed_c'] =  [abs(x) for x in tot_time_bed_c]\n","time['tot_time_last_c'] =  [abs(x) for x in tot_time_last_c]\n","time['tot_time_for_treatment_c'] =  [abs(x) for x in tot_time_for_treatment_c]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"04IUAMQJZG4R"},"source":["time.to_excel(\"/drive/My Drive/variables/time.xlsx\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"mDjlOwmagIep"},"source":["## Export Jana's Half of the Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":266},"id":"lLz2KPI_gIBs","executionInfo":{"elapsed":318,"status":"error","timestamp":1619924546573,"user":{"displayName":"Sim Bhattarai","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhpaBPF0DuLN5KkInccG6Ttqk5D8haa72YxXR_6=s64","userId":"11084487050159240062"},"user_tz":360},"outputId":"5eb18e92-089b-4a02-feed-223e7bcca368"},"source":["df['Number_of_Patients_In_Waiting_Before'] = Number_of_Patients_In_Waiting_Before\n","df['Number_of_Patients_In_Waiting_Before_CTAS'] = Number_of_Patients_In_Waiting_Before_CTAS\n","df['Number_of_Patients_In_Waiting_After'] = Number_of_Patients_In_Waiting_After\n","df['Number_of_Patients_In_Waiting_After_CTAS'] = Number_of_Patients_In_Waiting_After_CTAS\n","df['Number_of_Patient_Treated_After_Treatment_Before'] = Number_of_Patient_Treated_After_Treatment_Before\n","df['Number_of_Patient_Treated_After_Treatment_Before_CTAS'] = Number_of_Patient_Treated_After_Treatment_Before_CTAS\n","\n","df['Number_of_Patients_Waiting_For_Discharge'] = Number_of_Patients_Waiting_For_Discharge\n","df['Number_of_Patients_Waiting_For_Discharge_CTAS'] = Number_of_Patients_Waiting_For_Discharge_CTAS\n","\n","df['Number_of_Patients_Waiting_For_Admission'] = Number_of_Patients_Waiting_For_Admission\n","df['Number_of_Patients_Waiting_For_Admission_CTAS'] = Number_of_Patients_Waiting_For_Admission_CTAS\n","\n","df['tot_time_init_loc'] =  [abs(x) for x in tot_time_init_loc]\n","df['tot_time_init_treat_loc'] =  [abs(x) for x in tot_time_init_treat_loc]\n","df['tot_time_phys'] =  [abs(x) for x in tot_time_phys]\n","df['tot_time_consult'] =  [abs(x) for x in tot_time_consult]\n","df['tot_time_bed'] =  [abs(x) for x in tot_time_bed]\n","df['tot_time_last'] =  [abs(x) for x in tot_time_last]\n","\n","df['tot_time_init_treat_loc_c'] =  [abs(x) for x in tot_time_init_treat_loc_c]\n","df['tot_time_phys_c'] =  [abs(x) for x in tot_time_phys_c]\n","df['tot_time_consult_c'] =  [abs(x) for x in tot_time_consult_c]\n","df['tot_time_bed_c'] =  [abs(x) for x in tot_time_bed_c]\n","df['tot_time_last_c'] =  [abs(x) for x in tot_time_last_c]\n","df['tot_time_for_treatment_c'] =  [abs(x) for x in tot_time_for_treatment_c]"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-18-23d9ef2ba420>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Number_of_Patients_In_Waiting_After'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNumber_of_Patients_In_Waiting_After\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Number_of_Patients_In_Waiting_After_CTAS'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNumber_of_Patients_In_Waiting_After_CTAS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Number_of_Patient_Treated_After_Treatment_Before'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNumber_of_Patient_Treated_After_Treatment_Before\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Number_of_Patient_Treated_After_Treatment_Before_CTAS'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNumber_of_Patient_Treated_After_Treatment_Before_CTAS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'Number_of_Patient_Treated_After_Treatment_Before' is not defined"]}]},{"cell_type":"code","metadata":{"id":"tRlftJL5gseM"},"source":["df.to_excel(\"/drive/My Drive/variables/df_jana.xlsx\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"180iQ_pY58Ck"},"source":["## Mapping Doctors to Patient arrival\n","\n"]},{"cell_type":"code","metadata":{"id":"O-sTFGvV6Mnc"},"source":["def find_weekday(dt):\n","  wk = dt.weekday()\n","  if wk == 0:\n","    return \"Monday\"\n","  elif wk == 1:\n","    return \"Tuesday\"\n","  elif wk == 2:\n","    return \"Wednesday\"\n","  elif wk == 3:\n","    return \"Thursday\"\n","  elif wk == 4:\n","    return \"Friday\"\n","  elif wk == 5: \n","    return \"Saturday\"\n","  else:\n","    return \"Sunday\"\n","\n","\n","def find_num_of_doctors_at_arrival(patient_A_FC, df_phys, wk):\n","  num_docs = 0\n","  if (wk== \"Sunday\"):\n","    for i in range(0,24):\n","      if patient_A_FC == df_phys['hour'][i] and df_phys['day'][i] == wk:\n","        num_docs = df_phys['Physicians'][i]\n","        break\n","  elif (wk==  \"Monday\"):\n","    for i in range(24,48):\n","      if patient_A_FC == df_phys['hour'][i] and df_phys['day'][i] == wk:\n","        num_docs = df_phys['Physicians'][i]\n","        break\n","  elif (wk==  \"Tuesday\"):\n","    for i in range(48,72):\n","      if patient_A_FC == df_phys['hour'][i] and df_phys['day'][i] == wk:\n","        num_docs = df_phys['Physicians'][i]\n","        break\n","  elif (wk ==  \"Wednesday\"):\n","    for i in range(72,96):\n","      if patient_A_FC == df_phys['hour'][i] and df_phys['day'][i] == wk:\n","        num_docs = df_phys['Physicians'][i]\n","        break\n","  elif (wk ==  \"Thursday\"):\n","    for i in range(96,120):\n","      if patient_A_FC == df_phys['hour'][i] and df_phys['day'][i] == wk:\n","        num_docs = df_phys['Physicians'][i]\n","        break\n","  elif (wk == \"Friday\"):\n","    for i in range(120,145):\n","      if patient_A_FC == df_phys['hour'][i] and df_phys['day'][i] == wk:\n","        num_docs = df_phys['Physicians'][i]\n","        break\n","  elif (wk ==  \"Saturday\"):\n","    for i in range(145,167):\n","      if patient_A_FC == df_phys['hour'][i] and df_phys['day'][i] == wk:\n","        num_docs = df_phys['Physicians'][i]\n","        break\n","\n","\n","  return num_docs\n","  \n","Number_of_Physicians_At_Arrival = [0]*len(df)\n","Number_of_Physicians_At_3Hours_After = [0]*len(df)\n","Number_of_Physicians_At_2Hours_After = [0]*len(df)\n","Number_of_Physicians_At_1Hours_After = [0]*len(df)\n","Number_of_Physicians_At_3Hours_Before = [0]*len(df)\n","Number_of_Physicians_At_2Hours_Before = [0]*len(df)\n","Number_of_Physicians_At_1Hours_Before = [0]*len(df)\n","\n","for i in range(0, len(df)):\n","    Number_of_Physicians_At_Arrival[i] = find_num_of_doctors_at_arrival(df['FIRST_CONTACT_DT'][i].hour, df_phys, find_weekday(df['FIRST_CONTACT_DT'][i]))\n","    Number_of_Physicians_At_3Hours_Before[i] = find_num_of_doctors_at_arrival((df['FIRST_CONTACT_DT'][i].hour - 3)%24, df_phys, find_weekday(df['FIRST_CONTACT_DT'][i]))\n","    Number_of_Physicians_At_2Hours_Before[i] = find_num_of_doctors_at_arrival((df['FIRST_CONTACT_DT'][i].hour - 2)%24, df_phys,find_weekday(df['FIRST_CONTACT_DT'][i]))\n","    Number_of_Physicians_At_1Hours_Before[i] = find_num_of_doctors_at_arrival((df['FIRST_CONTACT_DT'][i].hour - 1)%24, df_phys,find_weekday(df['FIRST_CONTACT_DT'][i]))\n","    Number_of_Physicians_At_1Hours_After[i] = find_num_of_doctors_at_arrival((df['FIRST_CONTACT_DT'][i].hour + 1)%24, df_phys,find_weekday(df['FIRST_CONTACT_DT'][i]))\n","    Number_of_Physicians_At_2Hours_After[i] = find_num_of_doctors_at_arrival((df['FIRST_CONTACT_DT'][i].hour + 2)%24, df_phys,find_weekday(df['FIRST_CONTACT_DT'][i]))\n","    Number_of_Physicians_At_3Hours_After[i] = find_num_of_doctors_at_arrival((df['FIRST_CONTACT_DT'][i].hour + 3)%24, df_phys,find_weekday(df['FIRST_CONTACT_DT'][i]))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"JXHraqTqvL_D"},"source":["## Connecting Visit with Lab and DI\n","\n","---\n","Number of lab/DI per patient and the amount of time spend on a lab/DI for a patient\n"]},{"cell_type":"code","metadata":{"id":"k_hCbth8advI"},"source":["#Helper Function\n","def date_list_within_range_other(i,n):\n","  cut = 50\n","  if ((i - cut) <= 0):\n","    start = 0\n","  else:\n","    start = i - cut\n","  \n","  \n","  if ((i+cut) >= n):\n","    end = n\n","  else:\n","    end = i + cut\n","  return start, end"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pgOOr2xcZv3p"},"source":["#Declaration of all null result variables\n","Number_of_labs_for_patient = [0]*len(df)\n","Number_of_DI_for_patient = [0]*len(df)\n","Time_of_labs_for_patient = [0]*len(df)\n","Time_of_labs_for_patient = [0]*len(df)\n","Time_of_DI_for_patient = [0]*len(df)\n","Labs_Ordered_Hour_Before_Arrival = [0]*len(df)\n","Labs_Ordered_Hour_After_Arrival = [0]*len(df)\n","DIs_Ordered_Hour_Before_Arrival = [0]*len(df)\n","DIs_Ordered_Hour_After_Arrival = [0]*len(df)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"heuH8wUXa-oP"},"source":["### Number of Labs and DIs per patient"]},{"cell_type":"code","metadata":{"id":"IWz69WgeZqIw"},"source":["#Helper Function\n","def make_dictionary_for_count(data):\n","  values = data['VISIT_ID'].value_counts().keys().tolist()\n","  counts = data['VISIT_ID'].value_counts().tolist()\n","  return dict(zip(values, counts))\n","\n","\n","labs_dict = make_dictionary_for_count(df_labs)\n","DI_dict = make_dictionary_for_count(df_DI)\n","\n","def dict_lookup(patient_sequence, labs_dict):\n","  try: \n","    res = labs_dict[patient_sequence]\n","  except KeyError: \n","    res = 0\n","  return res\n","\n","\n","for i in range(0, len(df)):\n","  Number_of_labs_for_patient[i] = dict_lookup(df['DATA_SEQU'][i], labs_dict)\n","  Number_of_DI_for_patient[i] = dict_lookup(df['DATA_SEQU'][i], DI_dict)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"UOYmnsw2dQdb"},"source":["def find_labs_DI_before_hour_arrival_time(patient_arrival_time, labs_DI, start, end):\n","  result = 0\n","  for i in range(start, end):\n","      diff = (patient_arrival_time - labs_DI['ORD_REQ_DT'][i]).total_seconds() /60\n","      if diff > 0 and diff < 60:\n","        result = result+1\n","  return result\n","\n","def find_labs_DI_after_hour_arrival_time(patient_arrival_time, labs_DI, start, end):\n","  result = 0\n","  for i in range(start, end):\n","      diff = (patient_arrival_time - labs_DI['ORD_REQ_DT'][i]).total_seconds() /60\n","      if diff < 0 and diff > -60:\n","        result = result+1\n","  return result"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Md7ZRPYZdeyG"},"source":["for i in range(0, len(df)):\n","  start_l, end_l = date_list_within_range_other(i, len(df_labs))\n","  Labs_Ordered_Hour_Before_Arrival[i] = find_labs_DI_before_hour_arrival_time(df['FIRST_CONTACT_DT'][i], df_labs[start_l:end_l],start_l, end_l)\n","  Labs_Ordered_Hour_After_Arrival[i] = find_labs_DI_after_hour_arrival_time(df['FIRST_CONTACT_DT'][i], df_labs[start_l:end_l],start_l, end_l)\n","\n","for i in range(0, len(df)):\n","  start_d, end_d = date_list_within_range_other(i, len(df_DI))\n","  DIs_Ordered_Hour_Before_Arrival[i] = find_labs_DI_before_hour_arrival_time(df['FIRST_CONTACT_DT'][i], df_DI[start_d:end_d],start_d, end_d)\n","  DIs_Ordered_Hour_After_Arrival[i] = find_labs_DI_after_hour_arrival_time(df['FIRST_CONTACT_DT'][i], df_DI[start_d:end_d],start_d, end_d)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6MkKKERniTwK"},"source":["#Helper Function\n","def create_dict_for_time_delta(data):\n","  grouped = data.groupby('VISIT_ID', as_index=False).sum()\n","  d = grouped.set_index('VISIT_ID').transpose().to_dict()\n","  return d\n","\n","\n","def dict_lookup_array(patient_seq, dict): \n","  try:\n","    res = dict[patient_seq]['TIME_DELTA']\n","  except KeyError:\n","    res = 0\n","  return res\n","\n","labs_with_time = create_dict_for_time_delta(df_labs)\n","di_with_time = create_dict_for_time_delta(df_DI)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"IJdtnrLNkI5e"},"source":["for i in range(0, len(df)):\n","  Time_of_labs_for_patient[i] = dict_lookup_array(df['DATA_SEQU'][i], labs_with_time)\n","\n","for i in range(0, len(df)):\n","  Time_of_DI_for_patient[i] = dict_lookup_array(df['DATA_SEQU'][i], di_with_time)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zWcFopuqkXRn"},"source":["## What time of day was the patient's first contact?"]},{"cell_type":"code","metadata":{"id":"Bec8nWm_lB4W"},"source":["def time_of_day_category(patient_first_contact):\n","  patient_first_contact = patient_first_contact.time()\n","  midnight = time(0,0,0)\n","  morning_dawn = time(6,0,0)\n","  noon = time(12, 0, 0)\n","  evening = time(18,0,0)\n","  if patient_first_contact > midnight and patient_first_contact < morning_dawn:\n","    return \"Morning Dusk\"\n","  elif patient_first_contact < noon and patient_first_contact > morning_dawn:\n","    return \"Morning Dusk\"\n","  elif patient_first_contact < evening and patient_first_contact > noon:\n","    return \"Midday\"\n","  else: \n","    return \"Night/Evening\"\n","\n","time_of_day_of_arrival = [0]*len(df)\n","\n","for i in range(0, len(df)):\n","    time_of_day_of_arrival[i] = time_of_day_category(df['FIRST_CONTACT_DT'][i])"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"hyqrUYSpUPd1"},"source":["## Extracting Response Variable: Benchmark if They Meet Time to Physician\n","\n"]},{"cell_type":"code","metadata":{"id":"KiMhUGr0UOiB"},"source":[" Waiting_Time_Benchmark = [0]*len(df)\n","\n","def find_waiting_time_benchmark(patient_num, time_col, ctas):\n","  patient_ctas = ctas[patient_num]\n","  total_time_to_phys = time_col[patient_num] \n","  result = 0\n","  if (patient_ctas == 1):\n","    if (total_time_to_phys < 5):\n","      result = 1\n","  elif (patient_ctas == 2):\n","    if (total_time_to_phys <= 15):\n","      result = 1\n","  elif (patient_ctas == 3):\n","    if (total_time_to_phys <= 30):\n","      result = 1\n","  elif (patient_ctas == 4):\n","    if (total_time_to_phys <= 60):\n","      result = 1\n","  elif (patient_ctas == 5):\n","    if (total_time_to_phys <= 120):\n","      result = 1\n","  return result\n","\n","\n","for i in range(0, len(df)):\n","  Waiting_Time_Benchmark[i] = find_waiting_time_benchmark(i, tot_time_phys_c, ctas)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"yY2Ut9RaHHyN"},"source":["## Extracting Response Variable: Benchmark if They Meet Total Wait Time"]},{"cell_type":"code","metadata":{"id":"xdPyF4FbHQMn"},"source":["Total_Time_Benchmark = [0]*len(df)\n","\n","def find_total_time_benchmark(patient_num, time_col, admit_col, ctas):\n","  patient_ctas = ctas[patient_num]\n","  total_wait = time_col[patient_num] \n","  admit_decision = admit_col[patient_num]\n","  result = 0\n","  if (admit_decision == 0):\n","    if (patient_ctas == 1 or \n","        patient_ctas == 2 or \n","        patient_ctas == 3):\n","      if (total_wait <= 240):\n","        result = 1\n","    elif (patient_ctas == 4 or \n","          patient_ctas == 5):\n","      if (total_wait <= 120):\n","        result = 1\n","  elif (admit_decision == 1):\n","     if (total_wait <= 480):\n","       result = 1\n","  return result\n","\n","\n","for i in range(0, len(df)):\n","  Total_Time_Benchmark[i] = find_total_time_benchmark(i, tot_time_last_c, df['DISP_GROUP_Admitted'], ctas)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SvDOeEbRUKX3"},"source":["## Putting All the Variables Together"]},{"cell_type":"code","metadata":{"id":"CADqn6y7BLd-"},"source":["# already done! - jana\n","\n","# df['Number_of_Patients_In_Waiting_Before'] = Number_of_Patients_In_Waiting_Before\n","# df['Number_of_Patients_In_Waiting_Before_CTAS'] = Number_of_Patients_In_Waiting_Before_CTAS\n","# df['Number_of_Patients_In_Waiting_After'] = Number_of_Patients_In_Waiting_After\n","# df['Number_of_Patients_In_Waiting_After_CTAS'] = Number_of_Patients_In_Waiting_After_CTAS\n","# df['Number_of_Patient_Treated_After_Treatment_Before'] = Number_of_Patient_Treated_After_Treatment_Before\n","# df['Number_of_Patient_Treated_After_Treatment_Before_CTAS'] = Number_of_Patient_Treated_After_Treatment_Before_CTAS\n","\n","# df['Number_of_Patients_Waiting_For_Discharge'] = Number_of_Patients_Waiting_For_Discharge\n","# df['Number_of_Patients_Waiting_For_Discharge_CTAS'] = Number_of_Patients_Waiting_For_Discharge_CTAS\n","\n","# df['Number_of_Patients_Waiting_For_Admission'] = Number_of_Patients_Waiting_For_Admission\n","# df['Number_of_Patients_Waiting_For_Admission_CTAS'] = Number_of_Patients_Waiting_For_Admission_CTAS\n","\n","# df['tot_time_init_loc'] = tot_time_init_loc\n","# df['tot_time_init_treat_loc'] = tot_time_init_treat_loc\n","# df['tot_time_phys'] = tot_time_phys\n","# df['tot_time_consult'] = tot_time_consult\n","# df['tot_time_bed'] = tot_time_bed\n","# df['tot_time_last'] = tot_time_last\n","\n","# df['tot_time_init_treat_loc_c'] = tot_time_init_treat_loc_c\n","# df['tot_time_phys_c'] = tot_time_phys_c\n","# df['tot_time_consult_c'] = tot_time_consult_c\n","# df['tot_time_bed_c'] = tot_time_bed_c\n","# df['tot_time_last_c'] = tot_time_last_c\n","# df['tot_time_for_treatment_c'] = tot_time_for_treatment_c\n","\n","df['Number_of_Physicians_At_Arrival'] = Number_of_Physicians_At_Arrival\n","df['Number_of_Physicians_At_3Hours_After'] = Number_of_Physicians_At_3Hours_After\n","df['Number_of_Physicians_At_2Hours_After'] = Number_of_Physicians_At_2Hours_After\n","df['Number_of_Physicians_At_1Hours_After'] = Number_of_Physicians_At_1Hours_After\n","df['Number_of_Physicians_At_3Hours_Before'] = Number_of_Physicians_At_3Hours_Before\n","df['Number_of_Physicians_At_2Hours_Before'] = Number_of_Physicians_At_2Hours_Before\n","df['Number_of_Physicians_At_1Hours_Before'] = Number_of_Physicians_At_1Hours_Before\n","\n","df['Number_of_labs_for_patient'] = Number_of_labs_for_patient\n","df['Number_of_DI_for_patient'] = Number_of_DI_for_patient\n","df['Time_of_labs_for_patient'] = Time_of_labs_for_patient\n","df['Time_of_labs_for_patient'] = Time_of_labs_for_patient\n","df['Time_of_DI_for_patient'] = Time_of_DI_for_patient\n","\n","df['Labs_Ordered_Hour_Before_Arrival'] = Labs_Ordered_Hour_Before_Arrival\n","df['Labs_Ordered_Hour_After_Arrival'] = Labs_Ordered_Hour_After_Arrival\n","df['DIs_Ordered_Hour_Before_Arrival'] = DIs_Ordered_Hour_Before_Arrival\n","df['DIs_Ordered_Hour_After_Arrival'] = DIs_Ordered_Hour_After_Arrival\n","\n","df['time_of_day_of_arrival'] = time_of_day_of_arrival\n","\n","\n","###################################################################\n","############ Remaining Variables ##################################\n","##### I have exported the following as well ####################### \n","df['Waiting_Time_Benchmark'] = Waiting_Time_Benchmark\n","df['Total_Time_Benchmark'] = Total_Time_Benchmark"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"hBfifKama6Ud"},"source":["## Making Extracted Categorical Variables Into Binary Variables"]},{"cell_type":"code","metadata":{"id":"ENtoDHWYabLS"},"source":["df =  pd.get_dummies(df, columns=['time_of_day_of_arrival'])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"RK1Xeo-wlKyl"},"source":["df.to_excel(\"/drive/My Drive/df_sim_with_benchmark.xlsx\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9fgfMwf7bZQD"},"source":["## Exporting the Dataframe Into an xlsx"]},{"cell_type":"code","metadata":{"id":"xMA4AZ5bFtV6"},"source":["df.to_excel(\"/drive/My Drive/df_augmented.xlsx\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"IamktZJpULWR"},"source":["## Extract Age Group"]},{"cell_type":"code","metadata":{"id":"jiNdVdRdUKpc"},"source":["age_group = [0]*len(df)\n","\n","bins = [0,10,20,30,40,50,60,70,80,90,100,150]\n","labels = ['1-10', '11-20', '21-30', '31-40', '41-50', '51-60', '61-70', '71-80', '81-90', '91-100', '>100']\n","a = pd.cut(df['PTN_AGE'], bins=bins, labels=labels, right=False)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NQYewi8FViXH"},"source":["pd.get_dummies(pd.DataFrame(a), columns=['PTN_AGE']).to_excel(\"/drive/My Drive/age_cat.xlsx\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xJ8llo0uYksE"},"source":["## Extract Time to Decision Benchmark"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nRDxJg30YjZW","executionInfo":{"elapsed":415,"status":"ok","timestamp":1617095201917,"user":{"displayName":"Jana Osea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GinlnOlxUgArsya4p00riDGuYwkslYg9eB3cAOHzg=s64","userId":"11480627999511648530"},"user_tz":360},"outputId":"ee69a423-27ca-4e98-f722-f3be9d2382e1"},"source":["time.dtypes"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tot_time_init_loc            float64\n","tot_time_init_treat_loc      float64\n","tot_time_phys                float64\n","tot_time_consult             float64\n","tot_time_bed                 float64\n","tot_time_last                float64\n","tot_time_init_treat_loc_c    float64\n","tot_time_phys_c              float64\n","tot_time_consult_c           float64\n","tot_time_bed_c               float64\n","tot_time_last_c              float64\n","tot_time_for_treatment_c     float64\n","dtype: object"]},"metadata":{"tags":[]},"execution_count":27}]}]}