dac-national-dataset-eda.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceType":"datasetVersion","sourceId":8585699,"datasetId":5069291,"databundleVersionId":8731831}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Imports","metadata":{}},{"cell_type":"code","source":"import time\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport warnings\n\nwarnings.filterwarnings(\"ignore\", category=UserWarning, module=\"matplotlib\")","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:00:41.628688Z","iopub.execute_input":"2024-06-04T08:00:41.629061Z","iopub.status.idle":"2024-06-04T08:00:44.268616Z","shell.execute_reply.started":"2024-06-04T08:00:41.629031Z","shell.execute_reply":"2024-06-04T08:00:44.267424Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"# Constant","metadata":{}},{"cell_type":"code","source":"DATASET_PATH = '/kaggle/input/zocdoc/DAC_NationalDownloadableFile.csv'\n\n# Dictionary of doctors\ndict_drs = {\n    \"MD\": \"Doctor of Medicine\",\n    \"DO\": \"Doctor of Osteopathic Medicine\",\n    \"DC\": \"Doctor of Chiropractic\",\n    \"OD\": \"Doctor of Optometry\",\n    \"DPM\": \"Doctor of Podiatric Medicine\",\n    \"DDS\": \"Doctor of Dental Surgery\",\n    \"DDM\": \"Doctor of Dental Medicine\",\n    \"PSY\": \"Psychologist\"\n}\n\n# Dictionary of nurses\ndict_nurse = {\n    \"NP\": \"Nurse Practitioner\",\n    \"CNA\": \"Certified Nursing Assistant\",\n    \"CNS\": \"Clinical Nurse Specialist\",\n    \"CNM\": \"Certified Nurse Midwife\"\n}\n\n# Dictionary of assistants\ndict_assistant = {\n    \"PA\": \"Physician Assistant\",\n    \"AA\": \"Anesthesiologist Assistant\"\n}\n\n# Dictionary of specialities\ndr_specialities = {\n    'cardiologist': 'cardio',\n    'chiropractor': 'chiro',\n    'dentist': 'dent',\n    'dermatologist': 'derma',\n    'dietitian': 'diet',\n    'ent': 'ear',\n    'gastroenterologist': 'gas',\n    'neurosurgeon': 'neur',\n    'obgyn': 'ob|gyn',\n    'oncologist': 'onco',\n    'optometrist': 'opto',\n    'orthopedic': 'ortho',\n    'pain_management': 'pain',\n    'phy_therapist': 'phy',\n    'podiatrist': 'podia',\n    'pcd': 'primary care|pcd',\n    'psychiatrist': 'psy',\n    'rheumatologist': 'rhe',\n    'sports_medicine': 'sport',\n    'surgeon': 'surg',\n#     'orthopedic_surgeon': '',\n}\n\nspeciality_name = []\nfor (key,value) in dr_specialities.items():\n    speciality_name.append(key)","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:00:44.270806Z","iopub.execute_input":"2024-06-04T08:00:44.271264Z","iopub.status.idle":"2024-06-04T08:00:44.279781Z","shell.execute_reply.started":"2024-06-04T08:00:44.271232Z","shell.execute_reply":"2024-06-04T08:00:44.278553Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"# Code","metadata":{}},{"cell_type":"markdown","source":"### Step1: Load original national dataset","metadata":{}},{"cell_type":"code","source":"%%time\ndf = pd.read_csv(DATASET_PATH, low_memory=False)","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:00:44.281415Z","iopub.execute_input":"2024-06-04T08:00:44.282008Z","iopub.status.idle":"2024-06-04T08:01:13.279787Z","shell.execute_reply.started":"2024-06-04T08:00:44.281965Z","shell.execute_reply":"2024-06-04T08:01:13.278442Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"CPU times: user 20.3 s, sys: 4.02 s, total: 24.3 s\nWall time: 29 s\n","output_type":"stream"}]},{"cell_type":"code","source":"df.head(4)","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:01:13.283039Z","iopub.execute_input":"2024-06-04T08:01:13.283503Z","iopub.status.idle":"2024-06-04T08:01:13.331295Z","shell.execute_reply.started":"2024-06-04T08:01:13.283462Z","shell.execute_reply":"2024-06-04T08:01:13.329998Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"          NPI  Ind_PAC_ID      Ind_enrl_ID Provider Last Name  \\\n0  1790868909    42100042  I20100809000798           HAGHIGHI   \n1  1114033073    42100109  I20040319001256              JONES   \n2  1508823618    42100117  I20050426000780            GRIFFIN   \n3  1508823618    42100117  I20050426000780            GRIFFIN   \n\n  Provider First Name Provider Middle Name suff gndr Cred  \\\n0               BIJAN                  NaN  NaN    M  NaN   \n1               TAMMY                   JO  NaN    F  CNA   \n2               DAVID                  NaN  NaN    M   MD   \n3               DAVID                  NaN  NaN    M   MD   \n\n                                             Med_sch  ...  \\\n0  UNIVERSITY OF SOUTHERN CALIFORNIA KECK SCHOOL ...  ...   \n1                                              OTHER  ...   \n2           VANDERBILT UNIVERSITY SCHOOL OF MEDICINE  ...   \n3           VANDERBILT UNIVERSITY SCHOOL OF MEDICINE  ...   \n\n               adr_ln_1   adr_ln_2 ln_2_sprs      City/Town State   ZIP Code  \\\n0             1 HOAG DR        NaN       NaN  NEWPORT BEACH    CA  926634162   \n1   327 MEDICAL PARK DR        NaN       NaN     BRIDGEPORT    WV  263309006   \n2    1 SAINT FRANCIS DR        NaN       NaN     GREENVILLE    SC  296013955   \n3  317 SAINT FRANCIS DR  SUITE 220       NaN     GREENVILLE    SC  296013976   \n\n  Telephone Number ind_assgn grp_assgn                    adrs_id  \n0     9.498911e+09         Y         Y  CA926634162NE1XXXXDRXX300  \n1     6.813423e+09         Y         Y  WV263309006BR327XXDRXX400  \n2     5.138539e+09         Y         Y  SC296013955GR1XXXXDRXX400  \n3     8.642552e+09         Y         Y  SC296013976GR317XXDRXX402  \n\n[4 rows x 31 columns]","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>NPI</th>\n      <th>Ind_PAC_ID</th>\n      <th>Ind_enrl_ID</th>\n      <th>Provider Last Name</th>\n      <th>Provider First Name</th>\n      <th>Provider Middle Name</th>\n      <th>suff</th>\n      <th>gndr</th>\n      <th>Cred</th>\n      <th>Med_sch</th>\n      <th>...</th>\n      <th>adr_ln_1</th>\n      <th>adr_ln_2</th>\n      <th>ln_2_sprs</th>\n      <th>City/Town</th>\n      <th>State</th>\n      <th>ZIP Code</th>\n      <th>Telephone Number</th>\n      <th>ind_assgn</th>\n      <th>grp_assgn</th>\n      <th>adrs_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1790868909</td>\n      <td>42100042</td>\n      <td>I20100809000798</td>\n      <td>HAGHIGHI</td>\n      <td>BIJAN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>M</td>\n      <td>NaN</td>\n      <td>UNIVERSITY OF SOUTHERN CALIFORNIA KECK SCHOOL ...</td>\n      <td>...</td>\n      <td>1 HOAG DR</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NEWPORT BEACH</td>\n      <td>CA</td>\n      <td>926634162</td>\n      <td>9.498911e+09</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>CA926634162NE1XXXXDRXX300</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1114033073</td>\n      <td>42100109</td>\n      <td>I20040319001256</td>\n      <td>JONES</td>\n      <td>TAMMY</td>\n      <td>JO</td>\n      <td>NaN</td>\n      <td>F</td>\n      <td>CNA</td>\n      <td>OTHER</td>\n      <td>...</td>\n      <td>327 MEDICAL PARK DR</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>BRIDGEPORT</td>\n      <td>WV</td>\n      <td>263309006</td>\n      <td>6.813423e+09</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>WV263309006BR327XXDRXX400</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1508823618</td>\n      <td>42100117</td>\n      <td>I20050426000780</td>\n      <td>GRIFFIN</td>\n      <td>DAVID</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>M</td>\n      <td>MD</td>\n      <td>VANDERBILT UNIVERSITY SCHOOL OF MEDICINE</td>\n      <td>...</td>\n      <td>1 SAINT FRANCIS DR</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>GREENVILLE</td>\n      <td>SC</td>\n      <td>296013955</td>\n      <td>5.138539e+09</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>SC296013955GR1XXXXDRXX400</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1508823618</td>\n      <td>42100117</td>\n      <td>I20050426000780</td>\n      <td>GRIFFIN</td>\n      <td>DAVID</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>M</td>\n      <td>MD</td>\n      <td>VANDERBILT UNIVERSITY SCHOOL OF MEDICINE</td>\n      <td>...</td>\n      <td>317 SAINT FRANCIS DR</td>\n      <td>SUITE 220</td>\n      <td>NaN</td>\n      <td>GREENVILLE</td>\n      <td>SC</td>\n      <td>296013976</td>\n      <td>8.642552e+09</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>SC296013976GR317XXDRXX402</td>\n    </tr>\n  </tbody>\n</table>\n<p>4 rows × 31 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:01:13.332545Z","iopub.execute_input":"2024-06-04T08:01:13.332975Z","iopub.status.idle":"2024-06-04T08:01:13.360554Z","shell.execute_reply.started":"2024-06-04T08:01:13.332934Z","shell.execute_reply":"2024-06-04T08:01:13.359287Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2572154 entries, 0 to 2572153\nData columns (total 31 columns):\n #   Column                Dtype  \n---  ------                -----  \n 0   NPI                   int64  \n 1   Ind_PAC_ID            int64  \n 2   Ind_enrl_ID           object \n 3   Provider Last Name    object \n 4   Provider First Name   object \n 5   Provider Middle Name  object \n 6   suff                  object \n 7   gndr                  object \n 8   Cred                  object \n 9   Med_sch               object \n 10  Grd_yr                float64\n 11  pri_spec              object \n 12  sec_spec_1            object \n 13  sec_spec_2            object \n 14  sec_spec_3            object \n 15  sec_spec_4            object \n 16  sec_spec_all          object \n 17  Telehlth              object \n 18  Facility Name         object \n 19  org_pac_id            float64\n 20  num_org_mem           float64\n 21  adr_ln_1              object \n 22  adr_ln_2              object \n 23  ln_2_sprs             object \n 24  City/Town             object \n 25  State                 object \n 26  ZIP Code              object \n 27  Telephone Number      float64\n 28  ind_assgn             object \n 29  grp_assgn             object \n 30  adrs_id               object \ndtypes: float64(4), int64(2), object(25)\nmemory usage: 608.3+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"df['Cred'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:01:13.362214Z","iopub.execute_input":"2024-06-04T08:01:13.362659Z","iopub.status.idle":"2024-06-04T08:01:13.446327Z","shell.execute_reply.started":"2024-06-04T08:01:13.362620Z","shell.execute_reply":"2024-06-04T08:01:13.445139Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"Cred\nMD     401558\nPA      38702\nDO      24746\nNP      24595\nCNA     17890\nDC      12660\nOD      11942\nPT      11915\nDPM      8175\nCSW      7153\nCP       5816\nOT       1284\nAU       1123\nCNS       883\nMNT       778\nCNM       774\nAA        435\nDDS       363\nDDM       178\nPSY       112\nSCW         2\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### Step2: Make new dataset using the national dataset attributes","metadata":{}},{"cell_type":"code","source":"%%time\nnew_df = pd.DataFrame({\n    'enrl_id': df['Ind_enrl_ID'], # Extra\n    'name': df['Provider First Name'] + ' ' + df['Provider Middle Name'].fillna('') + ' ' + df['Provider Last Name'],\n    'job_title': df['pri_spec'],\n    'rating': np.nan,\n    'facility_name': df['Facility Name'],\n    'street_address': df['adr_ln_1'].fillna('') + ' ' + df['adr_ln_2'].fillna(''),\n    'address_locality': df['City/Town'],\n    'region': df['State'],\n    'postal_code': df['ZIP Code'],\n    'is_dr': 0,\n    'is_nurse': 0,\n    'is_assistant': 0,\n    'cred': df['Cred'],\n})","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:04:57.156716Z","iopub.execute_input":"2024-06-04T08:04:57.157077Z","iopub.status.idle":"2024-06-04T08:05:02.819570Z","shell.execute_reply.started":"2024-06-04T08:04:57.157050Z","shell.execute_reply":"2024-06-04T08:05:02.818241Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"CPU times: user 4.57 s, sys: 1.09 s, total: 5.67 s\nWall time: 5.66 s\n","output_type":"stream"}]},{"cell_type":"code","source":"new_df.head(3)","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:05:04.791585Z","iopub.execute_input":"2024-06-04T08:05:04.791998Z","iopub.status.idle":"2024-06-04T08:05:04.808276Z","shell.execute_reply.started":"2024-06-04T08:05:04.791967Z","shell.execute_reply":"2024-06-04T08:05:04.807103Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"           enrl_id             name  \\\n0  I20100809000798  BIJAN  HAGHIGHI   \n1  I20040319001256   TAMMY JO JONES   \n2  I20050426000780   DAVID  GRIFFIN   \n\n                                       job_title  rating  \\\n0                                      PATHOLOGY     NaN   \n1  CERTIFIED REGISTERED NURSE ANESTHETIST (CRNA)     NaN   \n2                         GYNECOLOGICAL ONCOLOGY     NaN   \n\n                                       facility_name        street_address  \\\n0         NEWPORT HARBOR PATHOLOGY MEDICAL GROUP INC            1 HOAG DR    \n1                         UNITED HOSPITAL CENTER INC  327 MEDICAL PARK DR    \n2  BON SECOURS MEDICAL GROUP GREENVILLE SPECIALTY...   1 SAINT FRANCIS DR    \n\n  address_locality region postal_code  is_dr  is_nurse  is_assistant cred  \n0    NEWPORT BEACH     CA   926634162      0         0             0  NaN  \n1       BRIDGEPORT     WV   263309006      0         0             0  CNA  \n2       GREENVILLE     SC   296013955      0         0             0   MD  ","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>enrl_id</th>\n      <th>name</th>\n      <th>job_title</th>\n      <th>rating</th>\n      <th>facility_name</th>\n      <th>street_address</th>\n      <th>address_locality</th>\n      <th>region</th>\n      <th>postal_code</th>\n      <th>is_dr</th>\n      <th>is_nurse</th>\n      <th>is_assistant</th>\n      <th>cred</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>I20100809000798</td>\n      <td>BIJAN  HAGHIGHI</td>\n      <td>PATHOLOGY</td>\n      <td>NaN</td>\n      <td>NEWPORT HARBOR PATHOLOGY MEDICAL GROUP INC</td>\n      <td>1 HOAG DR</td>\n      <td>NEWPORT BEACH</td>\n      <td>CA</td>\n      <td>926634162</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>I20040319001256</td>\n      <td>TAMMY JO JONES</td>\n      <td>CERTIFIED REGISTERED NURSE ANESTHETIST (CRNA)</td>\n      <td>NaN</td>\n      <td>UNITED HOSPITAL CENTER INC</td>\n      <td>327 MEDICAL PARK DR</td>\n      <td>BRIDGEPORT</td>\n      <td>WV</td>\n      <td>263309006</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>CNA</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>I20050426000780</td>\n      <td>DAVID  GRIFFIN</td>\n      <td>GYNECOLOGICAL ONCOLOGY</td>\n      <td>NaN</td>\n      <td>BON SECOURS MEDICAL GROUP GREENVILLE SPECIALTY...</td>\n      <td>1 SAINT FRANCIS DR</td>\n      <td>GREENVILLE</td>\n      <td>SC</td>\n      <td>296013955</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>MD</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### Step3: Impute values of 'is_dr', 'is_nurse', 'is_assistant'","metadata":{}},{"cell_type":"code","source":"%%time\nfor index, row in new_df.iterrows():\n    credential = row['cred']\n\n    if credential in dict_drs:\n        new_df.at[index, 'is_dr'] = 1\n    if credential in dict_nurse:\n        new_df.at[index, 'is_nurse'] = 1\n    if credential in dict_assistant:\n        new_df.at[index, 'is_assistant'] = 1","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:05:11.688499Z","iopub.execute_input":"2024-06-04T08:05:11.688952Z","iopub.status.idle":"2024-06-04T08:07:52.885201Z","shell.execute_reply.started":"2024-06-04T08:05:11.688919Z","shell.execute_reply":"2024-06-04T08:07:52.883981Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":"CPU times: user 2min 40s, sys: 281 ms, total: 2min 41s\nWall time: 2min 41s\n","output_type":"stream"}]},{"cell_type":"code","source":"new_df.head(3)","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:07:58.114813Z","iopub.execute_input":"2024-06-04T08:07:58.115191Z","iopub.status.idle":"2024-06-04T08:07:58.133135Z","shell.execute_reply.started":"2024-06-04T08:07:58.115160Z","shell.execute_reply":"2024-06-04T08:07:58.131914Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"           enrl_id             name  \\\n0  I20100809000798  BIJAN  HAGHIGHI   \n1  I20040319001256   TAMMY JO JONES   \n2  I20050426000780   DAVID  GRIFFIN   \n\n                                       job_title  rating  \\\n0                                      PATHOLOGY     NaN   \n1  CERTIFIED REGISTERED NURSE ANESTHETIST (CRNA)     NaN   \n2                         GYNECOLOGICAL ONCOLOGY     NaN   \n\n                                       facility_name        street_address  \\\n0         NEWPORT HARBOR PATHOLOGY MEDICAL GROUP INC            1 HOAG DR    \n1                         UNITED HOSPITAL CENTER INC  327 MEDICAL PARK DR    \n2  BON SECOURS MEDICAL GROUP GREENVILLE SPECIALTY...   1 SAINT FRANCIS DR    \n\n  address_locality region postal_code  is_dr  is_nurse  is_assistant cred  \n0    NEWPORT BEACH     CA   926634162      0         0             0  NaN  \n1       BRIDGEPORT     WV   263309006      0         1             0  CNA  \n2       GREENVILLE     SC   296013955      1         0             0   MD  ","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>enrl_id</th>\n      <th>name</th>\n      <th>job_title</th>\n      <th>rating</th>\n      <th>facility_name</th>\n      <th>street_address</th>\n      <th>address_locality</th>\n      <th>region</th>\n      <th>postal_code</th>\n      <th>is_dr</th>\n      <th>is_nurse</th>\n      <th>is_assistant</th>\n      <th>cred</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>I20100809000798</td>\n      <td>BIJAN  HAGHIGHI</td>\n      <td>PATHOLOGY</td>\n      <td>NaN</td>\n      <td>NEWPORT HARBOR PATHOLOGY MEDICAL GROUP INC</td>\n      <td>1 HOAG DR</td>\n      <td>NEWPORT BEACH</td>\n      <td>CA</td>\n      <td>926634162</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>I20040319001256</td>\n      <td>TAMMY JO JONES</td>\n      <td>CERTIFIED REGISTERED NURSE ANESTHETIST (CRNA)</td>\n      <td>NaN</td>\n      <td>UNITED HOSPITAL CENTER INC</td>\n      <td>327 MEDICAL PARK DR</td>\n      <td>BRIDGEPORT</td>\n      <td>WV</td>\n      <td>263309006</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>CNA</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>I20050426000780</td>\n      <td>DAVID  GRIFFIN</td>\n      <td>GYNECOLOGICAL ONCOLOGY</td>\n      <td>NaN</td>\n      <td>BON SECOURS MEDICAL GROUP GREENVILLE SPECIALTY...</td>\n      <td>1 SAINT FRANCIS DR</td>\n      <td>GREENVILLE</td>\n      <td>SC</td>\n      <td>296013955</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>MD</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Filter regions\nnew_df = new_df[new_df['region'].isin(['NY', 'NJ', 'PA', 'DE'])]","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:08:10.402583Z","iopub.execute_input":"2024-06-04T08:08:10.402975Z","iopub.status.idle":"2024-06-04T08:08:10.609569Z","shell.execute_reply.started":"2024-06-04T08:08:10.402942Z","shell.execute_reply":"2024-06-04T08:08:10.608262Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"new_df.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:08:10.854876Z","iopub.execute_input":"2024-06-04T08:08:10.855270Z","iopub.status.idle":"2024-06-04T08:08:11.086361Z","shell.execute_reply.started":"2024-06-04T08:08:10.855237Z","shell.execute_reply":"2024-06-04T08:08:11.084980Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 379976 entries, 36 to 2572134\nData columns (total 13 columns):\n #   Column            Non-Null Count   Dtype  \n---  ------            --------------   -----  \n 0   enrl_id           379976 non-null  object \n 1   name              379951 non-null  object \n 2   job_title         379976 non-null  object \n 3   rating            0 non-null       float64\n 4   facility_name     350946 non-null  object \n 5   street_address    379976 non-null  object \n 6   address_locality  379976 non-null  object \n 7   region            379976 non-null  object \n 8   postal_code       379976 non-null  object \n 9   is_dr             379976 non-null  int64  \n 10  is_nurse          379976 non-null  int64  \n 11  is_assistant      379976 non-null  int64  \n 12  cred              87446 non-null   object \ndtypes: float64(1), int64(3), object(9)\nmemory usage: 40.6+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"for index, row in new_df.iterrows():\n    job_title = row['job_title'].lower()  # Convert to lowercase to handle case insensitivity\n    \n    for speciality, keywords in dr_specialities.items():\n        if any(keyword in job_title for keyword in keywords.split('|')):\n            new_df.at[index, 'job_title'] = speciality\n            break","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:09:33.008079Z","iopub.execute_input":"2024-06-04T08:09:33.008464Z","iopub.status.idle":"2024-06-04T08:10:05.744341Z","shell.execute_reply.started":"2024-06-04T08:09:33.008435Z","shell.execute_reply":"2024-06-04T08:10:05.743216Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"new_df['job_title'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:10:10.333305Z","iopub.execute_input":"2024-06-04T08:10:10.333701Z","iopub.status.idle":"2024-06-04T08:10:10.364047Z","shell.execute_reply.started":"2024-06-04T08:10:10.333669Z","shell.execute_reply":"2024-06-04T08:10:10.362751Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"job_title\nphy_therapist                       74668\nNURSE PRACTITIONER                  40666\nINTERNAL MEDICINE                   26814\nDIAGNOSTIC RADIOLOGY                24681\nFAMILY PRACTICE                     17594\n                                    ...  \nMEDICAL GENETICS AND GENOMICS          28\nMEDICAL TOXICOLOGY                     12\nPERIPHERAL VASCULAR DISEASE             3\nORAL MEDICINE                           1\nORAL AND MAXILLOFACIAL PATHOLOGY        1\nName: count, Length: 62, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"(new_df['job_title'] == 'surgeon').sum()","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:10:13.048459Z","iopub.execute_input":"2024-06-04T08:10:13.048865Z","iopub.status.idle":"2024-06-04T08:10:13.085176Z","shell.execute_reply.started":"2024-06-04T08:10:13.048831Z","shell.execute_reply":"2024-06-04T08:10:13.083897Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"12519"},"metadata":{}}]},{"cell_type":"code","source":"filtered_df = new_df[new_df['job_title'].isin(speciality_name)]","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:10:14.922562Z","iopub.execute_input":"2024-06-04T08:10:14.922921Z","iopub.status.idle":"2024-06-04T08:10:14.993012Z","shell.execute_reply.started":"2024-06-04T08:10:14.922894Z","shell.execute_reply":"2024-06-04T08:10:14.991832Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"filtered_df.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:10:16.391377Z","iopub.execute_input":"2024-06-04T08:10:16.391814Z","iopub.status.idle":"2024-06-04T08:10:16.511738Z","shell.execute_reply.started":"2024-06-04T08:10:16.391776Z","shell.execute_reply":"2024-06-04T08:10:16.510654Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 175854 entries, 90 to 2572134\nData columns (total 13 columns):\n #   Column            Non-Null Count   Dtype  \n---  ------            --------------   -----  \n 0   enrl_id           175854 non-null  object \n 1   name              175850 non-null  object \n 2   job_title         175854 non-null  object \n 3   rating            0 non-null       float64\n 4   facility_name     159344 non-null  object \n 5   street_address    175854 non-null  object \n 6   address_locality  175854 non-null  object \n 7   region            175854 non-null  object \n 8   postal_code       175854 non-null  object \n 9   is_dr             175854 non-null  int64  \n 10  is_nurse          175854 non-null  int64  \n 11  is_assistant      175854 non-null  int64  \n 12  cred              40382 non-null   object \ndtypes: float64(1), int64(3), object(9)\nmemory usage: 18.8+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"filtered_df.head(3)","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:17:28.004471Z","iopub.execute_input":"2024-06-04T08:17:28.005486Z","iopub.status.idle":"2024-06-04T08:17:28.021924Z","shell.execute_reply.started":"2024-06-04T08:17:28.005449Z","shell.execute_reply":"2024-06-04T08:17:28.020432Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"             enrl_id                    name      job_title  rating  \\\n90   I20040325000440         jeffrey s kezis  dermatologist     NaN   \n91   I20040325000440         jeffrey s kezis  dermatologist     NaN   \n124  I20040204000061  alice kathleen roberts          obgyn     NaN   \n\n                        facility_name               street_address  \\\n90             hudson dermatology, pc          969 main st suite d   \n91             hudson dermatology, pc               29 fox st fl 4   \n124  regional womens health group llc  1245 highland ave suite 404   \n\n    address_locality region postal_code  is_dr  is_nurse  is_assistant cred  \n90          fishkill     NY   125241791      1         0             0   md  \n91      poughkeepsie     NY   126014732      1         0             0   md  \n124         abington     PA   190013725      1         0             0   md  ","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>enrl_id</th>\n      <th>name</th>\n      <th>job_title</th>\n      <th>rating</th>\n      <th>facility_name</th>\n      <th>street_address</th>\n      <th>address_locality</th>\n      <th>region</th>\n      <th>postal_code</th>\n      <th>is_dr</th>\n      <th>is_nurse</th>\n      <th>is_assistant</th>\n      <th>cred</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>90</th>\n      <td>I20040325000440</td>\n      <td>jeffrey s kezis</td>\n      <td>dermatologist</td>\n      <td>NaN</td>\n      <td>hudson dermatology, pc</td>\n      <td>969 main st suite d</td>\n      <td>fishkill</td>\n      <td>NY</td>\n      <td>125241791</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>md</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>I20040325000440</td>\n      <td>jeffrey s kezis</td>\n      <td>dermatologist</td>\n      <td>NaN</td>\n      <td>hudson dermatology, pc</td>\n      <td>29 fox st fl 4</td>\n      <td>poughkeepsie</td>\n      <td>NY</td>\n      <td>126014732</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>md</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>I20040204000061</td>\n      <td>alice kathleen roberts</td>\n      <td>obgyn</td>\n      <td>NaN</td>\n      <td>regional womens health group llc</td>\n      <td>1245 highland ave suite 404</td>\n      <td>abington</td>\n      <td>PA</td>\n      <td>190013725</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>md</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Lowercase the specific columns\nfor col in ['name', 'job_title', 'facility_name','street_address', 'address_locality', 'cred']:\n    filtered_df.loc[:, col] = filtered_df[col].str.lower()","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:20:34.302418Z","iopub.execute_input":"2024-06-04T08:20:34.302816Z","iopub.status.idle":"2024-06-04T08:20:34.544079Z","shell.execute_reply.started":"2024-06-04T08:20:34.302785Z","shell.execute_reply":"2024-06-04T08:20:34.542957Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"filtered_df.head(3)","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:20:36.289938Z","iopub.execute_input":"2024-06-04T08:20:36.290304Z","iopub.status.idle":"2024-06-04T08:20:36.307798Z","shell.execute_reply.started":"2024-06-04T08:20:36.290275Z","shell.execute_reply":"2024-06-04T08:20:36.306457Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"             enrl_id                    name      job_title  rating  \\\n90   I20040325000440         jeffrey s kezis  dermatologist     NaN   \n91   I20040325000440         jeffrey s kezis  dermatologist     NaN   \n124  I20040204000061  alice kathleen roberts          obgyn     NaN   \n\n                        facility_name               street_address  \\\n90             hudson dermatology, pc          969 main st suite d   \n91             hudson dermatology, pc               29 fox st fl 4   \n124  regional womens health group llc  1245 highland ave suite 404   \n\n    address_locality region postal_code  is_dr  is_nurse  is_assistant cred  \n90          fishkill     NY   125241791      1         0             0   md  \n91      poughkeepsie     NY   126014732      1         0             0   md  \n124         abington     PA   190013725      1         0             0   md  ","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>enrl_id</th>\n      <th>name</th>\n      <th>job_title</th>\n      <th>rating</th>\n      <th>facility_name</th>\n      <th>street_address</th>\n      <th>address_locality</th>\n      <th>region</th>\n      <th>postal_code</th>\n      <th>is_dr</th>\n      <th>is_nurse</th>\n      <th>is_assistant</th>\n      <th>cred</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>90</th>\n      <td>I20040325000440</td>\n      <td>jeffrey s kezis</td>\n      <td>dermatologist</td>\n      <td>NaN</td>\n      <td>hudson dermatology, pc</td>\n      <td>969 main st suite d</td>\n      <td>fishkill</td>\n      <td>NY</td>\n      <td>125241791</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>md</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>I20040325000440</td>\n      <td>jeffrey s kezis</td>\n      <td>dermatologist</td>\n      <td>NaN</td>\n      <td>hudson dermatology, pc</td>\n      <td>29 fox st fl 4</td>\n      <td>poughkeepsie</td>\n      <td>NY</td>\n      <td>126014732</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>md</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>I20040204000061</td>\n      <td>alice kathleen roberts</td>\n      <td>obgyn</td>\n      <td>NaN</td>\n      <td>regional womens health group llc</td>\n      <td>1245 highland ave suite 404</td>\n      <td>abington</td>\n      <td>PA</td>\n      <td>190013725</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>md</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Create a figure and a set of subplots\nfig, axes = plt.subplots(2, 2, figsize=(15, 15))  # 2 rows, 2 columns\n\n# Flatten the axes array for easy iteration\naxes = axes.flatten()\n\n# List of columns to plot\ncolumns = ['region', 'is_dr', 'is_nurse', 'is_assistant']\n\n# Plot histograms\nfor i, column in enumerate(columns):\n    sns.histplot(filtered_df[column], ax=axes[i])\n    axes[i].set_title(f'Histogram of {column}')\n\n# Adjust layout to prevent overlap\nplt.tight_layout()\n\n# Separate plot for 'job_title'\nplt.figure(figsize=(15, 8))\nsns.countplot(y=filtered_df['job_title'], order=filtered_df['job_title'].value_counts().index)\nplt.title('Count Plot of Job Title')\nplt.xlabel('Count')\nplt.ylabel('Specialities')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:20:43.688641Z","iopub.execute_input":"2024-06-04T08:20:43.689055Z","iopub.status.idle":"2024-06-04T08:20:46.096414Z","shell.execute_reply.started":"2024-06-04T08:20:43.689023Z","shell.execute_reply":"2024-06-04T08:20:46.095123Z"},"trusted":true},"execution_count":44,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1500 with 4 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AAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAAAAAAAlCNEBAAAAAKAEIToAALBF/t//+3/553/+5+y6667p2LFjBg8enIcffrh4vFAoZNKkSendu3c6duyYkSNH5k9/+lOTa7z00ks5/vjjU1FRkW7duuXEE0/M66+/3qTmsccey6GHHpoOHTqkb9++mTZt2nt6ufHGG7PPPvukQ4cOGTx4cObMmbNt3jQAAB96QnQAAGCzXn755Rx88MHZaaed8j//8z9ZsmRJzj///Oyyyy7FmmnTpuXiiy/OzJkz88ADD6Rz586prq7OW2+9Vaw5/vjj8/jjj2fevHm57bbbcs899+Tkk08uHm9sbMyRRx6Zfv36pb6+Pj/96U9zzjnn5Be/+EWx5r777ss3vvGNnHjiiXn00Udz9NFH5+ijj84f/vCH5rkZAAB8qLRv6QYAAIDt37//+7+nb9++ueqqq4r7BgwYUPxzoVDIhRdemIkTJ+ZLX/pSkuT//t//m169euWWW27J17/+9TzxxBOZO3duHnrooQwbNixJ8vOf/zyf//zn87Of/Sx9+vTJNddckzVr1uTKK69MWVlZPvGJT2TRokWZPn16MWy/6KKL8rnPfS6nn356kuTcc8/NvHnzcskll2TmzJnNdUsAAPiQ8El0AABgs2699dYMGzYsxxxzTHr27JlPfvKTufzyy4vHly1bloaGhowcObK4r2vXrhk+fHgWLlyYJFm4cGG6detWDNCTZOTIkWnbtm0eeOCBYs1hhx2WsrKyYk11dXWWLl2al19+uVjzznE21GwYBwAAPkhCdAAAYLOeeeaZXHbZZdl7771z++2359RTT81pp52Wq6++OknS0NCQJOnVq1eT83r16lU81tDQkJ49ezY53r59+3Tv3r1Jzcau8c4xStVsOP5uq1evTmNjY5MNAAC2lMe5AAAAm7V+/foMGzYsP/nJT5Ikn/zkJ/OHP/whM2fOzJgxY1q4u02bOnVqfvSjH7V0GwAA7KB8Eh0AANis3r17Z9CgQU32DRw4MMuXL0+SVFZWJklWrFjRpGbFihXFY5WVlVm5cmWT42vXrs1LL73UpGZj13jnGKVqNhx/t7PPPjuvvvpqcXvuuee27E0DAECE6AAAwBY4+OCDs3Tp0ib7/vjHP6Zfv35J/v5LRisrKzN//vzi8cbGxjzwwAOpqqpKklRVVeWVV15JfX19seauu+7K+vXrM3z48GLNPffck7fffrtYM2/evHz84x/PLrvsUqx55zgbajaM827l5eWpqKhosgEAwJYSogMAAJs1YcKE3H///fnJT36Sp556Ktdee21+8YtfpKamJknSpk2bjB8/Pj/+8Y9z6623ZvHixfnmN7+ZPn365Oijj07y90+uf+5zn8u4cePy4IMP5ne/+11qa2vz9a9/PX369EmSHHfccSkrK8uJJ56Yxx9/PNdff30uuuii1NXVFXv5/ve/n7lz5+b888/Pk08+mXPOOScPP/xwamtrm/2+AADQ+nkmOgAAsFmf+tSncvPNN+fss8/OlClTMmDAgFx44YU5/vjjizVnnHFGVq1alZNPPjmvvPJKDjnkkMydOzcdOnQo1lxzzTWpra3NEUcckbZt22b06NG5+OKLi8e7du2aO+64IzU1NRk6dGh69OiRSZMm5eSTTy7WfPrTn861116biRMn5gc/+EH23nvv3HLLLdl3332b52YAAPChIkQHAAC2yBe+8IV84QtfKHm8TZs2mTJlSqZMmVKypnv37rn22ms3Oc5+++2X//3f/91kzTHHHJNjjjlm0w0DAMAHwONcAAAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACghO06RD/nnHPSpk2bJts+++xTPP7WW2+lpqYmu+66a3beeeeMHj06K1asaHKN5cuXZ9SoUenUqVN69uyZ008/PWvXrm1Ss2DBghxwwAEpLy/PXnvtlVmzZr2nlxkzZqR///7p0KFDhg8fngcffHCbvGcAAAAAALYf23WIniSf+MQn8vzzzxe3e++9t3hswoQJ+c1vfpMbb7wxd999d/7617/mK1/5SvH4unXrMmrUqKxZsyb33Xdfrr766syaNSuTJk0q1ixbtiyjRo3K4YcfnkWLFmX8+PE56aSTcvvttxdrrr/++tTV1WXy5Ml55JFHsv/++6e6ujorV65snpsAAAAAAECL2O5D9Pbt26eysrK49ejRI0ny6quv5pe//GWmT5+ez372sxk6dGiuuuqq3Hfffbn//vuTJHfccUeWLFmSX/3qVxkyZEiOOuqonHvuuZkxY0bWrFmTJJk5c2YGDBiQ888/PwMHDkxtbW2++tWv5oILLij2MH369IwbNy5jx47NoEGDMnPmzHTq1ClXXnll898QAAAAAACazXYfov/pT39Knz59sscee+T444/P8uXLkyT19fV5++23M3LkyGLtPvvsk9133z0LFy5MkixcuDCDBw9Or169ijXV1dVpbGzM448/Xqx55zU21Gy4xpo1a1JfX9+kpm3bthk5cmSxppTVq1ensbGxyQYAAAAAwI5juw7Rhw8fnlmzZmXu3Lm57LLLsmzZshx66KF57bXX0tDQkLKysnTr1q3JOb169UpDQ0OSpKGhoUmAvuH4hmObqmlsbMybb76ZF198MevWrdtozYZrlDJ16tR07dq1uPXt23er7wEAAAAAAC2nfUs3sClHHXVU8c/77bdfhg8fnn79+uWGG25Ix44dW7CzLXP22Wenrq6u+LqxsVGQDgAAAACwA9muP4n+bt26dcvHPvaxPPXUU6msrMyaNWvyyiuvNKlZsWJFKisrkySVlZVZsWLFe45vOLapmoqKinTs2DE9evRIu3btNlqz4RqllJeXp6KioskGAAAAAMCOY4cK0V9//fU8/fTT6d27d4YOHZqddtop8+fPLx5funRpli9fnqqqqiRJVVVVFi9enJUrVxZr5s2bl4qKigwaNKhY885rbKjZcI2ysrIMHTq0Sc369eszf/78Yg0AAAAAAK3Tdh2i/8u//EvuvvvuPPvss7nvvvvy5S9/Oe3atcs3vvGNdO3aNSeeeGLq6ury29/+NvX19Rk7dmyqqqpy0EEHJUmOPPLIDBo0KCeccEJ+//vf5/bbb8/EiRNTU1OT8vLyJMkpp5ySZ555JmeccUaefPLJXHrppbnhhhsyYcKEYh91dXW5/PLLc/XVV+eJJ57IqaeemlWrVmXs2LEtcl8AAAAAAGge2/Uz0f/yl7/kG9/4Rv72t7/lIx/5SA455JDcf//9+chHPpIkueCCC9K2bduMHj06q1evTnV1dS699NLi+e3atcttt92WU089NVVVVencuXPGjBmTKVOmFGsGDBiQ2bNnZ8KECbnooouy22675Yorrkh1dXWx5thjj80LL7yQSZMmpaGhIUOGDMncuXPf88tGAQAAAABoXbbrEP26667b5PEOHTpkxowZmTFjRsmafv36Zc6cOZu8zogRI/Loo49usqa2tja1tbWbrAEAAAAAoHXZrh/nAgAAAAAALUmIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AACw1c4777y0adMm48ePL+576623UlNTk1133TU777xzRo8enRUrVjQ5b/ny5Rk1alQ6deqUnj175vTTT8/atWub1CxYsCAHHHBAysvLs9dee2XWrFnvGX/GjBnp379/OnTokOHDh+fBBx/cFm8TAACE6AAAwNZ56KGH8h//8R/Zb7/9muyfMGFCfvOb3+TGG2/M3Xffnb/+9a/5yle+Ujy+bt26jBo1KmvWrMl9992Xq6++OrNmzcqkSZOKNcuWLcuoUaNy+OGHZ9GiRRk/fnxOOumk3H777cWa66+/PnV1dZk8eXIeeeSR7L///qmurs7KlSu3/ZsHAOBDR4gOAABssddffz3HH398Lr/88uyyyy7F/a+++mp++ctfZvr06fnsZz+boUOH5qqrrsp9992X+++/P0lyxx13ZMmSJfnVr36VIUOG5Kijjsq5556bGTNmZM2aNUmSmTNnZsCAATn//PMzcODA1NbW5qtf/WouuOCC4ljTp0/PuHHjMnbs2AwaNCgzZ85Mp06dcuWVVzbvzQAA4ENBiA4AAGyxmpqajBo1KiNHjmyyv76+Pm+//XaT/fvss0923333LFy4MEmycOHCDB48OL169SrWVFdXp7GxMY8//nix5t3Xrq6uLl5jzZo1qa+vb1LTtm3bjBw5slgDAAAfpPYt3QAAALBjuO666/LII4/koYcees+xhoaGlJWVpVu3bk329+rVKw0NDcWadwboG45vOLapmsbGxrz55pt5+eWXs27duo3WPPnkkxvte/Xq1Vm9enXxdWNj4xa8WwAA+DufRAcAADbrueeey/e///1cc8016dChQ0u3s1WmTp2arl27Fre+ffu2dEsAAOxAhOgAAMBm1dfXZ+XKlTnggAPSvn37tG/fPnfffXcuvvjitG/fPr169cqaNWvyyiuvNDlvxYoVqaysTJJUVlZmxYoV7zm+4dimaioqKtKxY8f06NEj7dq122jNhmu829lnn51XX321uD333HPv+z4AAPDhI0QHAAA264gjjsjixYuzaNGi4jZs2LAcf/zxxT/vtNNOmT9/fvGcpUuXZvny5amqqkqSVFVVZfHixVm5cmWxZt68eamoqMigQYOKNe+8xoaaDdcoKyvL0KFDm9SsX78+8+fPL9a8W3l5eSoqKppsAACwpTwTHQAA2KwuXbpk3333bbKvc+fO2XXXXYv7TzzxxNTV1aV79+6pqKjI9773vVRVVeWggw5Kkhx55JEZNGhQTjjhhEybNi0NDQ2ZOHFiampqUl5eniQ55ZRTcskll+SMM87It7/97dx111254YYbMnv27OK4dXV1GTNmTIYNG5YDDzwwF154YVatWpWxY8c2090AAODDRIgOAAB8IC644IK0bds2o0ePzurVq1NdXZ1LL720eLxdu3a57bbbcuqpp6aqqiqdO3fOmDFjMmXKlGLNgAEDMnv27EyYMCEXXXRRdtttt1xxxRWprq4u1hx77LF54YUXMmnSpDQ0NGTIkCGZO3fue37ZKAAAfBCE6AAAwPuyYMGCJq87dOiQGTNmZMaMGSXP6devX+bMmbPJ644YMSKPPvroJmtqa2tTW1u7xb0CAMD75ZnoAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAEoToAAAAAABQghAdAAAAAABKEKIDAAAAAEAJQnQAAAAAAChBiA4AAAAAACUI0QEAAAAAoAQhOgAAAAAAlCBEBwAAAACAErbrEH3q1Kn51Kc+lS5duqRnz545+uijs3Tp0iY1I0aMSJs2bZpsp5xySpOa5cuXZ9SoUenUqVN69uyZ008/PWvXrm1Ss2DBghxwwAEpLy/PXnvtlVmzZr2nnxkzZqR///7p0KFDhg8fngcffPADf88AAAAAAGw/tusQ/e67705NTU3uv//+zJs3L2+//XaOPPLIrFq1qknduHHj8vzzzxe3adOmFY+tW7cuo0aNypo1a3Lffffl6quvzqxZszJp0qRizbJlyzJq1KgcfvjhWbRoUcaPH5+TTjopt99+e7Hm+uuvT11dXSZPnpxHHnkk+++/f6qrq7Ny5cptfyMAAAAAAGgR7Vu6gU2ZO3duk9ezZs1Kz549U19fn8MOO6y4v1OnTqmsrNzoNe64444sWbIkd955Z3r16pUhQ4bk3HPPzZlnnplzzjknZWVlmTlzZgYMGJDzzz8/STJw4MDce++9ueCCC1JdXZ0kmT59esaNG5exY8cmSWbOnJnZs2fnyiuvzFlnnbUt3j4AAAAAAC1su/4k+ru9+uqrSZLu3bs32X/NNdekR48e2XfffXP22WfnjTfeKB5buHBhBg8enF69ehX3VVdXp7GxMY8//nixZuTIkU2uWV1dnYULFyZJ1qxZk/r6+iY1bdu2zciRI4s1AAAAAAC0Ptv1J9Hfaf369Rk/fnwOPvjg7LvvvsX9xx13XPr165c+ffrksccey5lnnpmlS5fmpptuSpI0NDQ0CdCTFF83NDRssqaxsTFvvvlmXn755axbt26jNU8++WTJnlevXp3Vq1cXXzc2Nr6Pdw4AAAAAQEvZYUL0mpqa/OEPf8i9997bZP/JJ59c/PPgwYPTu3fvHHHEEXn66aez5557NnebTUydOjU/+tGPWrQHAAAAAADevx3icS61tbW57bbb8tvf/ja77bbbJmuHDx+eJHnqqaeSJJWVlVmxYkWTmg2vNzxHvVRNRUVFOnbsmB49eqRdu3YbrSn1LPYkOfvss/Pqq68Wt+eee24L3i0AAAAAANuL7TpELxQKqa2tzc0335y77rorAwYM2Ow5ixYtSpL07t07SVJVVZXFixdn5cqVxZp58+aloqIigwYNKtbMnz+/yXXmzZuXqqqqJElZWVmGDh3apGb9+vWZP39+sWZjysvLU1FR0WQDAAAAAGDHsV0/zqWmpibXXntt/vu//ztdunQpPsO8a9eu6dixY55++ulce+21+fznP59dd901jz32WCZMmJDDDjss++23X5LkyCOPzKBBg3LCCSdk2rRpaWhoyMSJE1NTU5Py8vIkySmnnJJLLrkkZ5xxRr797W/nrrvuyg033JDZs2cXe6mrq8uYMWMybNiwHHjggbnwwguzatWqjB07tvlvDAAAAAAAzWK7DtEvu+yyJMmIESOa7L/qqqvyrW99K2VlZbnzzjuLgXbfvn0zevToTJw4sVjbrl273HbbbTn11FNTVVWVzp07Z8yYMZkyZUqxZsCAAZk9e3YmTJiQiy66KLvttluuuOKKVFdXF2uOPfbYvPDCC5k0aVIaGhoyZMiQzJ079z2/bBQAAAAAgNZjuw7RC4XCJo/37ds3d99992av069fv8yZM2eTNSNGjMijjz66yZra2trU1tZudjwAAAAAAFqH7fqZ6AAAAAAA0JKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOAABs1tSpU/OpT30qXbp0Sc+ePXP00Udn6dKlTWreeuut1NTUZNddd83OO++c0aNHZ8WKFU1qli9fnlGjRqVTp07p2bNnTj/99Kxdu7ZJzYIFC3LAAQekvLw8e+21V2bNmvWefmbMmJH+/funQ4cOGT58eB588MEP/D0DAEAiRAcAALbA3XffnZqamtx///2ZN29e3n777Rx55JFZtWpVsWbChAn5zW9+kxtvvDF33313/vrXv+YrX/lK8fi6desyatSorFmzJvfdd1+uvvrqzJo1K5MmTSrWLFu2LKNGjcrhhx+eRYsWZfz48TnppJNy++23F2uuv/761NXVZfLkyXnkkUey//77p7q6OitXrmyemwEAwIdK+5ZuAAAA2P7NnTu3yetZs2alZ8+eqa+vz2GHHZZXX301v/zlL3Pttdfms5/9bJLkqquuysCBA3P//ffnoIMOyh133JElS5bkzjvvTK9evTJkyJCce+65OfPMM3POOeekrKwsM2fOzIABA3L++ecnSQYOHJh77703F1xwQaqrq5Mk06dPz7hx4zJ27NgkycyZMzN79uxceeWVOeuss5rxrgAA8GHgk+gAAMBWe/XVV5Mk3bt3T5LU19fn7bffzsiRI4s1++yzT3bfffcsXLgwSbJw4cIMHjw4vXr1KtZUV1ensbExjz/+eLHmndfYULPhGmvWrEl9fX2TmrZt22bkyJHFmndbvXp1Ghsbm2wAALClhOgAAMBWWb9+fcaPH5+DDz44++67b5KkoaEhZWVl6datW5PaXr16paGhoVjzzgB9w/ENxzZV09jYmDfffDMvvvhi1q1bt9GaDdd4t6lTp6Zr167FrW/fvu/vjQMA8KEkRAcAALZKTU1N/vCHP+S6665r6Va2yNlnn51XX321uD333HMt3RIAADsQz0QHAAC2WG1tbW677bbcc8892W233Yr7Kysrs2bNmrzyyitNPo2+YsWKVFZWFmsefPDBJtdbsWJF8diG/92w7501FRUV6dixY9q1a5d27dpttGbDNd6tvLw85eXl7+8NAwDwoeeT6AAAwGYVCoXU1tbm5ptvzl133ZUBAwY0OT506NDstNNOmT9/fnHf0qVLs3z58lRVVSVJqqqqsnjx4qxcubJYM2/evFRUVGTQoEHFmndeY0PNhmuUlZVl6NChTWrWr1+f+fPnF2sAAOCD5JPoAADAZtXU1OTaa6/Nf//3f6dLly7F54937do1HTt2TNeuXXPiiSemrq4u3bt3T0VFRb73ve+lqqoqBx10UJLkyCOPzKBBg3LCCSdk2rRpaWhoyMSJE1NTU1P8pPgpp5ySSy65JGeccUa+/e1v56677soNN9yQ2bNnF3upq6vLmDFjMmzYsBx44IG58MILs2rVqowdO7b5bwwAAK2eEB0AANisyy67LEkyYsSIJvuvuuqqfOtb30qSXHDBBWnbtm1Gjx6d1atXp7q6Opdeemmxtl27drntttty6qmnpqqqKp07d86YMWMyZcqUYs2AAQMye/bsTJgwIRdddFF22223XHHFFamuri7WHHvssXnhhRcyadKkNDQ0ZMiQIZk7d+57ftkoAAB8EIToAADAZhUKhc3WdOjQITNmzMiMGTNK1vTr1y9z5szZ5HVGjBiRRx99dJM1tbW1qa2t3WxPAADwj/JMdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACghPYt3QAAAAAAANuH5cuX58UXX2yRsXv06JHdd9+9RcbeFCE6AAAAAABZvnx59tlnYN58840WGb9jx0558skntrsgXYgOAAAAAEBefPHFvPnmGxn+7cmp6N2/WcdufP7ZPHDlj/Liiy8K0QEAAAAA2H5V9O6f7rt/vKXb2G74xaIAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOgAAAAAAFCCEB0AAAAAAEoQogMAAAAAQAlCdAAAAAAAKEGIDgAAAAAAJQjRAQAAAACgBCE6AAAAAACUIEQHAAAAAIAShOhbacaMGenfv386dOiQ4cOH58EHH2zplgAA4EPJ2hwAgOYgRN8K119/ferq6jJ58uQ88sgj2X///VNdXZ2VK1e2dGsAAPChYm0OAEBzEaJvhenTp2fcuHEZO3ZsBg0alJkzZ6ZTp0658sorW7o1AAD4ULE2BwCguQjRt9CaNWtSX1+fkSNHFve1bds2I0eOzMKFC1uwMwAA+HCxNgcAoDm1b+kGdhQvvvhi1q1bl169ejXZ36tXrzz55JMbPWf16tVZvXp18fWrr76aJGlsbNx2jb7L66+/niR56c9Ls3b1m802bpI0NixPktTX1xf7aE5t27bN+vXrm31cY3+4xv4wvmdjG9vYrXPcD/PYlZWVqaysbLbxNqwFC4VCs43Z2mzt2nx7WJcn1ua+phrb2MZuDWN/GN+zsT9cYy9dujRJy65XXn/99WZbp23p2lyIvg1NnTo1P/rRj96zv2/fvs3eS/2vzmv2MTc4+eSTW2xsAIDt1WuvvZauXbu2dBsfCtvTujyxNgcAtn8tuV75zGc+0+xjbm5tLkTfQj169Ei7du2yYsWKJvtXrFhR8pNLZ599durq6oqv169fn5deeim77rpr2rRps0373aCxsTF9+/bNc889l4qKimYZk23PvLY+5rT1Maetk3ltfVpiTguFQl577bX06dOnWcZrjbZ2bb49rMsTX0NaI3PaOpnX1sectj7mtHXantfmQvQtVFZWlqFDh2b+/Pk5+uijk/x98T1//vzU1tZu9Jzy8vKUl5c32detW7dt3OnGVVRU+KLSCpnX1sectj7mtHUyr61Pc8+pT6D/Y7Z2bb49rcsTX0NaI3PaOpnX1sectj7mtHXaHtfmQvStUFdXlzFjxmTYsGE58MADc+GFF2bVqlUZO3ZsS7cGAAAfKtbmAAA0FyH6Vjj22GPzwgsvZNKkSWloaMiQIUMyd+7c9/xCIwAAYNuyNgcAoLkI0bdSbW1tyce3bI/Ky8szefLk9/z4Kjs289r6mNPWx5y2Tua19TGnOzZrc1qaOW2dzGvrY05bH3PaOm3P89qmUCgUWroJAAAAAADYHrVt6QYAAAAAAGB7JUQHAAAAAIAShOgAAAAAAFCCEL0VmDFjRvr3758OHTpk+PDhefDBBzdZf+ONN2afffZJhw4dMnjw4MyZM6eZOmVrbM28Xn755Tn00EOzyy67ZJdddsnIkSM3+98BzW9r/65ucN1116VNmzY5+uijt22DbLWtndNXXnklNTU16d27d8rLy/Oxj33M1+Dt0NbO64UXXpiPf/zj6dixY/r27ZsJEybkrbfeaqZu2Zx77rknX/ziF9OnT5+0adMmt9xyy2bPWbBgQQ444ICUl5dnr732yqxZs7Z5n7Qe1uatj3V562Nd3jpZm7c+1uWtyw6/Li+wQ7vuuusKZWVlhSuvvLLw+OOPF8aNG1fo1q1bYcWKFRut/93vfldo165dYdq0aYUlS5YUJk6cWNhpp50KixcvbubO2ZStndfjjjuuMGPGjMKjjz5aeOKJJwrf+ta3Cl27di385S9/aebOKWVr53SDZcuWFT760Y8WDj300MKXvvSl5mmWLbK1c7p69erCsGHDCp///OcL9957b2HZsmWFBQsWFBYtWtTMnbMpWzuv11xzTaG8vLxwzTXXFJYtW1a4/fbbC7179y5MmDChmTunlDlz5hR++MMfFm666aZCksLNN9+8yfpnnnmm0KlTp0JdXV1hyZIlhZ///OeFdu3aFebOnds8DbNDszZvfazLWx/r8tbJ2rz1sS5vfXb0dbkQfQd34IEHFmpqaoqv161bV+jTp09h6tSpG63/2te+Vhg1alSTfcOHDy985zvf2aZ9snW2dl7fRdV5vAAAD+NJREFUbe3atYUuXboUrr766m3VIlvp/czp2rVrC5/+9KcLV1xxRWHMmDEW69uZrZ3Tyy67rLDHHnsU1qxZ01wt8j5s7bzW1NQUPvvZzzbZV1dXVzj44IO3aZ+8P1uyWD/jjDMKn/jEJ5rsO/bYYwvV1dXbsDNaC2vz1se6vPWxLm+drM1bH+vy1m1HXJd7nMsObM2aNamvr8/IkSOL+9q2bZuRI0dm4cKFGz1n4cKFTeqTpLq6umQ9ze/9zOu7vfHGG3n77bfTvXv3bdUmW+H9zumUKVPSs2fPnHjiic3RJlvh/czprbfemqqqqtTU1KRXr17Zd99985Of/CTr1q1rrrbZjPczr5/+9KdTX19f/NHSZ555JnPmzMnnP//5ZumZD561Eu+XtXnrY13e+liXt07W5q2PdTnJ9rdOat8io/KBePHFF7Nu3br06tWryf5evXrlySef3Og5DQ0NG61vaGjYZn2ydd7PvL7bmWeemT59+rzniw0t4/3M6b333ptf/vKXWbRoUTN0yNZ6P3P6zDPP5K677srxxx+fOXPm5Kmnnsp3v/vdvP3225k8eXJztM1mvJ95Pe644/Liiy/mkEMOSaFQyNq1a3PKKafkBz/4QXO0zDZQaq3U2NiYN998Mx07dmyhztjeWZu3PtblrY91eetkbd76WJeTbH/rcp9Eh1bmvPPOy3XXXZebb745HTp0aOl2eB9ee+21nHDCCbn88svTo0ePlm6HD8j69evTs2fP/OIXv8jQoUNz7LHH5oc//GFmzpzZ0q3xD1iwYEF+8pOf5NJLL80jjzySm266KbNnz865557b0q0B0MKsy3d81uWtl7V562Ndzrbmk+g7sB49eqRdu3ZZsWJFk/0rVqxIZWXlRs+prKzcqnqa3/uZ1w1+9rOf5bzzzsudd96Z/fbbb1u2yVbY2jl9+umn8+yzz+aLX/xicd/69euTJO3bt8/SpUuz5557btum2aT38/e0d+/e2WmnndKuXbvivoEDB6ahoSFr1qxJWVnZNu2ZzXs/8/qv//qvOeGEE3LSSSclSQYPHpxVq1bl5JNPzg9/+MO0bevzCjuaUmuliooKn0Jnk6zNWx/r8tbHurx1sjZvfazLSba/dbn/gnZgZWVlGTp0aObPn1/ct379+syfPz9VVVUbPaeqqqpJfZLMmzevZD3N7/3Ma5JMmzYt5557bubOnZthw4Y1R6tsoa2d03322SeLFy/OokWLitv/+T//J4cffngWLVqUvn37Nmf7bMT7+Xt68MEH56mnnip+45Ukf/zjH9O7d2+L9O3E+5nXN9544z0L8g3fjBUKhW3XLNuMtRLvl7V562Nd3vpYl7dO1uatj3U5yXa4TmqRX2fKB+a6664rlJeXF2bNmlVYsmRJ4eSTTy5069at0NDQUCgUCoUTTjihcNZZZxXrf/e73xXat29f+NnPflZ44oknCpMnTy7stNNOhcWLF7fUW2AjtnZezzvvvEJZWVnhP//zPwvPP/98cXvttdda6i3wLls7p+82ZsyYwpe+9KVm6pYtsbVzunz58kKXLl0KtbW1haVLlxZuu+22Qs+ePQs//vGPW+otsBFbO6+TJ08udOnSpfDrX/+68MwzzxTuuOOOwp577ln42te+1lJvgXd57bXXCo8++mjh0UcfLSQpTJ8+vfDoo48W/vznPxcKhULhrLPOKpxwwgnF+meeeabQqVOnwumnn1544oknCjNmzCi0a9euMHfu3JZ6C+xArM1bH+vy1se6vHWyNm99rMtbnx19XS5EbwV+/vOfF3bfffdCWVlZ4cADDyzcf//9xWOf+cxnCmPGjGlSf8MNNxQ+9rGPFcrKygqf+MQnCrNnz27mjtkSWzOv/fr1KyR5zzZ58uTmb5yStvbv6jtZrG+ftnZO77vvvsLw4cML5eXlhT322KPwb//2b4W1a9c2c9dsztbM69tvv10455xzCnvuuWehQ4cOhb59+xa++93vFl5++eXmb5yN+u1vf7vRfyM3zOOYMWMKn/nMZ95zzpAhQwplZWWFPfbYo3DVVVc1e9/suKzNWx/r8tbHurx1sjZvfazLW5cdfV3eplDwMw0AAAAAALAxnokOAAAAAAAlCNEBAAAAAKAEIToAAAAAAJQgRAcAAAAAgBKE6AAAAAAAUIIQHQAAAAAAShCiAwAAAABACUJ0AAAAAAAoQYgOQEaMGJHx48e3dBsAANDqbO9r7f79++fCCy9s6TYAtmttCoVCoaWbAKBlvfTSS9lpp53SpUuXlm4FAABale19rf3CCy+kc+fO6dSp02Zr+/fvn/Hjx2+T/1OgTZs2ufnmm3P00Ud/4NcG+Ee1b+kGAGh53bt3b+kWNqpQKGTdunVp394/VwAA7Ji217X2Bh/5yEdaugWA7Z7HuQDQ5EdM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Step4: Save finalised dataframe","metadata":{}},{"cell_type":"code","source":"filtered_df.to_csv('df_dac_finalised.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2024-06-04T08:20:55.758127Z","iopub.execute_input":"2024-06-04T08:20:55.758539Z","iopub.status.idle":"2024-06-04T08:20:56.928808Z","shell.execute_reply.started":"2024-06-04T08:20:55.758490Z","shell.execute_reply":"2024-06-04T08:20:56.927607Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}