florish child other fields.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd2a620e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  * Reading config from flourish.ini\n",
      "Loading Data Encryption (init)...\n",
      " * loading keys from /Users/mcturner/source/flourish-project-files/flourish/crypto_fields\n",
      " * loading rsa.restricted.public ... Done.\n",
      " * loading rsa.restricted.private ... Done.\n",
      " * loading rsa.local.public ... Done.\n",
      " * loading rsa.local.private ... Done.\n",
      " * loading aes.local ... Done.\n",
      " * loading aes.restricted ... Done.\n",
      " * loading salt.local ... Done.\n",
      " * loading salt.restricted ... Done.\n",
      " Done loading Data Encryption (init)...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mcturner/.venvs/flourish/lib/python3.9/site-packages/django_q/conf.py:139: UserWarning: Retry and timeout are misconfigured. Set retry larger than timeout, \n",
      "        failure to do so will cause the tasks to be retriggered before completion. \n",
      "        See https://django-q.readthedocs.io/en/latest/configure.html#retry for details.\n",
      "  warn(\n",
      "/Users/mcturner/.venvs/flourish/lib/python3.9/site-packages/simple_history/models.py:123: UserWarning: HistoricalRecords added to abstract model (HistoryManagerMixin) without inherit=True\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/mcturner/.venvs/flourish/lib/python3.9/site-packages/simple_history/models.py:123: UserWarning: HistoricalRecords added to abstract model (ScheduleModelMixin) without inherit=True\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/mcturner/.venvs/flourish/lib/python3.9/site-packages/simple_history/models.py:123: UserWarning: HistoricalRecords added to abstract model (VisitModelMixin) without inherit=True\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/Users/mcturner/.venvs/flourish/lib/python3.9/site-packages/django/db/models/base.py:321: RuntimeWarning: Model 'flourish_caregiver.maternaldiagnoseslist' was already registered. Reloading models is not advised as it can lead to inconsistencies, most notably with related models.\n",
      "  new_class._meta.apps.register_model(new_class._meta.app_label, new_class)\n",
      "/Users/mcturner/.venvs/flourish/lib/python3.9/site-packages/simple_history/models.py:123: UserWarning: HistoricalRecords added to abstract model (OnScheduleModelMixin) without inherit=True\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Data Encryption ...\n",
      " * found encryption keys in /Users/mcturner/source/flourish-project-files/flourish/crypto_fields.\n",
      " * using model django_crypto_fields.crypt.\n",
      " Done loading Data Encryption.\n",
      "Loading Edc Consent ...\n",
      " * checking for site consents ...\n",
      " * registered consents 'consents' from 'flourish_caregiver'\n",
      " * registered consents 'consents' from 'flourish_child'\n",
      " * registered consents 'consents' from 'pre_flourish'\n",
      " * flourish_caregiver.subjectconsent 1 covering 2020-07-01 UTC to 2025-06-30 UTC\n",
      " * flourish_caregiver.subjectconsent 2 covering 2020-07-01 UTC to 2025-06-30 UTC\n",
      " * flourish_caregiver.tbinformedconsent 1 covering 2020-07-01 UTC to 2025-06-30 UTC\n",
      " * flourish_child.childdummysubjectconsent 1 covering 2020-07-01 UTC to 2025-06-30 UTC\n",
      " * flourish_child.childdummysubjectconsent 2 covering 2020-07-01 UTC to 2025-06-30 UTC\n",
      " * pre_flourish.preflourishconsent 1 covering 2020-07-01 UTC to 2025-06-30 UTC\n",
      " Done loading Edc Consent.\n",
      "Loading Edc Device ...\n",
      "  * device id is '99'.\n",
      "  * device role is 'CentralServer'.\n",
      " Done loading Edc Device.\n",
      "Loading Edc Identifier ...\n",
      " Warning: 'identifier_prefix' has not been explicitly set. Using default '999'. See AppConfig.\n",
      " * identifier prefix: 999\n",
      " * check-digit modulus: 7\n",
      " Done loading Edc Identifier\n",
      "Loading Edc Lab ...\n",
      " * checking for labs ...\n",
      " Done loading Edc Lab.\n",
      "Loading Edc Navbar ...\n",
      " * checking for site navbars ...\n",
      " * searching edc_consent           \r         * registered navbars 'navbars' from 'edc_consent'\n",
      " * registered navbars 'navbars' from 'edc_device'\n",
      " * registered navbars 'navbars' from 'edc_navbar'\n",
      " * registered navbars 'navbars' from 'edc_reference'\n",
      " * registered navbars 'navbars' from 'edc_visit_schedule'\n",
      " * registered navbars 'navbars' from 'flourish_export'\n",
      " * registered navbars 'navbars' from 'flourish_dashboard'\n",
      " * registered navbars 'navbars' from 'flourish_follow'\n",
      " * registered navbars 'navbars' from 'pre_flourish_follow'\n",
      " * registered navbars 'navbars' from 'flourish_reports'\n",
      " * registered navbars 'navbars' from 'edc_base'\n",
      " * registered navbars 'navbars' from 'edc_data_manager'\n",
      " * registered navbars 'navbars' from 'edc_odk'\n",
      " * registered navbars 'navbars' from 'edc_protocol'\n",
      " * registered navbars 'navbars' from 'pre_flourish'\n",
      " * registered navbars 'navbars' from 'flourish_calendar'\n",
      " * registered navbars 'navbars' from 'flourish'\n",
      " Done loading Edc Navbar.             \n",
      " * checking for site prn_forms ...\n",
      "Loading Edc Registration ...                   \n",
      "  * using edc_registration.registeredsubject\n",
      " Done loading Edc Registration.\n",
      "Loading Edc Reference ...\n",
      " * checking for reference_model_configs ...\n",
      " * checking for labs ...\n",
      " * registered reference model configs from application 'flourish_reference'\n",
      " * checking for labs ...\n",
      " * registered reference model configs from application 'pre_flourish'\n",
      " Done loading Edc Reference.\n",
      "Loading Visit Schedules ...\n",
      " * checking site for module 'visit_schedules' ...\n",
      " * registered visit schedule from 'flourish_visit_schedule'\n",
      " * registered visit schedule from 'pre_flourish'\n",
      " Done loading Visit Schedules.\n",
      "Loading EDC Call Manager ...\n",
      " * call models are in app edc_call_manager.\n",
      " * checking for site model_callers ...\n",
      " * registered model caller '<class 'flourish_follow.model_callers.model_callers.WorkListFollowUpModelCaller'>'\n",
      " * registered model caller '<class 'pre_flourish_follow.model_callers.model_callers.PreFlourishWorkListFollowUpModelCaller'>'\n",
      " Done loading EDC Call Manager.\n",
      "Loading edc_metadata_rules ...\n",
      " * checking for metadata_rules ...\n",
      "   - imported metadata rules from 'flourish_metadata_rules.metadata_rules'\n",
      " Done loading edc_metadata_rules.\n",
      "Loading Edc Appointments ...\n",
      " * edc_appointment.appointment.\n",
      " * edc_appointment.appointment.\n",
      " * flourish_child.appointment.\n",
      " Done loading Edc Appointments.\n",
      "Loading Edc Base ...\n",
      " * default TIME_ZONE Africa/Gaborone.\n",
      " Done loading Edc Base.\n",
      "Loading Edc Facility ...\n",
      " * 7-Day Clinic MO(100 slots), TU(100 slots), WE(100 slots), TH(100 slots), FR(100 slots), SA(100 slots), SU(100 slots).\n",
      " * 5-Day Clinic MO(100 slots), TU(100 slots), WE(100 slots), TH(100 slots), FR(100 slots).\n",
      " Done loading Edc Facility.\n",
      "Loading Edc Metadata ...\n",
      " * using default metadata models from 'edc_metadata'\n",
      " Done loading Edc Metadata.\n",
      "Loading Edc Protocol ...\n",
      " * BHP142: Flourish.\n",
      " * Study opening date: 2020-07-01 UTC\n",
      " * Expected study closing date: 2025-06-30 UTC\n",
      " Done loading Edc Protocol.\n",
      "Loading Edc Visit Tracking ...\n",
      " * flourish_caregiver.maternalvisit uses model attr 'maternal_visit'\n",
      " * flourish_child.childvisit uses model attr 'child_visit'\n",
      " * pre_flourish.preflourishcaregivervisit uses model attr 'maternal_visit'\n",
      " Done loading Edc Visit Tracking.\n",
      "Loading Edc Timepoint ...\n",
      " * 'edc_appointment.appointment' is a timepoint model.\n",
      " * 'edc_appointment.historicalappointment' is a timepoint model.\n",
      " * 'flourish_child.appointment' is a timepoint model.\n",
      " * 'flourish_child.historicalappointment' is a timepoint model.\n",
      " Done loading Edc Timepoint.\n"
     ]
    }
   ],
   "source": [
    "import os, sys\n",
    "import pandas as pd\n",
    "import django\n",
    "from dateutil import parser\n",
    "from django.utils.timezone import make_aware\n",
    "\n",
    "sys.path.append('../..') # add path to project root dir\n",
    "os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'flourish.settings')\n",
    "os.environ[\"DJANGO_ALLOW_ASYNC_UNSAFE\"] = \"true\"\n",
    "import pytz, datetime\n",
    "\n",
    "# for more sophisticated setups, if you need to change connection settings (e.g. when using django-environ):\n",
    "#os.environ[\"DATABASE_URL\"] = \"postgres://myuser:mypassword@localhost:54324/mydb\"\n",
    "\n",
    "# Connect to Django ORM\n",
    "django.setup()\n",
    "import uuid\n",
    "from django.contrib.sites.models import Site\n",
    "from django.core.exceptions import ValidationError\n",
    "import pandas as pd\n",
    "from django_pandas.io import read_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dd558495",
   "metadata": {
    "scrolled": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 122\n",
       "caput ++           1\n",
       "Infant at NNU      1\n",
       "Name: other_birth_info, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qs = BirthData.objects.all()\n",
    "df = read_frame(qs)\n",
    "df['other_birth_info'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f32fa02c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****heent_no_other\n",
      "                                                    115\n",
      "Not present.                                          4\n",
      "Not present. Visit conducted via phone call           1\n",
      "Baby is small, admitted in NNU.                       1\n",
      "Baby in NNU                                           1\n",
      "Not present. Visit conducted through phone call.      1\n",
      "VISIT CONDUCTED VIA PHONE CALL.                       1\n",
      "Baby is at NNU.                                       1\n",
      "Name: heent_no_other, dtype: int64\n",
      "***resp_exam_other\n",
      "                                                    114\n",
      "Not present.                                          4\n",
      "sternal recession on oxygen per face mask             1\n",
      "Not present. Visit conducted via phone call           1\n",
      "Baby is small, admitted in NNU.                       1\n",
      "Baby in NNU                                           1\n",
      "Not present. Visit conducted through phone call.      1\n",
      "VISIT CONDUCTED VIA PHONE CALL.                       1\n",
      "Baby is at NNU.                                       1\n",
      "Name: resp_exam_other, dtype: int64\n",
      "****cardiac_exam_other\n",
      "                                                    116\n",
      "Not present.                                          3\n",
      "Not present. Visit conducted via phone call           1\n",
      "Baby is small, admitted in NNU.                       1\n",
      "Baby in NNU                                           1\n",
      "Not present. Visit conducted through phone call.      1\n",
      "VISIT CONDUCTED VIA PHONE CALL.                       1\n",
      "Baby is at NNU.                                       1\n",
      "Name: cardiac_exam_other, dtype: int64\n",
      "****abdominal_exam_other\n",
      "                                                    115\n",
      "Not present.                                          3\n",
      "Not present. Visit conducted via phone call           1\n",
      "Baby is small, admitted in NNU.                       1\n",
      "Baby in NNU                                           1\n",
      "Not present. Visit conducted through phone call.      1\n",
      "VISIT CONDUCTED VIA PHONE CALL.                       1\n",
      "Baby is at NNU.                                       1\n",
      "Not present                                           1\n",
      "Name: abdominal_exam_other, dtype: int64\n",
      "****skin_exam_other\n",
      "                                                    105\n",
      "Not present.                                          4\n",
      "MILD FACIAL HEAT RASH NOTED                           2\n",
      "Heat rash noted.                                      2\n",
      "mild  facial heat rash noted                          1\n",
      "skin dry ++                                           1\n",
      "Heat rash noted all over the body                     1\n",
      "Not present. Visit conducted via phone call           1\n",
      "Baby is small, admitted in NNU.                       1\n",
      "mild facial heat rash noted                           1\n",
      "mild heat rash on the face                            1\n",
      "Rash noted on the face.                               1\n",
      "Baby in NNU                                           1\n",
      "Not present. Visit conducted through phone call.      1\n",
      "VISIT CONDUCTED VIA PHONE CALL.                       1\n",
      "Baby is at NNU.                                       1\n",
      "Name: skin_exam_other, dtype: int64\n",
      "****neuro_exam_other\n",
      "                                                    115\n",
      "Not present.                                          4\n",
      "Not present. Visit conducted via phone call           1\n",
      "Baby is small, admitted in NNU.                       1\n",
      "Baby in NNU                                           1\n",
      "Not present. Visit conducted through phone call.      1\n",
      "VISIT CONDUCTED VIA PHONE CALL.                       1\n",
      "Baby is at NNU.                                       1\n",
      "Name: neuro_exam_other, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "qs = BirthExam.objects.all()\n",
    "df = read_frame(qs)\n",
    "print('''****heent_no_other''')\n",
    "print(df['heent_no_other'].value_counts())\n",
    "print('''***resp_exam_other''')\n",
    "print(df['resp_exam_other'].value_counts())\n",
    "print('''****cardiac_exam_other''')\n",
    "print(df['cardiac_exam_other'].value_counts())\n",
    "print('''****abdominal_exam_other''')\n",
    "print(df['abdominal_exam_other'].value_counts())\n",
    "print('''****skin_exam_other''')\n",
    "print(df['skin_exam_other'].value_counts())\n",
    "print('''****neuro_exam_other''')\n",
    "print(df['neuro_exam_other'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6a5c8cd0",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****other_reason_for_testing\n",
      "Had flu-like symptoms    4\n",
      "Pre-admission            3\n",
      "Flu like symptoms        1\n",
      "Name: other_reason_for_testing, dtype: int64\n",
      "****other_isolation_location\n",
      "hotel                       2\n",
      "Guest house                 2\n",
      "TLOTLO CONFERENCE CENTRE    1\n",
      "MOGODITSHANE LODGE          1\n",
      "Name: other_isolation_location, dtype: int64\n",
      "****other_vaccination_type\n",
      "Moderna    3\n",
      "Name: other_vaccination_type, dtype: int64\n",
      "****other_booster_vac_type\n",
      "Series([], Name: other_booster_vac_type, dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "qs = ChildCovid19.objects.all()\n",
    "df = read_frame(qs)\n",
    "print('''****other_reason_for_testing''')\n",
    "print(df['other_reason_for_testing'].value_counts())\n",
    "\n",
    "print('''****other_isolation_location''')\n",
    "print(df['other_isolation_location'].value_counts())\n",
    "\n",
    "print('''****other_vaccination_type''')\n",
    "print(df['other_vaccination_type'].value_counts())\n",
    "\n",
    "print('''****other_booster_vac_type''')\n",
    "print(df['other_booster_vac_type'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fed4c4c1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****reason_missed_other\n",
      "no documentation                       9\n",
      "undocumented, mom said child jabbed    5\n",
      "not documented                         3\n",
      "was not aware of the vaccine           3\n",
      "NO DOCUMENTATION-LOST NEW CARD         3\n",
      "NOT DOCUMENTED                         2\n",
      "UNDOCUMENTED                           2\n",
      "It is not documented.                  2\n",
      "taken , nurses forgot to document      2\n",
      "taken but No documentation             2\n",
      "mother did not know                    2\n",
      "not documented at 18 months            1\n",
      "unknown                                1\n",
      "undocumented                           1\n",
      "received but no documentation          1\n",
      "CANCELLED ON TABLE                     1\n",
      "mother said never missed vaccines      1\n",
      "undocumented but mother said taken     1\n",
      "not documented but mother said done    1\n",
      "mother said there was long line        1\n",
      "not stated                             1\n",
      "not specified                          1\n",
      "Name: reason_missed_other, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "qs = VaccinesMissed.objects.all()\n",
    "df = read_frame(qs)\n",
    "print('''****reason_missed_other''')\n",
    "print(df['reason_missed_other'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "736cfdff",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****child_chronic_other\n",
      "epigastric ulcers                      5\n",
      "Epilepsy                               5\n",
      "Chronic Suppurative Otitis Media       4\n",
      "recurrent laryngeal papiloma           4\n",
      "Attention Deficit Hyperactive (ADHD    4\n",
      "Endocarditis                           3\n",
      "AUTISM                                 3\n",
      "Cerebral palsy                         3\n",
      "Eye condition                          3\n",
      "TB                                     3\n",
      "CVA, Epilepsy                          3\n",
      "cerebral palsy                         1\n",
      "chronic sinusitis                      1\n",
      "Haemophilia A                          1\n",
      "Sinusitis                              1\n",
      "Name: child_chronic_other, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "qs = ChildMedicalHistory.objects.all()\n",
    "df = read_frame(qs)\n",
    "print('''****child_chronic_other''')\n",
    "print(df['child_chronic_other'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "be9720a9",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****name_hospital_other\n",
      "GPH    1\n",
      "Name: name_hospital_other, dtype: int64\n",
      "****reason_hospitalized_other\n",
      "Asthma                            2\n",
      "Acute Gastroenteritis             1\n",
      "Fracture of the left arm          1\n",
      "Cancer                            1\n",
      "CHEMICAL INTOXICATION             1\n",
      "Bleeding (Haemophilia A)          1\n",
      "Cancer(leukemia                   1\n",
      "Fever                             1\n",
      "Asthmatic attack                  1\n",
      "Jaundice                          1\n",
      "Cancer( Leukemia, lymphoma rel    1\n",
      "Vomiting and diarrhea             1\n",
      "Cancer(Leukemia                   1\n",
      "Cancer (Leukemia, lymphoma rel    1\n",
      "Diarrhoea and vomiting            1\n",
      "shortness of breath               1\n",
      "Name: reason_hospitalized_other, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "qs = ChildPreHospitalizationInline.objects.all()\n",
    "df = read_frame(qs)\n",
    "print('''****name_hospital_other''')\n",
    "print(df['name_hospital_other'].value_counts())\n",
    "\n",
    "print('''****reason_hospitalized_other''')\n",
    "print(df['reason_hospitalized_other'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a00b3764",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****reason_not_drawn_other\n",
      "Stool Not available                    20\n",
      "Visit conducted via phone call          5\n",
      "not required at this point              3\n",
      "Baby is in NNU                          3\n",
      "mother delivered outside study area     3\n",
      "RECTAL SWABS NOT YET PROCURED           2\n",
      "Not present.                            1\n",
      "medical complications(NNU)              1\n",
      "no swab                                 1\n",
      "swabs not yet procured                  1\n",
      "Baby is too small (premature)           1\n",
      "delivered outside study area            1\n",
      "rectal swabs not yet procured           1\n",
      "Not available                           1\n",
      "Name: reason_not_drawn_other, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "qs = ChildRequisition.objects.all()\n",
    "df = read_frame(qs)\n",
    "print('''****reason_not_drawn_other''')\n",
    "print(df['reason_not_drawn_other'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2acd64de",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****ethnicity_other\n",
      "Series([], Name: ethnicity_other, dtype: int64)\n",
      "****toilet_facility_other\n",
      "Neighbours toilet          2\n",
      "Outside flashing system    1\n",
      "Name: toilet_facility_other, dtype: int64\n",
      "****education_level_other\n",
      "Special Education        10\n",
      "Supplementary classes     1\n",
      "Rehabilitation Centre     1\n",
      "Name: education_level_other, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "qs = ChildSocioDemographic.objects.all()\n",
    "df = read_frame(qs)\n",
    "print('''****ethnicity_other''')\n",
    "print(df['ethnicity_other'].value_counts())\n",
    "\n",
    "print('''****toilet_facility_other''')\n",
    "print(df['toilet_facility_other'].value_counts())\n",
    "\n",
    "\n",
    "print('''****education_level_other''')\n",
    "print(df['education_level_other'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "aaafa590",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****information_provider_other\n",
      "Aunt                   9\n",
      "Clinician reporting    7\n",
      "clinician              3\n",
      "Clinician              3\n",
      "uncle                  2\n",
      "Child participant      1\n",
      "aunt                   1\n",
      "Sister                 1\n",
      "clinician reporting    1\n",
      "Child                  1\n",
      "child participant      1\n",
      "Uncle                  1\n",
      "Aunty                  1\n",
      "Name: information_provider_other, dtype: int64\n",
      "****info_source_other\n",
      "clinician reporting    5\n",
      "Clinician Reporting    3\n",
      "HOME VISIT             1\n",
      "Name: info_source_other, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "qs = ChildVisit.objects.all()\n",
    "df = read_frame(qs)\n",
    "print('''****information_provider_other''')\n",
    "print(df['information_provider_other'].value_counts())\n",
    "\n",
    "print('''****info_source_other''')\n",
    "print(df['info_source_other'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3a022f84",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****formula_water_other\n",
      "Readily made formula    1\n",
      "Name: formula_water_other, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "qs = InfantFeeding.objects.all()\n",
    "df = read_frame(qs)\n",
    "print('''****formula_water_other''')\n",
    "print(df['formula_water_other'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4ba1e6bd",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****water_used_other\n",
      "Series([], Name: water_used_other, dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "qs = InfantFeedingPractices.objects.all()\n",
    "df = read_frame(qs)\n",
    "print('''****water_used_other''')\n",
    "print(df['water_used_other'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29ea266e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
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