explorations/primero.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primero Explorations\n",
    "Using Magasin to explore the Primero API and datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell to install the library in your current environment\n",
    "!pip install primero-api\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: primero-api\n",
      "Version: 0.2.0\n",
      "Summary: A simple client for consuming data from Primero API\n",
      "Home-page: https://github.com/unicef/magasin-primero-paquet\n",
      "Author: merlos\n",
      "Author-email: merlos@users.github.com\n",
      "License: \n",
      "Location: /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages\n",
      "Requires: pandas, python-slugify, requests, requests-cache, requests-ratelimiter\n",
      "Required-by: \n"
     ]
    }
   ],
   "source": [
    "# Check it was installed correctly\n",
    "!pip show primero_api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Primero API library version 0.2.1\n",
      "Primero Server version 2.11.0-rc3\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('../primero_api/')\n",
    "import primero_api\n",
    "\n",
    "\n",
    "PRIMERO_USER='primero'\n",
    "PRIMERO_PASSWORD='primer0!'\n",
    "PRIMERO_SERVER_URL = 'http://localhost/'\n",
    "PRIMERO_SERVER_API_URL='http://localhost/api/v2/'\n",
    "\n",
    "\n",
    "primero = primero_api.PrimeroAPI(PRIMERO_USER, PRIMERO_PASSWORD, PRIMERO_SERVER_API_URL)\n",
    "\n",
    "## Note that PrimeroAPI caches the information and controls the speed. rate / duration (sec) is the number of api calls per sec \n",
    "## cache_expire= number of seconds.\n",
    "## primero = primero_api.PrimeroAPI(PRIMERO_USER, PRIMERO_PASSWORD, PRIMERO_SERVER_API_URL, rate=2, duration=1, cache_expire=3600)\n",
    "primero = primero_api.PrimeroAPI(PRIMERO_USER, PRIMERO_PASSWORD, PRIMERO_SERVER_API_URL, cache_expire=0)\n",
    "print('Primero API library version', primero.version())\n",
    "print('Primero Server version', primero.get_server_version())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Registration CP\n",
      "2 Caseload Summary CP\n",
      "3 Case status by case worker CP\n",
      "4 Cases by Agency CP\n",
      "5 Cases by Nationality\n",
      "6 Cases by Age and Sex\n",
      "7 Cases by Protection Concern\n",
      "8 Current Care Arrangements\n",
      "10 Follow up by month by Agency\n",
      "11 Follow up by week by Agency\n",
      "12 Cases per Month\n",
      "13 Cases with case plans\n"
     ]
    }
   ],
   "source": [
    "reports = primero.get_reports()\n",
    "\n",
    "for id, report in reports.items():\n",
    "  if report != None:\n",
    "    print(id, report.name)\n",
    "  else:\n",
    "    print(id, '** This report has no data **')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id 6\n",
      "name Cases by Age and Sex fr\n",
      "raw_data {'id': 6, 'name': {'en': 'Cases by Age and Sex', 'fr': 'Cases by Age and Sex', 'ar': 'Cases by Age and Sex'}, 'description': {'en': 'Number of cases broken down by age and sex', 'fr': 'Number of cases broken down by age and sex', 'ar': 'Number of cases broken down by age and sex'}, 'graph': True, 'graph_type': 'bar', 'exclude_empty_rows': False, 'record_type': 'case', 'module_id': 'primeromodule-cp', 'group_dates_by': 'date', 'group_ages': True, 'editable': False, 'disabled': False, 'filters': [{'value': ['open'], 'attribute': 'status'}, {'value': ['true'], 'attribute': 'record_state'}], 'fields': [{'name': 'age', 'display_name': {'en': 'Age', 'fr': '', 'ar': ''}, 'position': {'type': 'horizontal', 'order': 0}}, {'name': 'sex', 'display_name': {'en': 'Sex', 'fr': '', 'ar': ''}, 'position': {'type': 'vertical', 'order': 0}, 'option_labels': {'en': [{'id': 'male', 'display_text': 'Male'}, {'id': 'female', 'display_text': 'Female'}], 'fr': [], 'ar': []}}], 'report_data': {'0 - 5': {'_total': 447, 'male': {'_total': 235}, 'female': {'_total': 212}}, '6 - 11': {'_total': 506, 'male': {'_total': 255}, 'female': {'_total': 251}}, '12 - 17': {'_total': 541, 'male': {'_total': 287}, 'female': {'_total': 254}}, '18+': {'_total': 10, 'male': {'_total': 8}, 'female': {'_total': 2}}}}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "      <th>key</th>\n",
       "      <th>key_label</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>235</td>\n",
       "      <td>212</td>\n",
       "      <td>0 - 5</td>\n",
       "      <td>0 - 5</td>\n",
       "      <td>447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>255</td>\n",
       "      <td>251</td>\n",
       "      <td>6 - 11</td>\n",
       "      <td>6 - 11</td>\n",
       "      <td>506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>287</td>\n",
       "      <td>254</td>\n",
       "      <td>12 - 17</td>\n",
       "      <td>12 - 17</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>18+</td>\n",
       "      <td>18+</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   male  female      key key_label  total\n",
       "0   235     212    0 - 5     0 - 5    447\n",
       "1   255     251   6 - 11    6 - 11    506\n",
       "2   287     254  12 - 17   12 - 17    541\n",
       "3     8       2      18+       18+     10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Interact with a specific report by its id\n",
    "# lang is optional. Default is 'en'\n",
    "r = primero.get_report(6, lang='fr')\n",
    "\n",
    "# Display the id\n",
    "print('id', r.id)\n",
    "\n",
    "# name of the report \n",
    "print('name', r.name, r.lang)\n",
    "\n",
    "# raw data of the report as dict\n",
    "print('raw_data', r.report_data_dict)\n",
    "\n",
    "# pandas dataframe of the report\n",
    "df = r.to_pandas()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from seaborn) (1.26.4)\n",
      "Requirement already satisfied: pandas>=1.2 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from seaborn) (2.2.1)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from seaborn) (3.8.3)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.50.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.0)\n",
      "Requirement already satisfied: pillow>=8 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: six>=1.5 in /Users/jmerlostevar/Library/Caches/pypoetry/virtualenvs/dxint-hpo8hiaR-py3.12/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Age')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets display some graphs using seaborn\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# display the number of cases (total) per age group (key column)\n",
    "sns.barplot(x='key', y='total', data=df)\n",
    "\n",
    "# set the labels\n",
    "plt.ylabel('Total')\n",
    "plt.xlabel('Age') \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='key', ylabel='male'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dissagregate the number of cases per sex\n",
    "# We have the count of on male column and female cases on female column, the key label is the age group\n",
    "\n",
    "sns.barplot(x='key', y='male', data=df, color='blue', label='Male')\n",
    "sns.barplot(x='key', y='female', data=df, color='pink', label='Female', bottom=df['male']) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>enabled</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>status</th>\n",
       "      <th>flagged</th>\n",
       "      <th>owned_by</th>\n",
       "      <th>workflow</th>\n",
       "      <th>estimated</th>\n",
       "      <th>has_photo</th>\n",
       "      <th>module_id</th>\n",
       "      <th>...</th>\n",
       "      <th>consent_reporting</th>\n",
       "      <th>interview_subject</th>\n",
       "      <th>case_plan_approved</th>\n",
       "      <th>assessment_approved</th>\n",
       "      <th>assessment_due_date</th>\n",
       "      <th>case_status_reopened</th>\n",
       "      <th>consent_for_services</th>\n",
       "      <th>disclosure_other_orgs</th>\n",
       "      <th>withholding_info_reason</th>\n",
       "      <th>service_implemented_day_times</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>17</td>\n",
       "      <td>male</td>\n",
       "      <td>open</td>\n",
       "      <td>False</td>\n",
       "      <td>primero_cp_ar</td>\n",
       "      <td>new</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>primeromodule-cp</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>female</td>\n",
       "      <td>open</td>\n",
       "      <td>False</td>\n",
       "      <td>primero_cp_ar</td>\n",
       "      <td>new</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>primeromodule-cp</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>open</td>\n",
       "      <td>False</td>\n",
       "      <td>primero_cp_ar</td>\n",
       "      <td>new</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>primeromodule-cp</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>7</td>\n",
       "      <td>female</td>\n",
       "      <td>open</td>\n",
       "      <td>False</td>\n",
       "      <td>primero_cp_ar</td>\n",
       "      <td>new</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>primeromodule-cp</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "      <td>male</td>\n",
       "      <td>open</td>\n",
       "      <td>False</td>\n",
       "      <td>primero_cp_ar</td>\n",
       "      <td>new</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>primeromodule-cp</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>female</td>\n",
       "      <td>open</td>\n",
       "      <td>False</td>\n",
       "      <td>primero_cp_ar</td>\n",
       "      <td>new</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>primeromodule-cp</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1500</th>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>open</td>\n",
       "      <td>False</td>\n",
       "      <td>primero</td>\n",
       "      <td>care_plan</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>primeromodule-cp</td>\n",
       "      <td>...</td>\n",
       "      <td>true</td>\n",
       "      <td>individual</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-10-31</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>[fear]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>True</td>\n",
       "      <td>9</td>\n",
       "      <td>male</td>\n",
       "      <td>open</td>\n",
       "      <td>False</td>\n",
       "      <td>primero</td>\n",
       "      <td>new</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>primeromodule-cp</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-09-03</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1502</th>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "      <td>male</td>\n",
       "      <td>open</td>\n",
       "      <td>True</td>\n",
       "      <td>primero</td>\n",
       "      <td>new</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>primeromodule-cp</td>\n",
       "      <td>...</td>\n",
       "      <td>true</td>\n",
       "      <td>individual</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0004-10-21</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>[fear]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503</th>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>male</td>\n",
       "      <td>open</td>\n",
       "      <td>False</td>\n",
       "      <td>primero</td>\n",
       "      <td>new</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>primeromodule-cp</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1504 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      enabled  age     sex status  flagged       owned_by   workflow  \\\n",
       "0        True   17    male   open    False  primero_cp_ar        new   \n",
       "1        True   11  female   open    False  primero_cp_ar        new   \n",
       "2        True    2    male   open    False  primero_cp_ar        new   \n",
       "3        True    7  female   open    False  primero_cp_ar        new   \n",
       "4        True   14    male   open    False  primero_cp_ar        new   \n",
       "...       ...  ...     ...    ...      ...            ...        ...   \n",
       "1499     True    5  female   open    False  primero_cp_ar        new   \n",
       "1500     True    1  female   open    False        primero  care_plan   \n",
       "1501     True    9    male   open    False        primero        new   \n",
       "1502     True   14    male   open     True        primero        new   \n",
       "1503     True   11    male   open    False        primero        new   \n",
       "\n",
       "     estimated  has_photo         module_id  ...  consent_reporting  \\\n",
       "0        False      False  primeromodule-cp  ...                NaN   \n",
       "1        False      False  primeromodule-cp  ...                NaN   \n",
       "2          NaN      False  primeromodule-cp  ...                NaN   \n",
       "3        False      False  primeromodule-cp  ...                NaN   \n",
       "4        False      False  primeromodule-cp  ...                NaN   \n",
       "...        ...        ...               ...  ...                ...   \n",
       "1499       NaN      False  primeromodule-cp  ...                NaN   \n",
       "1500     False      False  primeromodule-cp  ...               true   \n",
       "1501      True      False  primeromodule-cp  ...                NaN   \n",
       "1502     False      False  primeromodule-cp  ...               true   \n",
       "1503     False      False  primeromodule-cp  ...                NaN   \n",
       "\n",
       "      interview_subject  case_plan_approved assessment_approved  \\\n",
       "0                   NaN                 NaN                 NaN   \n",
       "1                   NaN                 NaN                 NaN   \n",
       "2                   NaN                 NaN                 NaN   \n",
       "3                   NaN                 NaN                 NaN   \n",
       "4                   NaN                 NaN                 NaN   \n",
       "...                 ...                 ...                 ...   \n",
       "1499                NaN                 NaN                 NaN   \n",
       "1500         individual               False               False   \n",
       "1501                NaN               False               False   \n",
       "1502         individual               False               False   \n",
       "1503                NaN               False               False   \n",
       "\n",
       "     assessment_due_date case_status_reopened consent_for_services  \\\n",
       "0                    NaN                  NaN                  NaN   \n",
       "1                    NaN                  NaN                  NaN   \n",
       "2                    NaN                  NaN                  NaN   \n",
       "3                    NaN                  NaN                  NaN   \n",
       "4                    NaN                  NaN                  NaN   \n",
       "...                  ...                  ...                  ...   \n",
       "1499                 NaN                  NaN                  NaN   \n",
       "1500          2024-10-31                False                 True   \n",
       "1501          2024-09-03                False                False   \n",
       "1502          0004-10-21                False                 True   \n",
       "1503                 NaN                False                False   \n",
       "\n",
       "     disclosure_other_orgs withholding_info_reason  \\\n",
       "0                      NaN                     NaN   \n",
       "1                      NaN                     NaN   \n",
       "2                      NaN                     NaN   \n",
       "3                      NaN                     NaN   \n",
       "4                      NaN                     NaN   \n",
       "...                    ...                     ...   \n",
       "1499                   NaN                     NaN   \n",
       "1500                  True                  [fear]   \n",
       "1501                 False                     NaN   \n",
       "1502                  True                  [fear]   \n",
       "1503                 False                     NaN   \n",
       "\n",
       "     service_implemented_day_times  \n",
       "0                              NaN  \n",
       "1                              NaN  \n",
       "2                              NaN  \n",
       "3                              NaN  \n",
       "4                              NaN  \n",
       "...                            ...  \n",
       "1499                           NaN  \n",
       "1500                            []  \n",
       "1501                           NaN  \n",
       "1502                           NaN  \n",
       "1503                           NaN  \n",
       "\n",
       "[1504 rows x 49 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's do the same but with the cases raw data\n",
    "\n",
    "cases = primero.get_cases()\n",
    "# this call returns already a pandas dataframe\n",
    "cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the cases by age and plot the number of cases per age group\n",
    "sns.countplot(x='age', data=cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the cases dataframe with age on the x-axis and count on the y-axis, disaggregated by the sex column\n",
    "sns.histplot(data=cases, x='age', hue='sex', multiple='stack', palette={'male': 'lightblue', 'female': 'pink'})\n",
    "\n",
    "# set the x to display the age number \n",
    "plt.xticks(range(1, 19, 1))\n",
    "# Set the labels\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Cases Count by Age and Sex')\n",
    "plt.legend(title='Sex')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
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