analysis/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "latest-edwards",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'ebmdatalab'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-b0e3da93a14b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mutilities\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mregister_matplotlib_converters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/DataLab/mechanical-valve-anticoag/analysis/utilities.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mebmdatalab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcharts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_objects\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'ebmdatalab'"
     ]
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "from IPython.display import HTML, display\n",
    "import json\n",
    "from collections import Counter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from utilities import *\n",
    "\n",
    "register_matplotlib_converters()\n",
    "pd.set_option('display.float_format', '{:.0f}'.format)\n",
    "pd.set_option('display.max_colwidth', 100)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "prerequisite-newton",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../output/measure_doac_rx_mechanical_valve_rate.csv', parse_dates=['date'])\n",
    "\n",
    "def calculate_rate(df, value_col, population_col, rate_per=1000):\n",
    "    num_per_thousand = df[value_col]/(df[population_col]/rate_per)\n",
    "    return num_per_thousand\n",
    "\n",
    "df['rate'] = calculate_rate(df, 'doac', 'population')\n",
    "\n",
    "codelist = pd.read_csv('../codelists/opensafely-mechanical-or-artificial-valves.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "light-explosion",
   "metadata": {},
   "source": [
    "## DOAC Use in Patients with Mechanical Heart Valves\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf99254-244d-45f9-9a19-e6644a0fe144",
   "metadata": {},
   "source": [
    "### Total Number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "592aa0fb-887d-4450-930b-979e8a72e624",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-f1dc5b519c37>:39: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[rate_column][(df[numerator].isna())| (df[denominator].isna())] = np.nan\n"
     ]
    }
   ],
   "source": [
    "population = df.groupby(by='date')[['population']].sum().reset_index()\n",
    "events = df.groupby(by='date')[['doac']].mean().reset_index()\n",
    "total_df = df.groupby(by='date')[['rate']].mean().reset_index()\n",
    "\n",
    "total_df = total_df.merge(events, on='date').merge(population, on='date')\n",
    "\n",
    "def redact_small_numbers(df, n, numerator, denominator, rate_column):\n",
    "    \"\"\"\n",
    "    Takes counts df as input and suppresses low numbers.  Sequentially redacts\n",
    "    low numbers from numerator and denominator until count of redcted values >=n.\n",
    "    Rates corresponding to redacted values are also redacted.\n",
    "    \n",
    "    df: input df\n",
    "    n: threshold for low number suppression\n",
    "    numerator: numerator column to be redacted\n",
    "    denominator: denominator column to be redacted\n",
    "    \"\"\"\n",
    "    \n",
    "    def suppress_column(column):    \n",
    "        suppressed_count = column[column<=n].sum()\n",
    "        \n",
    "        #if 0 dont need to suppress anything\n",
    "        if suppressed_count == 0:\n",
    "            pass\n",
    "        \n",
    "        else:\n",
    "            df[column.name][df[column.name]<=n] = np.nan\n",
    "            \n",
    "\n",
    "            while suppressed_count <=n:\n",
    "                suppressed_count += column.min()\n",
    "                column.iloc[column.idxmin()] = np.nan   \n",
    "        return column\n",
    "    \n",
    "    \n",
    "    for column in [numerator, denominator]:\n",
    "        df[column] = suppress_column(df[column])\n",
    "    \n",
    "    df[rate_column][(df[numerator].isna())| (df[denominator].isna())] = np.nan\n",
    "    \n",
    "    return df    \n",
    "\n",
    "total_df = redact_small_numbers(total_df, 5, 'doac', 'population', 'rate')\n",
    "\n",
    "\n",
    "def plot_measures(df, title,column_to_plot, filename,category=False, y_label='Rate per 1000', interactive=True):\n",
    "\n",
    "    if interactive:\n",
    "\n",
    "        fig = go.Figure()\n",
    "\n",
    "        if category:\n",
    "            for unique_category in df[category].unique():\n",
    "\n",
    "                df_subset = df[df[category] == unique_category]\n",
    "                fig.add_trace(go.Scatter(\n",
    "                    x=df_subset['date'], y=df_subset[column_to_plot], name=unique_category))\n",
    "\n",
    "        else:\n",
    "            fig.add_trace(go.Scatter(\n",
    "                x=df['date'], y=df[column_to_plot]))\n",
    "\n",
    "        # Set title\n",
    "        fig.update_layout(\n",
    "            title_text=title,\n",
    "            hovermode='x',\n",
    "            title_x=0.5,\n",
    "            yaxis=dict(\n",
    "            range=[0, 7000]\n",
    "        )\n",
    "\n",
    "\n",
    "        )\n",
    "\n",
    "        fig.update_yaxes(title=y_label)\n",
    "        fig.update_xaxes(title=\"Date\")\n",
    "\n",
    "        # Add range slider\n",
    "        fig.update_layout(\n",
    "            xaxis=go.layout.XAxis(\n",
    "                rangeselector=dict(\n",
    "                    buttons=list([\n",
    "                        dict(count=1,\n",
    "                            label=\"1m\",\n",
    "                            step=\"month\",\n",
    "                            stepmode=\"backward\"),\n",
    "                        dict(count=6,\n",
    "                            label=\"6m\",\n",
    "                            step=\"month\",\n",
    "                            stepmode=\"backward\"),\n",
    "\n",
    "                        dict(count=1,\n",
    "                            label=\"1y\",\n",
    "                            step=\"year\",\n",
    "                            stepmode=\"backward\"),\n",
    "                        dict(step=\"all\")\n",
    "                    ])\n",
    "                ),\n",
    "                rangeslider=dict(\n",
    "                    visible=True\n",
    "                ),\n",
    "                type=\"date\"\n",
    "            )\n",
    "        )\n",
    "\n",
    "        fig.show()\n",
    "\n",
    "    else:\n",
    "\n",
    "        if category:\n",
    "            for unique_category in df[category].unique():\n",
    "\n",
    "                df_subset = df[df[category] == unique_category]\n",
    "\n",
    "                plt.plot(df_subset['date'], df_subset[column_to_plot], marker='o')\n",
    "        else:\n",
    "            plt.plot(df['date'], df[column_to_plot])\n",
    "\n",
    "        plt.ylabel(y_label)\n",
    "        plt.xlabel('Date')\n",
    "        plt.ylim(bottom=0)\n",
    "        plt.xticks(rotation='vertical')\n",
    "        plt.title(title)\n",
    "\n",
    "        if category:\n",
    "            plt.legend(df[category].unique(), bbox_to_anchor=(\n",
    "                1.04, 1), loc=\"upper left\")\n",
    "\n",
    "        else:\n",
    "            pass\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(f'../output/measure_{filename}.jpeg')\n",
    "        plt.show()\n",
    "        \n",
    "        plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87656f4b-0bf3-4d30-a219-e99fe60f8d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_measures(total_df, title='DOAC prescribing rate per 1000 patients with a mechanical valve', column_to_plot='rate', category=False, y_label='Rate per 1000', interactive=False, filename=\"total\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faced-soldier",
   "metadata": {},
   "source": [
    "### STP Breakdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "administrative-sodium",
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "df_stp = pd.read_csv('../output/measure_stp_rate.csv', parse_dates=['date'])\n",
    "df_stp['rate'] = calculate_rate(df_stp, 'doac', 'population')\n",
    "\n",
    "deciles_chart(df_stp, period_column='date', column='rate', title='Decile chart',ylabel='rate per 1000', interactive=False)\n",
    "plt.tight_layout()\n",
    "plt.savefig('../output/measure_stp.jpeg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def15f80-c9bd-4516-bf75-b6e33bb96766",
   "metadata": {},
   "source": [
    "### Mechanical Valve Type\n",
    "\n",
    "Mechanical valve type of those with mechanical valve currently prescribed a DOAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c7b9ba2-c046-4e35-8cc8-01856d59e88b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_low_values(df, value_col, population_col, term_col):\n",
    "    \n",
    "    df = df[df[value_col] > 0]\n",
    "    suppressed_count = df[df[value_col]<=5][value_col].sum()\n",
    "    population_suppressed_count = df[df[value_col]<=5][population_col].sum()\n",
    "    \n",
    "    if suppressed_count == 0:\n",
    "        pass\n",
    "\n",
    "    else:\n",
    "        df[value_col][df[value_col] <=5]  = np.nan\n",
    "\n",
    "        while suppressed_count <=10:\n",
    "\n",
    "            suppressed_count += df[value_col].min()\n",
    "            df[value_col].iloc[df[value_col].idxmin()] = np.nan \n",
    "            \n",
    "            population_suppressed_count += df[population_col].iloc[df[value_col].idxmin()]\n",
    "            df[population_col].iloc[df[value_col].idxmin()] = np.nan\n",
    "\n",
    "\n",
    "    dropped_rows = df[df[value_col].isnull()][term_col].values\n",
    "\n",
    "    df = df[df[value_col].notnull()]\n",
    "    \n",
    "    \n",
    "    \n",
    "    return df, dropped_rows, suppressed_count, population_suppressed_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e05557e3-e396-4ae0-831f-7fef379eacc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "valve_type_df = pd.read_csv('../output/measure_doac_rx_mechanical_valve_3_month_valve_code_rate.csv', parse_dates=['date'])\n",
    "valve_type_df_now = valve_type_df[valve_type_df['date']== '2021-05-01']\n",
    "valve_type_df_now = valve_type_df_now.drop('value', axis=1)\n",
    "\n",
    "\n",
    "\n",
    "valve_type_df_now = valve_type_df_now.merge(codelist, left_on='mechanical_valve_code', right_on='code', how='left')\n",
    "valve_type_df_now = valve_type_df_now.drop(columns='code')\n",
    "valve_type_df_now\n",
    "\n",
    "\n",
    "valve_type_df_now, dropped_rows, suppressed_count, population_suppressed_count = group_low_values(valve_type_df_now, 'doac_3_months', 'population', 'term')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "863d8066-fade-428c-978e-367647f3f2ab",
   "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>mechanical_valve_code</th>\n",
       "      <th>doac_3_months</th>\n",
       "      <th>population</th>\n",
       "      <th>date</th>\n",
       "      <th>term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2379.0</td>\n",
       "      <td>12000</td>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>174920003.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>Prosthetic mitral valve replacement (&amp; [Bjork ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mechanical_valve_code  doac_3_months  population       date  \\\n",
       "0                    NaN         2379.0       12000 2021-05-01   \n",
       "1            174920003.0          621.0        3000 2021-05-01   \n",
       "\n",
       "                                                term  \n",
       "0                                                NaN  \n",
       "1  Prosthetic mitral valve replacement (& [Bjork ...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valve_type_df_now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "70623aa4-3805-4a01-864e-d73043d281ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suppressed count: 0.0\n",
      "population suppressed: 0\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "print(f'suppressed count: {suppressed_count}')\n",
    "print(f'population suppressed: {population_suppressed_count}')\n",
    "print(dropped_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "impossible-walnut",
   "metadata": {},
   "source": [
    "### Demographic breakdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "blind-virus",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from collections import Counter\n",
    "\n",
    "# recent_df = pd.read_csv('../output/input_demographics.csv')\n",
    "# demographic_df = \n",
    "# demographic_df_doac = demographic_df[(demographic_df['mechanical_valve']==1)&(demographic_df['doac']==1)]\n",
    "\n",
    "\n",
    "\n",
    "# demographics = ['sex', 'age_band', 'atrial_fib', 'imd', 'eth2001']\n",
    "# demographic_counts = {}\n",
    "\n",
    "# for dem in demographics:\n",
    "    \n",
    "#     if dem == \"imd\":\n",
    "#         demographic_df_doac[\"imd\"] = pd.qcut(demographic_df_doac[\"imd\"], q=5,duplicates=\"drop\")  \n",
    "#     column = demographic_df_doac[dem]\n",
    "#     count_dict = Counter(column.dropna())\n",
    "    \n",
    "#     demographic_counts[dem] = count_dict\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "enabling-respect",
   "metadata": {},
   "source": [
    "#### Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sized-fitting",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sex_dict = demographic_counts['sex']\n",
    "# sex_df = pd.DataFrame.from_dict(sex_dict, orient='index').rename(columns={'index': 'row', 0: 'count'})\n",
    "\n",
    "# total = sex_df['count'].sum()\n",
    "\n",
    "# # other_count = 0\n",
    "# # def get_other_count(row, other_count):\n",
    "# #     count = row['count']\n",
    "# #     if count<=5:\n",
    "# #         other_count+=count\n",
    "\n",
    "# # sex_df.apply(lambda row: get_other_count(row, other_count), axis=1)\n",
    "# # sex_df = sex_df[sex_df['count']>5]      \n",
    "\n",
    "# # if other_count >5:\n",
    "# #     sex_df = sex_df.append(pd.DataFrame([other_count], columns=['count']))\n",
    "\n",
    "\n",
    "# def calculate_percent(row, total):\n",
    "#     row['%'] = (row['count']/total)*100\n",
    "#     return row\n",
    "# sex_df = sex_df.apply(lambda row: calculate_percent(row, total), axis=1)\n",
    "\n",
    "\n",
    "# sex_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "peripheral-joseph",
   "metadata": {},
   "source": [
    "#### Ethnicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "offshore-angel",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ethnicity_dict = demographic_counts['eth2001']\n",
    "# ethnicity_df = pd.DataFrame.from_dict(ethnicity_dict, orient='index').rename(columns={'index': 'row', 0: 'count'})\n",
    "\n",
    "# total = ethnicity_df['count'].sum()\n",
    "\n",
    "# # other_count = 0\n",
    "# # def get_other_count(row, other_count):\n",
    "# #     count = row['count']\n",
    "# #     if count<=5:\n",
    "# #         other_count+=count\n",
    "\n",
    "# # ethnicity_df.apply(lambda row: get_other_count(row, other_count), axis=1)\n",
    "# # ethnicity_df = ethnicity_df[ethnicity_df['count']>5]      \n",
    "\n",
    "# # if other_count >5:\n",
    "# #     ethnicity_df = ethnicity_df.append(pd.DataFrame([other_count], columns=['count']))\n",
    "\n",
    "\n",
    "# def calculate_percent(row, total):\n",
    "#     row['%'] = (row['count']/total)*100\n",
    "#     return row\n",
    "# ethnicity_df = ethnicity_df.apply(lambda row: calculate_percent(row, total), axis=1)\n",
    "\n",
    "\n",
    "# ethnicity_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confirmed-failure",
   "metadata": {},
   "source": [
    "#### IMD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "demographic-diary",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imd_dict = demographic_counts['imd']\n",
    "# imd_df = pd.DataFrame.from_dict(imd_dict, orient='index').rename(columns={'index': 'row', 0: 'count'})\n",
    "\n",
    "# total = imd_df['count'].sum()\n",
    "\n",
    "# # other_count = 0\n",
    "# # def get_other_count(row, other_count):\n",
    "# #     count = row['count']\n",
    "# #     if count<=5:\n",
    "# #         other_count+=count\n",
    "\n",
    "# # imd_df.apply(lambda row: get_other_count(row, other_count), axis=1)\n",
    "# # imd_df = imd_df[imd_df['count']>5]      \n",
    "\n",
    "# # if other_count >5:\n",
    "# #     imd_df = imd_df.append(pd.DataFrame([other_count], columns=['count']))\n",
    "\n",
    "\n",
    "# def calculate_percent(row, total):\n",
    "#     row['%'] = (row['count']/total)*100\n",
    "#     return row\n",
    "# imd_df = imd_df.apply(lambda row: calculate_percent(row, total), axis=1)\n",
    "\n",
    "\n",
    "# imd_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "american-vegetation",
   "metadata": {},
   "source": [
    "#### AF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "presidential-jason",
   "metadata": {},
   "outputs": [],
   "source": [
    "# af_dict = demographic_counts['atrial_fib']\n",
    "# af_df = pd.DataFrame.from_dict(af_dict, orient='index').rename(columns={'index': 'row', 0: 'count'})\n",
    "\n",
    "# total = af_df['count'].sum()\n",
    "\n",
    "# # other_count = 0\n",
    "# # def get_other_count(row, other_count):\n",
    "# #     count = row['count']\n",
    "# #     if count<=5:\n",
    "# #         other_count+=count\n",
    "\n",
    "# # af_df.apply(lambda row: get_other_count(row, other_count), axis=1)\n",
    "# # af_df = af_df[af_df['count']>5]      \n",
    "\n",
    "# # if other_count >5:\n",
    "# #     af_df = af_df.append(pd.DataFrame([other_count], columns=['count']))\n",
    "\n",
    "\n",
    "# def calculate_percent(row, total):\n",
    "#     row['%'] = (row['count']/total)*100\n",
    "#     return row\n",
    "# af_df = af_df.apply(lambda row: calculate_percent(row, total), axis=1)\n",
    "\n",
    "\n",
    "# af_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "operational-lambda",
   "metadata": {},
   "source": [
    "#### Age Band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "northern-senate",
   "metadata": {},
   "outputs": [],
   "source": [
    "# age_dict = demographic_counts['age_band']\n",
    "# age_df = pd.DataFrame.from_dict(age_dict, orient='index').rename(columns={'index': 'row', 0: 'count'})\n",
    "\n",
    "# total = age_df['count'].sum()\n",
    "\n",
    "# # other_count = 0\n",
    "# # def get_other_count(row, other_count):\n",
    "# #     count = row['count']\n",
    "# #     if count<=5:\n",
    "# #         other_count+=count\n",
    "\n",
    "# # age_df.apply(lambda row: get_other_count(row, other_count), axis=1)\n",
    "# # age_df = age_df[age_df['count']>5]      \n",
    "\n",
    "# # if other_count >5:\n",
    "# #     age_df = age_df.append(pd.DataFrame([other_count], columns=['count']))\n",
    "\n",
    "\n",
    "# def calculate_percent(row, total):\n",
    "#     row['%'] = (row['count']/total)*100\n",
    "#     return row\n",
    "# age_df = age_df.apply(lambda row: calculate_percent(row, total), axis=1)\n",
    "\n",
    "\n",
    "# age_df"
   ]
  }
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