analysis/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
   "metadata": {},
   "source": [
    "## DOAC Use in Patients with Mechanical Heart Valves\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "<ipython-input-25-4bafbfd1d2cc>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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[column] = suppress_column(df[column])\n",
      "/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  self._setitem_single_column(loc, value, pi)\n"
     ]
    }
   ],
   "source": [
    "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",
    "            column[column<=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.loc[(df[numerator].isna())| (df[denominator].isna()), rate_column] = np.nan\n",
    "    \n",
    "    return df\n",
    "\n",
    "population = df.groupby(by='date')[['population']].sum().reset_index()\n",
    "events = df.groupby(by='date')[['doac']].sum().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",
    "\n",
    "redacted_dfs = []\n",
    "for date in total_df['date'].unique():\n",
    "    \n",
    "    sub_df = total_df.loc[total_df['date']==date, :]\n",
    "\n",
    "    sub_df = redact_small_numbers(sub_df, 5, 'doac', 'population', 'rate')\n",
    "    redacted_dfs.append(sub_df)\n",
    "\n",
    "total_df = pd.concat(redacted_dfs, axis=0)\n",
    "total_df.to_csv('../output/doac_rate_total.csv')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# total_df = redact_small_numbers(total_df, 10, '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 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, top=df[column_to_plot].max() + df[column_to_plot].max()* 0.1)\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.savefig(f'../output/measure_{filename}.jpeg')\n",
    "    plt.show()\n",
    "    \n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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 in patients with a mechanical valve', column_to_plot='doac', category=False, y_label='Total number', interactive=False, filename=\"total\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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='Total number', interactive=False, filename=\"total_rate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STP Breakdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "code_folding": [],
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b-\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n",
      "/usr/local/lib/python3.9/site-packages/ebmdatalab/charts.py:95: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
      "  ax.plot(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_stp = pd.read_csv('../output/measure_stp_rate.csv', parse_dates=['date'])\n",
    "\n",
    "df_stp['rate'] = calculate_rate(df_stp, 'doac', 'population')\n",
    "\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",
   "metadata": {},
   "source": [
    "### Mechanical Valve Type\n",
    "\n",
    "Mechanical valve type of those with mechanical valve currently prescribed a DOAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_low_values_mv(df, value_col, population_col, term_col):\n",
    "    \n",
    "    df = df[df[value_col] > 0]\n",
    "    suppressed_count = df.loc[df[value_col]<=5, value_col].sum()\n",
    "    population_suppressed_count = df.loc[df[value_col]<=5, population_col].sum()\n",
    "    \n",
    "    if suppressed_count == 0:\n",
    "        pass\n",
    "\n",
    "    else:\n",
    "        df.loc[df[value_col] <=5, value_col]  = np.nan\n",
    "\n",
    "        while suppressed_count <=5:\n",
    "            suppressed_count += df[value_col].min()\n",
    "            df.loc[df[value_col].idxmin(), value_col] = np.nan \n",
    "            \n",
    "            population_suppressed_count += df.loc[df[value_col].idxmin(), population_col]\n",
    "   \n",
    "        df = df[df[value_col].notnull()]\n",
    "\n",
    "        other_row = {'mechanical_valve_code':'Other', 'doac_3_months':suppressed_count, 'population':population_suppressed_count, 'term':'-'}\n",
    "        df = df.append(other_row, ignore_index=True)\n",
    "    \n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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",
    "\n",
    "\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",
    "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', 'date'])\n",
    "valve_type_df_now = group_low_values_mv(valve_type_df_now, 'doac_3_months', 'population', 'term')\n",
    "valve_type_df_now.to_csv('../output/current_doac_valve_type.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2403</td>\n",
       "      <td>12000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>174920003</td>\n",
       "      <td>597</td>\n",
       "      <td>3000</td>\n",
       "      <td>Prosthetic mitral valve replacement (&amp; [Bjork Shiley] or [Carpentier Edwards])</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mechanical_valve_code  doac_3_months  population  \\\n",
       "0                    NaN           2403       12000   \n",
       "1              174920003            597        3000   \n",
       "\n",
       "                                                                             term  \n",
       "0                                                                             NaN  \n",
       "1  Prosthetic mitral valve replacement (& [Bjork Shiley] or [Carpentier Edwards])  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valve_type_df_now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demographic breakdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_low_values_dems(df, value_col, population_col, demographic):\n",
    "    \n",
    "    df = df[df[value_col] > 0]\n",
    "    suppressed_count = df.loc[df[value_col]<=5, value_col].sum()\n",
    "    population_suppressed_count = df.loc[df[value_col]<=5, population_col].sum()\n",
    "   \n",
    "    if suppressed_count == 0:\n",
    "        pass\n",
    "\n",
    "    else:\n",
    "        df.loc[df[value_col] <=5, value_col]  = np.nan\n",
    "\n",
    "        while suppressed_count <=5:\n",
    "            suppressed_count += df[value_col].min()\n",
    "            df.loc[df[value_col].idxmin(), value_col] = np.nan \n",
    "            \n",
    "            population_suppressed_count += df.loc[df[value_col].idxmin(), population_col]\n",
    "   \n",
    "        df = df[df[value_col].notnull()]\n",
    "\n",
    "        other_row = {demographic:'Other', 'doac_3_months':suppressed_count, 'population':population_suppressed_count,}\n",
    "        df = df.append(other_row, ignore_index=True)\n",
    "    \n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>sex</th>\n",
       "      <th>doac_3_months</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>F</td>\n",
       "      <td>1486</td>\n",
       "      <td>7557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>M</td>\n",
       "      <td>1478</td>\n",
       "      <td>7308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>U</td>\n",
       "      <td>36</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sex  doac_3_months  population\n",
       "96   F           1486        7557\n",
       "97   M           1478        7308\n",
       "98   U             36         135"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_df = pd.read_csv('../output/measure_doac_rx_mechanical_valve_3_month_sex_rate.csv', parse_dates=['date'])\n",
    "\n",
    "sex_df_now = sex_df[sex_df['date'] == '2021-05-01']\n",
    "sex_df_now = sex_df_now.drop(['value', 'date'], axis=1)\n",
    "sex_df_now = group_low_values_dems(sex_df_now, 'doac_3_months', 'population', 'sex')\n",
    "sex_df_now.to_csv('../output/current_doac_sex.csv')\n",
    "sex_df_now\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ethnicity"
   ]
  },
  {
   "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>eth2001</th>\n",
       "      <th>doac_3_months</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2709</td>\n",
       "      <td>13494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>70</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2709</td>\n",
       "      <td>13494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>70</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Other</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   eth2001  doac_3_months  population\n",
       "0      NaN           2709       13494\n",
       "1        1            140         763\n",
       "2        2             70         346\n",
       "3        3             49         196\n",
       "4        4             15         101\n",
       "5        5              7          50\n",
       "6        6              7          32\n",
       "7      NaN           2709       13494\n",
       "8        1            140         763\n",
       "9        2             70         346\n",
       "10       3             49         196\n",
       "11       4             15         101\n",
       "12       5              7          50\n",
       "13       6              7          32\n",
       "14   Other              6          36"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ethnicity_df = pd.read_csv('../output/measure_doac_rx_mechanical_valve_3_month_ethnicity_rate.csv', parse_dates=['date'])\n",
    "ethnicity_df_now = ethnicity_df[ethnicity_df['date'] == '2021-05-01']\n",
    "ethnicity_df_now = ethnicity_df_now.drop(['value', 'date'], axis=1)\n",
    "ethnicity_df_now = group_low_values_dems(ethnicity_df_now, 'doac_3_months', 'population', 'eth2001')\n",
    "ethnicity_df_now.to_csv('../output/current_doac_ethnicity.csv')\n",
    "ethnicity_df_now\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age Band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>age_band</th>\n",
       "      <th>doac_3_months</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>0-19</td>\n",
       "      <td>386</td>\n",
       "      <td>1861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>20-29</td>\n",
       "      <td>371</td>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>30-39</td>\n",
       "      <td>348</td>\n",
       "      <td>1830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>40-49</td>\n",
       "      <td>397</td>\n",
       "      <td>1884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>50-59</td>\n",
       "      <td>376</td>\n",
       "      <td>1941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>60-69</td>\n",
       "      <td>363</td>\n",
       "      <td>1846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>70-79</td>\n",
       "      <td>376</td>\n",
       "      <td>1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>80+</td>\n",
       "      <td>383</td>\n",
       "      <td>1861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age_band  doac_3_months  population\n",
       "289     0-19            386        1861\n",
       "290    20-29            371        1873\n",
       "291    30-39            348        1830\n",
       "292    40-49            397        1884\n",
       "293    50-59            376        1941\n",
       "294    60-69            363        1846\n",
       "295    70-79            376        1899\n",
       "296      80+            383        1861"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_df = pd.read_csv('../output/measure_doac_rx_mechanical_valve_3_month_age_rate.csv', parse_dates=['date'])\n",
    "\n",
    "age_df_now = age_df[age_df['date'] == '2021-05-01']\n",
    "age_df_now = age_df_now.drop(['value', 'date'], axis=1)\n",
    "age_df_now = group_low_values_dems(age_df_now, 'doac_3_months', 'population', 'age_band')\n",
    "age_df_now.to_csv('../output/current_doac_age.csv')\n",
    "age_df_now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IMD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>imd</th>\n",
       "      <th>doac_3_months</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "      <td>806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>1</td>\n",
       "      <td>574</td>\n",
       "      <td>2825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>2</td>\n",
       "      <td>544</td>\n",
       "      <td>2792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>3</td>\n",
       "      <td>606</td>\n",
       "      <td>2882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>4</td>\n",
       "      <td>556</td>\n",
       "      <td>2862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>5</td>\n",
       "      <td>559</td>\n",
       "      <td>2833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imd  doac_3_months  population\n",
       "192    0            161         806\n",
       "193    1            574        2825\n",
       "194    2            544        2792\n",
       "195    3            606        2882\n",
       "196    4            556        2862\n",
       "197    5            559        2833"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imd_df = pd.read_csv('../output/measure_doac_rx_mechanical_valve_3_month_imd_rate.csv', parse_dates=['date'])\n",
    "\n",
    "imd_df_now = imd_df[imd_df['date'] == '2021-05-01']\n",
    "imd_df_now = imd_df_now.drop(['value', 'date'], axis=1)\n",
    "imd_df_now = group_low_values_dems(imd_df_now, 'doac_3_months', 'population', 'imd')\n",
    "imd_df_now.to_csv('../output/current_doac_imd.csv')\n",
    "imd_df_now"
   ]
  }
 ],
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