notebooks_jupyter/local_runs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from itertools import product\n",
    "import numpy as np\n",
    "\n",
    "def local_patient_counts(\n",
    "    definitions, group, input_path, output_path, code_dict=\"\",definition_dict=\"\", categories=False, missing=False\n",
    "):\n",
    "    suffix = \"_filled\"\n",
    "    overlap = \"all_filled\"\n",
    "    if missing == True:\n",
    "        suffix = \"_missing\"\n",
    "        overlap = \"all_missing\"\n",
    "    if categories:\n",
    "        # df_population = pd.read_csv(\n",
    "        #     f\"output/{input_path}/simple_patient_counts_registered.csv\"\n",
    "        # ).set_index([\"group\", \"subgroup\"])\n",
    "        \n",
    "\n",
    "        df_append = pd.read_csv(\n",
    "            f\"../output/{input_path}/simple_patient_counts_categories_{group}_group_registered.csv\"\n",
    "        ).set_index([\"group\", \"subgroup\"])\n",
    "        for col in df_append.columns[df_append.columns.str.endswith('supplemented')]:\n",
    "            df_append=df_append.rename(columns={col: f\"{col}_filled\"})\n",
    "        \n",
    "        # df_append.drop(\"population\", inplace=True, axis=1)\n",
    "        for definition in definitions:\n",
    "            \n",
    "            # df_append[f\"population_{definition}\"] = df_population[definition+\"_filled\"]\n",
    "        # ensure definitions[n] in code_dict[definitions[n]] below refers to one of the definitions of interest\n",
    "            full_definitions = [\n",
    "                f\"{category}_{definition}\"\n",
    "                for category, definition in product(\n",
    "                    code_dict[definitions[1]].values(), [definition]\n",
    "                )\n",
    "            ]\n",
    "            for full_definition in full_definitions:\n",
    "                if missing:\n",
    "                    df_append[full_definition + suffix] = (\n",
    "                        df_append[f\"population_{definition}\"] - df_append[full_definition + \"_filled\"]\n",
    "                    )  \n",
    "                df_append[full_definition + \"_pct\"] = round(\n",
    "                    (df_append[full_definition + suffix].div(df_append[f\"population\"])) * 100, 1\n",
    "                )\n",
    "                \n",
    "                df_append[overlap + \"_pct\"] = round(\n",
    "                    (df_append[overlap].div(df_append[f\"population\"])) * 100, 1\n",
    "                )\n",
    "                # df_append[full_definition + \"_pct\"] = round(\n",
    "                #     (df_append[full_definition + suffix].div(df_append[f\"population_{definition}\"])) * 100, 1\n",
    "                # )\n",
    "                # df_append[overlap + \"_pct\"] = round(\n",
    "                #     (df_append[overlap].div(df_append[f\"population_{definition}\"])) * 100, 1\n",
    "                # )\n",
    "                # Combine count and percentage columns\n",
    "                df_append[full_definition] = (\n",
    "                    df_append[full_definition + suffix].apply(lambda x: \"{:,.0f}\".format(x))\n",
    "                    + \" (\"\n",
    "                    + df_append[full_definition + \"_pct\"].astype(str)\n",
    "                    + \")\"\n",
    "                )\n",
    "                df_append = df_append.drop(columns=[full_definition + suffix, full_definition + \"_pct\"])\n",
    "    else:\n",
    "        df_append = pd.read_csv(\n",
    "            f\"../output/{input_path}/simple_patient_counts_{group}_sus_registered.csv\"\n",
    "        ).set_index([\"group\", \"subgroup\"])\n",
    "        for definition in definitions:\n",
    "            if missing:\n",
    "                df_append[definition + suffix] = (\n",
    "                    df_append[\"population\"] - df_append[definition + \"_filled\"]\n",
    "                )    \n",
    "            df_append[definition + \"_pct\"] = round(\n",
    "                (df_append[definition + suffix].div(df_append[\"population\"])) * 100, 1\n",
    "            )\n",
    "            df_append[overlap + \"_pct\"] = round(\n",
    "                (df_append[overlap].div(df_append[\"population\"])) * 100, 1\n",
    "            )\n",
    "            \n",
    "        for definition in definitions: \n",
    "            # Percentage increase from adding SUS data\n",
    "            df_append[definition + \"_pp_inc\"] =round( df_append[definitions[1] + \"_pct\"] - df_append[definition + \"_pct\"],1)\n",
    "            # Combine count and percentage columns\n",
    "            df_append[definition] = (\n",
    "                df_append[definition + suffix].apply(lambda x: \"{:,.0f}\".format(x))\n",
    "                + \" (\"\n",
    "                + df_append[definition + \"_pct\"].astype(str)\n",
    "                + \")\"\n",
    "            )\n",
    "            df_append = df_append.drop(columns=[definition + suffix, definition + \"_pct\"])\n",
    "    df_append[overlap] = (\n",
    "        df_append[overlap].apply(lambda x: \"{:,.0f}\".format(x))\n",
    "        + \" (\"\n",
    "        + df_append[overlap + \"_pct\"].astype(str)\n",
    "        + \")\"\n",
    "    )\n",
    "    df_append = df_append.reset_index()\n",
    "    df_append = df_append.replace(\"True\", \"Yes\")\n",
    "    df_append = df_append.replace(\"False\", \"No\")\n",
    "    df_append = df_append.set_index([\"group\", \"subgroup\"])\n",
    "    df_append = df_append.drop(columns=[overlap + \"_pct\"])\n",
    "    if categories:\n",
    "        full_definitions = [\n",
    "                f\"{category}_{definition}\"\n",
    "                for category, definition in product(\n",
    "                    code_dict[definitions[1]].values(), definitions\n",
    "                )\n",
    "            ]\n",
    "        df_patient_counts = df_append[full_definitions]\n",
    "    else: \n",
    "        df_patient_counts = df_append[definitions + [overlap] + [\"population\"]+[definitions[0] + \"_pp_inc\"]]\n",
    "    # Final redaction step\n",
    "    df_patient_counts = df_patient_counts.replace(np.nan, \"-\")\n",
    "    df_patient_counts = df_patient_counts.replace(\"nan (nan)\", \"- (-)\")\n",
    "\n",
    "    for k, v in definition_dict.items():\n",
    "        df_patient_counts.columns = df_patient_counts.columns.str.replace(k,v) \n",
    "    df_patient_counts.columns = df_patient_counts.columns.str.replace(\"_\", \" \")\n",
    "   \n",
    "    if categories:\n",
    "        df_patient_counts.to_csv(\n",
    "                f\"../output/{output_path}/local_patient_counts_categories_{group}_registered.csv\"\n",
    "            )\n",
    "        display(f\"saved: ../output/{output_path}/local_patient_counts_categories_{group}_registered.csv\")\n",
    "    else:\n",
    "        df_patient_counts.to_csv(\n",
    "                f\"../output/{output_path}/local_patient_counts_registered.csv\"\n",
    "            )\n",
    "        display(f\"saved: ../output/{output_path}/local_patient_counts_registered.csv\")\n",
    "\n",
    "########################################################\n",
    "########################################################\n",
    "def local_state_change(\n",
    "    definitions, input_path,output_path, code_dict=\"\", definition_dict=\"\",\n",
    "):\n",
    "    for definition in definitions:\n",
    "            lowerlist_5 = [x.lower() for x in (list(code_dict[definition].values()))]\n",
    "            df_state_change = pd.read_csv(f'../output/{input_path}/simple_state_change_{definition}_registered.csv').set_index(definition)\n",
    "            df_state_change.columns = df_state_change.columns.str.replace(definition + \"_\", \"\")\n",
    "            #resort rows\n",
    "            df_state_change = df_state_change.reindex(list(code_dict[definition].values()))\n",
    "            df_state_change = df_state_change.reset_index()\n",
    "            \n",
    "            df_state_change[definition]=df_state_change[definition]+\": \" +df_state_change[\"n\"].apply(lambda x: \"{:,.0f}\".format(x))\n",
    "            df_state_change = df_state_change.set_index(definition)\n",
    "\n",
    "            for item in lowerlist_5 + list([\"any\"]):\n",
    "                df_state_change[item + \"_pct\"]= round(\n",
    "                        (df_state_change[item].div(df_state_change[\"n\"])) * 100, 1\n",
    "                    )\n",
    "            \n",
    "                df_state_change[item] = (\n",
    "                        df_state_change[item].apply(lambda x: \"{:,.0f}\".format(x))\n",
    "                        + \" (\"\n",
    "                        + df_state_change[item + \"_pct\"].astype(str)\n",
    "                        + \")\"\n",
    "                    )\n",
    "            df_state_change=df_state_change[lowerlist_5 + list([\"any\"])]\n",
    "            df_state_change = df_state_change.replace(\"nan (nan)\", \"- (-)\")\n",
    "            df_state_change = df_state_change.reset_index()\n",
    "            df_state_change = df_state_change.rename(definition_dict, axis='columns')\n",
    "            df_state_change.rename(columns={f'{definition_dict[definition]}':f'Latest Ethnicity-\\n{definition_dict[definition]}'}, inplace=True)\n",
    "            df_state_change = df_state_change.set_index(f'Latest Ethnicity-\\n{definition_dict[definition]}')\n",
    "            df_state_change.to_csv(\n",
    "                f\"../output/{output_path}/local_state_change_{definition}_registered.csv\"\n",
    "            )\n",
    "            display(f\"saved: ../output/{output_path}/local_state_change_{definition}_registered.csv\")\n",
    "\n",
    "\n",
    "def local_latest_common(definitions, input_path,output_path, code_dict=\"\", definition_dict=\"\",suffix=\"\",\n",
    "):\n",
    "    for definition in definitions:\n",
    "            if code_dict != \"\":\n",
    "                lowerlist_5 = [x.lower() for x in (list(code_dict[definition].values()))]\n",
    "            df_sum = pd.read_csv(f'../output/{input_path}/simple_latest_common_{definition}{suffix}_registered.csv').set_index(definition)\n",
    "            # sort rows by category index\n",
    "            df_sum.columns = df_sum.columns.str.replace(definition + \"_\", \"\")\n",
    "            df_sum.columns = df_sum.columns.str.lower()\n",
    "            df_sum = df_sum.reindex(list(code_dict[definition].values()))\n",
    "            df_sum = df_sum[lowerlist_5]\n",
    "            df_counts = pd.DataFrame(\n",
    "                np.diagonal(df_sum),\n",
    "                index=df_sum.index,\n",
    "            #   columns=[f\"matching (n={np.diagonal(df_sum).sum()})\"],\n",
    "            )\n",
    "\n",
    "            df_sum2 = df_sum.copy(deep=True)\n",
    "            np.fill_diagonal(df_sum2.values, 0)\n",
    "            df_diag = pd.DataFrame(\n",
    "                df_sum2.sum(axis=1),\n",
    "            )\n",
    "            df_out = df_counts.merge(df_diag, right_index=True, left_index=True)\n",
    "            # columns=round(df_out.sum()/df_out.sum(axis=1).sum()*100,1)\n",
    "\n",
    "            df_out.loc['Total']= df_out.sum()\n",
    "            df_out.columns=[f\"matching\",f\"not matching\"]\n",
    "            df_out[\"matching_pct\"] = round(df_out[\"matching\"]/(df_out[[\"matching\",\"not matching\"]].sum(axis=1))*100,1)\n",
    "            df_out[\"not matching_pct\"] = round(df_out[\"not matching\"]/(df_out[[\"matching\",\"not matching\"]].sum(axis=1))*100,1)\n",
    "            for item in[\"matching\",\"not matching\"]:\n",
    "                df_out[item] = (\n",
    "                            df_out[item].apply(lambda x: \"{:,.0f}\".format(x))\n",
    "                            + \" (\"\n",
    "                            + df_out[item + \"_pct\"].astype(str)\n",
    "                            + \")\"\n",
    "                        )\n",
    "            df_out = df_out[[\"matching\",\"not matching\"]]\n",
    "            df_out = df_out.reset_index()\n",
    "            df_out = df_out.rename(definition_dict, axis='columns')\n",
    "            df_out.rename(columns={f'{definition_dict[definition]}':f'Latest Ethnicity-\\n{definition_dict[definition]}'}, inplace=True)\n",
    "            df_out = df_out.set_index(f'Latest Ethnicity-\\n{definition_dict[definition]}')\n",
    "            df_out = df_out.replace(np.nan, \"-\")\n",
    "            df_out.to_csv(\n",
    "                f\"../output/{output_path}/local_latest_common_{definition}_registered.csv\"\n",
    "            )\n",
    "            display(f\"saved: ../output/{output_path}/local_latest_common_{definition}_registered.csv\")\n",
    "            \n",
    "            if code_dict != \"\":\n",
    "                lowerlist_5 = [x.lower() for x in (list(code_dict[definition].values()))]\n",
    "                df_sum = df_sum[lowerlist_5]\n",
    "            else:\n",
    "                df_sum = df_sum.reindex(sorted(df_sum.columns), axis=1)\n",
    "\n",
    "            # Combine count and percentage columns\n",
    "            df_sum[\"population\"]=df_sum.sum(axis = 1)\n",
    "            for item in lowerlist_5:\n",
    "                df_sum[item + \"_pct\"]= round(\n",
    "                        (df_sum[item].div(df_sum[\"population\"])) * 100, 1\n",
    "                    )\n",
    "                df_sum[item] = (\n",
    "                        df_sum[item].apply(lambda x: \"{:,.0f}\".format(x))\n",
    "                        + \" (\"\n",
    "                        + df_sum[item + \"_pct\"].astype(str)\n",
    "                        + \")\"\n",
    "                    )\n",
    "            df_sum = df_sum[lowerlist_5]\n",
    "            df_sum = df_sum.reset_index()\n",
    "            df_sum = df_sum.replace(\"nan (nan)\", \"- (-)\")\n",
    "            df_sum = df_sum.rename(definition_dict, axis='columns')\n",
    "            df_sum.rename(columns={f'{definition_dict[definition]}':f'Latest Ethnicity-\\n{definition_dict[definition]}'}, inplace=True)\n",
    "            df_sum = df_sum.set_index(f'Latest Ethnicity-\\n{definition_dict[definition]}')\n",
    "            df_sum.to_csv(\n",
    "                f\"../output/{output_path}/local_latest_common_{definition}_expanded_registered.csv\"\n",
    "            )\n",
    "            display(f\"saved: ../output/{output_path}/local_latest_common_{definition}_expanded_registered.csv\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'saved: ../output/released/made_locally/local_patient_counts_registered.csv'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'saved: ../output/released/made_locally/local_patient_counts_categories_5_registered.csv'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'saved: ../output/released/made_locally/local_patient_counts_categories_16_registered.csv'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'saved: ../output/released/made_locally/local_latest_common_ethnicity_new_5_registered.csv'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'saved: ../output/released/made_locally/local_latest_common_ethnicity_new_5_expanded_registered.csv'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'saved: ../output/released/made_locally/local_state_change_ethnicity_new_5_registered.csv'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from itertools import product\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "### CONFIGURE ###\n",
    "definitions_5 = ['ethnicity_new_5', 'ethnicity_5', 'ethnicity_primis_5']\n",
    "# definitions_16 = ['ethnicity_new_16', 'ethnicity_16', 'ethnicity_primis_16']\n",
    "definitions_sus_5 = ['ethnicity_new_5','any']\n",
    "definitions_sus_16 = ['ethnicity_new_16','any']\n",
    "definitions_supplemented_5 = ['ethnicity_new_5','supplemented']\n",
    "definitions_supplemented_16 = ['ethnicity_new_16','supplemented']\n",
    "covariates = ['_age_band','_sex','_region','_imd','_dementia','_diabetes','_hypertension','_learning_disability']\n",
    "input_path = 'released'\n",
    "# output_path_16 = 'from_jobserver/released\n",
    "suffixes = ['','_missing']\n",
    "suffix = ''\n",
    "code_dict_5 = {\n",
    "    \"imd\": {\n",
    "        0: \"Unknown\",\n",
    "        1: \"1 Most deprived\",\n",
    "        2: \"2\",\n",
    "        3: \"3\",\n",
    "        4: \"4\",\n",
    "        5: \"5 Least deprived\",\n",
    "    },\n",
    "    \"any\": {1: \"Asian\", 2: \"Black\", 3: \"Mixed\", 4: \"White\", 5: \"Other\"},\n",
    "    \"supplemented\": {1: \"Asian\", 2: \"Black\", 3: \"Mixed\", 4: \"White\", 5: \"Other\"},\n",
    "    \"ethnicity_new_5\": {1: \"Asian\", 2: \"Black\", 3: \"Mixed\", 4: \"White\", 5: \"Other\"},\n",
    "    \"ethnicity_primis_5\": {1: \"Asian\", 2: \"Black\", 3: \"Mixed\", 4: \"White\", 5: \"Other\"},\n",
    "}\n",
    "lowerlist_5 = [x.lower() for x in (list(code_dict_5[\"ethnicity_new_5\"].values()))]\n",
    "# Code dictionary\n",
    "code_dict_16 = {\n",
    "    \"imd\": {\n",
    "        0: \"Unknown\",\n",
    "        1: \"1 Most deprived\",\n",
    "        2: \"2\",\n",
    "        3: \"3\",\n",
    "        4: \"4\",\n",
    "        5: \"5 Least deprived\",\n",
    "    },\n",
    "    \"any\": {\n",
    "        1: \"Indian\",\n",
    "        2: \"Pakistani\",\n",
    "        3: \"Bangladeshi\",\n",
    "        4: \"Other_Asian\",\n",
    "        5: \"Caribbean\",\n",
    "        6: \"African\",\n",
    "        7: \"Other_Black\",\n",
    "        8: \"White_and_Black_Caribbean\",\n",
    "        9: \"White_and_Black_African\",\n",
    "        10: \"White_and_Asian\",\n",
    "        11: \"Other_Mixed\",\n",
    "        12: \"White_British\",\n",
    "        13: \"White_Irish\",\n",
    "        14: \"Other_White\",\n",
    "        15: \"Chinese\",\n",
    "        16: \"Any_other_ethnic_group\",\n",
    "    },\n",
    "    \"supplemented\": {\n",
    "        1: \"Indian\",\n",
    "        2: \"Pakistani\",\n",
    "        3: \"Bangladeshi\",\n",
    "        4: \"Other_Asian\",\n",
    "        5: \"Caribbean\",\n",
    "        6: \"African\",\n",
    "        7: \"Other_Black\",\n",
    "        8: \"White_and_Black_Caribbean\",\n",
    "        9: \"White_and_Black_African\",\n",
    "        10: \"White_and_Asian\",\n",
    "        11: \"Other_Mixed\",\n",
    "        12: \"White_British\",\n",
    "        13: \"White_Irish\",\n",
    "        14: \"Other_White\",\n",
    "        15: \"Chinese\",\n",
    "        16: \"Any_other_ethnic_group\",\n",
    "    },\n",
    "    \"ethnicity_16\": {\n",
    "        1: \"Indian\",\n",
    "        2: \"Pakistani\",\n",
    "        3: \"Bangladeshi\",\n",
    "        4: \"Other_Asian\",\n",
    "        5: \"Caribbean\",\n",
    "        6: \"African\",\n",
    "        7: \"Other_Black\",\n",
    "        8: \"White_and_Black_Caribbean\",\n",
    "        9: \"White_and_Black_African\",\n",
    "        10: \"White_and_Asian\",\n",
    "        11: \"Other_Mixed\",\n",
    "        12: \"White_British\",\n",
    "        13: \"White_Irish\",\n",
    "        14: \"Other_White\",\n",
    "        15: \"Chinese\",\n",
    "        16: \"Any_other_ethnic_group\",\n",
    "    },\n",
    "    \"ethnicity_new_16\": {\n",
    "        1: \"Indian\",\n",
    "        2: \"Pakistani\",\n",
    "        3: \"Bangladeshi\",\n",
    "        4: \"Other_Asian\",\n",
    "        5: \"Caribbean\",\n",
    "        6: \"African\",\n",
    "        7: \"Other_Black\",\n",
    "        8: \"White_and_Black_Caribbean\",\n",
    "        9: \"White_and_Black_African\",\n",
    "        10: \"White_and_Asian\",\n",
    "        11: \"Other_Mixed\",\n",
    "        12: \"White_British\",\n",
    "        13: \"White_Irish\",\n",
    "        14: \"Other_White\",\n",
    "        15: \"Chinese\",\n",
    "        16: \"Any_other_ethnic_group\",\n",
    "    },\n",
    "    \"ethnicity_primis_16\": {\n",
    "        1: \"Indian\",\n",
    "        2: \"Pakistani\",\n",
    "        3: \"Bangladeshi\",\n",
    "        4: \"Other_Asian\",\n",
    "        5: \"Caribbean\",\n",
    "        6: \"African\",\n",
    "        7: \"Other_Black\",\n",
    "        8: \"White_and_Black_Caribbean\",\n",
    "        9: \"White_and_Black_African\",\n",
    "        10: \"White_and_Asian\",\n",
    "        11: \"Other_Mixed\",\n",
    "        12: \"White_British\",\n",
    "        13: \"White_Irish\",\n",
    "        14: \"Other_White\",\n",
    "        15: \"Chinese\",\n",
    "        16: \"Any_other_ethnic_group\",\n",
    "    },\n",
    "}\n",
    "\n",
    "definition_dict = {\n",
    "        \"ethnicity_new_5\": \"5 SNOMED:2022\",\n",
    "        \"ethnicity_primis_5\": \"5 PRIMIS:2021\",\n",
    "        \"ethnicity_5\": \"5 CTV3:2020\",\n",
    "        \"ethnicity_new_16\": \"16 SNOMED:2022\",\n",
    "        \"ethnicity_primis_16\": \"16 PRIMIS:2021\",\n",
    "        \"ethnicity_16\": \"16 CTV3:2020\",\n",
    "        \"any\": \"Supplemented\",\n",
    "        \"supplemented\": \"Supplemented\"\n",
    "}\n",
    "\n",
    "output_path = f'{input_path}/made_locally'\n",
    "group_5 = \"5\"\n",
    "group_16=\"16\"\n",
    "\n",
    "exists = os.path.exists(f\"../output/{output_path}\")\n",
    "if not exists:\n",
    "    os.makedirs(f\"../output/{output_path}\")\n",
    "\n",
    "\n",
    "########################## SPECIFY ANALYSES TO RUN HERE ##############################\n",
    "\n",
    "\n",
    "def main():\n",
    "    local_patient_counts(\n",
    "            definitions_sus_5, group_5, input_path, output_path, code_dict_5, definition_dict, categories=False,missing=False\n",
    "        )\n",
    "    local_patient_counts(\n",
    "            definitions_supplemented_5, group_5, input_path, output_path, code_dict_5, definition_dict, categories=True, missing=False\n",
    "        )\n",
    "        \n",
    "    local_patient_counts(\n",
    "            definitions_supplemented_16, group_16, input_path, output_path, code_dict_16, definition_dict, categories=True, missing=False\n",
    "        )\n",
    "    local_latest_common(   ['ethnicity_new_5'], input_path,output_path, code_dict_5, definition_dict,suffix\n",
    "    )\n",
    "    local_state_change(\n",
    "    ['ethnicity_new_5'], input_path,output_path, code_dict_5, definition_dict,\n",
    "    )\n",
    "\n",
    "########################## DO NOT EDIT – RUNS SCRIPT ##############################\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  }
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