jasonreich/Hospitalisation Ratios.ipynb

## Hospitalisation Ratios.ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nfrom uk_covid19 import Cov19API\n%matplotlib inline",
      "execution_count": 20,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "england_only = [\n    \"areaType=nation\",\n    \"areaName=England\"\n]\ncases = {\n    \"date\": \"date\",\n    \"newCases\": \"newCasesBySpecimenDateRollingSum\",\n    \"newAdmissions\": \"newAdmissionsRollingSum\"\n}\napi_e = Cov19API(filters=england_only, structure=cases)\ndf_e = api_e.get_dataframe()",
      "execution_count": 25,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def index_and_window(df, name):\n    df['date'] = pd.to_datetime(df['date'])\n    df = df.set_index('date')\n    df = df[::-1]\n    return df.dropna()\n\ndf_e = index_and_window(df_e, 'England')",
      "execution_count": 26,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_e['ratio'] = df_e['newAdmissions'] / df_e['newCases'].shift(10)\ndf_e['ratio'][-180:].plot.line()",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='date'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_e",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": "            newCases  newAdmissions     ratio\ndate                                         \n2020-02-05       3.0            NaN       NaN\n2020-02-06       1.0            NaN       NaN\n2020-02-07       1.0            NaN       NaN\n2020-02-08       5.0            NaN       NaN\n2020-02-09       6.0            NaN       NaN\n...              ...            ...       ...\n2021-06-25   83983.0         1326.0  0.027480\n2021-06-26   90149.0         1371.0  0.027037\n2021-06-27       NaN         1426.0  0.026716\n2021-06-28       NaN         1489.0  0.026810\n2021-06-29       NaN         1590.0  0.027785\n\n[511 rows x 3 columns]",
            "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>newCases</th>\n      <th>newAdmissions</th>\n      <th>ratio</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-02-05</th>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2020-02-06</th>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2020-02-07</th>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2020-02-08</th>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2020-02-09</th>\n      <td>6.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2021-06-25</th>\n      <td>83983.0</td>\n      <td>1326.0</td>\n      <td>0.027480</td>\n    </tr>\n    <tr>\n      <th>2021-06-26</th>\n      <td>90149.0</td>\n      <td>1371.0</td>\n      <td>0.027037</td>\n    </tr>\n    <tr>\n      <th>2021-06-27</th>\n      <td>NaN</td>\n      <td>1426.0</td>\n      <td>0.026716</td>\n    </tr>\n    <tr>\n      <th>2021-06-28</th>\n      <td>NaN</td>\n      <td>1489.0</td>\n      <td>0.026810</td>\n    </tr>\n    <tr>\n      <th>2021-06-29</th>\n      <td>NaN</td>\n      <td>1590.0</td>\n      <td>0.027785</td>\n    </tr>\n  </tbody>\n</table>\n<p>511 rows × 3 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "_draft": {
      "nbviewer_url": "https://gist.github.com/f132aa1703ecded98967aadbed26efa4"
    },
    "gist": {
      "id": "f132aa1703ecded98967aadbed26efa4",
      "data": {
        "description": "Hospitalisation ratio",
        "public": true
      }
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.2+",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
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  "nbformat_minor": 2
}