{
"cells": [
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"import itertools\n",
"import collections\n",
"import json\n",
"import pandas as pd\n",
"import numpy as np\n",
"from scipy.stats import gmean\n",
"import datetime\n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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226 rows × 6 columns
\n",
"
"
],
"text/plain": [
" cases deaths cases_culm deaths_culm cases_diff deaths_diff\n",
"dateRep \n",
"2019-12-31 0 0 0 0 NaN NaN\n",
"2020-01-01 0 0 0 0 0.0 0.0\n",
"2020-01-02 0 0 0 0 0.0 0.0\n",
"2020-01-03 0 0 0 0 0.0 0.0\n",
"2020-01-04 0 0 0 0 0.0 0.0\n",
"... ... ... ... ... ... ...\n",
"2020-08-08 871 98 309005 46511 -79.0 49.0\n",
"2020-08-09 758 55 309763 46566 -113.0 -43.0\n",
"2020-08-10 1062 8 310825 46574 304.0 -47.0\n",
"2020-08-11 816 -48 311641 46526 -246.0 -56.0\n",
"2020-08-12 1148 0 312789 46526 332.0 48.0\n",
"\n",
"[226 rows x 6 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_by_day = pd.read_csv('data_by_day_uk.csv', index_col='dateRep', parse_dates=True)\n",
"data_by_day"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
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"\n",
" total_2015 previous_mean \n",
"week_ended \n",
"2020-01-03 13751 13870.2 \n",
"2020-01-10 18318 15783.4 \n",
"2020-01-17 16738 14985.8 \n",
"2020-01-24 15712 14457.2 \n",
"2020-01-31 14560 13841.0 \n",
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"2020-03-13 12475 12687.6 \n",
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"2020-10-16 10799 11224.8 \n",
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},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"deaths_by_week = pd.read_csv('deaths_by_week.csv', index_col='week_ended', parse_dates=True)\n",
"deaths_by_week"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
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},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_by_week = data_by_day.resample(pd.offsets.Week(weekday=4)).sum()\n",
"data_by_week"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
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"execution_count": 21,
"metadata": {},
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}
],
"source": [
"excess_deaths = deaths_by_week.loc['2020-03-20':, ['total_2020', 'previous_mean']].merge(\n",
" data_by_week['deaths'], left_index=True, right_index=True)\n",
"excess_deaths.rename(columns={'deaths': 'covid_deaths'}, inplace=True)\n",
"excess_deaths.dropna(inplace=True)\n",
"excess_deaths"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
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"
\n",
" \n",
" 2020-05-15 | \n",
" 16390.0 | \n",
" 11547.0 | \n",
" 3006 | \n",
" 4843.0 | \n",
"
\n",
" \n",
" 2020-05-22 | \n",
" 13839.0 | \n",
" 11281.0 | \n",
" 2449 | \n",
" 2558.0 | \n",
"
\n",
" \n",
" 2020-05-29 | \n",
" 11265.0 | \n",
" 9448.0 | \n",
" 2199 | \n",
" 1817.0 | \n",
"
\n",
" \n",
" 2020-06-05 | \n",
" 12106.0 | \n",
" 11325.6 | \n",
" 1697 | \n",
" 780.4 | \n",
"
\n",
" \n",
" 2020-06-12 | \n",
" 11302.0 | \n",
" 10703.6 | \n",
" 1388 | \n",
" 598.4 | \n",
"
\n",
" \n",
" 2020-06-19 | \n",
" 10694.0 | \n",
" 10698.2 | \n",
" 1018 | \n",
" -4.2 | \n",
"
\n",
" \n",
" 2020-06-26 | \n",
" 10282.0 | \n",
" 10605.6 | \n",
" 835 | \n",
" -323.6 | \n",
"
\n",
" \n",
" 2020-07-03 | \n",
" 10412.0 | \n",
" 10483.0 | \n",
" 765 | \n",
" -71.0 | \n",
"
\n",
" \n",
" 2020-07-10 | \n",
" 9941.0 | \n",
" 10509.4 | \n",
" 607 | \n",
" -568.4 | \n",
"
\n",
" \n",
" 2020-07-17 | \n",
" 10096.0 | \n",
" 10360.6 | \n",
" 517 | \n",
" -264.6 | \n",
"
\n",
" \n",
" 2020-07-24 | \n",
" 10159.0 | \n",
" 10311.6 | \n",
" 435 | \n",
" -152.6 | \n",
"
\n",
" \n",
" 2020-07-31 | \n",
" 10262.0 | \n",
" 10307.4 | \n",
" 445 | \n",
" -45.4 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2020 previous_mean covid_deaths excess\n",
"2020-03-20 12112.0 12007.4 149 104.6\n",
"2020-03-27 12507.0 11549.6 720 957.4\n",
"2020-04-03 18565.0 11681.4 2870 6883.6\n",
"2020-04-10 20929.0 11919.4 5868 9009.6\n",
"2020-04-17 24691.0 11850.6 6340 12840.4\n",
"2020-04-24 24303.0 11844.4 5845 12458.6\n",
"2020-05-01 20059.0 11318.4 4987 8740.6\n",
"2020-05-08 14428.0 10887.2 3850 3540.8\n",
"2020-05-15 16390.0 11547.0 3006 4843.0\n",
"2020-05-22 13839.0 11281.0 2449 2558.0\n",
"2020-05-29 11265.0 9448.0 2199 1817.0\n",
"2020-06-05 12106.0 11325.6 1697 780.4\n",
"2020-06-12 11302.0 10703.6 1388 598.4\n",
"2020-06-19 10694.0 10698.2 1018 -4.2\n",
"2020-06-26 10282.0 10605.6 835 -323.6\n",
"2020-07-03 10412.0 10483.0 765 -71.0\n",
"2020-07-10 9941.0 10509.4 607 -568.4\n",
"2020-07-17 10096.0 10360.6 517 -264.6\n",
"2020-07-24 10159.0 10311.6 435 -152.6\n",
"2020-07-31 10262.0 10307.4 445 -45.4"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"excess_deaths['excess'] = excess_deaths.total_2020 - excess_deaths.previous_mean\n",
"excess_deaths"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"excess_deaths[['covid_deaths', 'excess']].plot()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.385140247879974"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"excess_deaths.excess.sum() / excess_deaths.covid_deaths.sum()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" total_2020 | \n",
" previous_mean | \n",
" covid_deaths | \n",
" excess | \n",
" accounted_fraction | \n",
"
\n",
" \n",
" \n",
" \n",
" 2020-03-20 | \n",
" 12112.0 | \n",
" 12007.4 | \n",
" 149 | \n",
" 104.6 | \n",
" 1.424474 | \n",
"
\n",
" \n",
" 2020-03-27 | \n",
" 12507.0 | \n",
" 11549.6 | \n",
" 720 | \n",
" 957.4 | \n",
" 0.752037 | \n",
"
\n",
" \n",
" 2020-04-03 | \n",
" 18565.0 | \n",
" 11681.4 | \n",
" 2870 | \n",
" 6883.6 | \n",
" 0.416933 | \n",
"
\n",
" \n",
" 2020-04-10 | \n",
" 20929.0 | \n",
" 11919.4 | \n",
" 5868 | \n",
" 9009.6 | \n",
" 0.651305 | \n",
"
\n",
" \n",
" 2020-04-17 | \n",
" 24691.0 | \n",
" 11850.6 | \n",
" 6340 | \n",
" 12840.4 | \n",
" 0.493754 | \n",
"
\n",
" \n",
" 2020-04-24 | \n",
" 24303.0 | \n",
" 11844.4 | \n",
" 5845 | \n",
" 12458.6 | \n",
" 0.469154 | \n",
"
\n",
" \n",
" 2020-05-01 | \n",
" 20059.0 | \n",
" 11318.4 | \n",
" 4987 | \n",
" 8740.6 | \n",
" 0.570556 | \n",
"
\n",
" \n",
" 2020-05-08 | \n",
" 14428.0 | \n",
" 10887.2 | \n",
" 3850 | \n",
" 3540.8 | \n",
" 1.087325 | \n",
"
\n",
" \n",
" 2020-05-15 | \n",
" 16390.0 | \n",
" 11547.0 | \n",
" 3006 | \n",
" 4843.0 | \n",
" 0.620690 | \n",
"
\n",
" \n",
" 2020-05-22 | \n",
" 13839.0 | \n",
" 11281.0 | \n",
" 2449 | \n",
" 2558.0 | \n",
" 0.957389 | \n",
"
\n",
" \n",
" 2020-05-29 | \n",
" 11265.0 | \n",
" 9448.0 | \n",
" 2199 | \n",
" 1817.0 | \n",
" 1.210237 | \n",
"
\n",
" \n",
" 2020-06-05 | \n",
" 12106.0 | \n",
" 11325.6 | \n",
" 1697 | \n",
" 780.4 | \n",
" 2.174526 | \n",
"
\n",
" \n",
" 2020-06-12 | \n",
" 11302.0 | \n",
" 10703.6 | \n",
" 1388 | \n",
" 598.4 | \n",
" 2.319519 | \n",
"
\n",
" \n",
" 2020-06-19 | \n",
" 10694.0 | \n",
" 10698.2 | \n",
" 1018 | \n",
" -4.2 | \n",
" -242.380952 | \n",
"
\n",
" \n",
" 2020-06-26 | \n",
" 10282.0 | \n",
" 10605.6 | \n",
" 835 | \n",
" -323.6 | \n",
" -2.580346 | \n",
"
\n",
" \n",
" 2020-07-03 | \n",
" 10412.0 | \n",
" 10483.0 | \n",
" 765 | \n",
" -71.0 | \n",
" -10.774648 | \n",
"
\n",
" \n",
" 2020-07-10 | \n",
" 9941.0 | \n",
" 10509.4 | \n",
" 607 | \n",
" -568.4 | \n",
" -1.067910 | \n",
"
\n",
" \n",
" 2020-07-17 | \n",
" 10096.0 | \n",
" 10360.6 | \n",
" 517 | \n",
" -264.6 | \n",
" -1.953893 | \n",
"
\n",
" \n",
" 2020-07-24 | \n",
" 10159.0 | \n",
" 10311.6 | \n",
" 435 | \n",
" -152.6 | \n",
" -2.850590 | \n",
"
\n",
" \n",
" 2020-07-31 | \n",
" 10262.0 | \n",
" 10307.4 | \n",
" 445 | \n",
" -45.4 | \n",
" -9.801762 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" total_2020 previous_mean covid_deaths excess \\\n",
"2020-03-20 12112.0 12007.4 149 104.6 \n",
"2020-03-27 12507.0 11549.6 720 957.4 \n",
"2020-04-03 18565.0 11681.4 2870 6883.6 \n",
"2020-04-10 20929.0 11919.4 5868 9009.6 \n",
"2020-04-17 24691.0 11850.6 6340 12840.4 \n",
"2020-04-24 24303.0 11844.4 5845 12458.6 \n",
"2020-05-01 20059.0 11318.4 4987 8740.6 \n",
"2020-05-08 14428.0 10887.2 3850 3540.8 \n",
"2020-05-15 16390.0 11547.0 3006 4843.0 \n",
"2020-05-22 13839.0 11281.0 2449 2558.0 \n",
"2020-05-29 11265.0 9448.0 2199 1817.0 \n",
"2020-06-05 12106.0 11325.6 1697 780.4 \n",
"2020-06-12 11302.0 10703.6 1388 598.4 \n",
"2020-06-19 10694.0 10698.2 1018 -4.2 \n",
"2020-06-26 10282.0 10605.6 835 -323.6 \n",
"2020-07-03 10412.0 10483.0 765 -71.0 \n",
"2020-07-10 9941.0 10509.4 607 -568.4 \n",
"2020-07-17 10096.0 10360.6 517 -264.6 \n",
"2020-07-24 10159.0 10311.6 435 -152.6 \n",
"2020-07-31 10262.0 10307.4 445 -45.4 \n",
"\n",
" accounted_fraction \n",
"2020-03-20 1.424474 \n",
"2020-03-27 0.752037 \n",
"2020-04-03 0.416933 \n",
"2020-04-10 0.651305 \n",
"2020-04-17 0.493754 \n",
"2020-04-24 0.469154 \n",
"2020-05-01 0.570556 \n",
"2020-05-08 1.087325 \n",
"2020-05-15 0.620690 \n",
"2020-05-22 0.957389 \n",
"2020-05-29 1.210237 \n",
"2020-06-05 2.174526 \n",
"2020-06-12 2.319519 \n",
"2020-06-19 -242.380952 \n",
"2020-06-26 -2.580346 \n",
"2020-07-03 -10.774648 \n",
"2020-07-10 -1.067910 \n",
"2020-07-17 -1.953893 \n",
"2020-07-24 -2.850590 \n",
"2020-07-31 -9.801762 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"excess_deaths['accounted_fraction'] = excess_deaths.covid_deaths / excess_deaths.excess\n",
"excess_deaths"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" | \n",
" total_2020 | \n",
" previous_mean | \n",
" covid_deaths | \n",
" excess | \n",
" accounted_fraction | \n",
" covid_deaths_m2 | \n",
" excess_m2 | \n",
" accounted_fraction_m2 | \n",
"
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" \n",
" \n",
" \n",
" 2020-03-20 | \n",
" 12112.0 | \n",
" 12007.4 | \n",
" 149 | \n",
" 104.6 | \n",
" 1.424474 | \n",
" 149.0 | \n",
" 104.6 | \n",
" 1.424474 | \n",
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" \n",
" 2020-03-27 | \n",
" 12507.0 | \n",
" 11549.6 | \n",
" 720 | \n",
" 957.4 | \n",
" 0.752037 | \n",
" 434.5 | \n",
" 531.0 | \n",
" 0.818267 | \n",
"
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" \n",
" 2020-04-03 | \n",
" 18565.0 | \n",
" 11681.4 | \n",
" 2870 | \n",
" 6883.6 | \n",
" 0.416933 | \n",
" 1795.0 | \n",
" 3920.5 | \n",
" 0.457850 | \n",
"
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" \n",
" 2020-04-10 | \n",
" 20929.0 | \n",
" 11919.4 | \n",
" 5868 | \n",
" 9009.6 | \n",
" 0.651305 | \n",
" 4369.0 | \n",
" 7946.6 | \n",
" 0.549795 | \n",
"
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" \n",
" 2020-04-17 | \n",
" 24691.0 | \n",
" 11850.6 | \n",
" 6340 | \n",
" 12840.4 | \n",
" 0.493754 | \n",
" 6104.0 | \n",
" 10925.0 | \n",
" 0.558719 | \n",
"
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" \n",
" 2020-04-24 | \n",
" 24303.0 | \n",
" 11844.4 | \n",
" 5845 | \n",
" 12458.6 | \n",
" 0.469154 | \n",
" 6092.5 | \n",
" 12649.5 | \n",
" 0.481640 | \n",
"
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" \n",
" 2020-05-01 | \n",
" 20059.0 | \n",
" 11318.4 | \n",
" 4987 | \n",
" 8740.6 | \n",
" 0.570556 | \n",
" 5416.0 | \n",
" 10599.6 | \n",
" 0.510963 | \n",
"
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" \n",
" 2020-05-08 | \n",
" 14428.0 | \n",
" 10887.2 | \n",
" 3850 | \n",
" 3540.8 | \n",
" 1.087325 | \n",
" 4418.5 | \n",
" 6140.7 | \n",
" 0.719543 | \n",
"
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" \n",
" 2020-05-15 | \n",
" 16390.0 | \n",
" 11547.0 | \n",
" 3006 | \n",
" 4843.0 | \n",
" 0.620690 | \n",
" 3428.0 | \n",
" 4191.9 | \n",
" 0.817768 | \n",
"
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" \n",
" 2020-05-22 | \n",
" 13839.0 | \n",
" 11281.0 | \n",
" 2449 | \n",
" 2558.0 | \n",
" 0.957389 | \n",
" 2727.5 | \n",
" 3700.5 | \n",
" 0.737063 | \n",
"
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" \n",
" 2020-05-29 | \n",
" 11265.0 | \n",
" 9448.0 | \n",
" 2199 | \n",
" 1817.0 | \n",
" 1.210237 | \n",
" 2324.0 | \n",
" 2187.5 | \n",
" 1.062400 | \n",
"
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" \n",
" 2020-06-05 | \n",
" 12106.0 | \n",
" 11325.6 | \n",
" 1697 | \n",
" 780.4 | \n",
" 2.174526 | \n",
" 1948.0 | \n",
" 1298.7 | \n",
" 1.499961 | \n",
"
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" \n",
" 2020-06-12 | \n",
" 11302.0 | \n",
" 10703.6 | \n",
" 1388 | \n",
" 598.4 | \n",
" 2.319519 | \n",
" 1542.5 | \n",
" 689.4 | \n",
" 2.237453 | \n",
"
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" \n",
" 2020-06-19 | \n",
" 10694.0 | \n",
" 10698.2 | \n",
" 1018 | \n",
" -4.2 | \n",
" -242.380952 | \n",
" 1203.0 | \n",
" 297.1 | \n",
" 4.049142 | \n",
"
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" \n",
" 2020-06-26 | \n",
" 10282.0 | \n",
" 10605.6 | \n",
" 835 | \n",
" -323.6 | \n",
" -2.580346 | \n",
" 926.5 | \n",
" -163.9 | \n",
" -5.652837 | \n",
"
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" \n",
" 2020-07-03 | \n",
" 10412.0 | \n",
" 10483.0 | \n",
" 765 | \n",
" -71.0 | \n",
" -10.774648 | \n",
" 800.0 | \n",
" -197.3 | \n",
" -4.054739 | \n",
"
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" \n",
" 2020-07-10 | \n",
" 9941.0 | \n",
" 10509.4 | \n",
" 607 | \n",
" -568.4 | \n",
" -1.067910 | \n",
" 686.0 | \n",
" -319.7 | \n",
" -2.145762 | \n",
"
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" \n",
" 2020-07-17 | \n",
" 10096.0 | \n",
" 10360.6 | \n",
" 517 | \n",
" -264.6 | \n",
" -1.953893 | \n",
" 562.0 | \n",
" -416.5 | \n",
" -1.349340 | \n",
"
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" \n",
" 2020-07-24 | \n",
" 10159.0 | \n",
" 10311.6 | \n",
" 435 | \n",
" -152.6 | \n",
" -2.850590 | \n",
" 476.0 | \n",
" -208.6 | \n",
" -2.281879 | \n",
"
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" \n",
" 2020-07-31 | \n",
" 10262.0 | \n",
" 10307.4 | \n",
" 445 | \n",
" -45.4 | \n",
" -9.801762 | \n",
" 440.0 | \n",
" -99.0 | \n",
" -4.444444 | \n",
"
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" \n",
"
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"
"
],
"text/plain": [
" total_2020 previous_mean covid_deaths excess \\\n",
"2020-03-20 12112.0 12007.4 149 104.6 \n",
"2020-03-27 12507.0 11549.6 720 957.4 \n",
"2020-04-03 18565.0 11681.4 2870 6883.6 \n",
"2020-04-10 20929.0 11919.4 5868 9009.6 \n",
"2020-04-17 24691.0 11850.6 6340 12840.4 \n",
"2020-04-24 24303.0 11844.4 5845 12458.6 \n",
"2020-05-01 20059.0 11318.4 4987 8740.6 \n",
"2020-05-08 14428.0 10887.2 3850 3540.8 \n",
"2020-05-15 16390.0 11547.0 3006 4843.0 \n",
"2020-05-22 13839.0 11281.0 2449 2558.0 \n",
"2020-05-29 11265.0 9448.0 2199 1817.0 \n",
"2020-06-05 12106.0 11325.6 1697 780.4 \n",
"2020-06-12 11302.0 10703.6 1388 598.4 \n",
"2020-06-19 10694.0 10698.2 1018 -4.2 \n",
"2020-06-26 10282.0 10605.6 835 -323.6 \n",
"2020-07-03 10412.0 10483.0 765 -71.0 \n",
"2020-07-10 9941.0 10509.4 607 -568.4 \n",
"2020-07-17 10096.0 10360.6 517 -264.6 \n",
"2020-07-24 10159.0 10311.6 435 -152.6 \n",
"2020-07-31 10262.0 10307.4 445 -45.4 \n",
"\n",
" accounted_fraction covid_deaths_m2 excess_m2 \\\n",
"2020-03-20 1.424474 149.0 104.6 \n",
"2020-03-27 0.752037 434.5 531.0 \n",
"2020-04-03 0.416933 1795.0 3920.5 \n",
"2020-04-10 0.651305 4369.0 7946.6 \n",
"2020-04-17 0.493754 6104.0 10925.0 \n",
"2020-04-24 0.469154 6092.5 12649.5 \n",
"2020-05-01 0.570556 5416.0 10599.6 \n",
"2020-05-08 1.087325 4418.5 6140.7 \n",
"2020-05-15 0.620690 3428.0 4191.9 \n",
"2020-05-22 0.957389 2727.5 3700.5 \n",
"2020-05-29 1.210237 2324.0 2187.5 \n",
"2020-06-05 2.174526 1948.0 1298.7 \n",
"2020-06-12 2.319519 1542.5 689.4 \n",
"2020-06-19 -242.380952 1203.0 297.1 \n",
"2020-06-26 -2.580346 926.5 -163.9 \n",
"2020-07-03 -10.774648 800.0 -197.3 \n",
"2020-07-10 -1.067910 686.0 -319.7 \n",
"2020-07-17 -1.953893 562.0 -416.5 \n",
"2020-07-24 -2.850590 476.0 -208.6 \n",
"2020-07-31 -9.801762 440.0 -99.0 \n",
"\n",
" accounted_fraction_m2 \n",
"2020-03-20 1.424474 \n",
"2020-03-27 0.818267 \n",
"2020-04-03 0.457850 \n",
"2020-04-10 0.549795 \n",
"2020-04-17 0.558719 \n",
"2020-04-24 0.481640 \n",
"2020-05-01 0.510963 \n",
"2020-05-08 0.719543 \n",
"2020-05-15 0.817768 \n",
"2020-05-22 0.737063 \n",
"2020-05-29 1.062400 \n",
"2020-06-05 1.499961 \n",
"2020-06-12 2.237453 \n",
"2020-06-19 4.049142 \n",
"2020-06-26 -5.652837 \n",
"2020-07-03 -4.054739 \n",
"2020-07-10 -2.145762 \n",
"2020-07-17 -1.349340 \n",
"2020-07-24 -2.281879 \n",
"2020-07-31 -4.444444 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"excess_deaths['covid_deaths_m2'] = excess_deaths.covid_deaths.transform(lambda x: x.rolling(2, 1).mean())\n",
"excess_deaths['excess_m2'] = excess_deaths.excess.transform(lambda x: x.rolling(2, 1).mean())\n",
"excess_deaths['accounted_fraction_m2'] = excess_deaths.covid_deaths_m2 / excess_deaths.excess_m2\n",
"excess_deaths"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
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