+++ /dev/null
----
-jupyter:
- jupytext:
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- text_representation:
- extension: .md
- format_name: markdown
- format_version: '1.2'
- jupytext_version: 1.9.1
- kernelspec:
- display_name: Python 3
- language: python
- name: python3
----
-
-<!-- #region Collapsed="false" -->
-Data from:
-
-* [Office of National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwales) (Endland and Wales) Weeks start on a Saturday.
-* [Northern Ireland Statistics and Research Agency](https://www.nisra.gov.uk/publications/weekly-deaths) (Northern Ireland). Weeks start on a Saturday. Note that the week numbers don't match the England and Wales data.
-* [National Records of Scotland](https://www.nrscotland.gov.uk/statistics-and-data/statistics/statistics-by-theme/vital-events/general-publications/weekly-and-monthly-data-on-births-and-deaths/weekly-data-on-births-and-deaths) (Scotland). Note that Scotland uses ISO8601 week numbers, which start on a Monday.
-
-<!-- #endregion -->
-
-```python Collapsed="false"
-import itertools
-import collections
-import json
-import pandas as pd
-import numpy as np
-from scipy.stats import gmean
-
-import matplotlib as mpl
-import matplotlib.pyplot as plt
-%matplotlib inline
-```
-
-```python Collapsed="false"
-england_wales_filename = 'uk-deaths-data/publishedweek532020.xlsx'
-```
-
-```python Collapsed="false"
-raw_data_2015 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2015.csv',
- parse_dates=[1, 2], dayfirst=True,
- index_col=0,
- header=[0, 1]
- )
-dh15i = raw_data_2015.iloc[:, [2]]
-dh15i.columns = ['total_2015']
-# dh15i.head()
-```
-
-```python Collapsed="false"
-raw_data_2016 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2016.csv',
- parse_dates=[1, 2], dayfirst=True,
- index_col=0,
- header=[0, 1]
- )
-dh16i = raw_data_2016.iloc[:, [2]]
-dh16i.columns = ['total_2016']
-# dh16i.head()
-```
-
-```python Collapsed="false"
-raw_data_2017 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2017.csv',
- parse_dates=[1, 2], dayfirst=True,
- index_col=0,
- header=[0, 1]
- )
-dh17i = raw_data_2017.iloc[:, [2]]
-dh17i.columns = ['total_2017']
-# dh17i.head()
-```
-
-```python Collapsed="false"
-raw_data_2018 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2018.csv',
- parse_dates=[1, 2], dayfirst=True,
- index_col=0,
- header=[0, 1]
- )
-dh18i = raw_data_2018.iloc[:, [2]]
-dh18i.columns = ['total_2018']
-# dh18i.head()
-```
-
-```python Collapsed="false"
-raw_data_2019 = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2019.csv',
- parse_dates=[1, 2], dayfirst=True,
- index_col=0,
- header=[0, 1]
- )
-dh19i = raw_data_2019.iloc[:, [2]]
-dh19i.columns = ['total_2019']
-# dh19i.head()
-```
-
-```python Collapsed="false"
-raw_data_2020_i = pd.read_csv('uk-deaths-data/Weekly_Deaths_NI_2020.csv',
- parse_dates=[1], dayfirst=True,
- index_col=0,
- header=[0, 1]
- )
-deaths_headlines_i = raw_data_2020_i.iloc[:, [1]]
-deaths_headlines_i.columns = ['total_2020']
-deaths_headlines_i.tail()
-```
-
-```python Collapsed="false"
-raw_data_2019
-```
-
-```python Collapsed="false"
-
-```
-
-```python Collapsed="false"
-raw_data_s = pd.read_csv('uk-deaths-data/weekly-deaths-scotland.csv',
- index_col=0,
- header=0,
- skiprows=2
- )
-# raw_data_s
-```
-
-```python Collapsed="false"
-deaths_headlines_s = raw_data_s[reversed('2015 2016 2017 2018 2019 2020'.split())]
-deaths_headlines_s.columns = ['total_' + c for c in deaths_headlines_s.columns]
-deaths_headlines_s.reset_index(drop=True, inplace=True)
-deaths_headlines_s.index = deaths_headlines_s.index + 1
-deaths_headlines_s
-```
-
-```python Collapsed="false"
-
-```
-
-```python Collapsed="false"
-eng_xls = pd.read_excel(england_wales_filename,
- sheet_name="Weekly figures 2020",
- skiprows=[0, 1, 2, 3],
- header=0,
- index_col=[1]
- ).iloc[:91].T
-eng_xls
-```
-
-```python Collapsed="false"
-# eng_xls_columns
-```
-
-```python Collapsed="false"
-eng_xls_columns = list(eng_xls.columns)
-
-for i, c in enumerate(eng_xls_columns):
-# print(i, c, type(c), isinstance(c, float))
- if isinstance(c, float) and np.isnan(c):
- if eng_xls.iloc[0].iloc[i] is not pd.NaT:
- eng_xls_columns[i] = eng_xls.iloc[0].iloc[i]
-
-# np.isnan(eng_xls_columns[0])
-# eng_xls_columns
-
-eng_xls.columns = eng_xls_columns
-# eng_xls.columns
-```
-
-```python Collapsed="false"
-eng_xls['Total deaths, all ages']
-```
-
-```python Collapsed="false"
-eng_xls['Wales'].iloc[1:]
-```
-
-```python Collapsed="false"
-# raw_data_2020 = pd.read_csv('uk-deaths-data/publishedweek272020.csv',
-# parse_dates=[1], dayfirst=True,
-# index_col=0,
-# header=[0, 1])
-```
-
-```python Collapsed="false"
-
-```
-
-```python Collapsed="false"
-# raw_data_2020.head()
-```
-
-```python Collapsed="false"
-# raw_data_2020['W92000004', 'Wales']
-```
-
-```python Collapsed="false"
-raw_data_2019 = pd.read_csv('uk-deaths-data/publishedweek522019.csv',
- parse_dates=[1], dayfirst=True,
- index_col=0,
- header=[0, 1])
-# raw_data_2019.head()
-```
-
-```python Collapsed="false"
-raw_data_2018 = pd.read_csv('uk-deaths-data/publishedweek522018.csv',
- parse_dates=[1], dayfirst=True,
- index_col=0,
- header=[0, 1])
-# raw_data_2018.head()
-```
-
-```python Collapsed="false"
-raw_data_2017 = pd.read_csv('uk-deaths-data/publishedweek522017.csv',
- parse_dates=[1], dayfirst=True,
- index_col=0,
- header=[0, 1])
-# raw_data_2017.head()
-```
-
-```python Collapsed="false"
-raw_data_2016 = pd.read_csv('uk-deaths-data/publishedweek522016.csv',
- parse_dates=[1], dayfirst=True,
- index_col=0,
- header=[0, 1])
-# raw_data_2016.head()
-```
-
-```python Collapsed="false"
-raw_data_2015 = pd.read_csv('uk-deaths-data/publishedweek2015.csv',
- parse_dates=[1], dayfirst=True,
- index_col=0,
- header=[0, 1])
-# raw_data_2015.head()
-```
-
-```python Collapsed="false"
-dhw = eng_xls['Wales'].iloc[1:]
-dhe = eng_xls['Total deaths, all ages'].iloc[1:] - dhw
-deaths_headlines_e = pd.DataFrame({'total_2020': dhe.dropna()})
-deaths_headlines_w = pd.DataFrame({'total_2020': dhw.dropna()})
-```
-
-```python Collapsed="false"
-# deaths_headlines_e = raw_data_2020.iloc[:, [1]].copy()
-# deaths_headlines_e.columns = ['total_2020']
-# deaths_headlines_w = raw_data_2020['W92000004'].copy()
-# deaths_headlines_e.columns = ['total_2020']
-# deaths_headlines_w.columns = ['total_2020']
-# deaths_headlines_e.total_2020 -= deaths_headlines_w.total_2020
-# deaths_headlines_e.head()
-# deaths_headlines_e
-```
-
-```python Collapsed="false"
-dh19e = raw_data_2019.iloc[:, [1]]
-dh19w = raw_data_2019['W92000004']
-dh19e.columns = ['total_2019']
-dh19w.columns = ['total_2019']
-dh19e.total_2019 -= dh19w.total_2019
-dh19e.tail()
-```
-
-```python Collapsed="false"
-dh19w.head()
-```
-
-```python Collapsed="false"
-dh18e = raw_data_2018.iloc[:, [1]]
-dh18w = raw_data_2018['W92000004']
-dh18e.columns = ['total_2018']
-dh18w.columns = ['total_2018']
-dh18e.total_2018 -= dh18w.total_2018
-# dh18e.head()
-```
-
-```python Collapsed="false"
-dh17e = raw_data_2017.iloc[:, [1]]
-dh17w = raw_data_2017['W92000004']
-dh17e.columns = ['total_2017']
-dh17w.columns = ['total_2017']
-dh17e.total_2017 -= dh17w.total_2017
-# dh17e.head()
-```
-
-```python Collapsed="false"
-dh16e = raw_data_2016.iloc[:, [1]]
-dh16w = raw_data_2016['W92000004']
-dh16e.columns = ['total_2016']
-dh16w.columns = ['total_2016']
-dh16e.total_2016 -= dh16w.total_2016
-# dh16e.head()
-```
-
-```python Collapsed="false"
-dh15e = raw_data_2015.iloc[:, [1]]
-dh15w = raw_data_2015['W92000004']
-dh15e.columns = ['total_2015']
-dh15w.columns = ['total_2015']
-dh15e.total_2015 -= dh15w.total_2015
-# dh15e.head()
-```
-
-```python Collapsed="false"
-# dh18 = raw_data_2018.iloc[:, [1, 2]]
-# dh18.columns = ['total_2018', 'total_previous']
-# # dh18.head()
-```
-
-```python Collapsed="false"
-deaths_headlines_e = deaths_headlines_e.merge(dh19e['total_2019'], how='outer', left_index=True, right_index=True)
-deaths_headlines_e = deaths_headlines_e.merge(dh18e['total_2018'], how='outer', left_index=True, right_index=True)
-deaths_headlines_e = deaths_headlines_e.merge(dh17e['total_2017'], how='outer', left_index=True, right_index=True)
-deaths_headlines_e = deaths_headlines_e.merge(dh16e['total_2016'], how='outer', left_index=True, right_index=True)
-# deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)
-deaths_headlines_e = deaths_headlines_e.merge(dh15e['total_2015'], how='left', left_index=True, right_index=True)
-deaths_headlines_e
-```
-
-```python Collapsed="false"
-deaths_headlines_s = raw_data_s[reversed('2015 2016 2017 2018 2019 2020'.split())]
-deaths_headlines_s.columns = ['total_' + c for c in deaths_headlines_s.columns]
-deaths_headlines_s.reset_index(drop=True, inplace=True)
-deaths_headlines_s.index = deaths_headlines_s.index + 1
-deaths_headlines_s = deaths_headlines_s.loc[1:52]
-deaths_headlines_s
-```
-
-<!-- #region Collapsed="false" -->
-# Correction for missing data
-<!-- #endregion -->
-
-```python Collapsed="false"
-# deaths_headlines_s.loc[20, 'total_2020'] = 1000
-# deaths_headlines_s
-```
-
-```python Collapsed="false"
-deaths_headlines_w = deaths_headlines_w.merge(dh19w['total_2019'], how='outer', left_index=True, right_index=True)
-deaths_headlines_w = deaths_headlines_w.merge(dh18w['total_2018'], how='outer', left_index=True, right_index=True)
-deaths_headlines_w = deaths_headlines_w.merge(dh17w['total_2017'], how='outer', left_index=True, right_index=True)
-deaths_headlines_w = deaths_headlines_w.merge(dh16w['total_2016'], how='outer', left_index=True, right_index=True)
-# deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)
-deaths_headlines_w = deaths_headlines_w.merge(dh15w['total_2015'], how='left', left_index=True, right_index=True)
-deaths_headlines_w
-```
-
-```python Collapsed="false"
-deaths_headlines_i = deaths_headlines_i.merge(dh19i['total_2019'], how='outer', left_index=True, right_index=True)
-deaths_headlines_i = deaths_headlines_i.merge(dh18i['total_2018'], how='outer', left_index=True, right_index=True)
-deaths_headlines_i = deaths_headlines_i.merge(dh17i['total_2017'], how='outer', left_index=True, right_index=True)
-deaths_headlines_i = deaths_headlines_i.merge(dh16i['total_2016'], how='outer', left_index=True, right_index=True)
-deaths_headlines_i = deaths_headlines_i.merge(dh15i['total_2015'], how='left', left_index=True, right_index=True)
-deaths_headlines_i
-```
-
-```python Collapsed="false"
-deaths_headlines_s
-```
-
-```python Collapsed="false"
-deaths_headlines = deaths_headlines_e + deaths_headlines_w + deaths_headlines_i + deaths_headlines_s
-deaths_headlines
-```
-
-```python Collapsed="false"
-deaths_headlines_e['previous_mean'] = deaths_headlines_e['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
-deaths_headlines_w['previous_mean'] = deaths_headlines_w['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
-deaths_headlines_s['previous_mean'] = deaths_headlines_s['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
-deaths_headlines_i['previous_mean'] = deaths_headlines_i['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
-deaths_headlines['previous_mean'] = deaths_headlines['total_2019 total_2018 total_2017 total_2016 total_2015'.split()].apply(np.mean, axis=1)
-deaths_headlines
-```
-
-```python Collapsed="false"
-deaths_headlines['total_2020 total_2019 total_2018 total_2017 total_2016 total_2015'.split()].plot(figsize=(14, 8))
-```
-
-```python Collapsed="false"
-deaths_headlines[['total_2020', 'previous_mean']].plot(figsize=(10, 8))
-```
-
-```python Collapsed="false"
-deaths_headlines_i.plot()
-```
-
-```python Collapsed="false"
-# Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
-
-dhna = deaths_headlines.dropna()
-
-fig = plt.figure(figsize=(10, 10))
-ax = fig.add_subplot(111, projection="polar")
-
-theta = np.roll(
- np.flip(
- np.arange(len(dhna))/float(len(dhna))*2.*np.pi),
- 14)
-# l15, = ax.plot(theta, deaths_headlines['total_2015'], color="#b56363", label="2015") # 0
-# l16, = ax.plot(theta, deaths_headlines['total_2016'], color="#a4b563", label="2016") # 72
-# l17, = ax.plot(theta, deaths_headlines['total_2017'], color="#63b584", label="2017") # 144
-# l18, = ax.plot(theta, deaths_headlines['total_2018'], color="#6384b5", label="2018") # 216
-# l19, = ax.plot(theta, deaths_headlines['total_2019'], color="#a4635b", label="2019") # 288
-l15, = ax.plot(theta, dhna['total_2015'], color="#e47d7d", label="2015") # 0
-l16, = ax.plot(theta, dhna['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
-l17, = ax.plot(theta, dhna['total_2017'], color="#7de4a6", label="2017") # 144
-l18, = ax.plot(theta, dhna['total_2018'], color="#7da6e4", label="2018") # 216
-l19, = ax.plot(theta, dhna['total_2019'], color="#d07de4", label="2019") # 288
-
-lmean, = ax.plot(theta, dhna['previous_mean'], color="black", linestyle='dashed', label="mean")
-
-l20, = ax.plot(theta, dhna['total_2020'], color="red", label="2020")
-
-# deaths_headlines.total_2019.plot(ax=ax)
-
-def _closeline(line):
- x, y = line.get_data()
- x = np.concatenate((x, [x[0]]))
- y = np.concatenate((y, [y[0]]))
- line.set_data(x, y)
-
-[_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
-
-
-ax.set_xticks(theta)
-ax.set_xticklabels(dhna.index)
-plt.legend()
-plt.title("Deaths by week over years, all UK")
-plt.savefig('deaths-radar.png')
-plt.show()
-```
-
-<!-- #region Collapsed="false" -->
-# Excess deaths calculation
-<!-- #endregion -->
-
-```python Collapsed="false"
-# raw_data_2020.loc[12, 'Week ended']
-```
-
-```python Collapsed="false"
-eng_xls.loc[12, 'Week ended']
-```
-
-```python Collapsed="false"
-# raw_data_2020.iloc[-1]['Week ended']
-```
-
-```python Collapsed="false"
-deaths_headlines_e.total_2020.dropna().last_valid_index()
-```
-
-```python Collapsed="false"
-eng_xls.loc[deaths_headlines_e.total_2020.dropna().last_valid_index(), 'Week ended']
-```
-
-```python Collapsed="false"
-eng_xls.loc[27, 'Week ended']
-```
-
-```python Collapsed="false"
-# raw_data_2020.loc[12].droplevel(1)['Week ended']
-```
-
-```python Collapsed="false"
-# raw_data_2020.iloc[-1].droplevel(1)['Week ended']
-```
-
-```python Collapsed="false"
-(deaths_headlines.loc[12:].total_2020 - deaths_headlines.loc[12:].previous_mean).sum()
-```
-
-```python Collapsed="false"
-(deaths_headlines.loc[12:27].total_2020 - deaths_headlines.loc[12:27].previous_mean).sum()
-```
-
-```python Collapsed="false"
-deaths_headlines.previous_mean.sum()
-```
-
-```python Collapsed="false"
-# excess_death_data = {
-# 'start_date': str(eng_xls.loc[12, 'Week ended']),
-# 'end_date': str(eng_xls.loc[deaths_headlines_e.total_2020.dropna().last_valid_index(), 'Week ended']),
-# 'excess_deaths': (deaths_headlines.loc[12:].total_2020 - deaths_headlines.loc[12:].previous_mean).sum()
-# }
-
-# with open('excess_deaths.json', 'w') as f:
-# json.dump(excess_death_data, f)
-```
-
-```python Collapsed="false"
-# excess_death_data = {
-# 'start_date': str(eng_xls.loc[12, 'Week ended']),
-# 'end_date': str(eng_xls.loc[27, 'Week ended']),
-# 'excess_deaths': (deaths_headlines.loc[12:27].total_2020 - deaths_headlines.loc[12:27].previous_mean).sum()
-# }
-
-# with open('excess_deaths.json', 'w') as f:
-# json.dump(excess_death_data, f)
-```
-
-```python Collapsed="false"
-# excess_death_data = {
-# 'start_date': str(raw_data_2020.loc[12].droplevel(1)['Week ended']),
-# 'end_date': str(raw_data_2020.iloc[-1].droplevel(1)['Week ended']),
-# 'excess_deaths': (deaths_headlines.loc[12:].total_2020 - deaths_headlines.loc[12:].previous_mean).sum()
-# }
-
-# with open('excess_deaths.json', 'w') as f:
-# json.dump(excess_death_data, f)
-```
-
-```python Collapsed="false"
-eng_xls['Week ended']
-```
-
-```python Collapsed="false"
-# raw_data_2020.droplevel(1, axis='columns')['Week ended']
-```
-
-```python Collapsed="false"
-deaths_by_week = deaths_headlines.merge(eng_xls['Week ended'], left_index=True, right_index=True)
-deaths_by_week.rename(columns={'Week ended': 'week_ended'}, inplace=True)
-deaths_by_week.to_csv('deaths_by_week.csv', header=True, index=False)
-```
-
-```python Collapsed="false"
-# deaths_by_week = deaths_headlines.merge(raw_data_2020.droplevel(1, axis='columns')['Week ended'], left_index=True, right_index=True)
-# deaths_by_week.rename(columns={'Week ended': 'week_ended'}, inplace=True)
-# deaths_by_week.to_csv('deaths_by_week.csv', header=True, index=False)
-```
-
-<!-- #region Collapsed="false" -->
-# Plots for UK nations
-<!-- #endregion -->
-
-```python Collapsed="false"
-# Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
-
-fig = plt.figure(figsize=(10, 10))
-ax = fig.add_subplot(111, projection="polar")
-
-theta = np.roll(
- np.flip(
- np.arange(len(deaths_headlines_e))/float(len(deaths_headlines_e))*2.*np.pi),
- 14)
-l15, = ax.plot(theta, deaths_headlines_e['total_2015'], color="#e47d7d", label="2015") # 0
-l16, = ax.plot(theta, deaths_headlines_e['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
-l17, = ax.plot(theta, deaths_headlines_e['total_2017'], color="#7de4a6", label="2017") # 144
-l18, = ax.plot(theta, deaths_headlines_e['total_2018'], color="#7da6e4", label="2018") # 216
-l19, = ax.plot(theta, deaths_headlines_e['total_2019'], color="#d07de4", label="2019") # 288
-
-lmean, = ax.plot(theta, deaths_headlines_e['previous_mean'], color="black", linestyle='dashed', label="mean")
-
-l20, = ax.plot(theta, deaths_headlines_e['total_2020'], color="red", label="2020")
-
-# deaths_headlines.total_2019.plot(ax=ax)
-
-def _closeline(line):
- x, y = line.get_data()
- x = np.concatenate((x, [x[0]]))
- y = np.concatenate((y, [y[0]]))
- line.set_data(x, y)
-
-[_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
-
-
-ax.set_xticks(theta)
-ax.set_xticklabels(deaths_headlines_e.index)
-plt.legend()
-plt.title("Deaths by week over years, England")
-plt.savefig('deaths-radar_england.png')
-plt.show()
-```
-
-```python Collapsed="false"
-# Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
-
-fig = plt.figure(figsize=(10, 10))
-ax = fig.add_subplot(111, projection="polar")
-
-theta = np.roll(
- np.flip(
- np.arange(len(deaths_headlines_w))/float(len(deaths_headlines_w))*2.*np.pi),
- 14)
-l15, = ax.plot(theta, deaths_headlines_w['total_2015'], color="#e47d7d", label="2015") # 0
-l16, = ax.plot(theta, deaths_headlines_w['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
-l17, = ax.plot(theta, deaths_headlines_w['total_2017'], color="#7de4a6", label="2017") # 144
-l18, = ax.plot(theta, deaths_headlines_w['total_2018'], color="#7da6e4", label="2018") # 216
-l19, = ax.plot(theta, deaths_headlines_w['total_2019'], color="#d07de4", label="2019") # 288
-
-lmean, = ax.plot(theta, deaths_headlines_w['previous_mean'], color="black", linestyle='dashed', label="mean")
-
-l20, = ax.plot(theta, deaths_headlines_w['total_2020'], color="red", label="2020")
-
-
-def _closeline(line):
- x, y = line.get_data()
- x = np.concatenate((x, [x[0]]))
- y = np.concatenate((y, [y[0]]))
- line.set_data(x, y)
-
-[_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
-
-
-ax.set_xticks(theta)
-ax.set_xticklabels(deaths_headlines_w.index)
-plt.legend()
-plt.title("Deaths by week over years, Wales")
-plt.savefig('deaths-radar_wales.png')
-plt.show()
-```
-
-```python Collapsed="false"
-# Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
-
-fig = plt.figure(figsize=(10, 10))
-ax = fig.add_subplot(111, projection="polar")
-
-theta = np.roll(
- np.flip(
- np.arange(len(deaths_headlines_s))/float(len(deaths_headlines_s))*2.*np.pi),
- 14)
-l15, = ax.plot(theta, deaths_headlines_s['total_2015'], color="#e47d7d", label="2015") # 0
-l16, = ax.plot(theta, deaths_headlines_s['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
-l17, = ax.plot(theta, deaths_headlines_s['total_2017'], color="#7de4a6", label="2017") # 144
-l18, = ax.plot(theta, deaths_headlines_s['total_2018'], color="#7da6e4", label="2018") # 216
-l19, = ax.plot(theta, deaths_headlines_s['total_2019'], color="#d07de4", label="2019") # 288
-
-lmean, = ax.plot(theta, deaths_headlines_s['previous_mean'], color="black", linestyle='dashed', label="mean")
-
-l20, = ax.plot(theta, deaths_headlines_s['total_2020'], color="red", label="2020")
-
-
-def _closeline(line):
- x, y = line.get_data()
- x = np.concatenate((x, [x[0]]))
- y = np.concatenate((y, [y[0]]))
- line.set_data(x, y)
-
-[_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
-
-
-ax.set_xticks(theta)
-ax.set_xticklabels(deaths_headlines_s.index)
-plt.legend()
-plt.title("Deaths by week over years, Scotland")
-plt.savefig('deaths-radar_scotland.png')
-plt.show()
-```
-
-```python Collapsed="false"
-# Radar plot code taken from example at https://stackoverflow.com/questions/42878485/getting-matplotlib-radar-plot-with-pandas#
-
-fig = plt.figure(figsize=(10, 10))
-ax = fig.add_subplot(111, projection="polar")
-
-theta = np.roll(
- np.flip(
- np.arange(len(deaths_headlines_i))/float(len(deaths_headlines_i))*2.*np.pi),
- 14)
-l15, = ax.plot(theta, deaths_headlines_i['total_2015'], color="#e47d7d", label="2015") # 0
-l16, = ax.plot(theta, deaths_headlines_i['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
-l17, = ax.plot(theta, deaths_headlines_i['total_2017'], color="#7de4a6", label="2017") # 144
-l18, = ax.plot(theta, deaths_headlines_i['total_2018'], color="#7da6e4", label="2018") # 216
-l19, = ax.plot(theta, deaths_headlines_i['total_2019'], color="#d07de4", label="2019") # 288
-
-lmean, = ax.plot(theta, deaths_headlines_i['previous_mean'], color="black", linestyle='dashed', label="mean")
-
-l20, = ax.plot(theta, deaths_headlines_i['total_2020'], color="red", label="2020")
-
-
-def _closeline(line):
- x, y = line.get_data()
- x = np.concatenate((x, [x[0]]))
- y = np.concatenate((y, [y[0]]))
- line.set_data(x, y)
-
-[_closeline(l) for l in [l19, l18, l17, l16, l15, lmean]]
-
-
-ax.set_xticks(theta)
-ax.set_xticklabels(deaths_headlines_i.index)
-plt.legend()
-plt.title("Deaths by week over years, Northern Ireland")
-plt.savefig('deaths-radar_northern_ireland.png')
-plt.show()
-```
-
-```python Collapsed="false"
-# list(raw_data_2020.columns)
-```
-
-```python Collapsed="false"
-# deaths_headlines_e = raw_data_2020.iloc[:, [1]].copy()
-# deaths_headlines_e.columns = ['total_2020']
-# deaths_headlines_w = raw_data_2020['W92000004'].copy()
-# deaths_headlines_e.columns = ['total_2020']
-# deaths_headlines_w.columns = ['total_2020']
-# deaths_headlines_e.total_2020 -= deaths_headlines_w.total_2020
-# deaths_headlines_e.head()
-# deaths_headlines_e
-```
-
-```python Collapsed="false"
-
-```
-
-```python Collapsed="false"
-
-```
projects / covid19.git / commitdiff