--- /dev/null
+---
+jupyter:
+ jupytext:
+ formats: ipynb,md
+ text_representation:
+ extension: .md
+ format_name: markdown
+ format_version: '1.2'
+ jupytext_version: 1.3.4
+ kernelspec:
+ display_name: Python 3
+ language: python
+ name: python3
+---
+
+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.
+
+
+```python
+import itertools
+import collections
+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
+!ls uk-deaths-data
+```
+
+```python
+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
+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
+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
+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
+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
+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.head()
+```
+
+```python
+
+```
+
+```python
+
+```
+
+```python
+raw_data_s = pd.read_csv('uk-deaths-data/weekly-deaths-april-20-scotland.csv',
+ index_col=0,
+ header=0,
+ skiprows=2
+ )
+# raw_data_s
+```
+
+```python
+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
+
+```
+
+```python
+
+```
+
+```python
+
+```
+
+```python
+
+```
+
+```python
+
+```
+
+```python
+raw_data_2020 = pd.read_csv('uk-deaths-data/publishedweek182020.csv',
+ parse_dates=[1], dayfirst=True,
+ index_col=0,
+ header=[0, 1])
+```
+
+```python
+# raw_data_2020.head()
+```
+
+```python
+raw_data_2020['W92000004', 'Wales']
+```
+
+```python
+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
+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
+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
+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
+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
+deaths_headlines_e = raw_data_2020.iloc[:, [1]]
+deaths_headlines_e.columns = ['total_2020']
+deaths_headlines_w = raw_data_2020['W92000004']
+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
+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.head()
+```
+
+```python
+dh19w.head()
+```
+
+```python
+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
+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
+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
+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
+# dh18 = raw_data_2018.iloc[:, [1, 2]]
+# dh18.columns = ['total_2018', 'total_previous']
+# # dh18.head()
+```
+
+```python
+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
+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
+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
+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
+deaths_headlines = deaths_headlines_e + deaths_headlines_w + deaths_headlines_i + deaths_headlines_s
+deaths_headlines
+```
+
+```python
+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
+deaths_headlines['total_2020 total_2019 total_2018 total_2017 total_2016 total_2015'.split()].plot(figsize=(10, 8))
+```
+
+```python
+fig = plt.figure(figsize=(10, 10))
+ax = fig.add_subplot(111, projection="polar")
+
+theta = np.roll(
+ np.flip(
+ np.arange(len(deaths_headlines))/float(len(deaths_headlines))*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, deaths_headlines['total_2015'], color="#e47d7d", label="2015") # 0
+l16, = ax.plot(theta, deaths_headlines['total_2016'], color="#afc169", label="2016") # 72 , d0e47d
+l17, = ax.plot(theta, deaths_headlines['total_2017'], color="#7de4a6", label="2017") # 144
+l18, = ax.plot(theta, deaths_headlines['total_2018'], color="#7da6e4", label="2018") # 216
+l19, = ax.plot(theta, deaths_headlines['total_2019'], color="#d07de4", label="2019") # 288
+
+lmean, = ax.plot(theta, deaths_headlines['previous_mean'], color="black", linestyle='dashed', label="mean")
+
+l20, = ax.plot(theta, deaths_headlines['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.index)
+plt.legend()
+plt.title("Deaths by week over years, all UK")
+plt.savefig('deaths-radar.png')
+plt.show()
+```
+
+```python
+(deaths_headlines.loc[12:].total_2020 - deaths_headlines.loc[12:].previous_mean).sum()
+```
+
+```python
+
+```
projects / covid19.git / blobdiff