Covid-19: Comparison of Czech and German regions.
I joined several Czech regions to make them of similar size as the German states.
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CZ+DE: Covid19 by regions
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| """CZ - DE matric chart.""" | |
| # import csv | |
| import datetime | |
| import pandas | |
| import plotly.subplots as subplots | |
| import plotly.graph_objects as go | |
| # import requests | |
| path = "/home/michal/dev/coronavirus/regions/" | |
| supregions = [ | |
| { | |
| 'regions': ['Praha', 'Středočeský'], | |
| 'name': 'Prague and Central Bohemia' | |
| }, | |
| { | |
| 'regions': ['Karlovarský', 'Plzeňský', 'Jihočeský', 'Pardubický', 'Královéhradecký', 'Liberecký', 'Ústecký', 'Vysočina'], | |
| 'name': 'Bohemia w/o Prague and Centr.B.' | |
| }, | |
| { | |
| 'regions': ['Jihomoravský', 'Zlínský', 'Olomoucký', 'Moravskoslezský'], | |
| 'name': 'Moravia and Silesia' | |
| } | |
| ] | |
| # read data CZ | |
| infected_cz = pandas.read_csv(path + "cz_regions_infected.csv") | |
| basic_info_cz = pandas.read_csv(path + 'cz_regions.csv') | |
| data_cz = pandas.merge(infected_cz, basic_info_cz, on='name') | |
| names_cz = sorted(basic_info_cz["name"].tolist()) | |
| series_cz = [] | |
| for name in names_cz: | |
| series_cz.append(data_cz[(data_cz['name'] == name)]) | |
| dates_cz = [] | |
| for row in series_cz[0]['date']: | |
| date = datetime.datetime.strptime(row, "%Y-%m-%d") | |
| dates_cz.append(date) | |
| # join data CZ | |
| i = 0 | |
| for sr in supregions: | |
| selected = data_cz[data_cz['name'].isin(sr['regions'])] | |
| df = selected.groupby(['date']).sum() | |
| df = df.reset_index() | |
| df['name'] = [sr['name']] * df.shape[0] | |
| if i == 0: | |
| data_cz_transf = df | |
| else: | |
| data_cz_transf = pandas.concat([data_cz_transf, df]) | |
| i += 1 | |
| # read data | |
| infected_de = pandas.read_csv(path + "de_regions_infected.csv") | |
| basic_info_de = pandas.read_csv(path + 'de_regions.csv') | |
| data_de = pandas.merge(infected_de, basic_info_de, on='name') | |
| names_de = sorted(basic_info_de["name"].tolist()) | |
| series_de = [] | |
| for name in names_de: | |
| series_de.append(data_de[(data_de['name'] == name)]) | |
| dates_de = [] | |
| for row in series_de[-1]['date']: | |
| date = datetime.datetime.strptime(row, "%Y-%m-%d") | |
| dates_de.append(date) | |
| # join CZ + DE | |
| data = pandas.concat([data_cz_transf[['date', 'value', 'population', 'name']], data_de[['date', 'value', 'population', 'name']]]) | |
| names_cz_transf = [] | |
| for sr in supregions: | |
| names_cz_transf.append(sr['name']) | |
| names = names_cz_transf + names_de | |
| series = [] | |
| k = 0 | |
| for name in names: | |
| series.append(data[(data['name'] == name)]) | |
| k = 0 | |
| nnames = [] | |
| for name in names: | |
| if k < 3: | |
| prefix = 'CZ: ' | |
| else: | |
| prefix = 'DE: ' | |
| nnames.append(prefix + name) | |
| k += 1 | |
| # charting | |
| y_max = 60 | |
| colors = { | |
| 'primary': '#ce4414', | |
| 'secondary': '#9c948a', | |
| 'success': '#2f973e', | |
| 'info': '#138496', | |
| 'warning': '#ecaa1b', | |
| 'danger': '#c7291e', | |
| 'dark': '#772953', | |
| 'light': '#e9ecef', | |
| 'text-light': 'rgba(0, 0, 0, 0.9)', | |
| 'text-dark': '#fff', | |
| 'text-muted': '#868e96', | |
| 'text-primary': '#E95420', | |
| 'text-secondary': '#AEA79F', | |
| 'text-warning': '#EFB73E', | |
| 'text-danger': '#DF382C', | |
| 'text-success': '#38B44A', | |
| 'text-info': '#17a2b8' | |
| } | |
| dims = [4, 5] | |
| fig = subplots.make_subplots(dims[0], dims[1], shared_yaxes=True, subplot_titles=nnames) | |
| # fig.get_subplot(3,5)[0]['domain'] = (0.5, 0.5) | |
| fig.layout.annotations | |
| i = 1 | |
| j = 1 | |
| for s in series: | |
| for ss in series: | |
| traceg = go.Scatter( | |
| x=ss['date'].tolist(), | |
| y=(ss['value'] / ss['population'] * 100000).tolist(), | |
| name=ss['name'].tolist()[0], | |
| mode='lines', | |
| line={ | |
| 'color': colors['text-secondary'], | |
| 'width': 2 | |
| } | |
| ) | |
| fig.add_trace(traceg, row=i, col=j) | |
| trace = go.Scatter( | |
| x=s['date'].tolist(), | |
| y=(s['value'] / s['population'] * 100000).tolist(), | |
| name=s['name'].tolist()[0], | |
| mode='lines', | |
| line={ | |
| 'color': colors['dark'], | |
| 'width': 8 | |
| } | |
| ) | |
| # trace.textfont = {'color': colors['primary']} | |
| fig.add_trace(trace, row=i, col=j) | |
| fig.update_yaxes( | |
| range=[0, y_max], | |
| color=colors['text-secondary'], | |
| tickfont={'size': 30}, | |
| gridwidth=1, | |
| gridcolor=colors['light'], | |
| row=i, | |
| col=j | |
| ) | |
| fig.update_xaxes( | |
| tickformat="%d/3", | |
| # tickformat="%d.m.", | |
| color=colors['text-secondary'], | |
| tickfont={'size': 30}, | |
| nticks=4, | |
| gridwidth=2, | |
| gridcolor=colors['light'], | |
| ) | |
| j += 1 | |
| if j == (dims[1] + 1): | |
| j = 1 | |
| i += 1 | |
| fig.update_layout( | |
| height=1500, | |
| width=2000, | |
| # title_text="Covid 19 - Potvrzené případy na 100 000 obyvatel - Kraje ČR", | |
| showlegend=False, | |
| margin={ | |
| 't': 200 | |
| }, | |
| title={ | |
| 'font': { | |
| 'size': 35, | |
| 'color': colors['dark'] | |
| }, | |
| 'text': "<b>Covid 19 - Cumulative number of confirmed cases per 100,000 citizens - Czech and German regions</b>" | |
| }, | |
| font={"family": "Ubuntu", "color": colors['primary']}, | |
| template="plotly_white" | |
| ) | |
| # fig.show() | |
| # fig.write_image(path + "de_regions.png") | |
| fig.update_annotations( | |
| font={'size': 20} | |
| ) | |
| credit = go.layout.Annotation( | |
| text='Updated: 24/3/2020<br>Author: Michal Škop<br>@skopmichal<br>Data Source: Covid19CZ,<br>MZČR, Robert Koch Institut<br>CC-BY', | |
| align='left', | |
| font={'size': 20, 'color': colors['text-secondary']}, | |
| showarrow=False, | |
| xref='paper', | |
| yref='paper', | |
| x=1, | |
| y=0 | |
| ) | |
| fig.add_annotation(credit) | |
| # fig.show() | |
| fig.write_image(path + "cz_de_regions.png") | |
| fig.update_layout( | |
| height=1000, | |
| width=2000, | |
| ) | |
| fig.write_image(path + "cz_de_regions_twitter.png") |
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