Countries with highest confirmed cases of Covid-19 with Plotly
COVID-19 is the disease caused by a new coronavirus called SARS-CoV-2. World Health Organisation (WHO) first learned of the virus on 31 December 2019. The WHO declared the coronavirus outbreak a pandemic in March 2020. This article will show how to create an interactive line chart with plotly, showing the changes in confirmed cases over time.
The data used in this article is retrieved from the Johns Hopkins University who have made te data available on GitHub. More information about COVID-19 and the coronavirus is available from Coronavirus disease (COVID-19) advice for the public.
Plotly is an open-source graphing library for Python that produces interactive charts.
Load the data into a Pandas dataframe
The data is available on John Hopkins GitHub page. The data for the daily deaths from corona virus is in time_series_covid19_deaths_global.csv file. This file can either be downloaded and loaded into a dataframe or it can be loaded directly from GitHub as in the code below. Load and review the data.
1confirmed_path = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
2confirmed_df = pd.read_csv(confirmed_path)
3confirmed_df.shape
4"""
5(271, 315)
6"""
7
8confirmed_df.iloc[[0,1,2,-3,-2,-1], [0,1,2,-3,-2,-1]]
9"""
10 Province/State Country/Region Lat 11/25/20 11/26/20 11/27/20
110 NaN Afghanistan 33.939110 45490 45716 45839
121 NaN Albania 41.153300 34944 35600 36245
132 NaN Algeria 28.033900 78025 79110 80168
14268 NaN Yemen 15.552727 2124 2137 2148
15269 NaN Zambia -13.133897 17535 17553 17569
16270 NaN Zimbabwe -19.015438 9508 9623 9714
17"""
The data in the Country/Region is further broken down by Province/State for some
countries such as China or United Kingdom. A single row is created for each country
by grouping the data by Country/Region and summing up the number. the columns
'Province/State', 'Lat', 'Long' are removed from the dataset.
1# Drop columns 'Province/State', 'Lat', 'Long'
2confirmed_df.drop(['Province/State', 'Lat', 'Long'], axis = 1, inplace = True)
3
4country_df = confirmed_df.groupby('Country/Region').sum()
5country_df.shape
6"""
7(271, 312)
8"""
9
10country_df.iloc[[0,1,2,-3,-2,-1], [0,1,2,-3,-2,-1]]
11"""
12 Country/Region 1/22/20 1/23/20 11/25/20 11/26/20 11/27/20
130 Afghanistan 0 0 45490 45716 45839
141 Albania 0 0 34944 35600 36245
152 Algeria 0 0 78025 79110 80168
16268 Yemen 0 0 2124 2137 2148
17269 Zambia 0 0 17535 17553 17569
18270 Zimbabwe 0 0 9508 9623 9714
19"""
Display confirmed cases in top ten countries on the latest date
Sort the data by the values in the last column in descending order. A bar chart is created using Plotly with the top ten values. This displays an interactive chart that displays the value for each country when the bar is hovered over.
1Top_10 = (country_df
2 .sort_values(by=country_df.columns[-1], ascending=False)
3 [[country_df.columns[-1]]]
4 .head(10))
5bg_color = 'rgba(208, 225, 242, 1.0)'
6
7fig = go.Figure(
8 data = [go.Bar(x = list(Top_10.iloc[:, 0]),
9 y = list(Top_10.index),
10 hovertemplate = "%{y}: <br><br>Confirmed: %{x:,.0f}<extra></extra>",
11 orientation = 'h')]
12)
13
14fig.update_layout(
15 # Set default font
16 font = dict(
17 family = "Droid Sans",
18 size = 16
19 ),
20 # Set figure title
21 title = dict(
22 text = "<b>Top ten countries with highest confirmed cases of covid-19</b>",
23 xref = 'container',
24 yref = 'container',
25 x = 0.5,
26 y = 0.91,
27 xanchor = 'center',
28 yanchor = 'middle',
29 font = dict(family = 'Droid Sans', size = 24)
30 ),
31 # set x-axis
32 xaxis = dict(
33 title = dict(
34 text = 'Number of confirmed cases',
35 font = dict(family = 'Droid Sans', size = 18)
36 ),
37 showgrid = False,
38 linecolor = bg_color,
39 linewidth = 2,
40 showticklabels = True,
41 ),
42 # set y-axis
43 yaxis = dict(
44 showgrid = False,
45 linecolor = bg_color,
46 linewidth = 4,
47 ),
48 # set the plot bacground color
49 plot_bgcolor = bg_color,
50 # set the hover background color
51 hoverlabel = dict(
52 bgcolor = 'rgba(75, 152, 201, 0.2)',
53 font_size = 16
54 ),
55 paper_bgcolor = bg_color,
56)
57
58# Add annotation for the latest date
59fig.add_annotation(
60 xref = "paper",
61 yref = "paper",
62 x = 0.9,
63 y = 0.2,
64 text = f"<b>{pd.to_datetime(Top_10.columns[0]).strftime('%b %d %Y')}</b>",
65 showarrow = False,
66 font = dict(family = 'Droid Sans', size = 40))
67
68fig.update_traces(marker_color = 'rgb(75, 152, 201)')
69fig['layout']['yaxis']['autorange'] = "reversed"
70
71fig.show()
Top ten countries with highest confirmed cases of COVID-19
Display confirmed cases in US
Display a line chart showing the changes in the number of confirmed case over time for
a specific country. Start with the US as this has highest number of confirmed cases
in the world. The index is converted to datetime format so the axis is displayed
better.
1us_data = country_df.loc['US']
2# Convert the index to datetime format
3us_data.index = pd.to_datetime(us_data.index)
4
5def hov_text(ary_x, ary_y):
6 '''Create the hover text for each data point
7
8 Keyword arguments:
9 ary_x -- list of all the x data points
10 ary_y -- list of all the y data points
11
12 return: A list of hover texts for the selected country
13 with format to display
14 '''
15 txt = [f'''
16<b>{ary_x[i].strftime('%b %d %Y')}</b><br><br>
17Confirmed Cases: <b>{ary_y[i]:,.0F}</b><br>
18<extra></extra>
19''' for i in range(len(ary_x))]
20 return txt
21
22bg_color = 'rgba(208, 225, 242, 1.0)'
23line_color = 'rgba(75, 152, 201, 1.0)'
24grid_color = 'rgba(75, 152, 201, 0.3)'
25
26fig = go.Figure()
27fig.add_trace(go.Scatter(x = us_data.index,
28 y = us_data,
29 name = 'US',
30 hovertemplate = hov_text(us_data.index, us_data)))
31
32fig.update_traces(
33 hoverinfo = 'text+name',
34 mode = 'lines'
35)
36
37fig.update_layout(
38 # Set figure title
39 title = dict(
40 text = f'<b>Confirmed cases of COVID-19 in US</b>',
41 xref = 'container',
42 yref = 'container',
43 x = 0.5,
44 y = 0.9,
45 xanchor = 'center',
46 yanchor = 'middle',
47 font = dict(family = 'Droid Sans', size = 28)
48 ),
49 # set legend
50 legend = dict(
51 orientation = "h",
52 traceorder = 'normal',
53 font_size = 12,
54 x = 0.0,
55 y = -0.3,
56 xanchor = "left",
57 yanchor = "top"
58 ),
59 # set x-axis
60 xaxis = dict(
61 title = 'Date',
62 linecolor = line_color,
63 linewidth = 2,
64 gridcolor = grid_color,
65 showticklabels = True,
66 ticks = 'outside',
67 ),
68 # set y-axis
69 yaxis = dict(
70 title = 'Number of confirmed cases',
71 linecolor = line_color,
72 linewidth = 2,
73 gridcolor = grid_color,
74 showticklabels = True,
75 ticks = 'outside',
76 ),
77 # set the plot bacground color
78 plot_bgcolor = bg_color,
79 paper_bgcolor = bg_color,
80)
81
82fig.show()
Confirmed cases of COVID-19 in US
Create a function to display confirmed cases for any country
The code above can be wrapped up in a function to create a chart for any country specified.
1def hov_text(ary_x, ary_y):
2 '''Create the hover text for each data point
3
4 Keyword arguments:
5 ary_x -- list of all the x data points
6 ary_y -- list of all the y data points
7
8 return: A list of hover texts for the selected country
9 with format to display
10 '''
11 txt = [f'''
12<b>{ary_x[i].strftime('%b %d %Y')}</b><br><br>
13Confirmed Cases: <b>{ary_y[i]:,.0F}</b><br>
14<extra></extra>
15''' for i in range(len(ary_x))]
16 return txt
17
18
19def plot_confirmed_cases_for_country(df, country):
20 sel_data = df.loc[country]
21 # Convert the index to datetime format
22 sel_data.index = pd.to_datetime(sel_data.index)
23
24 bg_color = 'rgba(208, 225, 242, 1.0)'
25 line_color = 'rgba(75, 152, 201, 1.0)'
26 grid_color = 'rgba(75, 152, 201, 0.3)'
27
28 fig = go.Figure()
29 fig.add_trace(go.Scatter(x = sel_data.index,
30 y = sel_data,
31 name = 'US',
32 hovertemplate = hov_text(sel_data.index, sel_data)))
33
34 fig.update_traces(
35 hoverinfo = 'text+name',
36 mode = 'lines'
37 )
38
39 fig.update_layout(
40 # Set figure title
41 title = dict(
42 text = f'<b>Confirmed cases of COVID-19 in {country}</b>',
43 xref = 'container',
44 yref = 'container',
45 x = 0.5,
46 y = 0.9,
47 xanchor = 'center',
48 yanchor = 'middle',
49 font = dict(family = 'Droid Sans', size = 28)
50 ),
51 # set legend
52 legend = dict(
53 orientation = "h",
54 traceorder = 'normal',
55 font_size = 12,
56 x = 0.0,
57 y = -0.3,
58 xanchor = "left",
59 yanchor = "top"
60 ),
61 # set x-axis
62 xaxis = dict(
63 title = 'Date',
64 linecolor = line_color,
65 linewidth = 2,
66 gridcolor = grid_color,
67 showticklabels = True,
68 ticks = 'outside',
69 ),
70 # set y-axis
71 yaxis = dict(
72 title = 'Number of confirmed cases',
73 linecolor = line_color,
74 linewidth = 2,
75 gridcolor = grid_color,
76 showticklabels = True,
77 ticks = 'outside',
78 ),
79 # set the plot bacground color
80 plot_bgcolor = bg_color,
81 paper_bgcolor = bg_color,
82 )
83
84 return fig
Call this function with the dataframe and the country name to create the plot.
1fig = plot_confirmed_cases_for_country(country_df, 'China')
2fig.show()
Confirmed cases of COVID-19 in China
Display confirmed cases for top ten countries
Get the ten country names for the countries with the highest confirmed cases on the latest date.
1top_countries = (country_df
2 .sort_values(by = country_df.columns[-1], ascending = False)
3 .head(10)
4 .index)
5
6"""
7['US', 'India', 'Brazil', 'France', 'Russia',
8'Spain', 'United Kingdom', 'Italy', 'Argentina', 'Colombia']
9"""
Transpose the data so that the date is the index and each row represents all the data
for that date. Set the index to be datetime format.
1wide_df = country_df.T
2wide_df.index = pd.to_datetime(wide_df.index)
3
4wide_df.shape
5"""
6(311, 191)
7"""
8
9wide_df.iloc[[0,1,2,-3,-2,-1], [0,1,2,-3,-2,-1]]
10"""
11Country/Region Afghanistan Albania Algeria Yemen Zambia Zimbabwe
122020-01-22 0 0 0 0 0 0
132020-01-23 0 0 0 0 0 0
142020-01-24 0 0 0 0 0 0
152020-11-25 45490 34944 78025 2124 17535 9508
162020-11-26 45716 35600 79110 2137 17553 9623
172020-11-27 45839 36245 80168 2148 17569 9714
18"""
The data is extracted for the top ten countries and displayed on a line chart.
1def hov_text(ary_x, ary_y, country):
2 '''Create the hover text for each data point
3
4 Keyword arguments:
5 ary_x -- list of all the x data points
6 ary_y -- list of all the y data points
7 country -- name of country
8
9 return: A list of hover texts for the selected country
10 with format to display
11 '''
12 txt = [f'''
13<b>{country}<b><br><br>
14Date: <b>{ary_x[i].strftime('%b %d %Y')}</b><br>
15Confirmed Cases: <b>{ary_y[i]:,.0F}</b><br>
16<extra></extra>
17''' for i in range(len(ary_x))]
18 return txt
19
20bg_color = 'rgba(208, 225, 242, 1.0)'
21line_color = 'rgba(75, 152, 201, 1.0)'
22grid_color = 'rgba(75, 152, 201, 0.3)'
23
24df = wide_df[top_countries]
25
26fig = go.Figure()
27for c in top_countries:
28 fig.add_trace(go.Scatter(x = df.index,
29 y = df[c],
30 name = c,
31 hovertemplate = hov_text(df.index, df[c], c)))
32
33fig.update_traces(
34 hoverinfo = 'text+name',
35 mode = 'lines'
36)
37
38fig.update_layout(
39 # Set figure title
40 title = dict(
41 text = f'<b>Confirmed cases of COVID-19 in top ten countries</b>',
42 xref = 'container',
43 yref = 'container',
44 x = 0.5,
45 y = 0.9,
46 xanchor = 'center',
47 yanchor = 'middle',
48 font = dict(family = 'Droid Sans', size = 28)
49 ),
50 # set legend
51 legend = dict(
52 orientation = "h",
53 traceorder = 'normal',
54 font_size = 12,
55 x = 0.0,
56 y = -0.3,
57 xanchor = "left",
58 yanchor = "top"
59 ),
60 # set x-axis
61 xaxis = dict(
62 title = 'Date',
63 linecolor = line_color,
64 linewidth = 2,
65 gridcolor = grid_color,
66 showticklabels = True,
67 ticks = 'outside',
68 ),
69 # set y-axis
70 yaxis = dict(
71 title = 'Number of confirmed cases',
72 linecolor = line_color,
73 linewidth = 2,
74 gridcolor = grid_color,
75 showticklabels = True,
76 ticks = 'outside',
77 ),
78 showlegend = True,
79 # set the plot bacground color
80 plot_bgcolor = bg_color,
81 paper_bgcolor = bg_color,
82)
83
84fig.show()
Confirmed cases of COVID-19 for top ten countries
Conclusion
Pandas and Plotly are a powerful combination to load data, clean data and create interactive charts. Johns Hopkins University has done the hard work of collecting the COVID-19 data and making it available. The data can be loaded directly from the csv file in GitHub. Plotly is used to create bar charts and line charts to display information on countries with the highest confirmed cases of COVID-19.