Data is Beautiful

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Coronavirus Time Series Line and Bar Chart

Dr. Shahin Rostami

2020-03-26

3 min read

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Preamble
Introduction
Visualising the Table
Conclusion

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You can access this notebook and more by getting the e-book, Data is Beautiful.

Preamble¶

In [1]:

import numpy as np                   # for multi-dimensional containers 
import pandas as pd                  # for DataFrames
import plotly
import plotly.graph_objects as go    # for data visualisation
from plotly.subplots import make_subplots

Introduction¶

In this section, we're going to use daily confirmed cases data for COVID-19 in the UK made available at coronavirus.data.gov.uk to create a time series plot. Our goal will be to visualise the number of new cases and cumulative cases over time.

No special restrictions or limitations on using the item’s content have been provided.

Visualising the Table¶

The first step is to read the CSV data into a pandas.DataFrame and display the first five samples.

In [2]:

data = pd.read_csv('https://datacrayon.com/datasets/coronavirus-cases_latest.csv')
data.head()

Out[2]:

	areaType	areaName	areaCode	date	newCasesByPublishDate	cumCasesByPublishDate
0	nation	England	E92000001	2020-07-22	519.0	255038
1	nation	Northern Ireland	N92000002	2020-07-22	9.0	5868
2	nation	Scotland	S92000003	2020-07-22	10.0	18484
3	nation	Wales	W92000004	2020-07-22	22.0	16987
4	nation	England	E92000001	2020-07-21	399.0	254519

Let's filter this data to only include rows where the Area name is England.

In [3]:

data = data[data['areaName']=='England']
data.head()

Out[3]:

	areaType	areaName	areaCode	date	newCasesByPublishDate	cumCasesByPublishDate
0	nation	England	E92000001	2020-07-22	519.0	255038
4	nation	England	E92000001	2020-07-21	399.0	254519
8	nation	England	E92000001	2020-07-20	535.0	254120
12	nation	England	E92000001	2020-07-19	672.0	253585
16	nation	England	E92000001	2020-07-18	796.0	252913

This data looks ready to plot. We have our dates in a column named Specimen date, the new daily cases in a column named Daily lab-confirmed cases, and the daily cumulative cases in a column named Cumulative lab-confirmed cases. For this plot, we'll enable a secondary y-axis so that we can present our cumulative cases as a line, and our new cases with bars.

In [4]:

from plotly.subplots import make_subplots

fig = make_subplots(specs=[[{"secondary_y": True}]])

fig.add_trace(go.Scatter(x=data['date'], y=data['cumCasesByPublishDate'],
                         mode='lines+markers',
                         name='Total Cases',
                         line_color='crimson'),
                         secondary_y=True)

fig.add_trace(go.Bar(x=data['date'], y=data['newCasesByPublishDate'],
                     name='New Cases',
                     marker_color='darkslategray'),
                     secondary_y=False)
fig.show()

It's an interactive plot, so you can hover over it to get more information.

Conclusion¶

In this section, we went on a rather quick journey. This involved loading in the CSV data directly from a web resource, and then plotting lines and bars to the same plot.