Using Python and Pandas to look at Pandemic Data

The script and supporting files in this repository are intended to show how the Python Pandas module can be used to analyze data, specifically COVID-19 data.

I am going to recommend 3 data sets to “investigate”:


WHO Data

The repository comes with the WHO data file from 06 April 2020 (WHO-COVID-19-global-data.csv). The simplest run of the script will use this WHO data file.

To download the latest file go to the Who Overview Map and download the Map Data from the link on the lower right hand side.

This CSV file will need clean up. Remove spaces from column titles. Some rows have spaces in the country names and so spaces have shifted columns (Belize and Palestine). You will need to combine the name and shift the data back to the correct columns. Welcome to the world of data.


John Hopkins University (JHU) Center for Systems Science and Engineering (CSSE) Data

The John Hopikns Unversity CSSE data is widely used in the media and either drives or is incorporated into many other data sets.
More importantly for our purposes, this wonderful institution of higher learning makes the raw data available on a public repository (GitHub).

CSSEGISandData on GitHub

I’ve cloned the repository so that it sits as a subdirectory in my pandas_for_pandemic_data folder and I refresh it every day.

# Clones the pands_for_pandemic_data Repository
git clone

# Change into the pands_for_pandemic_data Repository
cd pandas_for_pandemic_data

# Clones the John Hopkins University CSSE Data
git clone

# Refresh the JHU Data
cd COVID-19
git pull
# Example of refreshing the JHU repository
Claudias-iMac:COVID-19 claudia$ git pull
remote: Enumerating objects: 148, done.
remote: Counting objects: 100% (148/148), done.
remote: Compressing objects: 100% (13/13), done.
remote: Total 252 (delta 135), reused 140 (delta 135), pack-reused 104
Receiving objects: 100% (252/252), 1.25 MiB | 6.51 MiB/s, done.
Resolving deltas: 100% (157/157), completed with 14 local objects.
865c933c..f3dea791 master -> origin/master
513b21a4..493821d3 web-data -> origin/web-data
Updating 865c933c..f3dea791
csse_covid_19_data/UID_ISO_FIPS_LookUp_Table.csv | 7141 ++++++++++----------
…/csse_covid_19_daily_reports/04-06-2020.csv | 2810 ++++++++
…/time_series_covid19_confirmed_US.csv | 6508 +++++++++---------
…/time_series_covid19_confirmed_global.csv | 527 +-
…/time_series_covid19_deaths_US.csv | 6508 +++++++++---------
…/time_series_covid19_deaths_global.csv | 527 +-
…/time_series_covid19_recovered_global.csv | 499 +-
7 files changed, 13668 insertions(+), 10852 deletions(-)
create mode 100644 csse_covid_19_data/csse_covid_19_daily_reports/04-06-2020.csv
Claudias-iMac:COVID-19 claudia$

Feel free to put it elsewhere in your directory structure. The script sets the default path in the arguments section at the bottom. You can either update the default path directly or use the -d option when you execute the script to redirect script to look there for the daily files.


New York Times Data

The New York Times has also shared their data. This repository only contains data for the US. They share two flavors:

  • US State Level data
  • US County Level data

They do a good job of keeping the data set very clean. Its all numeric and so far I’ve not seen any missing data which is rare for any data set.

== Number of MISSING values in each column:
date 0
state 0
fips 0
cases 0
deaths 0
dtype: int64

New York Times US Data GitHub Repository

I took the same approach with this repository as I did for the JHU data. I’ve cloned the repository so that it sits as a subdirectory in my pandas_for_pandemic_data folder and I refresh it every day.


In general, the script will take in a CSV data file, turn it into a Pandas Data Frame, and execute a set of commands against the data. The main section manages which options to execute and sends the relevant data frame to a function that prints out the various analysis statements for the data frame. In general, this is what will be shown for each data frame.

  • Describe the data (pandas method showing interesting statistical facts about the data)
  • Show the shape of the data frame (number of rows and columns)
  • Show the first and last 5 lines of data
  • List the column headings
  • Show the data type of each column
  • Look for the total number of missing values in each column
  • Sum the columns (only makes sense for columns holding numeric data)

The various options let you control which data set you want to investigate and filter. The output is sent to your screen. These are just some of the actions available to you with Pandas. Once you have the data in a Pandas data frame you can query the data frame for the data that is meaningful to you.

Cheatsheet for script

python -hDisplay all the options available (Help)
python todays_totals.pyWHO Data
Without any options, the script will load the local WHO data file from 6 April into a Pandas Data Frame and run some commands to investigate the data.
Reminder: If you download a fresh WHO CSV file please note the updates I list above so that you can cleanly import the CSV into a Data Frame
python -c “MX”WHO Data Filtered for a Specific Country
Note: use the 2 letter country code as an argument with the -c option
python -tJohn Hopkins University CSSE Data
The -t option will look for todays daily log file in the JHU CSSE (remember to clone the repository)
python -t -c “Mexico”John Hopkins University CSSE Data
The -t -c “country or region” option will let you filter for a country
python -t -s “California”John Hopkins University CSSE Data
The -t -s “state” option filters the JHU data set for a state or province
python -t -f 06037John Hopkins University CSSE Data
The -t -f  FIPS option filters the JHU data set for a FIPS county code.   Note: FIPS code 06037 is for Los Angeles County
python -nNew York Times Data
US Totals Only for the full NY Times data set with the -n option
(remember to clone the repository)
python -n -f 6
python -n -p “California”
New York Times Data
This data set has both “state” and “fips” but fips represents FIPS State Code so in this example 6 is the FIPS state code. 
This should get you exactly the same data as ** python -n -p “California”**
Script CLI Cheat Sheet

The script will give you an idea of how to load pandemic data into a Pandas data frame and interrogate the data. Executing it with the -h option will give you help on the options.

(pandas) Claudias-iMac:pandas_for_pandemic_data claudia$ python -h
[-t] [-n]
Script Description
optional arguments:
-h, --help show this help message and exit
Set path to CSSE Dailty Report folder
csse_covid_19_daily_reports. Default is ./COVID-19/css
Filer on 2 letter Country Region. Example: "US"
Filer on Province State. Example: "California"
-s SPECIFIC_DAY, --specific_day SPECIFIC_DAY
File for specific day. Example: 04-01-2020
-f FIPS, --fips FIPS FIPS County Code Example: 06037 (Los Angeles County)
-w, --who_data_file Analyze the WHO data file provided
-t, --today_csse Analyze todays file in the CSSE repo
-n, --new_york_times Analyze the New York Times Data
Usage: 'python todays_totals'

Running the script without any parameters yields some information on the WHO data set from 6 April which is part of the repository. This information includes:

A description of the data frame including some statistical data on the numeric values.

The rows and

Example output for WHO Data:

(pandas) Claudias-iMac:pandas_for_pandemic_data claudia$ python
==================== DATA FRAME CHECK ====================
==================== WHO Data Frame from WHO-COVID-19-global-data.csv ====================

== Describe the Data Frame:
Deaths CumulativeDeaths Confirmed CumulativeConfirmed
count 6786.000000 6786.000000 6786.000000 6786.000000
mean 9.971412 104.946360 178.487179 2313.689213
std 73.268455 761.569677 1183.935476 12918.006118
min 0.000000 0.000000 0.000000 1.000000
25% 0.000000 0.000000 0.000000 4.000000
50% 0.000000 0.000000 2.000000 26.000000
75% 0.000000 3.000000 25.000000 235.000000
max 2003.000000 15889.000000 33510.000000 307318.000000

== Shape of the Data Frame:
(6786, 8)

== SAMPLE (first and last 5 rows):
day Country CountryName Region Deaths CumulativeDeaths Confirmed CumulativeConfirmed
0 2/25/20 AF Afghanistan EMRO 0 0 1 1
1 2/26/20 AF Afghanistan EMRO 0 0 0 1
2 2/27/20 AF Afghanistan EMRO 0 0 0 1
3 2/28/20 AF Afghanistan EMRO 0 0 0 1
4 2/29/20 AF Afghanistan EMRO 0 0 0 1
day Country CountryName Region Deaths CumulativeDeaths Confirmed CumulativeConfirmed
6781 4/2/20 ZW Zimbabwe AFRO 0 1 0 8
6782 4/3/20 ZW Zimbabwe AFRO 0 1 0 8
6783 4/4/20 ZW Zimbabwe AFRO 0 1 1 9
6784 4/5/20 ZW Zimbabwe AFRO 0 1 0 9
6785 4/6/20 ZW Zimbabwe AFRO 0 1 0 9

== Column Headings of the data set:
['day' 'Country' 'CountryName' 'Region' 'Deaths' 'CumulativeDeaths'
'Confirmed' 'CumulativeConfirmed']

== Column default data type:
day object
Country object
CountryName object
Region object
Deaths int64
CumulativeDeaths int64
Confirmed int64
CumulativeConfirmed int64
dtype: object

== Number of MISSING values in each column:
day 0
Country 85
CountryName 0
Region 62
Deaths 0
CumulativeDeaths 0
Confirmed 0
CumulativeConfirmed 0
dtype: int64

== Sum just the numeric columns in the Data Frame:
Deaths 67666
CumulativeDeaths 712166
Confirmed 1211214
CumulativeConfirmed 15700695
dtype: int64

(pandas) Claudias-iMac:pandas_for_pandemic_data claudia$

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