# Johns Hopkins COVID-19 Dataset in Pandas

COVID-19 is ravaging the globe. Let’s look at the excellent Johns Hopkins dataset using Pandas. This will serve both as a guideline for getting the data and exploring on your own, as well as an example of Pandas multi-indexing in an easy to understand situation. I am currently involved in science-responds.

My favorite links: worldometerarcGISProjections

A few more: COVID-19 dashnCoV201991-DIVOC

# Python 3.6+ required
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from urllib.error import HTTPError
plt.style.use('ggplot')


Anyway, now that we’ve made some basic imports, let’s write a function that can read in a datafile from GitHub:

def get_day(day: pd.Timestamp):

# Read in a datafile from GitHub
try:
"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/"
"master/csse_covid_19_data/csse_covid_19_daily_reports/"
f"{day:%m-%d-%Y}.csv",
)
except HTTPError:
return pd.DataFrame()

# Cleanup - sadly, the format has changed a bit over time - we can normalize that here
table.columns = [
f.replace("/", "_")
.replace(" ", "_")
.replace("Latitude", "Lat")
.replace("Longitude", "Long_")
for f in table.columns
]

# This column is new in recent datasets
if "Admin2" not in table.columns:

# New datasets have these, but they are not very useful for now
table.drop(
columns=["FIPS", "Combined_Key", "Lat", "Long_"], errors="ignore", inplace=True
)

# If the last update time was useful, we would make this day only, rather than day + time
#   table["Last_Update"] = pd.to_datetime(table["Last_Update"]).dt.normalize()
#
# However, last update is odd, let's just make this the current day
table["Last_Update"] = day

# Make sure indexes are not NaN, which causes later bits to not work. 0 isn't
# perfect, but good enough.
# Return as a multindex
return table.fillna(0).set_index(
["Last_Update", "Country_Region", "Province_State", "Admin2"], drop=True
)


Now let’s loop over all days and build a multi-index DataFrame with the whole dataset. We’ll be doing quite a bit of cleanup here as well. If you do this outside of a function, you should never modify an object in multiple cells; ideally you create an object like df, and make any modifications and replacements in the same cell. That way, running any cell again or running a cell multiple times will not cause unusual errors and problems to show up.

def get_all_days(end_day = None):

# Assume current day - 1 is the latest dataset if no end given
if end_day is None:
end_day = pd.Timestamp.now().normalize()

# Make a list of all dates
date_range = pd.date_range("2020-01-22", end_day)

# Create a generator that returns each day's dataframe
day_gen = (get_day(day) for day in date_range)

# Make a big dataframe, NaN is 0
df = pd.concat(day_gen).fillna(0).astype(int)

# Remove a few duplicate keys
df = df.groupby(level=df.index.names).sum()

# Sometimes active is not filled in; we can compute easily
df["Active"] = np.clip(
df["Confirmed"] - df["Deaths"] - df["Recovered"], 0, None
)

# Change in confirmed cases (placed in a pleasing location in the table)
df.insert(
1,
"ΔConfirmed",
.diff()
.fillna(0)
.astype(int),
)

# Change in deaths
df.insert(
3,
"ΔDeaths",
.diff()
.fillna(0)
.astype(int),
)

return df


If this were a larger/real project, it would be time to bundle up the functions above and put them into a .py file - notebooks are for experimentation, teaching, and high level manipulation. Functions and classes should normally move to normal Python files when ready.

Let’s look at a few lines of this DataFrame to see what we have:

df = get_all_days()
df

Confirmed ΔConfirmed Deaths ΔDeaths Recovered Active
Last_Update Country_Region Province_State Admin2
2020-01-22 Hong Kong Hong Kong 0 0 0 0 0 0 0
Japan 0 0 2 0 0 0 0 2
Macau Macau 0 1 0 0 0 0 1
Mainland China Anhui 0 1 0 0 0 0 1
Beijing 0 14 0 0 0 0 14
... ... ... ... ... ... ... ... ... ...
2020-05-22 West Bank and Gaza 0 0 423 0 2 0 346 75
Western Sahara 0 0 6 0 0 0 6 0
Yemen 0 0 209 12 33 0 11 165
Zambia 0 0 920 54 7 0 336 577
Zimbabwe 0 0 51 0 4 0 18 29

201895 rows × 6 columns

The benefit of doing this all at once, in one DataFrame, should quickly become apparent. We can now use simple selection and grouping to “ask” almost anything about our dataset.

As an example, let’s look at just the US portion of the dataset. We’ll use the pandas selection .xs:

us = df.xs("US", level="Country_Region")
us

Confirmed ΔConfirmed Deaths ΔDeaths Recovered Active
2020-01-22 Washington 0 1 0 0 0 0 1
2020-01-23 Washington 0 1 0 0 0 0 1
2020-01-24 Chicago 0 1 0 0 0 0 1
Washington 0 1 0 0 0 0 1
2020-01-25 Illinois 0 1 0 0 0 0 1
... ... ... ... ... ... ... ... ...
2020-05-22 Wyoming Sweetwater 25 0 0 0 0 25
Teton 100 0 0 0 0 100
Uinta 13 0 0 0 0 13
Unassigned 0 0 11 0 0 0
Washakie 20 0 0 0 0 20

178989 rows × 6 columns

Notice we have counties (early datasets just have one “county” called "0"). If we were only interested in states, we can group by the remaining levels and sum out the "Admin2" (county and similar) dimension:

by_state = us.groupby(level=("Last_Update", "Province_State")).sum()
by_state

Confirmed ΔConfirmed Deaths ΔDeaths Recovered Active
Last_Update Province_State
2020-01-22 Washington 1 0 0 0 0 1
2020-01-23 Washington 1 0 0 0 0 1
2020-01-24 Chicago 1 0 0 0 0 1
Washington 1 0 0 0 0 1
2020-01-25 Illinois 1 0 0 0 0 1
... ... ... ... ... ... ... ...
2020-05-22 Virginia 34950 813 1136 36 0 33814
Washington 19265 148 1050 6 0 18215
West Virginia 1705 112 72 2 0 1633
Wisconsin 14396 511 496 9 0 13900
Wyoming 803 2 12 0 0 802

5164 rows × 6 columns

Using the same selector as before, we can pick out North Carolina:

by_state.xs("North Carolina", level="Province_State")

Confirmed ΔConfirmed Deaths ΔDeaths Recovered Active
Last_Update
2020-03-10 7 0 0 0 0 7
2020-03-11 7 0 0 0 0 7
2020-03-12 15 8 0 0 0 15
2020-03-13 17 2 0 0 0 17
2020-03-14 24 7 0 0 0 24
... ... ... ... ... ... ...
2020-05-18 19207 534 693 7 0 18514
2020-05-19 19239 32 693 0 0 18546
2020-05-20 20262 1023 726 33 0 19536
2020-05-21 20512 250 728 2 0 19784
2020-05-22 22110 1598 775 47 0 21335

74 rows × 6 columns

We can look at all of US, as well:

all_states = by_state.groupby(level="Last_Update").sum()
all_states

Confirmed ΔConfirmed Deaths ΔDeaths Recovered Active
Last_Update
2020-01-22 1 0 0 0 0 1
2020-01-23 1 0 0 0 0 1
2020-01-24 2 0 0 0 0 2
2020-01-25 2 0 0 0 0 2
2020-01-26 5 0 0 0 0 5
... ... ... ... ... ... ...
2020-05-18 1508308 21552 90347 785 283178 1418271
2020-05-19 1528568 20260 91921 1574 289392 1436983
2020-05-20 1551853 23285 93439 1518 294312 1458754
2020-05-21 1577147 25294 94702 1263 298418 1482741
2020-05-22 1600937 23790 95979 1277 350135 1505273

122 rows × 6 columns

#### US total cases

Let’s try a simple plot first; this is the one you see quite often.

plt.figure(figsize=(10,5))
all_states.Confirmed.plot(logy=True, style='o');


#### Italy, new cases per day

As another example, let’s view the new cases per day for Italy. We will add a rolling mean, just to help guide the eye through the fluctuations - it is not a fit or anything fancy.

interesting = df.xs("Italy", level="Country_Region").groupby(level="Last_Update").sum()

plt.figure(figsize=(10,5))
interesting.ΔConfirmed.rolling(5, center=True).mean().plot(style='-', label='Rolling mean')
interesting.ΔConfirmed.plot(style='o', label="Data")
plt.ylabel("New cases per day")
plt.legend();


#### Italy, transmission rate

It’s more interesting to instead look at the transmission rate per day, which is new cases / active cases. The colors in the plot start changing when Italy implemented a lockdown on the 11th, and change over 14 days, which is roughly 1x the time to first symptoms. The lockdown make take longer than that to take full effect. There were several partial steps taken before the full lockdown on the 4th and 9th. Notice the transmission is slowing noticeably!

interesting = df.xs("Italy", level="Country_Region").groupby(level="Last_Update").sum()
growth = interesting.ΔConfirmed / interesting.Active
growth = growth['2020-02-24':]

# Color based on lockdown (which happened in 3 stages, 4th, 9th, and 11th)
lockdown = growth.index - pd.Timestamp('2020-03-11')
lockdown = np.clip(lockdown.days, 0, 14) / 14

fix, ax = plt.subplots(figsize=(10,5))
ax.scatter(growth.index, growth, cmap='cool', c=lockdown)

ax.set_ylabel("new cases / active cases")

#set ticks every week
ax.xaxis.set_major_locator(mdates.WeekdayLocator())
#set major ticks format
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))



#### US, transmission rate

Same plot for the US. The colors in the plot start changing when the US started the 15 plan to slow the spread, and change over 14 days, which is roughly 1x the time to first symptoms. Each state has implemented different guidelines, so the effect will be spread out even futher. Again, we are see the effect of the lockdown!

interesting = df.xs("US", level="Country_Region").groupby(level="Last_Update").sum()
growth = interesting.ΔConfirmed / interesting.Active
growth = growth['2020-03-01':]

# Not really a full lockdown, just a distancing guideline + local lockdowns later
lockdown = growth.index - pd.Timestamp('2020-03-15')
lockdown = np.clip(lockdown.days, 0, 14) / 14

fix, ax = plt.subplots(figsize=(10,5))
ax.scatter(growth.index, growth, cmap='cool', c=lockdown)

ax.set_ylabel("new cases / active cases")

#set ticks every week
ax.xaxis.set_major_locator(mdates.WeekdayLocator())
#set major ticks format
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))