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:
        table = pd.read_csv(
            "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:
        table["Admin2"] = None

    # 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",
        df.groupby(level=("Country_Region", "Province_State", "Admin2"))["Confirmed"]
        .diff()
        .fillna(0)
        .astype(int),
    )
    
    # Change in deaths
    df.insert(
        3,
        "ΔDeaths",
        df.groupby(level=("Country_Region", "Province_State", "Admin2"))["Deaths"]
        .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 Incidence_Rate Case-Fatality_Ratio
Last_Update Country_Region Province_State Admin2
2020-01-22 Hong Kong Hong Kong 0 0 0 0 0 0 0 0 0
Japan 0 0 2 0 0 0 0 2 0 0
Macau Macau 0 1 0 0 0 0 1 0 0
Mainland China Anhui 0 1 0 0 0 0 1 0 0
Beijing 0 14 0 0 0 0 14 0 0
... ... ... ... ... ... ... ... ... ... ... ...
2020-09-20 West Bank and Gaza 0 0 35686 683 262 9 23700 11724 699 0
Western Sahara 0 0 10 0 1 0 8 1 1 10
Yemen 0 0 2026 0 586 1 1227 213 6 28
Zambia 0 0 14131 61 330 0 13365 436 76 2
Zimbabwe 0 0 7683 11 225 0 5924 1534 51 2

665959 rows × 8 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 Incidence_Rate Case-Fatality_Ratio
Last_Update Province_State Admin2
2020-01-22 Washington 0 1 0 0 0 0 1 0 0
2020-01-23 Washington 0 1 0 0 0 0 1 0 0
2020-01-24 Chicago 0 1 0 0 0 0 1 0 0
Washington 0 1 0 0 0 0 1 0 0
2020-01-25 Illinois 0 1 0 0 0 0 1 0 0
... ... ... ... ... ... ... ... ... ... ...
2020-09-20 Wyoming Teton 498 5 1 0 0 497 2122 0
Uinta 334 4 2 0 0 332 1651 0
Unassigned 1 0 0 0 0 1 0 0
Washakie 113 0 6 0 0 107 1447 5
Weston 23 0 0 0 0 23 332 0

563565 rows × 8 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 Incidence_Rate Case-Fatality_Ratio
Last_Update Province_State
2020-01-22 Washington 1 0 0 0 0 1 0 0
2020-01-23 Washington 1 0 0 0 0 1 0 0
2020-01-24 Chicago 1 0 0 0 0 1 0 0
Washington 1 0 0 0 0 1 0 0
2020-01-25 Illinois 1 0 0 0 0 1 0 0
... ... ... ... ... ... ... ... ... ...
2020-09-20 Virginia 140395 849 3013 25 0 137382 207734 272
Washington 82548 349 2037 0 0 80511 44530 50
West Virginia 14062 173 314 3 0 13748 33428 97
Wisconsin 101227 1665 1242 1 0 99985 89617 36
Wyoming 4872 91 49 0 0 4823 18757 20

12182 rows × 8 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 Incidence_Rate Case-Fatality_Ratio
Last_Update
2020-03-10 7 0 0 0 0 7 0 0
2020-03-11 7 0 0 0 0 7 0 0
2020-03-12 15 8 0 0 0 15 0 0
2020-03-13 17 2 0 0 0 17 0 0
2020-03-14 24 7 0 0 0 24 0 0
... ... ... ... ... ... ... ... ...
2020-09-16 188024 1137 3149 38 0 184875 173477 143
2020-09-17 189576 1552 3180 31 0 186396 175167 142
2020-09-18 190973 1397 3207 27 0 187766 176622 139
2020-09-19 192247 1274 3235 28 0 189012 178046 144
2020-09-20 193547 1300 3243 8 0 190304 179629 140

195 rows × 8 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 Incidence_Rate Case-Fatality_Ratio
Last_Update
2020-01-22 1 0 0 0 0 1 0 0
2020-01-23 1 0 0 0 0 1 0 0
2020-01-24 2 0 0 0 0 2 0 0
2020-01-25 2 0 0 0 0 2 0 0
2020-01-26 5 0 0 0 0 5 0 0
... ... ... ... ... ... ... ... ...
2020-09-16 6630051 37709 196763 982 2525573 6433633 5297992 5873
2020-09-17 6674411 44360 197633 870 2540334 6477143 5354793 5885
2020-09-18 6723933 49522 198570 937 2556465 6525683 5426452 5839
2020-09-19 6768119 44186 199282 712 2577446 6569138 5483633 5824
2020-09-20 6804814 36695 199509 227 2590671 6605611 5531698 5794

243 rows × 8 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');

png

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();

png

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'))

png

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'))

png

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