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
from typing import Optional
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 valid_name(name: str) -> bool:
    "Return True if this is a valid name in the dataset. Faster parsing this way."
    
    return name.replace("/", "_").replace(" ", "_") in {
        "Country_Region", "Province_State", "Admin2", "Confirmed", "Deaths", "Recovered"
    }
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",
            usecols=valid_name,
        )
    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(" ", "_")
        for f in table.columns
    ]
    
    # This column is new in recent datasets
    if "Admin2" not in table.columns:
        table["Admin2"] = None
    
    # 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: Optional[pd.Timestamp] = None) -> pd.DataFrame:

    # 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
Last_Update Country_Region Province_State Admin2
2020-01-22 China Unknown 0 0 0 0 0 0 0
Hong Kong Hong Kong 0 0 0 0 0 0 0
Japan 0 0 2 0 0 0 0 2
Kiribati 0 0 0 0 0 0 0 0
Macau Macau 0 1 0 0 0 0 1
... ... ... ... ... ... ... ... ... ...
2021-09-14 Vietnam 0 0 635055 10508 15936 276 0 619119
West Bank and Gaza 0 0 374768 2660 3837 6 0 370931
Yemen 0 0 8502 50 1608 4 0 6894
Zambia 0 0 208049 89 3635 2 0 204414
Zimbabwe 0 0 126817 418 4550 7 0 122267

2098002 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
Last_Update Province_State Admin2
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
... ... ... ... ... ... ... ... ...
2021-09-14 Wyoming Teton 4679 24 11 0 0 4668
Uinta 3248 22 21 5 0 3227
Unassigned 0 0 0 0 0 0
Washakie 1148 19 27 0 0 1121
Weston 845 7 7 1 0 838

1740149 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
... ... ... ... ... ... ... ...
2021-09-14 Virginia 814738 3659 12118 29 0 802620
Washington 609911 2751 7037 56 0 602874
West Virginia 213179 1473 3261 23 0 209918
Wisconsin 760401 2488 8646 14 0 751755
Wyoming 82463 638 918 39 0 81545

33004 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
... ... ... ... ... ... ...
2021-09-10 1279500 5877 15075 71 0 1264425
2021-09-11 1279500 0 15075 0 0 1264425
2021-09-12 1279500 0 15075 0 0 1264425
2021-09-13 1303390 23890 15247 172 0 1288143
2021-09-14 1308150 4760 15305 58 0 1292845

554 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
... ... ... ... ... ... ...
2021-09-10 40863868 223369 658992 2326 0 40216857
2021-09-11 40925410 61542 659691 699 0 40277712
2021-09-12 40959217 33807 659970 279 0 40311250
2021-09-13 41221266 262049 662106 2136 0 40571668
2021-09-14 41365161 143895 663929 1823 0 40713025

602 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');

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")
Text(0, 0.5, 'new cases / active cases')

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")
Text(0, 0.5, 'new cases / active cases')

png


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