import pandas as pd
    import matplotlib.pyplot as plt
    import matplotlib.dates as mdates
    import seaborn as sn

    df = (pd.read_csv("/tmp/overview_2022-03-21.csv") # i just used curl beforehand
        .assign(date=lambda x: pd.to_datetime(x["date"]))
        .set_index("date")
        .melt(value_vars=[
                "newCasesBySpecimenDate",
                "covidOccupiedMVBeds",
                "newAdmissions",
                "newDeaths28DaysByDeathDate"],
            var_name="Data", ignore_index=False)
        .assign(Data=lambda x: x["Data"].replace({
            "newCasesBySpecimenDate": "New Cases",
            "newAdmissions": "Admissions",
            "newDeaths28DaysByDeathDate": "Deaths",
            "covidOccupiedMVBeds": "Ventilated"
        }))
    )
    ax = sn.scatterplot(data=df, x=df.index, y=df["value"], hue="Data")
    ax.set(xlabel="Date", ylabel="Daily rate", yscale="log")
    ax.xaxis.set_major_formatter(mdates.DateFormatter("%b"))
    plt.show()

I spend 2 minutes on the pandas part and 20 minutes on the plotting part, which really says it all. Seaborn's support for smoothing is really bad and doesn't play nicely with datetimes for some reason, so if I wanted smoothing I'd need to do it myself. And the other stuff I left out requires going into matplotlib's documentation which I don't want to spend time on.

pandas is as good or better than R's dataframe manipulation, but R's plotting tools are best in class. I hate all the python plotting libraries.