Plotly

<Fig> = px.line(<DF>, x=col_key, y=col_key)            # Or: px.line(x=<list>, y=<list>)
<Fig>.update_layout(margin=dict(t=0, r=0, b=0, l=0))   # Also `paper_bgcolor='rgb(0, 0, 0)'`.
<Fig>.write_html/json/image('<path>')                  # <Fig>.show() displays the plot.

<Fig> = px.area/bar/box(<DF>, x=col_key, y=col_key)    # Also `color=col_key`.
<Fig> = px.scatter(<DF>, x=col_key, y=col_key)         # Also `color/size/symbol=col_key`.
<Fig> = px.scatter_3d(<DF>, x=col_key, y=col_key, …)   # `z=col_key`. Also color/size/symbol.
<Fig> = px.histogram(<DF>, x=col_key [, nbins=<int>])  # Number of bins depends on DF size.

Displays a line chart of total coronavirus deaths per million grouped by continent:

Displays a multi-axis line chart of total coronavirus cases and changes in prices of Bitcoin, Dow Jones and gold:

# $ pip3 install pandas selenium plotly lxml
import pandas as pd, selenium.webdriver, plotly.graph_objects as go


def main():
    covid, (bitcoin, gold, dow) = get_covid_cases(), get_tickers()
    df = wrangle_data(covid, bitcoin, gold, dow)
    display_data(df)

def get_covid_cases():
    url = 'https://covid.ourworldindata.org/data/owid-covid-data.csv'
    df = pd.read_csv(url, usecols=['location', 'date', 'total_cases'], parse_dates=['date'])
    df = df[df.location == 'World']
    s = df.set_index('date').total_cases
    return s.rename('Total Cases')

def get_tickers():
    with selenium.webdriver.Chrome() as driver:
        symbols = {'Bitcoin': 'BTC-USD', 'Gold': 'GC=F', 'Dow Jones': '%5EDJI'}
        for name, symbol in symbols.items():
            yield get_ticker(driver, name, symbol)

def get_ticker(driver, name, symbol):
    url = f'https://finance.yahoo.com/quote/{symbol}/history/'
    driver.get(url + '?period1=1579651200&period2=9999999999')
    if buttons := driver.find_elements('xpath', '//button[@name="reject"]'):
        buttons[0].click()
    dataframes = pd.read_html(driver.page_source, parse_dates=['Date'])
    s = dataframes[0].set_index('Date').Open
    return s.rename(name)

def wrangle_data(covid, bitcoin, gold, dow):
    df = pd.concat([bitcoin, gold, dow], axis=1)  # Creates table by joining columns on dates.
    df = df.sort_index().interpolate()            # Sorts rows by date and interpolates NaN-s.
    df = df.loc['2020-02-23':'2021-12-20']        # Keeps rows between specified dates.
    df = (df / df.iloc[0]) * 100                  # Calculates percentages relative to day 1.
    df = df.join(covid)                           # Adds column with covid cases.
    return df.sort_values(df.index[-1], axis=1)   # Sorts columns by last day's value.

def display_data(df):
    figure = go.Figure()
    for col_name in reversed(df.columns):
        yaxis = 'y1' if col_name == 'Total Cases' else 'y2'
        trace = go.Scatter(x=df.index, y=df[col_name], name=col_name, yaxis=yaxis)
        figure.add_trace(trace)
    figure.update_layout(
        width=944,
        height=423,
        yaxis1=dict(title='Total Cases', rangemode='tozero'),
        yaxis2=dict(title='%', rangemode='tozero', overlaying='y', side='right'),
        colorway=['#EF553B', '#636EFA', '#00CC96', '#FFA152'],
        legend=dict(x=1.08)
    )
    figure.show()

if __name__ == '__main__':
    main()