如何用Jupyter Notebook制作新冠病毒疫情追踪器？

from datetime import datetimeimport re
from IPython.display import displayimport numpy as npimport pandas as pdimport plotly.graph_objects as gofrom plotly.subplots import make_subplots
pd.options.display.max_columns = 12

date_pattern = re.compile(r"\d{1,2}/\d{1,2}/\d{2}")def reformat_dates(col_name: str) -> str:    # for columns which are dates, I'd much rather they were in day/month/year format    try:        return date_pattern.sub(datetime.strptime(col_name, "%m/%d/%y").strftime("%d/%m/%Y"), col_name, count=1)    except ValueError:        return col_name

# this github repo contains timeseries data for all coronavirus cases: https://github.com/CSSEGISandData/COVID-19confirmed_cases_url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/" \                      "csse_covid_19_time_series/time_series_19-covid-Confirmed.csv"deaths_url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/" \             "csse_covid_19_time_series/time_series_19-covid-Deaths.csv"

renamed_columns_map = {   "Country/Region": "country",   "Province/State": "location",   "Lat": "latitude",   "Long": "longitude"}
cols_to_drop = ["location", "latitude", "longitude"]
confirmed_cases_df = (   pd.read_csv(confirmed_cases_url)   .rename(columns=renamed_columns_map)   .rename(columns=reformat_dates)   .drop(columns=cols_to_drop))deaths_df = (   pd.read_csv(deaths_url)   .rename(columns=renamed_columns_map)   .rename(columns=reformat_dates)   .drop(columns=cols_to_drop))
display(confirmed_cases_df.head())display(deaths_df.head())

# extract out just the relevant geographical data and join it to another .csv which has the country codes.# The country codes are required for the plotting function to identify countries on the mapgeo_data_df = confirmed_cases_df[["country"]].drop_duplicates()country_codes_df = (    pd.read_csv(        "country_code_mapping.csv",        usecols=["country", "alpha-3_code"],        index_col="country"))geo_data_df = geo_data_df.join(country_codes_df, how="left", on="country").set_index("country")

# my .csv file of country codes and the COVID-19 data source disagree on the names of some countries. This # dataframe should be empty, otherwise it means I need to edit the country name in the .csv to matchgeo_data_df[(pd.isnull(geo_data_df["alpha-3_code"])) & (geo_data_df.index != "Cruise Ship")

dates_list = (    deaths_df.filter(regex=r"(\d{2}/\d{2}/\d{4})", axis=1)    .columns    .to_list()




    
)
# create a mapping of date -> dataframe, where each df holds the daily counts of cases and deaths per countrycases_by_date = {}for date in dates_list:    confirmed_cases_day_df = (        confirmed_cases_df        .filter(like=date, axis=1)        .rename(columns=lambda col: "confirmed_cases")    )    deaths_day_df = deaths_df.filter(like=date, axis=1).rename(columns=lambda col: "deaths")    cases_df = confirmed_cases_day_df.join(deaths_day_df).set_index(confirmed_cases_df["country"])
    date_df = (        geo_data_df.join(cases_df)        .groupby("country")        .agg({"confirmed_cases": "sum", "deaths": "sum", "alpha-3_code": "first"})    )    date_df = date_df[date_df["confirmed_cases"] > 0].reset_index()        cases_by_date[date] = date_df    # the dataframe for each day looks something like this:cases_by_date[dates_list[-1]].head()

# helper function for when we produce the frames for the map animationdef frame_args(duration):    return {        "frame": {"duration": duration},        "mode": "immediate",        "fromcurrent": True,        "transition": {"duration": duration, "easing": "linear"},    }

fig = make_subplots(rows=2, cols=1, specs=[[{"type": "scattergeo"}], [{"type": "xy"}]], row_heights=[0.8, 0.2])
# set up the geo data, the slider, the play and pause buttons, and the titlefig.layout.geo = {"showcountries": True}fig.layout.sliders = [{"active": 0, "steps": []}]fig.layout.updatemenus = [    {        "type": "buttons",        "buttons": [            {                "label": "▶",  # play symbol                "method": "animate",                "args": [None, frame_args(250)],            },            {                "label": "◼",                "method": "animate",  # stop symbol                "args": [[None], frame_args(0)],            },        ],        "showactive": False,        "direction": "left",    }]fig.layout.title = {"text": "COVID-19 Case Tracker", "x": 0.5}

frames = []steps = []# set up colourbar tick values, ranging from 1 to the highest num. of confirmed cases for any country thus farmax_country_confirmed_cases = cases_by_date[dates_list[-1]]["confirmed_cases"].max()
# to account for the significant variance in number of cases, we want the scale to be logarithmic...high_tick = np.log1p(max_country_confirmed_cases)low_tick = np.log1p(1)log_tick_values = np.geomspace(low_tick, high_tick, num=6)
# ...however, we want the /labels/ on the scale to be the actual number of cases (i.e. not log(n_cases))visual_tick_values = np.expm1(log_tick_values).astype(int)# explicitly set max cbar value, otherwise it might be max - 1 due to a rounding errorvisual_tick_values[-1] = max_country_confirmed_cases  visual_tick_values = [f"{val:,}" for val in visual_tick_values]
# generate line chart data# list of tuples: [(confirmed_cases, deaths), ...]cases_deaths_totals = [(df.filter(like="confirmed_cases").astype("uint32").agg("sum")[0],                         df.filter(like="deaths").astype("uint32").agg("sum")[0])                           for df in cases_by_date.values()]
confirmed_cases_totals = [daily_total[0] for daily_total in cases_deaths_totals]deaths_totals =[daily_total[1] for daily_total in cases_deaths_totals]

# this loop generates the data for each framefor i, (date, data) in enumerate(cases_by_date.items(), start=1):    df = data
    # the z-scale (for calculating the colour for each country) needs to be logarithmic    df["confirmed_cases_log"] = np.log1p(df["confirmed_cases"])
    df["text"] = (        date        + "
"        + df["country"]        + "
Confirmed cases: "        + df["confirmed_cases"].apply(lambda x: "{:,}".format(x))        + "
Deaths: "        + df["deaths"].apply(lambda x: "{:,}".format(x))    )
    # create the choropleth chart    choro_trace = go.Choropleth(        **{            "locations": df["alpha-3_code"],            "z": df["confirmed_cases_log"],            "zmax": high_tick,            "zmin": low_tick,            "colorscale": "reds",            "colorbar"




    
: {                "ticks": "outside",                "ticktext": visual_tick_values,                "tickmode": "array",                "tickvals": log_tick_values,                "title": {"text": "Confirmed Cases"},                "len": 0.8,                "y": 1,                "yanchor": "top"            },            "hovertemplate": df["text"],            "name": "",            "showlegend": False        }    )        # create the confirmed cases trace    confirmed_cases_trace = go.Scatter(        x=dates_list,        y=confirmed_cases_totals[:i],        mode="markers" if i == 1 else "lines",        name="Total Confirmed Cases",        line={"color": "Red"},        hovertemplate="%{x}
Total confirmed cases: %{y:,}"    )            # create the deaths trace    deaths_trace = go.Scatter(        x=dates_list,        y=deaths_totals[:i],        mode="markers" if i == 1 else "lines",        name="Total Deaths",        line={"color": "Black"},        hovertemplate="%{x}
Total deaths: %{y:,}"    )
    if i == 1:        # the first frame is what the figure initially shows...        fig.add_trace(choro_trace, row=1, col=1)        fig.add_traces([confirmed_cases_trace, deaths_trace], rows=[2, 2], cols=[1, 1])    # ...and all the other frames are appended to the `frames` list and slider    frames.append(dict(data=[choro_trace, confirmed_cases_trace, deaths_trace], name=date))
    steps.append(        {"args": [[date], frame_args(0)], "label": date, "method": "animate",}    )
# tidy up the axes and finalise the chart ready for displayfig.update_xaxes(range=[0, len(dates_list)-1], visible=False)fig.update_yaxes(range=[0, max(confirmed_cases_totals)])fig.frames = framesfig.layout.sliders[0].steps = stepsfig.layout.geo.domain = {"x": [0,1], "y": [0.2, 1]}fig.update_layout(height=650, legend={"x": 0.05, "y": 0.175, "yanchor": "top", "bgcolor": "rgba(0, 0, 0, 0)"})fig

renamed_columns_map = {    "Country/Region": "country",    "Province/State": "location",    "Lat": "latitude",    "Long": "longitude"}
confirmed_cases_df = (    pd.read_csv(confirmed_cases_url)    .rename(columns=renamed_columns_map)    .rename(columns=reformat_dates)    .fillna(method="bfill", axis=1))deaths_df = (    pd.read_csv(deaths_url)    .rename(columns=renamed_columns_map)    .rename(columns=reformat_dates)    .fillna(method="bfill", axis=1))
display(confirmed_cases_df.head())display(deaths_df.head())

fig = go.Figure()
geo_data_cols = ["country", "location", "latitude", "longitude"]geo_data_df = confirmed_cases_df[geo_data_cols]dates_list = (    confirmed_cases_df.filter(regex=r"(\d{2}/\d{2}/\d{4})", axis=1)    .columns    .to_list())
# create a mapping of date -> dataframe, where each df holds the daily counts of cases and deaths per countrycases_by_date = {}for date in dates_list:    # get a pd.Series of all cases for the current day    confirmed_cases_day_df = (        confirmed_cases_df.filter(like=date, axis=1)        .rename(columns=lambda col: "confirmed_cases")        .astype("uint32")    )        # get a pd.Series of all deaths for the current day    deaths_day_df = (        deaths_df.filter(like=date, axis=1)        .rename(columns=lambda col: "deaths")        .astype("uint32")    )        cases_df = confirmed_cases_day_df.join(deaths_day_df)  # combine the cases and deaths dfs    cases_df = geo_data_df.join(cases_df)  # add in the geographical data    cases_df = cases_df[cases_df["confirmed_cases"] > 0]  # get rid of any rows where there were no cases        cases_by_date[date] = cases_df    # each dataframe looks something like this:cases_by_date[dates_list[-1]].head()

# generate the data for each dayfig.data = []for date, df in cases_by_date.items():    df["confirmed_cases_norm"] = np.log1p(df["confirmed_cases"])    df["text"] = (        date        + "
"        + df["country"]        + "
"        + df["location"]        + "
Confirmed cases: "        + df["confirmed_cases"].astype(str)        + "
Deaths: "        + df["deaths"].astype(str)    )    fig.add_trace(        go.Scattergeo(            name="",            lat=df["latitude"],            lon=df["longitude"],            visible=False,            hovertemplate=df["text"],            showlegend=False,            marker={                "size": df["confirmed_cases_norm"] * 100,                "color": "red",                "opacity": 0.75,                "sizemode": "area",            },        )    )

# sort out the nitty gritty of the annotations and slider stepsannotation_text_template = "Worldwide Totals" \                           "
{date}

" \                           "Confirmed cases: {confirmed_cases:,d}
" \                           "Deaths: {deaths:,d}
" \                           "Mortality rate: {mortality_rate:.1%}"annotation_dict = {    "x": 0.03,    "y": 0.35,    "width": 150,    "height": 110,    "showarrow": False,    "text": "",    "valign": "middle",    "visible": False,    "bordercolor": "black"