Producido | AI campamento de base tecnológica (ID: rgznai100)
La nueva corona tenía brote de neumonía en una escala global. Para entender la distribución mundial de la epidemia, hay técnicos Jupyter Notebook dibuja dos tipos de epidemia mapa de contorno ( Gráfico coropletas ) y gráficos de dispersión .
El primero muestra la propagación de la epidemia de la situación de un país / región: los colores en el mapa del país / región de la más profunda, más se confirmaron casos. Uno de los actores clave para la animación gráfica, pero también se puede utilizar el control deslizante para cambiar manualmente la fecha.
El segundo diagrama de dispersión punto rojo indica el número de casos confirmados logarítmicamente su tamaño y la proporción de un lugar particular. Una resolución más alta de esta tabla, los datos presentados en la situación de la epidemia es el nivel estatal / provincial.
El mapa brote pantalla la claridad final, el siguiente es todo el código compartido:
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"
Correspondencia de contorno
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")
salida:
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()
salida:
# 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 + "<br>" + df["country"] + "<br>Confirmed cases: " + df["confirmed_cases"].apply(lambda x: "{:,}".format(x)) + "<br>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": "<b>Confirmed Cases</b>"}, "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}<br>Total confirmed cases: %{y:,}<extra></extra>" ) # 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}<br>Total deaths: %{y:,}<extra></extra>" )
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
epidemia de dispersión
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()
salida:
# 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 + "<br>" + df["country"] + "<br>" + df["location"] + "<br>Confirmed cases: " + df["confirmed_cases"].astype(str) + "<br>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 = "<b>Worldwide Totals</b>" \ "<br>{date}<br><br>" \ "Confirmed cases: {confirmed_cases:,d}<br>" \ "Deaths: {deaths:,d}<br>" \ "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",}
steps = []for i, data in enumerate(fig.data): step = { "method": "update", "args": [ {"visible": [False] * len(fig.data)}, {"annotations": [dict(annotation_dict) for _ in range(len(fig.data))]}, ], "label": dates_list[i], }
# toggle the i'th trace and annotation box to visible step["args"][0]["visible"][i] = True step["args"][1]["annotations"][i]["visible"] = True
df = cases_by_date[dates_list[i]] confirmed_cases = df["confirmed_cases"].sum() deaths = df["deaths"].sum() mortality_rate = deaths / confirmed_cases step["args"][1]["annotations"][i]["text"] = annotation_text_template.format( date=dates_list[i], confirmed_cases=confirmed_cases, deaths=deaths, mortality_rate=mortality_rate, )
steps.append(step)
sliders = [ { "active": 0, "currentvalue": {"prefix": "Date: "}, "steps": steps, "len": 0.9, "x": 0.05, }]
first_annotation_dict = {**annotation_dict}first_annotation_dict.update( { "visible": True, "text": annotation_text_template.format( date="10/01/2020", confirmed_cases=44, deaths=1, mortality_rate=0.0227 ), })fig.layout.title = {"text": "COVID-19 Case Tracker", "x": 0.5}fig.update_layout( height=650, margin={"t": 50, "b": 20, "l": 20, "r": 20}, annotations=[go.layout.Annotation(**first_annotation_dict)], sliders=sliders,)fig.data[0].visible = True # set the first data point visible
fig
# save the figure locally as an interactive HTML pagefig.update_layout(height=1000)fig.write_html("nCoV_tracker.html")
来源：
https://mfreeborn.github.io/blog/2020/03/15/interactive-coronavirus-map-with-jupyter-notebook#Chart-1---A-Choropleth-Chart
【end】
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