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Analyse du taux de propagation COVID-19 et publication des résultats sur dstack.ai

Préface du traducteur



Bonjour Ă  tous, je n’ai rien Ă©crit Ă  Habr depuis longtemps, mais il y avait une raison. Je travaille dans le domaine de l'analyse de donnĂ©es depuis plusieurs annĂ©es, et la chose la plus importante que j'ai compris pendant cette pĂ©riode est qu'il y a trĂšs peu d'outils dans l'analyse de donnĂ©es, une grande variĂ©tĂ© d'outils. J'Ă©tais prĂ©occupĂ© par plusieurs choses, dont la difficultĂ© Ă  laquelle un spĂ©cialiste de l'analyse des donnĂ©es est confrontĂ© lorsqu'il essaie de partager les rĂ©sultats de son travail avec un gestionnaire ou mĂȘme un collĂšgue de l'atelier. Habituellement, tous les outils disponibles, e-mail, messagerie instantanĂ©e, dropbox, etc. sont utilisĂ©s ici. Andrei et l'un de nos autres amis ont dĂ©cidĂ© d'essayer de faire quelque chose de significatif dans ce domaine et aujourd'hui, je veux vous parler de ce que nous avons fait. Dans la situation oĂč nous nous sommes tous retrouvĂ©s Ă  cause du virus COVID-19, le problĂšme de la publication et de la discussion des rĂ©sultats de la recherche est peut-ĂȘtre devenu encore plus pertinent.que jamais auparavant.


, , dstack.ai , . , , , , - , .
, vitaly at dstack.ai.



COVID-19 — , SARS-CoV-2 (2019-nCoV). — , /, .



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COVID-19


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  • Python, pandas plot.ly . , data science Python .
  • COVID-19.
  • Python dstack.ai API , .

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dstack.ai, dstack Python ( conda – ).


import pandas as pd
import plotly.express as px
from dstack import create_frame

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url = "https://data.humdata.org/hxlproxy/api/data-preview.csv?url=https%3A%2F%2Fraw.githubusercontent.com%2FCSSEGISandData%2FCOVID-19%2Fmaster%2Fcsse_covid_19_data%2Fcsse_covid_19_time_series%2Ftime_series_19-covid-Confirmed.csv&filename=time_series_2019-ncov-Confirmed.csv"
df = pd.read_csv(url) #    URL
df.head() #   5 ,

données téléchargées


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#   (2-  )    
cols = [df.columns[1]] + list(df.columns[-2:]) 
last_2_days = df[df["Province/State"].isnull()][cols].copy() # 
last_2_days # ,


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d1 = last_2_days.columns[-1]  #  
d2 = last_2_days.columns[-2]  #  
last_2_days["delta"] = last_2_days[d1] - last_2_days[d2]
last_2_days["delta%"] = last_2_days["delta"] / last_2_days[d2]
last_2_days


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  1. covid19/speed ( , – ). , : https://dstack.ai/<user>/covid19/speed.

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dstack config --token <token> --user <user>

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min_cases = 50
#    
top_speed_frame = create_frame("covid19/speed")
# 
sort_by_cols = ["delta", "delta%"]
for col in sort_by_cols:
    top = last_2_days[last_2_days[last_2_days.columns[1]] > min_cases].
                 sort_values(by=[col], ascending=False).head(50)
    #    
    top_speed_frame.commit(top, f"Top 50 countries with the " \
                                f"fastest growing number of confirmed " \
                                f"Covid-19 cases (at least {min_cases})", 
                                {"Sort by": col})

top_speed_frame.push()

: https://dstack.ai/cheptsov/covid19/speed.



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#  
cdf = df[(df["Country/Region"]=="Italy") & (df["Province/State"].isnull())][df.columns[4:]].T
#    
cdf = cdf.rename(columns={cdf.columns[0]:"confirmed"})

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fig = px.line(cdf, x=cdf.index, y="confirmed")
fig.show()


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delta = (cdf.shift(-1) - cdf)
delta.tail() # ,     ,


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fig = px.line(delta, x=delta.index, y="confirmed")
fig.show()


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def plots_by_country(country):
    cdf = df[(df["Country/Region"]==country) & (df["Province/State"].isnull())][df.columns[4:]].T
    cdf = cdf.rename(columns={cdf.columns[0]:"confirmed"})
    cfig = px.line(cdf, x=cdf.index, y="confirmed")
    delta = (cdf.shift(-1) - cdf).rename(columns={"confirmed": "confirmed per day"})
    cdfig = px.line(delta, x=cdf.index, y="confirmed per day")
    delta_p = ((cdf.shift(-1) - cdf) / cdf.shift(-1)).rename(columns={"confirmed": "confirmed per day %"})
    cdpfig = px.line(delta_p, x=cdf.index, y="confirmed per day %")
    return (cfig, cdfig, cdpfig)

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(fig1, fig2, fig3) = plots_by_country("Austria")
fig1.show()
fig2.show()
fig3.show()




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#  30       
countries = df[df["Province/State"].isnull()].sort_values(by=[df.columns[-1]], ascending=False)[["Country/Region"]].head(30)

#           
frame = create_frame("covid19/speed_by_country")
for c in countries["Country/Region"].tolist():
    print(c)
    (fig1, fig2, fig3) = plots_by_country(c)
    frame.commit(fig1, f"Confirmed cases in {c}", {"Country": c, "Chart": "All cases"})
    frame.commit(fig2, f"New confirmed cases in {c}", {"Country": c, "Chart": "New cases"})
    frame.commit(fig3, f"New confirmed cases in {c} in %", {"Country": c, "Chart": "New cases (%)"})

frame.push()

https://dstack.ai/cheptsov/covid19/speed_by_country.



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t1 = df[(df["Country/Region"]=="Italy") & (df["Province/State"].isnull())][df.columns[4:]].T
t1 = t1.rename(columns={t1.columns[0]:"confirmed"})
t1.reset_index() #    
t1["Country/Region"] = "Italy" #    
t1.tail() # ,


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def country_df(country):
    cdf = df[(df["Country/Region"]==country) & (df["Province/State"].isnull())][df.columns[4:]].T
    cdf = cdf.rename(columns={cdf.columns[0]:"confirmed"})
    delta = (cdf.shift(-1) - cdf).rename(columns={"confirmed": "confirmed per day"})
    delta.reset_index()
    delta["Country/Region"] = country
    delta_p = ((cdf.shift(-1) - cdf) / cdf.shift(-1)).rename(columns={"confirmed": "confirmed per day %"})
    delta_p.reset_index()
    delta_p["Country/Region"] = country
    cdf.reset_index()
    cdf["Country/Region"] = country
    return (cdf, delta, delta_p)

10 , 3 , , , :


#  10     -   
top10 = df[df["Province/State"].isnull()].sort_values(by=[df.columns[-1]], ascending=False)[["Country/Region"]].head(10)

# ,       
top = []
top_delta = []
top_delta_p = []
for c in top10["Country/Region"].tolist():
    (x, y, z) = country_df(c)
    top.append(x)
    top_delta.append(y)
    top_delta_p.append(z)

test = pd.concat(top) #     
#  
px.line(test, x=test.index, y="confirmed", color='Country/Region').show()


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frame = create_frame("covid19/speed_by_country_all")

top10df = pd.concat(top)
fig = px.line(top10df, x=top10df.index, y="confirmed", color='Country/Region')
frame.commit(fig, "Confirmed cases in top 10 countries", {"Country": "Top 10", "Chart": "All cases"})

top10df_delta = pd.concat(top_delta)
fig = px.line(top10df_delta, x=top10df_delta.index, y="confirmed per day", color='Country/Region')
frame.commit(fig, "New confirmed cases in top 10 countries", {"Country": "Top 10", "Chart": "New cases"})

top10df_delta_p = pd.concat(top_delta_p)
fig = px.line(top10df_delta_p, x=top10df_delta_p.index, y="confirmed per day %", color='Country/Region')
frame.commit(fig, "New confirmed cases in top 10 countries in %", {"Country": "Top 10", "Chart": "New cases (%)"})

for c in countries["Country/Region"].tolist():
    print(c)
    (fig1, fig2, fig3) = plots_by_country(c)
    frame.commit(fig1, f"Confirmed cases in {c}", {"Country": c, "Chart": "All cases"})
    frame.commit(fig2, f"New confirmed cases in {c}", {"Country": c, "Chart": "New cases"})
    frame.commit(fig3, f"New confirmed cases in {c} in %", {"Country": c, "Chart": "New cases (%)"})

frame.push()

: https://dstack.ai/cheptsov/covid19/speed_by_country_all.


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