译者序

大家好，我很久没写信给Habr了，但是那是有原因的。我在数据分析领域工作了很多年，在这段时间里，我了解到的最重要的一点是，数据分析工具很少，各种各样的工具。我担心几件事，其中之一是数据分析专家在尝试与经理或什至车间的同事共享工作成果时遇到的困难。通常，这里使用任何可用的工具，电子邮件，IM，投递箱等。安德烈（Andrei）和我们的另一个朋友决定在这方面做一些有意义的事情，今天我想告诉大家我们做了什么。在大家都因COVID-19病毒而发现自己的情况下，发表和讨论研究结果的问题可能变得更为紧迫，比以往任何时候都。

因此，我叫Vitaliy，我是dstack.ai的共同创始人，并向您介绍几天前Andrey写的文本的译文。它更多地面向初学者，但即使是高级数据分析专家，尤其是对可视化工具感兴趣的专家，也可以在这里找到一些有趣的东西，这些东西可以帮助他们的日常工作。
我为所有示例编写了代码，因此您可以直接在dstack.ai上向我写信。

不明飞行物护理分钟

大流行的COVID-19是由SARS-CoV-2冠状病毒（2019-nCoV）引起的潜在严重的急性呼吸道感染，已在全球正式宣布。关于Habré的很多信息都涉及此主题-始终记住，它既可靠又有用，反之亦然。

我们敦促您不要批评任何已发布的信息。

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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 ,

下载数据

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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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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)

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#  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.

. , , , . GitHub ( , , , – ) .

, :

COVID-19传播速率分析并在dstack.ai上发布结果

译者序

不明飞行物护理分钟

我们敦促您不要批评任何已发布的信息。

COVID-19

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