疫情数据分析平台工作报告【6.5】疫情地图

数据结构如下。

我们用第一个csv文件。列信息如下。

导入依赖。

import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.io as pio
pio.renderers.default = "browser"

# Read Data
df = pd.read_csv("dataset/covid_19_data.csv")
# Rename columns
df = df.rename(columns={'Country/Region': 'Country'})
df = df.rename(columns={'ObservationDate': 'Date'})
# Manipulate Dataframe
df_countries = df.groupby(['Country', 'Date']).sum().reset_index().sort_values('Date', ascending=False)
print(df_countries)
df_countries = df_countries.drop_duplicates(subset=['Country'])
df_countries = df_countries[df_countries['Confirmed'] > 0]
# Create the Choropleth

fig = go.Figure(data=go.Choropleth(
    locations=df_countries['Country'],
    locationmode='country names',
    z=df_countries['Confirmed'],
    colorscale='reds',
    marker_line_color='black',
    marker_line_width=0.5,
))
fig.update_layout(
    title_text='Confirmed Cases as of 2021/5/3',
    title_x=0.5,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    )
)
fig.show()

第一部分先读取数据

# Read Data
df = pd.read_csv("dataset/covid_19_data.csv")

为列改名,便于操作

df = df.rename(columns={'Country/Region': 'Country'})
df = df.rename(columns={'ObservationDate': 'Date'})

重排数据并清洗

df_countries = df.groupby(['Country', 'Date']).sum().reset_index().sort_values('Date', ascending=False)
print(df_countries)
df_countries = df_countries.drop_duplicates(subset=['Country'])
df_countries = df_countries[df_countries['Confirmed'] > 0]
# Create the Choropleth

画图

fig = go.Figure(data=go.Choropleth(
    locations=df_countries['Country'],
    locationmode='country names',
    z=df_countries['Confirmed'],
    colorscale='reds',
    marker_line_color='black',
    marker_line_width=0.5,
))
fig.update_layout(
    title_text='Confirmed Cases as of 2021/5/3',
    title_x=0.5,
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    )
)
fig.show()

上面的是静态图,没有交互动能。但是相对的,渲染、画图的速度都会变快,同时保存下来的数据占用也小了。

导入依赖

import numpy as np
import plotly.express as px
import pandas as pd
import plotly.graph_objs as go
import plotly.io as pio

读数据、改列名

pio.renderers.default = "browser"
df = pd.read_csv("dataset/covid_19_data.csv")
df = df.rename(columns={'Country/Region': 'Country'})
df = df.rename(columns={'ObservationDate': 'Date'})

清洗

# Manipulating the original dataframe
df_countrydate = df[df['Confirmed'] > 0]
df_countrydate['Date'] = pd.to_datetime(df_countrydate['Date']).apply(lambda x: x.strftime('%Y-%m-%d'))

df_countrydate = df_countrydate.groupby(['Country', 'Date']).sum().reset_index().sort_values('Date', ascending=True)
print(df_countrydate)

画图

fig = px.choropleth(df_countrydate,
                    locations="Country",
                    locationmode="country names",
                    color="Confirmed",
                    hover_name="Country",
                    animation_frame="Date",
                    )
fig.update_layout(
    title_text='Global Spread of Coronavirus',
    title_x=0.5,
    geo=dict(
        showframe=False,
        showcoastlines=False,
    ))

fig.show()

上述是使用python生成展示为html形式的图。
此外,还可以使用前端绘图,例如Echarts。

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 30     累计确诊人数如下：
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