如何在 Python 中制作赛车条形图可视化

Question

how Animated Bar Chart Race Python: How to make a bar change its position automatically. For example, in the below code example while for countries like USA having more values, the bar should gradually move up.

import plotly.express as px

import pandas as pd
import plotly.graph_objects as go
import numpy as np 

url='https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'

def read_file(url):
    df = pd.read_csv(url)
    return df

def filter_specific_country(df, selected_countries):
    df1=df[df['Country/Region'].isin(selected_countries) ]
    countrywise_grouped_df = df1.groupby(df['Country/Region']).sum().drop(['Lat','Long'], axis=1)
    countrywise_grouped_df
    return countrywise_grouped_df

def transpose_and_reformat_data(df):
    df_t=df.transpose()
    df_t.reset_index(inplace=True)
    df_t.rename(columns={'Country/Region':'Index_Col', 'index':'Dates'}, inplace=True)
    return df_t

confirmed_dataset = read_file(url)
selected_countries=['India','China','Italy','Spain','France','Australia','Germany','Japan','Korea, South','Pakistan',
                    'Russia','United Kingdom','Canada','Iran','Brazil','Singapore','South Africa','US']
ds=filter_specific_country(confirmed_dataset,selected_countries)
data=transpose_and_reformat_data(ds).melt(id_vars=["Dates"], var_name="Country", value_name="Confirmed_Count")
#plot_title="Global Spread of Covid-19 : (Selected Countries)"
plot_title='Visualizing the spread of Novel Coronavirus COVID-19 (2019-nCoV) - Created by Dibyendu Banerjee'
fig = px.bar(data, y="Country", x="Confirmed_Count", color="Country",
  animation_frame="Dates", range_x=[1,14000000], orientation='h' )
fig.update_layout(title=plot_title,yaxis_title='Countries', xaxis_tickangle=90, font=dict(family="Arial",size=10,color="#7f7f7f"))
fig.show()

Answer 1

As far as I know, bar chart tracing using potly is not feasible. There is already a dedicated library that I will use to answer your question. Since the data is at the daily level, it will take a long time to play back, so I will need to resample or summarize the data into years.

from raceplotly.plots import barplot
import plotly.express as px
import plotly.graph_objects as go

import pandas as pd
import numpy as np 

url='https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'

def read_file(url):
    df = pd.read_csv(url)
    return df

def filter_specific_country(df, selected_countries):
    df1=df[df['Country/Region'].isin(selected_countries) ]
    countrywise_grouped_df = df1.groupby(df['Country/Region']).sum().drop(['Lat','Long'], axis=1)
    countrywise_grouped_df
    return countrywise_grouped_df

def transpose_and_reformat_data(df):
    df_t=df.transpose()
    df_t.reset_index(inplace=True)
    df_t.rename(columns={'Country/Region':'Index_Col', 'index':'Dates'}, inplace=True)
    return df_t

confirmed_dataset = read_file(url)
selected_countries=['India','China','Italy','Spain','France','Australia','Germany','Japan','Korea, South','Pakistan',
                    'Russia','United Kingdom','Canada','Iran','Brazil','Singapore','South Africa','US']
ds=filter_specific_country(confirmed_dataset,selected_countries)
data=transpose_and_reformat_data(ds).melt(id_vars=["Dates"], var_name="Country", value_name="Confirmed_Count")

covid_race = barplot(data, item_column='Country', value_column='Confirmed_Count',time_column='Dates')
covid_race.plot(item_label='Top 10 crops', value_label = 'Covid Confirmed Count', date_format='%Y/%m/%d', frame_duration=800)