Centro Europeu de Prevenção e Controlo das Doenças (ECDC) Casos de COVID-19

Este bloco de notas documenta os URLs e o código de exemplo para aceder aos URLs do conjunto de dados de Casos de Covid-19 do Centro Europeu de Prevenção e Controlo das Doenças (ECDC) de diferentes formatos de ficheiro de conjunto de dados alojados no Armazenamento de Blobs do Azure:¶ CSV: https://pandemicdatalake.blob.core.windows.net/public/curated/covid-19/ecdc_cases/latest/ecdc_cases.csv

JSON: https://pandemicdatalake.blob.core.windows.net/public/curated/covid-19/ecdc_cases/latest/ecdc_cases.json

JSONL: https://pandemicdatalake.blob.core.windows.net/public/curated/covid-19/ecdc_cases/latest/ecdc_cases.jsonl

Parquet: https://pandemicdatalake.blob.core.windows.net/public/curated/covid-19/ecdc_cases/latest/ecdc_cases.parquet

Transfira o ficheiro do conjunto de dados utilizando a capacidade de transferência incorporada a partir de um URL http no Pandas. Pandas tem leitores para vários formatos de arquivo:

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_parquet.html

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html

import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt

df = pd.read_parquet("https://pandemicdatalake.blob.core.windows.net/public/curated/covid-19/ecdc_cases/latest/ecdc_cases.parquet")
df.head(10)

df.dtypes

df.groupby('countries_and_territories').first().filter(['continent_exp','cases', 'deaths','date_rep'])

df.groupby('continent_exp').agg({'countries_and_territories': 'count','cases': 'count','deaths': 'count'})

import plotly.graph_objects as go
import plotly.express as px
import matplotlib.pyplot as plt

df.loc[: , ['countries_and_territories', 'cases', 'deaths']].groupby(['countries_and_territories'
         ]).max().sort_values(by='cases',ascending=False).reset_index()[:15].style.background_gradient(cmap='rainbow')

df_Worldwide=df[df['countries_and_territories']=='United_States_of_America']

df.plot(kind='line',x='date_rep',y="cases",grid=True)
df.plot(kind='line',x='date_rep',y="deaths",grid=True)
#df_Worldwide.plot(kind='line',x='date_rep',y="confirmed_change",grid=True)
#df_Worldwide.plot(kind='line',x='date_rep',y="deaths_change",grid=True)

Amostra não disponível para esta combinação de plataforma/pacote.

Amostra não disponível para esta combinação de plataforma/pacote.

# Azure storage access info
blob_account_name = "pandemicdatalake"
blob_container_name = "public"
blob_relative_path = "curated/covid-19/ecdc_cases/latest/ecdc_cases.parquet"
blob_sas_token = r""

# Allow SPARK to read from Blob remotely
wasbs_path = 'wasbs://%s@%s.blob.core.windows.net/%s' % (blob_container_name, blob_account_name, blob_relative_path)
spark.conf.set(
  'fs.azure.sas.%s.%s.blob.core.windows.net' % (blob_container_name, blob_account_name),
  blob_sas_token)
print('Remote blob path: ' + wasbs_path)

# SPARK read parquet, note that it won't load any data yet by now
df = spark.read.parquet(wasbs_path)
print('Register the DataFrame as a SQL temporary view: source')
df.createOrReplaceTempView('source')

# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Amostra não disponível para esta combinação de plataforma/pacote.

# Azure storage access info
blob_account_name = "pandemicdatalake"
blob_container_name = "public"
blob_relative_path = "curated/covid-19/ecdc_cases/latest/ecdc_cases.parquet"
blob_sas_token = r""

# Allow SPARK to read from Blob remotely
wasbs_path = 'wasbs://%s@%s.blob.core.windows.net/%s' % (blob_container_name, blob_account_name, blob_relative_path)
spark.conf.set(
  'fs.azure.sas.%s.%s.blob.core.windows.net' % (blob_container_name, blob_account_name),
  blob_sas_token)
print('Remote blob path: ' + wasbs_path)

# SPARK read parquet, note that it won't load any data yet by now
df = spark.read.parquet(wasbs_path)
print('Register the DataFrame as a SQL temporary view: source')
df.createOrReplaceTempView('source')

# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))