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Public Holidays

Public Holidays

來自 PyPI 假日套件和 Wikipedia 的全球國定假日資料,涵蓋 1970 年至 2099 年的 38 個國家或地區。

每個資料列都會載明假日資訊,指出特定日期、國家或地區,以及多數人是否具有帶薪休假。

磁碟區與保留期

此資料集以 Parquet 格式儲存, 此為快照集,包含從 1970-01-01 到 2099-01-01 的假期資訊。 資料大小約為 500KB。

儲存位置

此資料集儲存於美國東部 Azure 區域。 建議您在美國東部配置計算資源,以確保同質性。

其他資訊

此資料集合併的資料來源是 Wikipedia (WikiMedia Foundation Inc) 及 PyPI 假日套件

提供的合併資料集由 Creative Commons Attribution-ShareAlike 3.0 Unported License 所規範。

如果您對資料來源有任何疑問,請傳送電子郵件至

通知

Microsoft 係依「現況」提供 Azure 開放資料集。 針對 貴用戶對資料集的使用,Microsoft 不提供任何明示或默示的擔保、保證或條件。 在 貴用戶當地法律允許的範圍內,針對因使用資料集而導致的任何直接性、衍生性、特殊性、間接性、附隨性或懲罰性損害或損失,Microsoft 概不承擔任何責任。

此資料集是根據 Microsoft 接收來源資料的原始條款所提供。 資料集可能包含源自 Microsoft 的資料。

Access

Available inWhen to use
Azure Notebooks

Quickly explore the dataset with Jupyter notebooks hosted on Azure or your local machine.

Azure Databricks

Use this when you need the scale of an Azure managed Spark cluster to process the dataset.

Azure Synapse

Use this when you need the scale of an Azure managed Spark cluster to process the dataset.

Preview

countryOrRegion holidayName normalizeHolidayName countryRegionCode date
Norway Søndag Søndag NO 12/28/2098 12:00:00 AM
Sweden Söndag Söndag SE 12/28/2098 12:00:00 AM
Australia Boxing Day Boxing Day AU 12/26/2098 12:00:00 AM
Hungary Karácsony másnapja Karácsony másnapja HU 12/26/2098 12:00:00 AM
Austria Stefanitag Stefanitag AT 12/26/2098 12:00:00 AM
Canada Boxing Day Boxing Day CA 12/26/2098 12:00:00 AM
Croatia Sveti Stjepan Sveti Stjepan HR 12/26/2098 12:00:00 AM
Czech 2. svátek vánoční 2. svátek vánoční CZ 12/26/2098 12:00:00 AM
Denmark Anden juledag Anden juledag DK 12/26/2098 12:00:00 AM
England Boxing Day Boxing Day null 12/26/2098 12:00:00 AM
Name Data type Unique Values (sample) Description
countryOrRegion string 38 Sweden
Norway

國家或地區完整名稱。

countryRegionCode string 35 SE
NO

國碼/區域碼的格式請參閱這裡

date timestamp 20,665 2037-01-01 00:00:00
2032-01-01 00:00:00

假日的日期。

holidayName string 483 Søndag
Söndag

假日的全名。

isPaidTimeOff boolean 3 True

指出多數人在此日期是否具有帶薪休假 (目前僅適用於美國、英國和印度)。 如果為 Null,則表示不明。

normalizeHolidayName string 438 Søndag
Söndag

假日的正規化名稱。

Select your preferred service:

Azure Notebooks

Azure Databricks

Azure Synapse

Azure Notebooks

Package: Language: Python Python
In [1]:
# This is a package in preview.
from azureml.opendatasets import PublicHolidays

from datetime import datetime
from dateutil import parser
from dateutil.relativedelta import relativedelta


end_date = datetime.today()
start_date = datetime.today() - relativedelta(months=1)
hol = PublicHolidays(start_date=start_date, end_date=end_date)
hol_df = hol.to_pandas_dataframe()
ActivityStarted, to_pandas_dataframe ActivityStarted, to_pandas_dataframe_in_worker Looking for parquet files... Reading them into Pandas dataframe... Reading Processed/part-00000-tid-8575944798531137721-7b2fbd47-2ae5-45fd-b8b5-daa663d33177-649-c000.snappy.parquet under container holidaydatacontainer Done. ActivityCompleted: Activity=to_pandas_dataframe_in_worker, HowEnded=Success, Duration=955.3 [ms] ActivityCompleted: Activity=to_pandas_dataframe, HowEnded=Success, Duration=958.23 [ms]
In [2]:
hol_df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 34 entries, 25706 to 25739 Data columns (total 6 columns): countryOrRegion 34 non-null object holidayName 34 non-null object normalizeHolidayName 34 non-null object isPaidTimeOff 1 non-null object countryRegionCode 34 non-null object date 34 non-null datetime64[ns] dtypes: datetime64[ns](1), object(5) memory usage: 1.9+ KB
In [1]:
# Pip install packages
import os, sys

!{sys.executable} -m pip install azure-storage-blob
!{sys.executable} -m pip install pyarrow
!{sys.executable} -m pip install pandas
In [2]:
# Azure storage access info
azure_storage_account_name = "azureopendatastorage"
azure_storage_sas_token = r""
container_name = "holidaydatacontainer"
folder_name = "Processed"
In [3]:
from azure.storage.blob import BlockBlobServicefrom azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient

if azure_storage_account_name is None or azure_storage_sas_token is None:
    raise Exception(
        "Provide your specific name and key for your Azure Storage account--see the Prerequisites section earlier.")

print('Looking for the first parquet under the folder ' +
      folder_name + ' in container "' + container_name + '"...')
container_url = f"https://{azure_storage_account_name}.blob.core.windows.net/"
blob_service_client = BlobServiceClient(
    container_url, azure_storage_sas_token if azure_storage_sas_token else None)

container_client = blob_service_client.get_container_client(container_name)
blobs = container_client.list_blobs(folder_name)
sorted_blobs = sorted(list(blobs), key=lambda e: e.name, reverse=True)
targetBlobName = ''
for blob in sorted_blobs:
    if blob.name.startswith(folder_name) and blob.name.endswith('.parquet'):
        targetBlobName = blob.name
        break

print('Target blob to download: ' + targetBlobName)
_, filename = os.path.split(targetBlobName)
blob_client = container_client.get_blob_client(targetBlobName)
with open(filename, 'wb') as local_file:
    blob_client.download_blob().download_to_stream(local_file)
In [4]:
# Read the parquet file into Pandas data frame
import pandas as pd

print('Reading the parquet file into Pandas data frame')
df = pd.read_parquet(filename)
In [5]:
# you can add your filter at below
print('Loaded as a Pandas data frame: ')
df
In [6]:
 

Azure Databricks

Package: Language: Python Python
In [1]:
# This is a package in preview.
# You need to pip install azureml-opendatasets in Databricks cluster. https://docs.microsoft.com/en-us/azure/data-explorer/connect-from-databricks#install-the-python-library-on-your-azure-databricks-cluster
from azureml.opendatasets import PublicHolidays

from datetime import datetime
from dateutil import parser
from dateutil.relativedelta import relativedelta


end_date = datetime.today()
start_date = datetime.today() - relativedelta(months=1)
hol = PublicHolidays(start_date=start_date, end_date=end_date)
hol_df = hol.to_spark_dataframe()
ActivityStarted, to_spark_dataframe ActivityStarted, to_spark_dataframe_in_worker ActivityCompleted: Activity=to_spark_dataframe_in_worker, HowEnded=Success, Duration=2221.62 [ms] ActivityCompleted: Activity=to_spark_dataframe, HowEnded=Success, Duration=2223.36 [ms]
In [2]:
display(hol_df.limit(5))
countryOrRegionholidayNamenormalizeHolidayNameisPaidTimeOffcountryRegionCodedate
NorwaySøndagSøndagnullNO2019-06-16T00:00:00.000+0000
South AfricaYouth DayYouth DaynullZA2019-06-16T00:00:00.000+0000
SwedenSöndagSöndagnullSE2019-06-16T00:00:00.000+0000
UkraineТрійцяТрійцяnullUA2019-06-16T00:00:00.000+0000
ArgentinaDía Pase a la Inmortalidad del General Martín Miguel de Güemes [Day Pass to the Immortality of General Martín Miguel de Güemes]Día Pase a la Inmortalidad del General Martín Miguel de Güemes [Day Pass to the Immortality of General Martín Miguel de Güemes]nullAR2019-06-17T00:00:00.000+0000
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "holidaydatacontainer"
blob_relative_path = "Processed"
blob_sas_token = r""
In [2]:
# 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)
In [3]:
# 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')
In [4]:
# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Azure Synapse

Package: Language: Python Python
In [33]:
# This is a package in preview.
from azureml.opendatasets import PublicHolidays

from datetime import datetime
from dateutil import parser
from dateutil.relativedelta import relativedelta


end_date = datetime.today()
start_date = datetime.today() - relativedelta(months=1)
hol = PublicHolidays(start_date=start_date, end_date=end_date)
hol_df = hol.to_spark_dataframe()
In [34]:
# Display top 5 rows
display(hol_df.limit(5))
Out[34]:
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "holidaydatacontainer"
blob_relative_path = "Processed"
blob_sas_token = r""
In [2]:
# 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)
In [3]:
# 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')
In [4]:
# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))