Gezintiyi Atla

US Population by County

US Census Population Decennial County

2000 ve 2010 On Yıllık Sayımında her kullanılan her ABD vilayeti için cinsiyet ve ırka göre ABD nüfusu.

Bu veri kümesi ABD Sayım Bürosu’nun On Yıllık Sayım Veri Kümesi API’lerinden kullanılır. Bu veri kümesinin kullanımıyla ilgili hüküm ve koşullar için Hizmet Şartları ve İlkeler ve Bildirimler’i gözden geçirin.

Hacim ve Saklama

Bu veri kümesi Parquet biçiminde depolanır ve 2000 ile 2010 yıllarına ait verileri içerir.

Depolama Konumu

Bu veri kümesi Doğu ABD Azure bölgesinde depolanır. Benzeşim için Doğu ABD’deki işlem kaynaklarının ayrılması önerilir.

İlgili Veri Kümeleri

Bildirimler

MICROSOFT, AZURE AÇIK VERİ KÜMELERİNİ “OLDUĞU GİBİ” SAĞLAR. MICROSOFT, VERİ KÜMELERİNİ KULLANMANIZLA İLGİLİ AÇIK VEYA ÖRTÜLÜ HİÇBİR GARANTİ VEYA TAAHHÜTTE BULUNMAZ. YEREL KANUNLARINIZIN İZİN VERDİĞİ ÖLÇÜDE, MICROSOFT DOĞRUDAN, BAĞLI, ÖZEL, DOLAYLI, TESADÜFİ VEYA CEZA GEREKTİRENLER DE DAHİL OLMAK ÜZERE HERHANGİ BİR HASAR YA DA KAYIPLA İLGİLİ HİÇBİR SORUMLULUK KABUL ETMEZ.

Bu veri kümesi Microsoft’un kaynak verileri aldığı orijinal hükümler kapsamında sağlanır. Veri kümesi Microsoft’tan alınan verileri içerebilir.

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

decennialTime stateName countyName population race sex minAge maxAge year
2010 Texas Crockett County 123 WHITE ALONE Male 5 9 2010
2010 Texas Crockett County 1 ASIAN ALONE Female 67 69 2010
2010 Texas Crockett County 111 WHITE ALONE Female 55 59 2010
2010 Texas Crockett County 64 TWO OR MORE RACES null 2010
2010 Texas Crockett County 18 null Male 85 2010
2010 Texas Crockett County 16 AMERICAN INDIAN AND ALASKA NATIVE ALONE Female 2010
2010 Texas Crockett County 7 WHITE ALONE Male 21 21 2010
2010 Texas Crockett County 45 null Female 85 2010
2010 Texas Crockett County 0 NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE Female 67 69 2010
2010 Texas Crockett County 4 SOME OTHER RACE ALONE Male 67 69 2010
Name Data type Unique Values (sample) Description
countyName string 1,960 Washington County
Jefferson County

İlçe adı.

decennialTime string 2 2010
2000

Gerçekleşen on yıllık sayım zamanı, örn. 2010, 2000.

maxAge int 23 64
49

Yaş aralığının üst sınırı. Null ise tüm yaşlar için geçerlidir veya yaş aralığının üst sınırı yoktur, örn. yaş > 85.

minAge int 23 35
15

Yaş aralığının alt sınırı. Null ise tüm yaşlar için geçerlidir.

population int 47,229 1
2

Bu kesimin nüfusu.

race string 8 ASIAN ALONE
NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE

Sayım verilerindeki yarış kategorisi. Null ise tüm yarışlar için geçerlidir.

sex string 3 Male
Female

Erkek veya kadın. Null ise her iki cins için geçerlidir.

stateName string 52 Texas
Georgia

ABD’deki eyaletin adı.

year int 2 2010
2000

On yıllık sürenin yılı (tamsayı).

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 UsPopulationCounty

population = UsPopulationCounty()
population_df = population.to_pandas_dataframe()
ActivityStarted, to_pandas_dataframe
ActivityStarted, to_pandas_dataframe_in_worker
Looking for parquet files...
Reading them into Pandas dataframe...
Reading release/us_population_county/year=2000/part-00177-tid-926394737839939592-51ecde30-440a-40fd-9b41-831814678ab5-1919150.c000.snappy.parquet under container censusdatacontainer
Reading release/us_population_county/year=2010/part-00178-tid-926394737839939592-51ecde30-440a-40fd-9b41-831814678ab5-1919151.c000.snappy.parquet under container censusdatacontainer
Done.
ActivityCompleted: Activity=to_pandas_dataframe_in_worker, HowEnded=Success, Duration=11624.4 [ms]
ActivityCompleted: Activity=to_pandas_dataframe, HowEnded=Success, Duration=11659.25 [ms]
In [2]:
population_df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 3664512 entries, 0 to 1855295
Data columns (total 8 columns):
decennialTime    object
stateName        object
countyName       object
population       int32
race             object
sex              object
minAge           float64
maxAge           float64
dtypes: float64(2), int32(1), object(5)
memory usage: 237.6+ MB
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 = "censusdatacontainer"
folder_name = "release/us_population_county/"
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.
from azureml.opendatasets import UsPopulationCounty

population = UsPopulationCounty()
population_df = population.to_spark_dataframe()
ActivityStarted, to_spark_dataframe ActivityStarted, to_spark_dataframe_in_worker ActivityCompleted: Activity=to_spark_dataframe_in_worker, HowEnded=Success, Duration=3770.1 [ms] ActivityCompleted: Activity=to_spark_dataframe, HowEnded=Success, Duration=3771.78 [ms]
In [2]:
display(population_df.limit(5))
decennialTimestateNamecountyNamepopulationracesexminAgemaxAgeyear
2010TexasCrockett County123WHITE ALONEMale592010
2010TexasCrockett County1ASIAN ALONEFemale67692010
2010TexasCrockett County111WHITE ALONEFemale55592010
2010TexasCrockett County64TWO OR MORE RACESnullnullnull2010
2010TexasCrockett County18nullMale85null2010
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "censusdatacontainer"
blob_relative_path = "release/us_population_county/"
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 [39]:
# This is a package in preview.
from azureml.opendatasets import UsPopulationCounty

population = UsPopulationCounty()
population_df = population.to_spark_dataframe()
In [40]:
# Display top 5 rows
display(population_df.limit(5))
Out[40]:
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "censusdatacontainer"
blob_relative_path = "release/us_population_county/"
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'))