生產者物價指數 (PPI) 用於衡量一段時間內國內生產者就其生產所獲售價的平均變化情況。 PPI 中包含的價格取自所涵蓋之產品和服務的第一次商業交易。
每月會發佈大約 10,000 個個別產品和產品群的 PPI。 PPI 適用於美國經濟中商品生產業中幾乎所有產業的生產 (礦業、製造業、農業、漁業和林業),還有天然氣、電力、建造,以及與生產業所生產商品相比具有競爭力的商品,例如廢棄物和廢料。 根據 2007 年經濟普查中報告的收入,PPI 計劃涵蓋約 72% 的服務業總產值。 資料包含下列產業:批發和零售貿易、運輸和倉儲、資訊、金融和保險、房地產經紀、出租和租賃、專業、科學和技術服務、行政管理、支援和廢棄物管理服務、醫療保健和社會救助、住宿。
https://download.bls.gov/pub/time.series/wp/wp.txt 讀我檔案 是包含此資料集詳細資訊的檔案,位於原始資料集位置。 如需其他資訊,請參閱常見問題集。
此資料集來自美國勞工統計局 (BLS) 所發佈的生產者物價指數資料。 如需此資料集相關的使用條款及條件,請參閱 Copyright Information (連結與著作權資訊) 及 Important Web Site Notices (重要網站聲明)。
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。
相關資料集
- US Producer Price Index - Industry (美國生產者物價指數 - 產業)
- US Consumer Price Index (美國消費者物價指數)
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 in | When 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
item_code | group_code | series_id | year | period | value | footnote_codes | seasonal | series_title | group_name | item_name |
---|---|---|---|---|---|---|---|---|---|---|
120922 | 05 | WPU05120922 | 2008 | M06 | 100 | nan | U | PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted | Fuels and related products and power | Prepared bituminous coal underground mine, mechanically crushed/screened/sized only |
120922 | 05 | WPU05120922 | 2008 | M07 | 104.6 | nan | U | PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted | Fuels and related products and power | Prepared bituminous coal underground mine, mechanically crushed/screened/sized only |
120922 | 05 | WPU05120922 | 2008 | M08 | 104.4 | nan | U | PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted | Fuels and related products and power | Prepared bituminous coal underground mine, mechanically crushed/screened/sized only |
120922 | 05 | WPU05120922 | 2008 | M09 | 98.3 | nan | U | PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted | Fuels and related products and power | Prepared bituminous coal underground mine, mechanically crushed/screened/sized only |
120922 | 05 | WPU05120922 | 2008 | M10 | 101.5 | nan | U | PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted | Fuels and related products and power | Prepared bituminous coal underground mine, mechanically crushed/screened/sized only |
120922 | 05 | WPU05120922 | 2008 | M11 | 95.2 | nan | U | PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted | Fuels and related products and power | Prepared bituminous coal underground mine, mechanically crushed/screened/sized only |
120922 | 05 | WPU05120922 | 2008 | M12 | 96.7 | nan | U | PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted | Fuels and related products and power | Prepared bituminous coal underground mine, mechanically crushed/screened/sized only |
120922 | 05 | WPU05120922 | 2009 | M01 | 104.2 | nan | U | PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted | Fuels and related products and power | Prepared bituminous coal underground mine, mechanically crushed/screened/sized only |
120922 | 05 | WPU05120922 | 2009 | M02 | 113.2 | nan | U | PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted | Fuels and related products and power | Prepared bituminous coal underground mine, mechanically crushed/screened/sized only |
120922 | 05 | WPU05120922 | 2009 | M03 | 121 | nan | U | PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted | Fuels and related products and power | Prepared bituminous coal underground mine, mechanically crushed/screened/sized only |
Name | Data type | Unique | Values (sample) | Description |
---|---|---|---|---|
footnote_codes | string | 3 | nan P |
識別資料數列的註腳。 大部分的值為 Null。 請參閱 https://download.bls.gov/pub/time.series/wp/wp.footnote。 |
group_code | string | 56 | 02 01 |
識別索引涵蓋之主要商品群組名稱的代碼。 請參閱 https://download.bls.gov/pub/time.series/wp/wp.group 中的群組代碼與名稱。 |
group_name | string | 56 | Processed foods and feeds Farm products |
索引涵蓋的主要商品群組名稱。 請參閱 https://download.bls.gov/pub/time.series/wp/wp.group 中的群組代碼與名稱。 |
item_code | string | 2,949 | 1 11 |
識別與資料觀測有關的項目。 請參閱 https://download.bls.gov/pub/time.series/wp/wp.item 中的項目代碼及名稱。 |
item_name | string | 3,410 | Warehousing, storage, and related services Security guard services |
項目的完整名稱。 請參閱 https://download.bls.gov/pub/time.series/wp/wp.item 中的項目代碼及名稱。 |
period | string | 13 | M06 M07 |
識別觀測資料的週期。 請參閱 https://download.bls.gov/pub/time.series/wp/wp.period 中的期間值清單。 |
seasonal | string | 2 | U S |
識別資料是否季節性調整的代碼。 S=季節性調整;U=未調整 |
series_id | string | 5,458 | WPU601 WPU011 |
識別特定序列的代碼。 時間序列是指在持續時間間隔內的較長一段時間中,所觀測到的一組資料。 如需代碼、名稱、起迄年份等數列的詳細資料,請參閱 https://download.bls.gov/pub/time.series/wp/wp.series 。 |
series_title | string | 4,379 | PPI Commodity data for Metal treatment services, not seasonally adjusted PPI Commodity data for Mining services, not seasonally adjusted |
特定序列的標題。 時間序列是指在持續時間間隔內的較長一段時間中,所觀測到的一組資料。 如需識別碼、名稱、起迄年份等數列詳細資料,請參閱 https://download.bls.gov/pub/time.series/wp/wp.series 。 |
value | float | 6,788 | 100.0 99.0999984741211 |
商品的物價指數。 |
year | int | 26 | 2018 2017 |
識別觀測年份。 |
Azure Notebooks
# This is a package in preview.
from azureml.opendatasets import UsLaborPPICommodity
labor = UsLaborPPICommodity()
labor_df = labor.to_pandas_dataframe()
labor_df.info()
# 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
# Azure storage access info
azure_storage_account_name = "azureopendatastorage"
azure_storage_sas_token = r""
container_name = "laborstatisticscontainer"
folder_name = "ppi_commodity/"
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)
# 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)
# you can add your filter at below
print('Loaded as a Pandas data frame: ')
df
Azure Databricks
# This is a package in preview.
from azureml.opendatasets import UsLaborPPICommodity
labor = UsLaborPPICommodity()
labor_df = labor.to_spark_dataframe()
display(labor_df.limit(5))
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "laborstatisticscontainer"
blob_relative_path = "ppi_commodity/"
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'))
Azure Synapse
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "laborstatisticscontainer"
blob_relative_path = "ppi_commodity/"
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'))