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README.md
__init__.py
blsapi.py
demo.py
fetch.py
wrangle.py

README.md

Jobs Report Data Ingestion

This document describes how to perform data ingestion and wrangling using the ingest code in this library to fetch data from the BLS API.

For more information see: BLS Developers

Getting Started

In order to make authenticated requests to the BLS API, you need to first obtain an API key. Without the API key you will be allowed to request data for multiple series for the past three years. In order to parameterize your request and get additional data, you need the key.

To obtain the key, go to the BLS Registration page and fill out the form. They will email you the key (you'll also have to click on a link to validate your email). Once you have the key, you can use it in requests to the BLS API.

Using the BLS API code

The Python module blsapi provides three functions for interacting with data on the BLS website:

  • single_series: allows you to download a single time series for the past three years
  • multiple_series: allows you to download multiple time series for the past three years
  • bls_series: allows you to make authenticated, parameterized requests.

All of these methods require a TimeSeries code to work. Unfortunately, there is no index of TimeSeries ids on the BLS website, you'll have to find the ID from the page of the data set you're looking for. However, if you use the Data Tools section of the BLS site, you can find what you're looking for. In particular, for the CPS data see the CSV file ln.series which gives the IDs of various data. I've initially identified three timeseries of interest:

series = [
    'LNS10000000', # Population Level
    'LNS12000000', # Employment Level
    'LNS13000000', # Unemployment Level
]

In order to download a single time series, with data for the past three years, simply use the function as follows:

from blsapi import single_series
data = single_series('LNS10000000')

The data will be returned as a Python dictionary, loaded from the JSON response. Multiple series for the past three years is just as easy:

from blsapi import mutiple_series
data = multiple_series(['LNS10000000', 'LNS12000000', 'LNS13000000'])

Just pass in a list of series ids that you wish to fetch. Note that you have to pass at least 1 time series, and no more than 25 time series.

However, for the most part you want to perform authenticated requests. You do this by sending in your API key with every request. By doing this you can access more data than just the past three years, and parameterize your request with various arguments. There are two ways that the blsapi will find your API key:

  1. Store it as an environment variable, BLS_API_KEY.
  2. Pass it in to the function as registrationKey.

If you'd like to use the environment variable, in your terminal type:

$ export BLS_API_KEY=yourkey

Then in Python:

from blsapi import bls_series
data = bls_series(series, endyear="2010", startyear="2000")

This function can fetch data for 1 to 25 timeseries that you pass into it. The parameters that you can pass in are as follows:

  • startyear (4 digit year as a string)
  • endyear (4 digit year as a string)
  • catalog (True or False)
  • calculations (True or False)
  • annualaverage (True or False)
  • registrationKey (api key from BLS website)

For example, to fetch the data and pretty print it as a table:

import prettytable

from blsapi import *

## Demo Series
series = ['LNS12000000','LNS13000000', 'LNS10000000']
result = bls_series(series, startyear='2010', endyear='2015')

## Pretty print a table of the results
for s in result['Results']['series']:
    table = prettytable.PrettyTable(["series id","year","period","value","footnotes"])
    for item in s['data']:
        row   = [
            s['seriesID'],                  # Series ID
            item['year'],                   # Year
            item['period'],                 # Period
            item['value'],                  # Value
        ]

        # Footnotes
        row.append(", ".join([fn['text'] for fn in item['footnotes'] if 'text' in fn]))

        table.add_row(row)

    print table.get_string()

Hope this helps you get started using the BLS API data!

Important CPS TimeSeries

  • Civilian Labor Force Level - LNS11000000
  • Civilian Labor Force Participation Rate - LNS11300000
  • Employment Level - LNS12000000
  • Employment-Population Ratio - LNS12300000
  • Employed, Usually Work Full Time - LNS12500000
  • Employed, Usually Work Part Time - LNS12600000
  • Unemployment Level - LNS13000000
  • Unemployment Rate - LNS14000000
  • Unemployment Rate - 16-19 Years - LNS14000012
  • Unemployment Rate - 20 Years & Over Men - LNS14000025
  • Unemployment Rate - 20 Years & Over Women - LNS14000026
  • Unemployment Rate - White - LNS14000003
  • Unemployment Rate - Black or African American - LNS14000006
  • Unemployment Rate - Asian - LNS14032183
  • Unemployment Rate - Hispanic or Latino - LNS14000009
  • Unemployment Rate - 25 Years & Over, Less than a High School Diploma - LNS14027659
  • Unemployment Rate - 25 Years & Over, High School Graduates No College - LNS14027660
  • Unemployment Rate - 25 Years & Over, Some College or Associate Degree - LNS14027689
  • Unemployment Rate - 25 Years & Over, Bachelor's Degree and Higher - LNS14027662
  • Number Unemployed For Less Than 5 weeks - LNS13008396
  • Number Unemployed For 5-14 Weeks - LNS13008756
  • Number Unemployed For 15 Weeks & Over - LNS13008516
  • Number Unemployed For 27 Weeks & Over - LNS13008636
  • Average Weeks Unemployed - LNS13008275
  • Median Weeks Unemployed - LNS13008276
  • Unemployment Level Job Losers - LNS13023621
  • Unemployment Level Job Losers On Layoff - LNS13023653
  • Unemployment Level Job Losers Not on Layoff - LNS13025699
  • Unemployment Level Job Leavers - LNS13023705
  • Unemployment Level Reentrants To Labor Force - LNS13023557
  • Unemployment Level New Entrants - LNS13023569
  • Persons At Work Part Time for Economic Reasons - LNS12032194
  • Not in Labor Force - LNS15000000
  • Marginally Attached to Labor Force - LNU05026642
  • Discouraged Workers - LNU05026645
  • Alternative measure of labor underutilization U-6 - LNS13327709
  • Multiple Jobholders Level - LNS12026619
  • Multiple Jobholders as a Percent of Total Employed - LNS12026620
  • Employment Level, Nonag. Industries, With a Job not at Work, Bad Weather - LNU02036012
  • Employment Level, Nonag. Industries, At Work 1-34 Hrs, Usually Work Full time, Bad Weather - LNU02033224