bls-datasets 0.0.9
Python library for retrieving BLS datasets

# bls-datasets
Making datasets easily accessible to python scripts.



Integrated datasets include:
- [Occupational Employment Statistics (OES)](https://www.bls.gov/oes/)
- [Quarterly Census of Employment and Wages (QCEW)](https://www.bls.gov/cew/)
- *Coming soon: Employment Projections and Definition files*

For looking up BLS data via series-id lookups, please checkout OliverSherouse's library: [BLS](https://github.com/OliverSherouse/bls)
# Usage

```
>>> from bls_datasets import oes, qcew

# OES example:

>>> df_oes = oes.get_data(year=2017)
>>> df_oes.columns
Index(['OCC_CODE', 'OCC_TITLE', 'OCC_GROUP', 'TOT_EMP', 'EMP_PRSE', 'H_MEAN',
       'A_MEAN', 'MEAN_PRSE', 'H_PCT10', 'H_PCT25', 'H_MEDIAN', 'H_PCT75',
       'H_PCT90', 'A_PCT10', 'A_PCT25', 'A_MEDIAN', 'A_PCT75', 'A_PCT90',
       'ANNUAL', 'HOURLY'],
      dtype='object')

# Which occupation had the highest total employment in 2017?

>>> detailed = df_oes[df_oes.OCC_GROUP == 'detailed']
>>> detailed[detailed.TOT_EMP == detailed.TOT_EMP.max()].OCC_TITLE
772    Retail Salespersons

# QCEW example:
>>> df_qcew = qcew.get_data('industry', rtype='dataframe', year='2017',
...             qtr='1', industry='10')
>>> df_qcew.columns
Index(['area_fips', 'own_code', 'industry_code', 'agglvl_code', 'size_code',
       'year', 'qtr', 'disclosure_code', 'qtrly_estabs', 'month1_emplvl',
       'month2_emplvl', 'month3_emplvl', 'total_qtrly_wages',
       'taxable_qtrly_wages', 'qtrly_contributions', 'avg_wkly_wage',
       'lq_disclosure_code', 'lq_qtrly_estabs', 'lq_month1_emplvl',
       'lq_month2_emplvl', 'lq_month3_emplvl', 'lq_total_qtrly_wages',
       'lq_taxable_qtrly_wages', 'lq_qtrly_contributions', 'lq_avg_wkly_wage',
       'oty_disclosure_code', 'oty_qtrly_estabs_chg',
       'oty_qtrly_estabs_pct_chg', 'oty_month1_emplvl_chg',
       'oty_month1_emplvl_pct_chg', 'oty_month2_emplvl_chg',
       'oty_month2_emplvl_pct_chg', 'oty_month3_emplvl_chg',
       'oty_month3_emplvl_pct_chg', 'oty_total_qtrly_wages_chg',
       'oty_total_qtrly_wages_pct_chg', 'oty_taxable_qtrly_wages_chg',
       'oty_taxable_qtrly_wages_pct_chg', 'oty_qtrly_contributions_chg',
       'oty_qtrly_contributions_pct_chg', 'oty_avg_wkly_wage_chg',
       'oty_avg_wkly_wage_pct_chg'],
      dtype='object')

# What were the average weekly earnings in Fresno County for 2017 Q1?

# FIPS code, area title
# 06019, Fresno County, California

>>> fresno = df_qcew[(df_qcew.own_code == 0) & (df_qcew.area_fips == '06019')]
>>> fresno.avg_wkly_wage.values[0]
803


```

# Installation
`pip install bls-datasets`

# Documentation
Documentation coming soon. Please reference the docstrings in the source code for now.


# Notes on datasets


**OES**

OES consists of occupational statistics, primarily: employment, age, and salary. To learn more about this survey, you can visit this [link](https://www.bls.gov/oes/oes_emp.htm).

Note that due to idiosyncrasies in earlier OES datasets, this package only allows data access starting in 2014. Earlier files are available, although, they are given different naming patterns, are often broken into multiple excel spreadsheets due to size constraints of older excel version, and they do not always consist of the same datacuts. I will not integrate any earlier years, unless I see it necessary, or receive enough user requests.

**QCEW**

QCEW consists of employer reported occupational statistics. Data can be cut/sliced by area, industry or company size. To learn more about this survey, you can visit this [link](https://www.bls.gov/cew/)


Common gotchas with QCEW data:
- Datatypes are not always what you expect them to be. Reference the following tables when performing dataframe operations
  - [Quarterly data slice layout](https://data.bls.gov/cew/doc/access/csv_data_slices.htm##QTR_LAYOUT)
  - [Annual averages slice layout](https://data.bls.gov/cew/doc/access/csv_data_slices.htm##ANNUAL_LAYOUT)
- Due to employer confidentiality, some of the figures may be unavailable. This is especially true when making more granular data cuts. Do check the `disclosure_code` columns for this.