Metadata-Version: 2.1
Name: bls-datasets
Version: 0.0.8
Summary: Python library for retrieving BLS datasets
Home-page: https://github.com/mbkupfer/bls-datasets
Author: Maxim Kupfer
Author-email: mbkupfer@gmail.com
License: UNKNOWN
Description: # 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 ad Definition files*
        
        For looking up BLS data via series-id lookups, please checkout OlverSherouse'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 of 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.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Office/Business
Classifier: Topic :: Office/Business :: Financial
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
