pandect 0.4.0

Simple wrappers to load and convert common data file types

Simple wrappers to load and convert common data file types

Features

• Uses file extension as heuristic to determine input format

• Provides metadata using pyreadstat objects when appropriate

• Supports: csv, tsv, xlsx, sav, dta (unreliable), sqlite3

• Loads data into pandas.DataFrame

• Provides command line utilities: sav2dta, pandect

Examples

Load a data file into a pandas.DataFrame object:

>>> import pandect
>>> data, meta = pandect.load(input_file_name)

Save a pandas.DataFrame object as a data file:

>>> import pandas
>>> import pandect
>>> data = pandas.DataFrame([{'a': 1, 'b': 2}, {'a': 3, 'b': 4}])
>>> pandect.save(data=data, output='out.sav')
>>>

Save a pandas.DataFrame object as a data file with metadata:

>>> import pandas
>>> import pandect
>>> import pyreadstat
>>> data = pandas.DataFrame([{'a': 1, 'b': 2}, {'a': 3, 'b': 4}])
>>> meta = pyreadstat.metadata_container()
>>> meta.column_names_to_labels = {'a': 'A Label', 'b': 'B Label'}
>>> pandect.save(data=data, output='out.sav', meta=meta)
>>>

Convert a data file at the command line:

$ pandect input.csv output.dta

Convert sav data file to dta data file:

$ sav2dta some_file.sav

• Derives output file name from input file name (here: some_file.dta)

• This is a convenience utility for a common task

• It is basically a special case of the pandect utility

Limitations

• Loading dta files is unreliable (bug in pyreadstat, might segfault)

License

• Free software: MIT license

Documentation

• https://pandect.readthedocs.io/

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.