qeds 0.4.3

Python library for loading/cleaning data used in qeds training

This package provides a simplified interface to datasets that we use frequently.

Loading data

To see a list of available datasets run

import qeds
qeds.data.available()

To load one of the listed datasets run

df = qeds.data.load("dataset_name")

where dataset_name is replaced by one of the names returned by qeds.data.available().

When you first load a dataset, qeds will fetch the data from somewhere online. It will then save a local copy of the data to your hard drive. Subsequent requests to load a dataset (even in different python sessions) will first attempt to load the data from your hard drive and only fetch from online if necessary.

Configuration

The qeds library is configurable. Below is a listing of available configuration options.

To see a list of valid configuration options run

import qeds
qeds.data.config.describe_options()

To set a configuration use valourm.data.options[section.option] = value.

For example, to set the configuration option for the BLS api_key I would call:

import qeds
qeds.data.options["bls.api_key"] = "MY_API_KEY"

Developer docs

Contributing datasets

To contribute a dataset you need to implement a function _retrieve_{name} inside the file data/retrieve.py. This function is responsible for obtaining the data either “by hand” (data hard coded into the function) or from online. The function must return a pandas DataFrame with the data.