hoopoe 0.0.9

Hoopoe is a data enrichment service that makes it easy to improve your data-centric services and increase the accuracy of your ML models.

Hoopoe

A fast and easy way to enrich your data

Hoopoe is a data enrichment service that makes it easy to improve your data-centric services and increase the accuracy of your ML models.

Hoopoe saves you time in scraping data and loading it to Pandas Dataframes. Instead, just specify the data you already have in your dataframe and its type, and specify the desired data you would like to add.

Current development status

Hoopoe is in its early days, and offered as a pre-alpha version, therefore, bugs are expected. In addition, the current dataset avialability is low. You can check the list below to find out which datasets and translations are already available. This list will be updated from time to time.

Available datasets

• US states
• State name abbriviation (USPS abbreviation)
• Full state name
• State population
• State capital city:
• State capital city name
• State capital city population:
• Population
• MSA/µSA population (Metropolitan statistical area / Micropolitan statistical area)
• CSA population (Combined statistical area)
• State area:
• State area rank
• Country calling codes (international call prefixes)
• Country name
• International calling code

Units of measure

• Celsius
• farenheit
• Kelvin

Work in progress datasets

• US states ISO 3166 code
• US states area
• US states time zone
• US states density
• US population rank
• US states income rank
• US states density rank
• Population by country
• Capital City by country
• Countries time zone
• Countries official language

How to use

Currently Hoopoe supports enrichments of Pandas Dataframes and single strings. For basic usage, after installation, import hoopoe and use the hoopoe enrich function as follows:
hoopoe.enrich(data, source_data_name, source_data_type, target_data_type)

data - Either a string or a Pandas dataframe.
source_data_name - the dataframe column name of the column you want to enrich.
source_data_type - the Hoopoe source data type. For example us_state_name_abbr for a column holding abbriviations of us state.
target_data_type - the target Hoopoe data type you would like Hoopoe to return. For example, for country name, return the target date type phone_prefix for country calling code.

Enrichment options

Category	Source data type desc.	Hoopoe source_data_type	Target data type desc.	Hoopoe target_data_type
Places	Two letter abbriviations of USA states names	us_state_name_abbr	Full USA states name	us_state_name_full
Places	Full USA states name	us_state_name_full	Two letter abbriviations of USA states name	us_state_name_abbr
Places	Full USA states name	us_state_name_full	State population (2019)	us_state_population
Places	Full USA state name	us_state_name_full	State capital city name	us_state_capital_city
Places	Capital city name	us_capital_city_name	City population 2019 est.	us_capital_city_population
Places	Capital city name	us_capital_city_name	City population (Metropolitan/Micropolitan Statistical Area - MSA) 2019 est.	us_capital_city_population_msa
Places	Capital city name	us_capital_city_name	City population (Combined Statistical Area - CSA) 2019 est.	us_capital_city_population_csa
Places	Full USA state name	us_state_name_full	State area rank	us_state_area_rank
Phone numbers	Intenational phone prefix	phone_prefix	Country name	country_name
Units	Celsius	celsius	Farenheit	farenheit
Units	Celsius	celsius	Kelvin	Kelvin
Units	Farenheit	farenheit	Celsius	celsius
Units	Farenheit	farenheit	Kelvin	Kelvin
Units	Kelvin	Kelvin	Celsius	celsius
Units	Kelvin	Kelvin	Farenheit	farenheit

Installing

To install Hoopoe, run the following in your virtualenv:

$ pip install hoopoe

Developing Hoopoe

To install Hoopoe, along with the tools you need to develop and run tests, run the following in your virtualenv:

$ pip install -e .[dev]

Dependencies

• Pandas
  
  

Dev dependencies (only for contributers)

• pytest
• check-manifest

Licence

MIT License