Datenguide Python

• Free software: MIT license
• Documentation: https://datenguidepy.readthedocs.io/

The package provides easy access to German publicly available regional statistics. It does so by providing a wrapper for the GraphQL API of the Datenguide project.

Features

Overview of available statistics and regions:

The package provides DataFrames with the available statistics and regions, which can be queried by the user without having to refer to expert knowledge on regional statistics or the documentation of the underlying GraphQL API

Build and Execute Queries:

The package provides the user an object oriented interface to build queries that fetch certain statistics and return the results as a pandas DataFrame for further analysis.

Automatic inclusion of relevant meta data

Queries automatically retrieve some meta data along with the actual data to give the user more convenient access to the statistics without having to worry about too many technichal details

Full fidelity data

The package provides full fidelity data access to the datenguide API. This allows all use cases to use precicely the data that they need if it is available. It also means that most data cleaning has to be done by the user.

Quick Start

Install

To use the package install the package (command line):

pip install datenguidepy

Minimal example

To see the package work and obtain a DataFrame containing some statistics, the followin constitutes a minimal example.

from datenguidepy import Query

q = Query.region('01')
q.add_field('BEV001')
result_df = q.results()

Complex examples

These examples is intendend to illustrate many of the package's features at the same time. The idea is to give an impression of some of the possibilities. A more detailed explanation of the functionality can be found in the the rest of the documentation.

q = Query.region(['02','11'])
stat = q.add_field('BEVSTD')
stat.add_args({'year' : [2011,2012]})
stat2 = q.add_field('AI1601')
stat2.add_args({'year' : [2011,2012]})
q.results(
    verbose_statistics = True,
    add_units = True,
).iloc[:,:7]

	id	name	year	Verfügbares Einkommen je Einwohner (AI1601)	AI1601_unit	Bevölkerungsstand (BEVSTD)	BEVSTD_unit
0	02	Hamburg	2011	22695	EUR	1718187	Anzahl
1	02	Hamburg	2012	22971	EUR	1734272	Anzahl
0	11	Berlin	2011	18183	EUR	3326002	Anzahl
1	11	Berlin	2012	18380	EUR	3375222	Anzahl

q = Query.region('11')
stat = q.add_field('BEVSTD')
stat.add_args({
    'GES' : 'GESW',
    'statistics' : 'R12411',
    'NAT' : 'ALL',
    'year' : [1995,1996]
})
stat.add_field('GES')
stat.add_field('NAT')
q.results(verbose_enums = True).iloc[:,:6]

	id	name	GES	NAT	year	BEVSTD
0	11	Berlin	weiblich	Ausländer(innen)	1995	191378
1	11	Berlin	weiblich	Deutsche	1995	1605762
2	11	Berlin	weiblich	Gesamt	1995	1797140
3	11	Berlin	weiblich	Deutsche	1996	1590407
4	11	Berlin	weiblich	Ausländer(innen)	1996	195301
5	11	Berlin	weiblich	Gesamt	1996	1785708

Get information on fields and meta data

Get information on region ids

# from datenguidepy import get_regions

 get_regions()

Use pandas query() functionality to get specific regions. E.g., if you want to get all IDs on "Bundeländer" use. For more information on "nuts" levels see Wikipedia.

get_regions().query("level == 'nuts1'")

Get information on statistic shortnames

#  from datenguidepy import get_statistics

  get_statistics()
  # return statistical descriptions in English
  get_statistics(target_language = 'en')

Get information on single fields

You can further information about description, possible arguments, fields and enum values on a field you added to a query.

q = Query.region("01")
stat = q.add_field("BEV001")
stat.get_info()

Further information

For detailed examples see the notebooks within the use_case folder.

For a detailed documentation of all statistics and fields see the Datenguide API.

Credits

All this builds on the great work of Datenguide and their GraphQL API datenguide/datenguide-api

The data is retrieved via the Datenguide API from the "Statistische Ämter des Bundes und der Länder". Data being used via this package has to be credited according to the "Datenlizenz Deutschland – Namensnennung – Version 2.0".

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