wikirepo is a Python package that provides a framework to easily source and leverage standardized Wikidata information. The goal is to create an intuitive interface so that Wikidata can function as a common read-write repository for public statistics.
wikirepo can be downloaded from PyPI via pip or sourced directly from this repository:
pip install wikirepo
git clone https://github.com/andrewtavis/wikirepo.git cd wikirepo python setup.py install
wikirepo's data structure is built around Wikidata.org. Human-readable access to Wikidata statistics is achieved through converting requests into Wikidata's Quantity IDs (QIDs) and Property IDs (PIDs), with the Python package wikidata serving as a basis for data loading and indexing. See the documentation for a structured overview of the currently available properties.
wikirepo's main access function, wikirepo.data.query, returns a
pandas.DataFrame of locations and property data across time.
Each query needs the following inputs:
- locations: the locations that data should be queried for
- Strings are accepted for
Earth, continents, and countries
- Get all country names with
- The user can also pass Wikidata QIDs directly
- Strings are accepted for
- depth: the geographic level of the given locations to query
- A depth of 0 is the locations themselves
- Greater depths correspond to lower geographic levels (states of countries, etc.)
- A dictionary of locations is generated for lower depths (see second example below)
- timespan: start and end
datetime.dateobjects defining when data should come from
- If not provided, then the most recent data will be retrieved with annotation for when it's from
- Further arguments: the names of modules in wikirepo/data directories
- These are passed to arguments corresponding to their directories
- Data will be queried for these properties for the given
interval, with results being merged as dataframe columns
Queries are also able to access information in Wikidata sub-pages for locations. For example: if inflation rate is not found on the location's main page, then wikirepo checks the location's economic topic page as inflation_rate.py is found in wikirepo/data/economic (see Germany and economy of Germany).
wikirepo further provides a unique dictionary class,
EntitiesDict, that stores all loaded Wikidata entities during a query. This speeds up data retrieval, as entities are loaded once and then accessed in the
EntitiesDict object for any other needed properties.
Examples of wikirepo.data.query follow:
Querying Information for Given Countries
import wikirepo from wikirepo.data import wd_utils from datetime import date ents_dict = wd_utils.EntitiesDict() # Strings must match their Wikidata English page names countries = ["Germany", "United States of America", "People's Republic of China"] # countries = ["Q183", "Q30", "Q148"] # we could also pass QIDs # data.incl_lctn_lbls(lctn_lvls='country') # or all countries` depth = 0 timespan = (date(2009, 1, 1), date(2010, 1, 1)) interval = "yearly" df = wikirepo.data.query( ents_dict=ents_dict, locations=countries, depth=depth, timespan=timespan, interval=interval, climate_props=None, demographic_props=["population", "life_expectancy"], economic_props="median_income", electoral_poll_props=None, electoral_result_props=None, geographic_props=None, institutional_props="human_dev_idx", political_props="executive", misc_props=None, verbose=True, ) col_order = [ "location", "qid", "year", "executive", "population", "life_exp", "human_dev_idx", "median_income", ] df = df[col_order] df.head(6)
|United States of America||Q30||2010||Barack Obama||3.08746e+08||78.5415||0.914||43585|
|United States of America||Q30||2009||George W. Bush||nan||78.3902||0.91||nan|
|People's Republic of China||Q148||2010||Wen Jiabao||1.35976e+09||75.236||0.706||nan|
|People's Republic of China||Q148||2009||Wen Jiabao||nan||75.032||0.694||nan|
Querying Information for all US Counties
# Note: >3000 regions, expect a 45 minute runtime import wikirepo from wikirepo.data import lctn_utils, wd_utils from datetime import date ents_dict = wd_utils.EntitiesDict() country = "United States of America" # country = "Q30" # we could also pass its QID depth = 2 # 2 for counties, 1 for states and territories sub_lctns = True # for all # Only valid sub-locations given the timespan will be queried timespan = (date(2016, 1, 1), date(2018, 1, 1)) interval = "yearly" us_counties_dict = lctn_utils.gen_lctns_dict( ents_dict=ents_dict, locations=country, depth=depth, sub_lctns=sub_lctns, timespan=timespan, interval=interval, verbose=True, ) df = wikirepo.data.query( ents_dict=ents_dict, locations=us_counties_dict, depth=depth, timespan=timespan, interval=interval, climate_props=None, demographic_props="population", economic_props=None, electoral_poll_props=None, electoral_result_props=None, geographic_props="area", institutional_props="capital", political_props=None, misc_props=None, verbose=True, ) df[df["population"].notnull()].head(6)
|United States of America||California||Alameda County||Q107146||2018||1.6602e+06||2127||Oakland|
|United States of America||California||Contra Costa County||Q108058||2018||1.14936e+06||2078||Martinez|
|United States of America||California||Marin County||Q108117||2018||263886||2145||San Rafael|
|United States of America||California||Napa County||Q108137||2018||141294||2042||Napa|
|United States of America||California||San Mateo County||Q108101||2018||774155||1919||Redwood City|
|United States of America||California||Santa Clara County||Q110739||2018||1.9566e+06||3377||San Jose|
Upload Data (WIP)
wikirepo.data.upload will be the core of the eventual wikirepo upload feature. The goal is to record edits that a user makes to a previously queried or baseline dataframe such that these changes can then be pushed back to Wikidata. With the addition of Wikidata login credentials as a wikirepo feature (WIP), the unique information in the edited dataframe could then be uploaded to Wikidata for all to use.
The same process used to query information from Wikidata could be reversed for the upload process. Dataframe columns could be linked to their corresponding Wikidata properties, whether the time qualifiers are a point in time or spans using start time and end time could be derived through the defined variables in the module header, and other necessary qualifiers for proper data indexing could also be included. Source information could also be added in corresponding columns to the given property edits.
Pseudocode for how this process could function follows:
In the first example, changes are made to a
df.copy() of a queried dataframe. pandas is then used to compare the new and original dataframes after the user has added information that they have access to.
import wikirepo from wikirepo.data import lctn_utils, wd_utils from datetime import date credentials = wd_utils.login() ents_dict = wd_utils.EntitiesDict() country = "Country Name" depth = 2 sub_lctns = True timespan = (date(2000,1,1), date(2018,1,1)) interval = 'yearly' lctns_dict = lctn_utils.gen_lctns_dict() df = wikirepo.data.query() df_copy = df.copy() # The user checks for NaNs and adds data df_edits = pd.concat([df, df_copy]).drop_duplicates(keep=False) wikirepo.data.upload(df_edits, credentials)
In the next example
data.data_utils.gen_base_df is used to create a dataframe with dimensions that match a time series that the user has access to. The data is then added to the column that corresponds to the property to which it should be added. Source information could further be added via a structured dictionary generated for the user.
import wikirepo from wikirepo.data import data_utils, wd_utils from datetime import date credentials = wd_utils.login() locations = "Country Name" depth = 0 # The user defines the time parameters based on their data timespan = (date(1995,1,2), date(2010,1,2)) # (first Monday, last Sunday) interval = 'weekly' base_df = data_utils.gen_base_df() base_df['data'] = data_for_matching_time_series source_data = wd_utils.gen_source_dict('Source Information') base_df['data_source'] = [source_data] * len(base_df) wikirepo.data.upload(base_df, credentials)
Put simply: a full featured wikirepo.data.upload function would realize the potential of a single read-write repository for all public information.
wikirepo/maps is a further goal of the project, as it combines wikirepo's focus on easy to access open source data and quick high level analytics.
As in wikirepo.data.query, passing the
interval arguments could access GeoJSON files stored on Wikidata, thus providing mapping files in parallel to the user's data. These files could then be leveraged using existing Python plotting libraries to provide detailed presentations of geographic analysis.
Similar to the potential of adding statistics through wikirepo.data.upload, GeoJSON map files could also be uploaded to Wikidata using appropriate arguments. The potential exists for a myriad of variable maps given
interval information that would allow all wikirepo users to get the exact mapping file that they need for their given task.