isogeocoder 0.0.1

isogeocoder is a tool that generates and assigns standardized iso compliant unique identification numbers or codes for entities based on location information. It can create and assign codes based on a country's administrative division (geo-location) or any administrative level depending on the use case.

About isogeocoder

isogeocoder is a tool that generates and assigns standardized iso compliant unique identification numbers or codes for entities based on location information. It can create and assign codes based on a country's administrative division (geo-location) or any administrative level depending on the use case. Examples include:

• Unique school identity for an education management information system.
• Unique health facility identity for a health management information system.
• Standard administrative level geocode for country planning.
• Administrative level identity generation in digital addressing system.

Dependencies

Dependencies isogeocoder is built on the pandas framework; so all pandas operations can work on isogeocoder. Dataframes generated from each administrative level are saved as CSV files.

We also recommended using the Jupyter notebook.

Installation

Install isogeocoder with pip3

pip3 install isogeocoder

Usage/Examples

isogeocoder is a combination of two libraries, one is the iso library, a standard country dataset manipulation; and the geo library which generates administrative level code on your dataset and generates a unique id using the index of your entity dataset. In the examples, we use the Sierra Leone school list provided by the MInistry of Basic and Senior Secondary Education, dataset Sierra Leone School List

GEO Examples:

Generating a unique school identity for an EMIS system

from  isogeocoder import geo

schools_df = geo.data('sl_school_list.csv')

#columns
schools_df.columns 
#output
Index(['idregion', 'iddistrict', 'idchiefdom', 'idsection', 'sch_type',
       'idschool_name'],
      dtype='object')
Region = geo.level1(schools_df,'idregion')
Region
District = geo.level2(schools_df,Region,'iddistrict')
District
Chiefdom = geo.level3(schools_df,District,'idchiefdom')
Chiefdom
Section = geo.level4(schools_df,Chiefdom,'idsection')
Section
<<<<<<< HEAD
Schools = geo.uniqueid(schools_df,Section,'idschool_name')
Schools
=======
>>>>>>> 1bbc060061aa62acd289c8d1934f713be29d7028
School_Type = geo.categorical(schools_df,'sch_type',encoding_type='integer')
School_Type
school_masterlist = geo.gencode(Section,Schools,cat_df=School_Type,level_column='idchiefdom',uniqueid_column='idschool_name_edited_code',title='emis_code',sep='-')
school_masterlist

Generating administrative level coding in digital addressing system

from  isogeocoder import geo

schools_df = geo.data('sl_school_list.csv')

#columns
schools_df.columns 
#output
Index(['idregion', 'iddistrict', 'idchiefdom', 'idsection', 'sch_type',
       'idschool_name'],
      dtype='object')
Region = geo.level1(schools_df,'idregion')
Region
District = geo.level2(schools_df,Region,'iddistrict')
District
Chiefdom = geo.level3(schools_df,District,'idchiefdom')
Chiefdom
Section = geo.level4(schools_df,Chiefdom,'idsection')
Section
Region_Alpha = geo.alpha_coder(Region,column='idregion',clen=2)
District_alpha = geo.alpha_coder(District,column='iddistrict',clen=3,add_char='D')
Alpha_df = geo.alpha_merger(region_alpha,district_alpha,'idregion',sufixs=['Reg','Dis'],level=1,sep='-')
Chiefdom_level = Chiefdom[['iddistrict_code','idchiefdom','idchiefdom_code']]
Chiefdom_Alpha= geo.alpha_merger_l3(alpha_df,Chiefdom_level,'idchiefdom_code','iddistrict_code',2,sep='-')
Section_level =   Section[['idchiefdom_code','idsection','idsection_code']]
digital_addressing = geo.alpha_merger_l4(l3,Section_level,'idsection_code','idchiefdom_code',4,sep='-')
digital_addressing

ISO Example:

import pandas as pd
from  isogeocoder import iso
continents_df = pd.read_csv(iso.countries_data())
subdivision_df = pd.read_csv(iso.subdiv_data())
iso.continents(continents_df)
dataframe of subregions in a continent
iso.subregions(continents_df,'Africa',level=1,sep='-')
iso.countries(continents_df,'Africa',level=2,sep='-')
iso.country(subdivision_df,'Sierra Leone')

Documentation

Documentation is available here

Contributing

PR requests are highly welcome, fork and commit your changes

Authors

• moinina
• mjames21
• Mosesvb

License

MIT