Geonames Postal Codes and Geocoordinates

DataHerb Metadata

DataHerb ID: geonames_postal_codes_geocoordinates
DataHerb Name: Geonames Postal Codes and Geocoordinates
Contributors: Datumorphism
Description: Postal codes and their geocoordinates parsed from geonames data. The dataset covers the following countries: AD, AR, AS, AT, AU, AX, AZ, BD, BE, BG, BM, BR, BY, CA, CH, CL, CO, CR, CZ, DE, DK, DO, DZ, EE, ES, FI, FM, FO, FR, GB, GF, GG, GL, GP, GT, GU, HR, HU, IE, IM, IN, IS, IT, JE, JP, KR, LI, LK, LT, LU, LV, MC, MD, MH, MK, MP, MQ, MT, MW, MX, MY, NC, NL, NO, NZ, PH, PK, PL, PM, PR, PT, PW, RE, RO, RS, RU, SE, SG, SI, SJ, SK, SM, TH, TR, UA, US, UY, VA, VI, WF, YT, ZA.
GitHub Repository: datumorphism/geonames-postal-codes-geocoordinates

DataHerb Leaves: Files in Dataset

File:
Format: csv
Size: 23M

Fields

country_code: Alpha 2 country code where the postal code is affiliated with
postal_code: postal code in string format
latitude: latitude of the postal code
longitude: longitude of the postal code

Preview

Import Data

Copy the following code and paste into a Google Spreadsheet cell.

=IMPORTDATA("https://raw.githubusercontent.com/datumorphism/geonames-postal-codes-geocoordinates/master/dataset/postal_codes_and_coordinates.csv")

Load data into a Pandas DataFrame:

import pandas as pd
url = "https://raw.githubusercontent.com/datumorphism/geonames-postal-codes-geocoordinates/master/dataset/postal_codes_and_coordinates.csv"
df = pd.read_csv(url)

File:
Format: json
Size: 69M

Fields

country_code: alpha 2 country code the postal code belongs
postal_code: postal code in string format
latitude: latitude of the postal code
longitude: longitude of the postal code

Import Data

Load data into a Pandas DataFrame:

import pandas as pd
url = "https://raw.githubusercontent.com/datumorphism/geonames-postal-codes-geocoordinates/master/dataset/postal_codes_and_coordinates.json"
df = pd.read_json(url)

References:

Geonames Zip Download Server