Places

cities.tsv

Column Descriptions

Column ID	Description
geonameid	Unique place ID
city_name	Display name of Place
lat	Latitude
lon	Longitude
pop	Population
ccode	Country code (using current country borders)
cname	Country name (using current country borders)
region	Country's region (using current country borders)
continent	Country continent (using current country borders)
least_developed	Boolean according to least developed countries UN classification
metroid	Metro area ID

Sample Rows

(first 5 rows)

geonameid	city_name	lat	lon	pop	ccode	cname	region	continent	least_developed
5210117.0	Saint Marys	41.42784	-78.56086	13070	USA	United States	Northern America	Americas	False
524294.0	Murom	55.575	42.0426	126931	RUS	Russia	Eastern Europe	Europe	False
2883591.0	Kropp	54.41667	9.51667	6320	DEU	Germany	Western Europe	Europe	False
3407882.0	Altamira	-3.20333	-52.20639	70888	BRA	Brazil	South America	Americas	False
8224780.0	Cradley Heath	52.47214	-2.08212	5001	GBR	United Kingdom	Northern Europe	Europe	False
...

(last 5 rows)

geonameid	city_name	lat	lon	pop	ccode	cname	region	continent	least_developed
5211683.0	Sharon	41.23311	-80.4934	14038	USA	United States	Northern America	Americas	False
3014646.0	Grigny	48.65412	2.39343	24940	FRA	France	Western Europe	Europe	False
2981879.0	Saint-Alban	43.6927	1.4102	5379	FRA	France	Western Europe	Europe	False
2850808.0	Rain	48.69029	10.91611	8438	DEU	Germany	Western Europe	Europe	False
2162683.0	Innisfail	-17.52209	146.03102	8262	AUS	Australia	Australia and New Zealand	Oceania	False

Pandas import

pd.read_csv("cities.tsv", sep="\t", na_values="null", true_values="true", false_values="false", converters={"geonameid":float}, usecols=("geonameid", "ccode", "cname", "region", "continent"))