Python Data Visualization Essentials Guide

city City	city_ascii City Ascii	lat Lat	lng Lng	country Country	iso2 Iso2	iso3 Iso3	admin_name Admin Name	capital Capital	population Population	id Id
Tokyo	Tokyo	35.6897	139.6922	Japan	JP	JPN	Tōkyō	primary	37977000	1392685764
Jakarta	Jakarta	-6.2146	106.8451	Indonesia	ID	IDN	Jakarta	primary	34540000	1360771077
Delhi	Delhi	28.66	77.23	India	IN	IND	Delhi	admin	29617000	1356872604
Mumbai	Mumbai	18.9667	72.8333	India	IN	IND	Mahārāshtra	admin	23355000	1356226629
Manila	Manila	14.5958	120.9772	Philippines	PH	PHL	Manila	primary	23088000	1608618140
Shanghai	Shanghai	31.1667	121.4667	China	CN	CHN	Shanghai	admin	22120000	1156073548
São Paulo	Sao Paulo	-23.5504	-46.6339	Brazil	BR	BRA	São Paulo	admin	22046000	1076532519
Seoul	Seoul	37.5833	127	Korea, South	KR	KOR	Seoul	primary	21794000	1410836482
Mexico City	Mexico City	19.4333	-99.1333	Mexico	MX	MEX	Ciudad de México	primary	20996000	1484247881
Guangzhou	Guangzhou	23.1288	113.259	China	CN	CHN	Guangdong	admin	20902000	1156237133


CREATE TABLE worldcities (
  "city" VARCHAR,
  "city_ascii" VARCHAR,
  "lat" DOUBLE,
  "lng" DOUBLE,
  "country" VARCHAR,
  "iso2" VARCHAR,
  "iso3" VARCHAR,
  "admin_name" VARCHAR,
  "capital" VARCHAR,
  "population" DOUBLE,
  "id" BIGINT
);