World Cities (40 rows, CSV)
A curated world-cities dataset — 40 major cities with country, latitude, longitude, and population. A realistic geospatial fixture for testing map plots, geocoding, and CSV→GeoJSON conversion. GeoJSON twin included.
name,country,latitude,longitude,population
Tokyo,JP,35.6762,139.6503,37400068
Delhi,IN,28.7041,77.1025,32941308
Shanghai,CN,31.2304,121.4737,29210808
São Paulo,BR,-23.5505,-46.6333,22620000
Mexico City,MX,19.4326,-99.1332,22085140
Cairo,EG,30.0444,31.2357,21750020
Mumbai,IN,19.076,72.8777,20961472
Beijing,CN,39.9042,116.4074,20896820
Dhaka,BD,23.8103,90.4125,22478116
Osaka,JP,34.6937,135.5023,19222665
New York,US,40.7128,-74.006,18804000
Karachi,PK,24.8607,67.0011,16459472
Buenos Aires,AR,-34.6037,-58.3816,15490000
Istanbul,TR,41.0082,28.9784,15519267
Kolkata,IN,22.5726,88.3639,14850000
Lagos,NG,6.5244,3.3792,15388000
Manila,PH,14.5995,120.9842,14406059
Rio de Janeiro,BR,-22.9068,-43.1729,13458000
Los Angeles,US,34.0522,-118.2437,12447000
Moscow,RU,55.7558,37.6173,12537954
Paris,FR,48.8566,2.3522,11017000
London,GB,51.5074,-0.1278,9648110
Lima,PE,-12.0464,-77.0428,11044607
Bangkok,TH,13.7563,100.5018,10722000
Seoul,KR,37.5665,126.978,9963452
Jakarta,ID,-6.2088,106.8456,10770487
Bogotá,CO,4.711,-74.0721,11167392
Chicago,US,41.8781,-87.6298,8865000
Toronto,CA,43.6532,-79.3832,6255000
Sydney,AU,-33.8688,151.2093,5312000
Berlin,DE,52.52,13.405,3677472
Madrid,ES,40.4168,-3.7038,3223334
Nairobi,KE,-1.2921,36.8219,4397073
Lisbon,PT,38.7223,-9.1393,544851
Dubai,AE,25.2048,55.2708,3331420
Singapore,SG,1.3521,103.8198,5453600
Johannesburg,ZA,-26.2041,28.0473,5635000
Stockholm,SE,59.3293,18.0686,975551
Vienna,AT,48.2082,16.3738,1897000
Amsterdam,NL,52.3676,4.9041,872680
Specifications
- Rows
- 40
- Schema
- name, country, latitude, longitude, population
- Note
- curated sample of major cities; coordinates are real
What is a .csv file?
CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.
How to use this file
Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.
Code examples
import pandas as pd
df = pd.read_csv("world-cities.csv")
print(df.head())
print(df.dtypes)Related files
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Generated by generation/data_graphs.py. Free for any use, no attribution required — license.