| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| st.title("World Map") | |
| places_seen = pd.DataFrame({ | |
| 'cities' : ['Madison', 'Pittsburgh', 'Dallas', 'San Francisco','Los Angeles','Orlando', 'Miami', 'Tampa Bay', 'Buffalo','Boston','Washington D.C.', 'Atlanta', 'Houston', 'Austin','San Diego','Richmond', 'Myrtle Beach', 'Milwaukee','Detroit','UIUC', 'Purdue', 'Columbus','Lancaster','Alamo','Delaware', 'Cleveland', 'Louisville', 'Nashville','San Diego','Concord', 'Rochester', 'Santa Domingo', 'Toronto','San Juan','Las Vegas', 'Niagara Falls', 'Grand Canyon','Hyderabad','Guntur','Mutlur','Bapatla','Vijayawada','Ongole','Tirupati'], | |
| 'lat' : [43.073051, 40.440624, 32.776665, 37.774929,34.0522,28.5384,25.7617,27.7634,42.8864,42.3601,38.9072,33.7490,29.7694,30.2672,32.7157,37.5407,33.6891,43.0389,42.3314,40.1020,40.4237,39.9612,40.0379,26.1837,38.9108,41.4993,38.2527,36.1627,32.7157,43.2081,43.1566,18.4861,43.6532,18.4655,36.1716,43.0962,36.1069,17.3850, 16.3067, 16.1580, 15.9039, 16.5062, 15.5057, 13.6288] | |
| ,'lon' : [-89.401230, -79.995888, -96.796989, -122.419418,-118.2437,-81.3789,-80.1918,-82.5437,-78.8784,-71.0589,-77.0369,-84.3880,-95.3698,-97.7431,-117.1611,-77.4360,-78.8867,-87.9065,-83.0458,-88.2272,-86.9212,-82.9988,-76.3055, -98.1231,-75.5277,-81.6944,-85.7585,-86.7816,-117.1611,-71.5376,-77.6088,-69.9312,-79.3832,-66.1057,-115.1391,-79.0377,-112.1129, 78.4867, 80.4365,80.4841, 80.4671, 80.6480, 80.0499, 79.4192]}) | |
| st.map(places_seen) | |
| st.dataframe(places_seen) | |