hello = "hello world!" hello = hello + " how are you?" hello

hello.title()

import pandas as pd

cities = pd.DataFrame({ "name": ["Athens", "Bratislava", "Copenhagen", "Dublin"], "area": [39, 367.6, 86.2, 115], "elevation": [170, 152, 14, 20], "population": [664046, 429564, 602481, 553165] } )

cities

cities["area"].max()

cities.loc[ cities['area'].idxmax() ]

cities.mean(numeric_only=True)

import altair as alt

alt.Chart(cities).mark_bar().encode(x='name', y='area')

alt.Chart(cities).mark_bar().encode( x='name', y='area', tooltip=['name', 'area', 'elevation', 'population'], ).properties( width=200, height=200 )

# we load the dataset from Geonames using a URL, a web address countries = pd.read_csv("http://www.geonames.org/countryInfoCSV", sep='\t', keep_default_na=False) # we pass two further parameters: first we specify that the file is tab-separated, # and then we ask it not to translate NA into 'not a number' as it refers to North America # let's take a look at the first rows in the dataset: countries.head()

# string replace in continent column with a dictionary of find-replace pairs: countries = countries.replace( { "continent": { "AF": "Africa", "AN": "Antarctica", "AS": "Asia", "EU": "Europe", "OC": "Oceania", "NA": "North America", "SA": "South America" }})

countries = countries[ countries["population"]>0] countries = countries[ countries["areaInSqKm"]>0]

alt.Chart(countries).mark_circle().encode( alt.X('areaInSqKm').scale(type='log'), alt.Y('population').scale(type='log'), color='continent', tooltip = ["name", "capital", "areaInSqKm", "population", "continent"] ).interactive().properties( width=600, height=400 )