The greatest value of a picture is when it forces us to notice what we never expected to see.
seaborn
plotsMany, many more in these categories - these are just our focus for today!
import pandas as pd
mx_csv = "http://personal.tcu.edu/kylewalker/mexico.csv"
mx = pd.read_csv(mx_csv)
mx.head()
How about sorting our data?
seaborn
seaborn
zac = mx[mx.name == 'Zacatecas'].drop(['name', 'FID', 'gdp08', 'mus09'], axis = 1).squeeze()
zac.name = 'Zacatecas'
zac.plot(kind = 'pie', figsize = (6, 6))
seaborn
dfw = pd.read_csv('http://personal.tcu.edu/kylewalker/data/pct_college.csv')
sns.lineplot(x = "year", y = "pct_college",
hue = "county", data = dfw)
seaborn
seaborn
lmplot
and regplot
functionspandas
: .corr()