Exploration on hexagonal binning and contour areas

import numpy as np

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

data = pd.read_csv ( "kc_tax.csv" )

print (data.head ())

print (data.shape)

print ( "" , data.info ())

# Take a subset of King County, Washington
# Tax data for assessed values ​​for tax purposes
# "$ 600,000 and Legs Total Living Area" 100 & amp;
# "2000

data = data.loc [(data [ ’TaxAssessedValue’ ] " 600000 ) & amp; 

(data [ ’SqFtTotLiving’ ]" 100 ) & amp; 

(data [ ’SqFtTotLiving’ ] " 2000 )]

# As you can see in the information
# that the posts are incomplete

data [ ’TaxAssessedValue’ ]. isnull (). values. any ()

x = data [ ’SqFtTotLiving’ ]

y = data [ ’TaxAssessedValue’ ]

fig = sns.jointplot (x, y, kind = " hex " , 

color = "# 4CB391" )

fig.fig.subplots_adjust (top = 0.85 )

fig.set_axis_labels ( ’ Total Sq.Ft of Living Space’ , 

’Assessed Value for Tax Purposes’ )

fig.fig.suptitle ( ’Tax Assessed vs. Total Living Space’ , 

size = 18 );