First, like usual we will get the data. This time we want just the GDP per Capita measure and the population measure.

import wbdata
import pandas as pd
import datetime
dates =  (datetime.datetime(2016, 1, 1),datetime.datetime(2017, 1, 1))
levels = ["HIC","MIC","LIC"]
indicators = {"SP.POP.TOTL":"Population","NY.GDP.PCAP.CD":"GDP per Capita"}
dataArray = []
for level in levels:
    countries = [i["id"] for i in wbdata.get_country(incomelevel=level, display=False)]
    data  = wbdata.get_dataframe(indicators, country=countries, data_date=dates)
    data["Income Level"] = level
    dataArray.append(data)
df = pd.concat(dataArray)
df.dropna(inplace=True)
df

Let’s apply the log transformations again as well.

import numpy as np
df["GDP per Capita"] = df["GDP per Capita"].apply(np.log)
df["Population"] = df["Population"].apply(np.log)

Bring up the pair plots for these two for a reminder.

sns.pairplot(df, hue="Income Level")
plt.show()

Now, let’s call kdeplot() from seaborn by giving x and y arguments.

sns.kdeplot(df["Population"],df["GDP per Capita"])
plt.show()

These lines look a little ugly, let’s get some shading in there. Setting shade=True will do this.

sns.kdeplot(df["Population"],df["GDP per Capita"],shade=True)
plt.show()

Another change we might want to make is to get rid of farthest shading, it will look better without it. We can do this with shade_lowest=False.

sns.kdeplot(df["Population"],df["GDP per Capita"],shade=True,shade_lowest=False)
plt.show()

This is the full distribution, instead of plotting everything together let’s plot each income level alone.