How to set axis to logarithmic scale in a seaborn jointplot? I can't find any log arguments in seaborn.jointplot

Notebook

import seaborn as sns
import pandas as pddf = pd.read_csv("https://storage.googleapis.com/mledu-datasets/california_housing_train.csv", sep=",")g = sns.jointplot(x="total_bedrooms",y="median_house_value",data = df,kind="reg",logx=True)

• 300 rows of sample data of the relevant columns, incase the data link dies

median_house_value,total_bedrooms
66900.0,1283.0
80100.0,1901.0
85700.0,174.0
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25000.0,33.0
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100000.0,139.0
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84700.0,440.0
70100.0,573.0
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61100.0,301.0
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32500.0,49.0
62800.0,248.0
67500.0,95.0
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100000.0,820.0
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100000.0,176.0
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221000.0,1946.0
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231800.0,786.0
206100.0,520.0
100000.0,63.0
274600.0,565.0
84700.0,1527.0

After you create the plot, you can set the axes to be log scale, using matplotlib's ax.set_xscale('log') and ax.set_yscale('log').

In this case, we need to get the axis from the JointGrid created by jointplot. If you catch the JointGrid returned as g, then the joint axis is g.ax_joint.

For example:

g = sns.jointplot(x="predictions",y="targets",data = calibration_data,kind="reg",logx=True,)g.ax_joint.set_xscale('log')
g.ax_joint.set_yscale('log')