How to set seaborn jointplot axis to log scale

Question

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

import seaborn as sns
import pandas as pd

df = 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
73400.0,337.0
65500.0,326.0
74000.0,236.0
82400.0,680.0
48500.0,168.0
58400.0,1175.0
48100.0,309.0
86500.0,801.0
62000.0,483.0
48600.0,248.0
70400.0,464.0
45000.0,378.0
69100.0,587.0
94900.0,322.0
25000.0,33.0
44000.0,386.0
27500.0,24.0
44400.0,360.0
59200.0,243.0
50000.0,95.0
71300.0,129.0
53500.0,397.0
100000.0,139.0
71100.0,322.0
80900.0,270.0
68600.0,191.0
74300.0,294.0
65800.0,394.0
67500.0,262.0
146300.0,196.0
113800.0,171.0
95800.0,113.0
107800.0,220.0
40000.0,373.0
88500.0,246.0
91200.0,666.0
102800.0,104.0
64000.0,389.0
84700.0,440.0
70100.0,573.0
142500.0,72.0
88400.0,913.0
75500.0,492.0
43300.0,523.0
46700.0,218.0
63700.0,287.0
72700.0,610.0
42500.0,136.0
53400.0,283.0
60800.0,262.0
58600.0,382.0
66400.0,366.0
67500.0,387.0
79200.0,337.0
63100.0,275.0
67700.0,581.0
40000.0,199.0
62200.0,634.0
70700.0,340.0
60300.0,545.0
61200.0,325.0
69400.0,373.0
96000.0,268.0
60600.0,395.0
70800.0,454.0
60400.0,403.0
143000.0,365.0
80800.0,530.0
67500.0,316.0
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84300.0,606.0
107200.0,480.0
59400.0,416.0
63900.0,375.0
69400.0,328.0
62500.0,835.0
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70800.0,490.0
86200.0,202.0
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140300.0,217.0
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61100.0,301.0
67500.0,289.0
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62000.0,244.0
61200.0,231.0
125000.0,235.0
55200.0,340.0
87500.0,99.0
50000.0,238.0
30000.0,448.0
87500.0,103.0
93800.0,81.0
47500.0,18.0
68900.0,379.0
41000.0,1257.0
32500.0,49.0
62800.0,248.0
67500.0,95.0
67500.0,272.0
58800.0,43.0
53800.0,25.0
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53800.0,46.0
54300.0,536.0
51300.0,57.0
43900.0,280.0
66400.0,958.0
62800.0,515.0
94500.0,97.0
65600.0,65.0
81300.0,94.0
66900.0,290.0
66800.0,2331.0
76100.0,89.0
65600.0,1997.0
84700.0,354.0
100000.0,820.0
47800.0,1228.0
82600.0,705.0
112500.0,54.0
65400.0,499.0
61400.0,277.0
65900.0,800.0
47500.0,203.0
58600.0,512.0
155000.0,19.0
66700.0,654.0
67500.0,476.0
60600.0,625.0
96300.0,273.0
61800.0,409.0
68200.0,192.0
68900.0,714.0
82200.0,787.0
100000.0,176.0
100900.0,295.0
32900.0,386.0
42500.0,468.0
69400.0,858.0
68500.0,352.0
58800.0,258.0
124700.0,849.0
72100.0,221.0
76900.0,1326.0
90000.0,1349.0
104100.0,566.0
93400.0,1039.0
95000.0,2224.0
67500.0,187.0
50000.0,91.0
92900.0,444.0
382400.0,1222.0
83700.0,284.0
65800.0,109.0
199300.0,2555.0
167400.0,760.0
137500.0,481.0
55400.0,556.0
93400.0,410.0
91800.0,851.0
98000.0,831.0
54200.0,487.0
81000.0,861.0
100000.0,367.0
57400.0,411.0
158500.0,3923.0
353100.0,2000.0
176400.0,514.0
62300.0,406.0
110700.0,606.0
78500.0,3098.0
121300.0,1859.0
318100.0,1542.0
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500001.0,438.0
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87500.0,85.0
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51300.0,2634.0
61100.0,1395.0
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145200.0,2315.0
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125200.0,763.0
110900.0,2186.0
159000.0,1839.0
220500.0,463.0
124100.0,1714.0
199400.0,1217.0
183900.0,1387.0
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500001.0,562.0
69600.0,1529.0
321900.0,399.0
148200.0,361.0
22500.0,1743.0
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50000.0,166.0
230200.0,1652.0
345500.0,82.0
116500.0,876.0
113500.0,827.0
172900.0,365.0
198100.0,538.0
67400.0,1719.0
169100.0,847.0
240600.0,157.0
193800.0,74.0
161100.0,711.0
156300.0,374.0
66300.0,109.0
81700.0,875.0
122900.0,682.0
214300.0,661.0
158200.0,946.0
143400.0,1070.0
217400.0,845.0
308600.0,481.0
111400.0,849.0
42500.0,10.0
173400.0,268.0
187200.0,702.0
214500.0,751.0
63000.0,525.0
221000.0,1946.0
90000.0,68.0
231800.0,786.0
206100.0,520.0
100000.0,63.0
274600.0,565.0
84700.0,1527.0

tmdavison · Accepted Answer · 2021-10-11T09:56:02.237

16

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')

edited Oct 11 '21 at 09:56

answered Jun 27 '18 at 11:00

tmdavison

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3

That works! for other users, you might want to change your axes limits using g.ax_joint.set_xlim([xmin,xmax]) – Rutger Hofste Jun 27 '18 at 12:23

How to set seaborn jointplot axis to log scale

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