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Log-log lmplot with seaborn

Can the function lmplot from Seaborn plot on a log-log scale? This is lmplot on a normal scale

import numpy as np
import pandas as pd
import seaborn as sns
x =  10**arange(1, 10)
y = 10** arange(1,10)*2
df1 = pd.DataFrame( data=y, index=x )
df2 = pd.DataFrame(data = {'x': x, 'y': y}) 
sns.lmplot('x', 'y', df2)

sns.lmplot('x', 'y', df2)

      • 2
    • I'm a little confused because your code draws two plots. Are you trying to reproduce the first plot with seaborn? Or do you want to draw the lmplot on top of the first plot?

If you just want to plot a simple regression, it will be easier to use seaborn.regplot. This seems to work (although I'm not sure where the y axis minor grid goes)

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

x = 10 ** np.arange(1, 10)
y = x * 2
data = pd.DataFrame(data={'x': x, 'y': y})

f, ax = plt.subplots(figsize=(7, 7))
ax.set(xscale="log", yscale="log")
sns.regplot("x", "y", data, ax=ax, scatter_kws={"s": 100})

enter image description here

If you need to use lmplot for other purposes, this is what comes to mind, but I'm not sure what's happening with the x axis ticks. If someone has ideas and it's a bug in seaborn, I'm happy to fix it:

grid = sns.lmplot('x', 'y', data, size=7, truncate=True, scatter_kws={"s": 100})
grid.set(xscale="log", yscale="log")

enter image description here

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Call the seaborn function first. It returns a FacetGrid object which has an axes attribute (a 2-d numpy array of matplotlib Axes). Grab the Axes object and pass that to the call to df1.plot.

import numpy as np
import pandas as pd
import seaborn as sns

x =  10**np.arange(1, 10)
y = 10**np.arange(1,10)*2
df1 = pd.DataFrame(data=y, index=x)
df2 = pd.DataFrame(data = {'x': x, 'y': y})

fgrid = sns.lmplot('x', 'y', df2)    
ax = fgrid.axes[0][0]
df1.plot(ax=ax)        

ax.set_xscale('log')
ax.set_yscale('log')
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      • 1
    • In theory it should be simpler, and you could just do fgrid.set(xscale="log", yscale="log"), but due to a combination of some matplotlib weirdness and the way lmplot works this doesn't quite get what we want.

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