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Seaborn jointplot colour marginal plots separately

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4

I want to colour my marginal plots separately for each variable.

d1 = np.random.normal(10,1,100)
d2 = np.random.gamma(1,2,100)
col1 = sns.color_palette()[0]
col2 = sns.color_palette()[1]
col3 = sns.color_palette()[2]

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'))
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

Produces:

I want to colour each marginal plot separately. But when I assign arguments to the marginal axes they colour both marginal plots with the same arguments .

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'), marginal_kws=dict(hist_kws= 'color': col2))
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

I can colour the 'facecolors' but not the axes themselves. Any help very much appreciated!

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'), marginal_kws=dict(hist_kws= 'color': col2))
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

# new code
jp.ax_marg_x.set_facecolor(col1)
jp.ax_marg_y.set_facecolor(col3)

python python-3.x matplotlib seaborn

share|improve this question

asked Mar 21 at 15:54

Tommy LeesTommy Lees

16811

add a comment | 

4

I want to colour my marginal plots separately for each variable.

d1 = np.random.normal(10,1,100)
d2 = np.random.gamma(1,2,100)
col1 = sns.color_palette()[0]
col2 = sns.color_palette()[1]
col3 = sns.color_palette()[2]

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'))
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

Produces:

I want to colour each marginal plot separately. But when I assign arguments to the marginal axes they colour both marginal plots with the same arguments .

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'), marginal_kws=dict(hist_kws= 'color': col2))
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

I can colour the 'facecolors' but not the axes themselves. Any help very much appreciated!

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'), marginal_kws=dict(hist_kws= 'color': col2))
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

# new code
jp.ax_marg_x.set_facecolor(col1)
jp.ax_marg_y.set_facecolor(col3)

python python-3.x matplotlib seaborn

share|improve this question

asked Mar 21 at 15:54

Tommy LeesTommy Lees

16811

add a comment | 

I want to colour my marginal plots separately for each variable.

d1 = np.random.normal(10,1,100)
d2 = np.random.gamma(1,2,100)
col1 = sns.color_palette()[0]
col2 = sns.color_palette()[1]
col3 = sns.color_palette()[2]

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'))
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

Produces:

I want to colour each marginal plot separately. But when I assign arguments to the marginal axes they colour both marginal plots with the same arguments .

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'), marginal_kws=dict(hist_kws= 'color': col2))
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

I can colour the 'facecolors' but not the axes themselves. Any help very much appreciated!

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'), marginal_kws=dict(hist_kws= 'color': col2))
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

# new code
jp.ax_marg_x.set_facecolor(col1)
jp.ax_marg_y.set_facecolor(col3)

python python-3.x matplotlib seaborn

share|improve this question

asked Mar 21 at 15:54

Tommy LeesTommy Lees

16811

d1 = np.random.normal(10,1,100)
d2 = np.random.gamma(1,2,100)
col1 = sns.color_palette()[0]
col2 = sns.color_palette()[1]
col3 = sns.color_palette()[2]

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'))
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'), marginal_kws=dict(hist_kws= 'color': col2))
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'), marginal_kws=dict(hist_kws= 'color': col2))
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

# new code
jp.ax_marg_x.set_facecolor(col1)
jp.ax_marg_y.set_facecolor(col3)

share|improve this question

asked Mar 21 at 15:54

Tommy LeesTommy Lees

16811

share|improve this question

asked Mar 21 at 15:54

Tommy LeesTommy Lees

16811

asked Mar 21 at 15:54

Tommy LeesTommy Lees

16811

asked Mar 21 at 15:54

Tommy LeesTommy Lees

16811

1 Answer
1

active

oldest

votes

5

You can do it by accessing the patches of two marginal plots and changing their face colors.

import seaborn as sns
import numpy as np

# define data here

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'), color=col2)
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

for patch in jp.ax_marg_x.patches:
 patch.set_facecolor(col1)

for patch in jp.ax_marg_y.patches:
 patch.set_facecolor(col3) 

share|improve this answer

edited Mar 21 at 16:55

answered Mar 21 at 16:22

BazingaaBazingaa

15.1k21230

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  And how do I get the annotation to show? I have added: annot_kws=dict(stat="r") but it doesn't show up anywhere on the plot?
  
  – Tommy Lees
  Mar 21 at 16:55
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Sorry I realise new question but just thought about that! Awesome answer
  
  – Tommy Lees
  Mar 21 at 16:56
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @TommyLees: I would recommend to post a new question on that as it could be of potential importance for many. In the mean time, I will work on the answer and let you know asap.
  
  – Bazingaa
  Mar 21 at 16:58
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  thank you very much I did just that here - stackoverflow.com/q/55285704/9940782
  
  – Tommy Lees
  Mar 21 at 17:06
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @TommyLees: By annotation, you mean the equation of the best fit straight line, right?
  
  – Bazingaa
  Mar 21 at 17:07
  
  

  
  
  
  

 | 
show 2 more comments

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5

You can do it by accessing the patches of two marginal plots and changing their face colors.

import seaborn as sns
import numpy as np

# define data here

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'), color=col2)
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

for patch in jp.ax_marg_x.patches:
 patch.set_facecolor(col1)

for patch in jp.ax_marg_y.patches:
 patch.set_facecolor(col3) 

share|improve this answer

edited Mar 21 at 16:55

answered Mar 21 at 16:22

BazingaaBazingaa

15.1k21230

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  And how do I get the annotation to show? I have added: annot_kws=dict(stat="r") but it doesn't show up anywhere on the plot?
  
  – Tommy Lees
  Mar 21 at 16:55
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Sorry I realise new question but just thought about that! Awesome answer
  
  – Tommy Lees
  Mar 21 at 16:56
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @TommyLees: I would recommend to post a new question on that as it could be of potential importance for many. In the mean time, I will work on the answer and let you know asap.
  
  – Bazingaa
  Mar 21 at 16:58
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  thank you very much I did just that here - stackoverflow.com/q/55285704/9940782
  
  – Tommy Lees
  Mar 21 at 17:06
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @TommyLees: By annotation, you mean the equation of the best fit straight line, right?
  
  – Bazingaa
  Mar 21 at 17:07
  
  

  
  
  
  

 | 
show 2 more comments

5

You can do it by accessing the patches of two marginal plots and changing their face colors.

import seaborn as sns
import numpy as np

# define data here

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'), color=col2)
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

for patch in jp.ax_marg_x.patches:
 patch.set_facecolor(col1)

for patch in jp.ax_marg_y.patches:
 patch.set_facecolor(col3) 

share|improve this answer

edited Mar 21 at 16:55

answered Mar 21 at 16:22

BazingaaBazingaa

15.1k21230

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  And how do I get the annotation to show? I have added: annot_kws=dict(stat="r") but it doesn't show up anywhere on the plot?
  
  – Tommy Lees
  Mar 21 at 16:55
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Sorry I realise new question but just thought about that! Awesome answer
  
  – Tommy Lees
  Mar 21 at 16:56
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @TommyLees: I would recommend to post a new question on that as it could be of potential importance for many. In the mean time, I will work on the answer and let you know asap.
  
  – Bazingaa
  Mar 21 at 16:58
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  thank you very much I did just that here - stackoverflow.com/q/55285704/9940782
  
  – Tommy Lees
  Mar 21 at 17:06
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @TommyLees: By annotation, you mean the equation of the best fit straight line, right?
  
  – Bazingaa
  Mar 21 at 17:07
  
  

  
  
  
  

 | 
show 2 more comments

You can do it by accessing the patches of two marginal plots and changing their face colors.

import seaborn as sns
import numpy as np

# define data here

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'), color=col2)
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

for patch in jp.ax_marg_x.patches:
 patch.set_facecolor(col1)

for patch in jp.ax_marg_y.patches:
 patch.set_facecolor(col3) 

share|improve this answer

edited Mar 21 at 16:55

answered Mar 21 at 16:22

BazingaaBazingaa

15.1k21230

import seaborn as sns
import numpy as np

# define data here

jp = sns.jointplot(d1, d2, kind="hex", annot_kws=dict(stat="r"), joint_kws=dict(bins='log'), color=col2)
ax = jp.ax_joint
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3", label="1:1")

for patch in jp.ax_marg_x.patches:
 patch.set_facecolor(col1)

for patch in jp.ax_marg_y.patches:
 patch.set_facecolor(col3) 

share|improve this answer

edited Mar 21 at 16:55

answered Mar 21 at 16:22

BazingaaBazingaa

15.1k21230

answered Mar 21 at 16:22

BazingaaBazingaa

15.1k21230

answered Mar 21 at 16:22

BazingaaBazingaa

15.1k21230