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Changing distance between categories when plotting FacetGrid with error bars

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I am using FacetGrid from Seaborn together with errorbar from Matplotlib to generate a graph that has two categories in the y axis. I want to center the two categories (get them closer to each other and more distant to the margins).

I have tried to change height/aspect to achieve that without success. I am sharing the code below. Thanks!

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

cat = ['a','b','a','a','a','b','b','a'] 
var=[3,5,4,6,7,5,6,3] 
sd_var = [0.1,0.3,0.1,0.5,0.4,0.2,0.1,0.2] 

df = pd.DataFrame('cat' : cat, 'var' : var, 'sd_var' : sd_var)

g = sns.FacetGrid(data=df, hue="cat", aspect=1, height=6) 
g.map(plt.errorbar, "cat", "var", "sd_var", marker="o",
linestyle='',markersize='6', capsize=4, elinewidth=2)

plt.show() 

python matplotlib seaborn errorbar facet-grid

share|improve this question

edited Mar 28 at 1:11

Daniela Osorio

asked Mar 26 at 23:18

Daniela OsorioDaniela Osorio

111 bronze badge

add a comment | 

0

I am using FacetGrid from Seaborn together with errorbar from Matplotlib to generate a graph that has two categories in the y axis. I want to center the two categories (get them closer to each other and more distant to the margins).

I have tried to change height/aspect to achieve that without success. I am sharing the code below. Thanks!

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

cat = ['a','b','a','a','a','b','b','a'] 
var=[3,5,4,6,7,5,6,3] 
sd_var = [0.1,0.3,0.1,0.5,0.4,0.2,0.1,0.2] 

df = pd.DataFrame('cat' : cat, 'var' : var, 'sd_var' : sd_var)

g = sns.FacetGrid(data=df, hue="cat", aspect=1, height=6) 
g.map(plt.errorbar, "cat", "var", "sd_var", marker="o",
linestyle='',markersize='6', capsize=4, elinewidth=2)

plt.show() 

python matplotlib seaborn errorbar facet-grid

share|improve this question

edited Mar 28 at 1:11

Daniela Osorio

asked Mar 26 at 23:18

Daniela OsorioDaniela Osorio

111 bronze badge

add a comment | 

I am using FacetGrid from Seaborn together with errorbar from Matplotlib to generate a graph that has two categories in the y axis. I want to center the two categories (get them closer to each other and more distant to the margins).

I have tried to change height/aspect to achieve that without success. I am sharing the code below. Thanks!

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

cat = ['a','b','a','a','a','b','b','a'] 
var=[3,5,4,6,7,5,6,3] 
sd_var = [0.1,0.3,0.1,0.5,0.4,0.2,0.1,0.2] 

df = pd.DataFrame('cat' : cat, 'var' : var, 'sd_var' : sd_var)

g = sns.FacetGrid(data=df, hue="cat", aspect=1, height=6) 
g.map(plt.errorbar, "cat", "var", "sd_var", marker="o",
linestyle='',markersize='6', capsize=4, elinewidth=2)

plt.show() 

python matplotlib seaborn errorbar facet-grid

share|improve this question

edited Mar 28 at 1:11

Daniela Osorio

asked Mar 26 at 23:18

Daniela OsorioDaniela Osorio

111 bronze badge

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

cat = ['a','b','a','a','a','b','b','a'] 
var=[3,5,4,6,7,5,6,3] 
sd_var = [0.1,0.3,0.1,0.5,0.4,0.2,0.1,0.2] 

df = pd.DataFrame('cat' : cat, 'var' : var, 'sd_var' : sd_var)

g = sns.FacetGrid(data=df, hue="cat", aspect=1, height=6) 
g.map(plt.errorbar, "cat", "var", "sd_var", marker="o",
linestyle='',markersize='6', capsize=4, elinewidth=2)

plt.show() 

share|improve this question

edited Mar 28 at 1:11

Daniela Osorio

asked Mar 26 at 23:18

Daniela OsorioDaniela Osorio

111 bronze badge

share|improve this question

edited Mar 28 at 1:11

Daniela Osorio

asked Mar 26 at 23:18

Daniela OsorioDaniela Osorio

111 bronze badge

asked Mar 26 at 23:18

Daniela OsorioDaniela Osorio

111 bronze badge

asked Mar 26 at 23:18

Daniela OsorioDaniela Osorio

111 bronze badge

1 Answer
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0

You are pretty close to it

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

# replace label with actual number
cat = [1,2,1,1,1,2,2,1] 
var=[3,5,4,6,7,5,6,3] 
sd_var = [0.1,0.3,0.1,0.5,0.4,0.2,0.1,0.2] 

df = pd.DataFrame('cat' : cat, 'var' : var, 'sd_var' : sd_var)

g = sns.FacetGrid(data=df, hue="cat", aspect=1, height=6) 
g.map(plt.errorbar, "cat", "var", "sd_var", marker="o",
linestyle='',markersize='6', capsize=4, elinewidth=2)

# place the ticks at center by widening the plot
plt.xlim((0, 3))
# fix ticks at the number encoding for each class
g.fig.axes[0].xaxis.set_ticks([1, 2])
# name the numbers
g.fig.axes[0].xaxis.set_ticklabels(['a', 'b'])
plt.show()

The trick is to make use of relative length of the x-limit and your number encoding for the classes.

share|improve this answer

answered Mar 28 at 1:29

PM HuiPM Hui

15666 bronze badges

add a comment | 

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0

You are pretty close to it

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

# replace label with actual number
cat = [1,2,1,1,1,2,2,1] 
var=[3,5,4,6,7,5,6,3] 
sd_var = [0.1,0.3,0.1,0.5,0.4,0.2,0.1,0.2] 

df = pd.DataFrame('cat' : cat, 'var' : var, 'sd_var' : sd_var)

g = sns.FacetGrid(data=df, hue="cat", aspect=1, height=6) 
g.map(plt.errorbar, "cat", "var", "sd_var", marker="o",
linestyle='',markersize='6', capsize=4, elinewidth=2)

# place the ticks at center by widening the plot
plt.xlim((0, 3))
# fix ticks at the number encoding for each class
g.fig.axes[0].xaxis.set_ticks([1, 2])
# name the numbers
g.fig.axes[0].xaxis.set_ticklabels(['a', 'b'])
plt.show()

The trick is to make use of relative length of the x-limit and your number encoding for the classes.

share|improve this answer

answered Mar 28 at 1:29

PM HuiPM Hui

15666 bronze badges

add a comment | 

0

You are pretty close to it

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

# replace label with actual number
cat = [1,2,1,1,1,2,2,1] 
var=[3,5,4,6,7,5,6,3] 
sd_var = [0.1,0.3,0.1,0.5,0.4,0.2,0.1,0.2] 

df = pd.DataFrame('cat' : cat, 'var' : var, 'sd_var' : sd_var)

g = sns.FacetGrid(data=df, hue="cat", aspect=1, height=6) 
g.map(plt.errorbar, "cat", "var", "sd_var", marker="o",
linestyle='',markersize='6', capsize=4, elinewidth=2)

# place the ticks at center by widening the plot
plt.xlim((0, 3))
# fix ticks at the number encoding for each class
g.fig.axes[0].xaxis.set_ticks([1, 2])
# name the numbers
g.fig.axes[0].xaxis.set_ticklabels(['a', 'b'])
plt.show()

The trick is to make use of relative length of the x-limit and your number encoding for the classes.

share|improve this answer

answered Mar 28 at 1:29

PM HuiPM Hui

15666 bronze badges

add a comment | 

You are pretty close to it

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

# replace label with actual number
cat = [1,2,1,1,1,2,2,1] 
var=[3,5,4,6,7,5,6,3] 
sd_var = [0.1,0.3,0.1,0.5,0.4,0.2,0.1,0.2] 

df = pd.DataFrame('cat' : cat, 'var' : var, 'sd_var' : sd_var)

g = sns.FacetGrid(data=df, hue="cat", aspect=1, height=6) 
g.map(plt.errorbar, "cat", "var", "sd_var", marker="o",
linestyle='',markersize='6', capsize=4, elinewidth=2)

# place the ticks at center by widening the plot
plt.xlim((0, 3))
# fix ticks at the number encoding for each class
g.fig.axes[0].xaxis.set_ticks([1, 2])
# name the numbers
g.fig.axes[0].xaxis.set_ticklabels(['a', 'b'])
plt.show()

The trick is to make use of relative length of the x-limit and your number encoding for the classes.

share|improve this answer

answered Mar 28 at 1:29

PM HuiPM Hui

15666 bronze badges

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

# replace label with actual number
cat = [1,2,1,1,1,2,2,1] 
var=[3,5,4,6,7,5,6,3] 
sd_var = [0.1,0.3,0.1,0.5,0.4,0.2,0.1,0.2] 

df = pd.DataFrame('cat' : cat, 'var' : var, 'sd_var' : sd_var)

g = sns.FacetGrid(data=df, hue="cat", aspect=1, height=6) 
g.map(plt.errorbar, "cat", "var", "sd_var", marker="o",
linestyle='',markersize='6', capsize=4, elinewidth=2)

# place the ticks at center by widening the plot
plt.xlim((0, 3))
# fix ticks at the number encoding for each class
g.fig.axes[0].xaxis.set_ticks([1, 2])
# name the numbers
g.fig.axes[0].xaxis.set_ticklabels(['a', 'b'])
plt.show()

share|improve this answer

answered Mar 28 at 1:29

PM HuiPM Hui

15666 bronze badges

answered Mar 28 at 1:29

PM HuiPM Hui

15666 bronze badges

answered Mar 28 at 1:29

PM HuiPM Hui

15666 bronze badges