Combine various nautical facet lattices into a single section
I have three different datasets where I create a facet, each
a = sns.FacetGrid(data1, col="overlap", hue="comp")
a = (g.map(sns.kdeplot, "val",bw=0.8))
b = sns.FacetGrid(data2, col="overlap", hue="comp")
b = (g.map(sns.kdeplot, "val",bw=0.8))
c = sns.FacetGrid(data3, col="overlap", hue="comp")
c = (g.map(sns.kdeplot, "val",bw=0.8))
Each of these graphs has three subheadings on one line, so I end up with nine graphs.
I would like to combine these plots in subplot settings like this
f, (ax1, ax2, ax3) = plt.subplots(3,1) ax1.a ax2.b ax3.c
How can i do this?
source to share
A FacetGrid creates its own shape. Combining multiple shapes into one is not an easy task. Also, there is no such thing as subheading lines that you can add to a shape. Therefore, you will need to manipulate the axes individually.
However, it would be easier to find workarounds. For example. if the displayed data frames have the same structure as the question code appears to be, you can combine the data into a single frame with a new column and use it as a row
facet grid attribute .
import numpy as np; np.random.seed(3)
import pandas as pd
import seaborn.apionly as sns
import matplotlib.pyplot as plt
def get_data(n=266, s=[5,13]):
val = np.c_[np.random.poisson(lam=s[0], size=n),
np.random.poisson(lam=s[1], size=n)].T.flatten()
comp = [s[0]]*n + [s[1]]*n
ov = np.random.choice(list("ABC"), size=2*n)
return pd.DataFrame({"val":val, "overlap":ov, "comp":comp})
data1 = get_data(s=[9,11])
data2 = get_data(s=[7,19])
data3 = get_data(s=[1,27])
#option1 combine
for i, df in enumerate([data1,data2,data3]):
df["data"] = ["data{}".format(i+1)] * len(df)
data = data1.append(data2)
data = data.append(data3)
bw = 2
a = sns.FacetGrid(data, col="overlap", hue="comp", row="data")
a = (a.map(sns.kdeplot, "val",bw=bw ))
plt.show()
You can also loop over data blocks and axes to obtain the desired result.
import numpy as np; np.random.seed(3)
import pandas as pd
import seaborn.apionly as sns
import matplotlib.pyplot as plt
def get_data(n=266, s=[5,13]):
val = np.c_[np.random.poisson(lam=s[0], size=n),
np.random.poisson(lam=s[1], size=n)].T.flatten()
comp = [s[0]]*n + [s[1]]*n
ov = np.random.choice(list("ABC"), size=2*n)
return pd.DataFrame({"val":val, "overlap":ov, "comp":comp})
data1 = get_data(s=[9,11])
data2 = get_data(s=[7,19])
data3 = get_data(s=[1,27])
#option2 plot each subplot individually
data = [data1,data2,data3]
bw = 2
fig, axes = plt.subplots(3,3, sharex=True, sharey=True)
for i in range(3):
for j in range(3):
x = data[i]
x = x[x["overlap"] == x["overlap"].unique()[j]]
for hue in x["comp"].unique():
d = x[x["comp"] == hue]
sns.kdeplot(d["val"], ax=axes[i,j], bw=bw, label=hue )
plt.show()
source to share