How to create a figure with subfigures and subplots

Question

I am learning the breast cancer classification dataset in python. I am trying to plot histograms for each features, how am I able to arrange those histograms into three groups? Like the following screenshot:

What I am trying to achieve

Here is the code I used:

from sklearn.datasets import load_breast_cancer  # sample data
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

data = load_breast_cancer()

# Turn the feature data into a dataframe
df = pd.DataFrame(data.data, columns = data.feature_names)

# Add the target columns, and fill it with the target data
df["target"] = data.target

# display(df.head())
   mean radius  mean texture  mean perimeter  mean area  mean smoothness  mean compactness  mean concavity  mean concave points  mean symmetry  mean fractal dimension  radius error  texture error  perimeter error  area error  smoothness error  compactness error  concavity error  concave points error  symmetry error  fractal dimension error  worst radius  worst texture  worst perimeter  worst area  worst smoothness  worst compactness  worst concavity  worst concave points  worst symmetry  worst fractal dimension  target
0        17.99         10.38          122.80     1001.0          0.11840           0.27760          0.3001              0.14710         0.2419                 0.07871        1.0950         0.9053            8.589      153.40          0.006399            0.04904          0.05373               0.01587         0.03003                 0.006193         25.38          17.33           184.60      2019.0            0.1622             0.6656           0.7119                0.2654          0.4601                  0.11890       0
1        20.57         17.77          132.90     1326.0          0.08474           0.07864          0.0869              0.07017         0.1812                 0.05667        0.5435         0.7339            3.398       74.08          0.005225            0.01308          0.01860               0.01340         0.01389                 0.003532         24.99          23.41           158.80      1956.0            0.1238             0.1866           0.2416                0.1860          0.2750                  0.08902       0
2        19.69         21.25          130.00     1203.0          0.10960           0.15990          0.1974              0.12790         0.2069                 0.05999        0.7456         0.7869            4.585       94.03          0.006150            0.04006          0.03832               0.02058         0.02250                 0.004571         23.57          25.53           152.50      1709.0            0.1444             0.4245           0.4504                0.2430          0.3613                  0.08758       0
3        11.42         20.38           77.58      386.1          0.14250           0.28390          0.2414              0.10520         0.2597                 0.09744        0.4956         1.1560            3.445       27.23          0.009110            0.07458          0.05661               0.01867         0.05963                 0.009208         14.91          26.50            98.87       567.7            0.2098             0.8663           0.6869                0.2575          0.6638                  0.17300       0
4        20.29         14.34          135.10     1297.0          0.10030           0.13280          0.1980              0.10430         0.1809                 0.05883        0.7572         0.7813            5.438       94.44          0.011490            0.02461          0.05688               0.01885         0.01756                 0.005115         22.54          16.67           152.20      1575.0            0.1374             0.2050           0.4000                0.1625          0.2364                  0.07678       0

# plotting
plotnumber = 1
fig = plt.figure(figsize=(20, 20))

for column in df.drop('target', axis=1):
    if plotnumber <= 30:
        plt.subplot(5, 6, plotnumber)
        sns.distplot(df[df['target'] == 0][column], label = 'malignant')
        sns.distplot(df[df['target'] == 1][column], label = 'benign')
        plt.legend()
        plt.title(column)
    plotnumber += 1

fig.tight_layout()

What I have so far

I want to divide them into three groups: 'mean' group, 'error' group and 'worst' group. And each group includes 10 plots (5 row, 2 column)

These don't answer the question

• How to plot in multiple subplots
  • Doesn't show how to group the subplots
• Matplotlib different size subplots
  • Also doesn't show how to group the subplots

Answer 1

• This requires using matplotlib.figure.subfigures, and then adding .subplots to each subfigure.
  • Figure subfigure
  • Arranging multiple Axes in a Figure
    • Nested Axes layouts
• In seaborn v0.11.2, sns.distplot is deprecated and replaced by sns.histplot.
• itertools.chain is used to flatten groups, but other methods can be found in How do I make a flat list out of a list of lists?.
• Tested in python 3.10, pandas 1.4.2, matplotlib 3.5.1, seaborn 0.11.2
  • If you are using Anaconda, then use conda update --all, otherwise update packages with pip. Alternatively, create a new environment with the appropriate package versions.

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_breast_cancer  # sample data
from itertools import chain  # to lazily flatten the nested list

# starting with the sample dataframe in the op
data = load_breast_cancer()
df = pd.DataFrame(data.data, columns=data.feature_names)
df["target"] = data.target

# change the target name to what should be in the legend
df.target = df.target.map({0: 'Malignant', 1: 'Benign'})

# create the groups of column names for each set of subplots
col_groups = [df.columns[df.columns.str.contains(v)] for v in ['mean', 'error', 'worst']]

# create the subfigures and subplots
fig = plt.figure(figsize=(20, 20), constrained_layout=True)
subfigs = fig.subfigures(1, 3, width_ratios=[1, 1, 1], wspace=.15)

axs0 = subfigs[0].subplots(5, 2)
axs0 = axs0.flatten()
subfigs[0].suptitle('Mean Values', fontsize=20)

axs1 = subfigs[1].subplots(5, 2)
axs1 = axs1.flatten()
subfigs[1].suptitle('Standard Error Values', fontsize=20)

axs2 = subfigs[2].subplots(5, 2)
axs2 = axs2.flatten()
subfigs[2].suptitle('Worst Values', fontsize=20)

# create a flattened list of tuples containing an axes and column name
groups = chain(*[list(zip(axes, group)) for axes, group in zip([axs0, axs1, axs2], col_groups)])

# iterate through each axe and column
for ax, col in groups:
    sns.histplot(data=df, x=col, hue='target', kde=True, stat='density', ax=ax)
    l = ax.get_legend()  # remove this line to keep default legend
    l.remove()  # remove this line to keep default legend
    
# get the existing label text, otherwise use a custom list (e.g labels = ['Malignant', 'Benign'])
# remove this line to keep default legend
labels = [v.get_text() for v in l.get_texts()]

# add a single legend at the top of the figure; change loc and bbox_to_anchor to move the legend
# remove this line to keep default legend
fig.legend(title='Tumor Classification', handles=l.legendHandles, labels=labels, loc='lower center', ncol=2, bbox_to_anchor=(0.5, -0.03))

fig.suptitle('Breast Cancer Data', fontsize=30, y=1.05)
fig.savefig('test.png', bbox_inches="tight")
plt.show()

Plotted with default legends