How can I wrap subplot columns

Question

I've been struggling with visualizing subplots column wrapping in Seaborn histogram plots (kdeplot, histplot). Tried various things including fig, ax & enumerate(zip(df.columns, ax.flatten()).

Here's the dataset

 for col in df.columns:
  plt.figure(figsize = (3,3))
  sns.histplot(df, x = col, kde = True, bins = 40, hue = 'Dataset', fill = True)
  plt.show();

How can the plots be done with other seaborn plots or plots with facet wrap functionality?

Answer 1

• seaborn.displot with kind='hist' can be used to create subplots / facets, where col_wrap specifies the number of columns.
  • See How to plot in multiple subplots for specifying nrows and ncols when using axes-level plots.
  • See Figure-level vs. axes-level functions
• The data for 'Female' and 'Male' should be shown separately, because gender statistics are often different, so presenting them together can skew the impression of the data.
• Plotting separate FacetGrids for each 'Gender' produces the best display option.
• seaborn histplot and displot output doesn't match
• Tested in python 3.11.3, pandas 2.0.1, matplotlib 3.7.1, seaborn 0.12.2

import pandas as pd
import seaborn as sns

# load the dataset downloaded from https://www.kaggle.com/uciml/indian-liver-patient-records
df = pd.read_csv('d:/data/kaggle/indian_liver_patient.csv')

# convert the data to a long form
dfm = df.melt(id_vars=['Gender', 'Dataset'])

# plot the data for each gender
for gender, data in dfm.groupby('Gender'):
    
    g = sns.displot(kind='hist', data=data, x='value', col='variable', hue='Dataset',
                    hue_order=[1, 2], common_norm=False, common_bins=False,
                    multiple='dodge', kde=True, col_wrap=3, height=2.5, aspect=2,
                    facet_kws={'sharey': False, 'sharex': False}, palette='tab10')
    
    fig = g.fig
    
    fig.suptitle(f'Gender: {gender}', y=1.02)

    fig.savefig(f'hist_{gender}.png', bbox_inches='tight')

• The only problem with this option is common_bins=False means the bins of the two hue groups don't align. However, setting it to True causes sharex=False to be ignored, so all of the x-axis limits will be 0 - 2000, as can be seen in this plot.

---

• The plot generated by the following code has too many columns
  • col_wrap can't be used if row is also in use.

g = sns.displot(kind='hist', data=dfm, x='value', row='Dataset', col='variable', hue='Gender',
                common_norm=False, common_bins=False, multiple='dodge', kde=True,
                facet_kws={'sharey': False, 'sharex': False})

g.fig.savefig('hist.png')

• The following plot does not separate the data by 'Gender'.

g = sns.displot(kind='hist', data=dfm, x='value', col='variable', col_wrap=3,
                hue='Dataset', common_norm=False, common_bins=False,
                multiple='dodge', kde=True, height=2.5, aspect=2,
                facet_kws={'sharey': False, 'sharex': False}, palette='tab10')

---

• The following option correctly allows common_bins=True to be used.

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

# load the dataset
df = pd.read_csv('d:/data/kaggle/indian_liver_patient.csv')

# convert the data to a long form
dfm = df.melt(id_vars=['Gender', 'Dataset'])

# iterate through the data for each gender
for gen, data in dfm.groupby('Gender'):
    
    # create the figure and axes
    fig, axes = plt.subplots(3, 3, figsize=(11, 5), sharex=False, sharey=False, tight_layout=True)
    
    # flatten the array of axes
    axes = axes.flatten()
    
    # iterate through each axes and variable category
    for ax, (var, sel) in zip(axes, data.groupby('variable')):
        
        sns.histplot(data=sel, x='value', hue='Dataset', hue_order=[1, 2], kde=True, ax=ax,
                     common_norm=False, common_bins=True, multiple='dodge', palette='tab10')
        
        ax.set(xlabel='', title=var.replace('_', ' ').title())
        ax.spines[['top', 'right']].set_visible(False)
    
    # remove all the legends except for Aspartate Aminotrnsferase, which will be move to used for the figure
    for ax in np.append(axes[:5], axes[6:]):
        ax.get_legend().remove()
        
    sns.move_legend(axes[5], bbox_to_anchor=(1, 0.5), loc='center left', frameon=False)
        
    fig.suptitle(f'Gender: {gen}', y=1.02)
    
    fig.savefig(f'hist_{gen}.png', bbox_inches='tight')

---

• Some columns in df have significant outliers. Removing them will improve the histogram visualization.

from scipy.stats import zscore
from typing import Literal


def remove_outliers(data: pd.DataFrame, method: Literal['std', 'z'] = 'std') -> pd.DataFrame:
    # remove outliers with std or zscore
    if method == 'std':
        std = data.value.std()
        low = data.value.mean() - std * 3
        high = data.value.mean() + std * 3
        data = data[data.value.between(low, high)] 
    else:
        data = data[(np.abs(zscore(data['value'])) < 3)]
    return data


# iterate through the data for each gender
for gen, data in dfm.groupby('Gender'):
    
    ...
    
    # iterate through each axes and variable category
    for ax, (var, sel) in zip(axes, data.groupby('variable')):
        
        # remove outliers of specified columns
        if var in df.columns[2:7]:
            sel = remove_outliers(sel)
        
        sns.histplot(data=sel, x='value', hue='Dataset', hue_order=[1, 2], kde=True, ax=ax,
                     common_norm=False, common_bins=True, multiple='dodge', palette='tab10')

        ....
     ....