seaborn is not plotting within defined subplots

I am trying to plot two displots side by side with this code

fig,(ax1,ax2) = plt.subplots(1,2)

sns.displot(x =X_train['Age'], hue=y_train, ax=ax1)
sns.displot(x =X_train['Fare'], hue=y_train, ax=ax2)

It returns the following result (two empty subplots followed by one displot each on two lines)-

If I try the same code with violinplot, it returns result as expected

fig,(ax1,ax2) = plt.subplots(1,2)

sns.violinplot(y_train, X_train['Age'], ax=ax1)
sns.violinplot(y_train, X_train['Fare'], ax=ax2)

Why is displot returning a different kind of output and what can I do to output two plots on the same line?

Answer

• seaborn.distplot has been DEPRECATED in seaborn 0.11 and is replaced with the following:
  • displot(), a figure-level function with a similar flexibility over the kind of plot to draw. This is a FacetGrid, and does not have the ax parameter, so it will not work with matplotlib.pyplot.subplots.
  • histplot(), an axes-level function for plotting histograms, including with kernel density smoothing. This does have the ax parameter, so it will work with matplotlib.pyplot.subplots.
• It is applicable to any of the seaborn FacetGrid plots that there is no ax parameter. Use the equivalent axes-level plot.
  • Look at the documentation for the figure-level plot to find the appropriate axes-level plot function for your needs.
  • See Figure-level vs. axes-level functions
• Because the histogram of two different columns is desired, it’s easier to use histplot.
• See How to plot in multiple subplots for a number of different ways to plot into maplotlib.pyplot.subplots
• Also review seaborn histplot and displot output doesn’t match
• Tested in seaborn 0.11.1 & matplotlib 3.4.2

fig, (ax1, ax2) = plt.subplots(1, 2)

sns.histplot(x=X_train['Age'], hue=y_train, ax=ax1)
sns.histplot(x=X_train['Fare'], hue=y_train, ax=ax2)

Imports and DataFrame Sample

import seaborn as sns
import matplotlib.pyplot as plt

# load data
penguins = sns.load_dataset("penguins", cache=False)

# display(penguins.head())
  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g     sex
0  Adelie  Torgersen            39.1           18.7              181.0       3750.0    MALE
1  Adelie  Torgersen            39.5           17.4              186.0       3800.0  FEMALE
2  Adelie  Torgersen            40.3           18.0              195.0       3250.0  FEMALE
3  Adelie  Torgersen             NaN            NaN                NaN          NaN     NaN
4  Adelie  Torgersen            36.7           19.3              193.0       3450.0  FEMALE

Axes Level Plot

• With the data in a wide format, use sns.histplot

# select the columns to be plotted
cols = ['bill_length_mm', 'bill_depth_mm']

# create the figure and axes
fig, axes = plt.subplots(1, 2)
axes = axes.ravel()  # flattening the array makes indexing easier

for col, ax in zip(cols, axes):
    sns.histplot(data=penguins[col], kde=True, stat='density', ax=ax)

fig.tight_layout()
plt.show()

Figure Level Plot

• With the dataframe in a long format, use displot

# create a long dataframe
dfl = penguins.melt(id_vars='species', value_vars=['bill_length_mm', 'bill_depth_mm'], var_name='bill_size', value_name='vals')

# display(dfl.head())
  species       bill_size  vals
0  Adelie  bill_length_mm  39.1
1  Adelie   bill_depth_mm  18.7
2  Adelie  bill_length_mm  39.5
3  Adelie   bill_depth_mm  17.4
4  Adelie  bill_length_mm  40.3

# plot
sns.displot(data=dfl, x='vals', col='bill_size', kde=True, stat='density', common_bins=False, common_norm=False, height=4, facet_kws={'sharey': False, 'sharex': False})

---

Multiple DataFrames

• If there are multiple dataframes, they can be combined with pd.concat, and use .assign to create an identifying 'source' column, which can be used for row=, col=, or hue=

# list of dataframe
lod = [df1, df2, df3]

# create one dataframe with a new 'source' column to use for row, col, or hue
df = pd.concat((d.assign(source=f'df{i}') for i, d in enumerate(lod, 1)), ignore_index=True)

• See Import multiple csv files into pandas and concatenate into one DataFrame to read multiple files into a single dataframe with an identifying column.