Difficulty plotting a split Violinplot using Seaborn and a Pandas Dataframe-CodePudding

I have dataframe full of likelihoods from a model I am using to identify points of interest on a set of images. The rows correspond to an image and the columns correspond to a label. There are "Left" and "Right" versions of the labels. I would like to use the split=True keyword to show the L and R sides on the same violin plot.

I have already created separate violin plots for labels "LH1" and "RH1" as can be seen here:

But I am trying to produce a plot with 5 violins, split left and right. Like this example from seaborn:

Seaborn requires a hue parameter which I imagine in my case would be the categorical information "Left" or "Right". As such, I restructured/reshaped my dataframe removing "L" or "R" prefixes in the labels and adding the information as a category under the column "chirality". This is approximately what I have at the moment:

  df = pd.DataFrame.from_dict(
        {'H1': {0: 0.55, 1: 0.56, 2: 0.46, 3: 0.93, 4: 0.74, 5: 0.35, 6: 0.75, 7: 0.86, 8: 0.81, 9: 0.88},
         'H2': {0: 0.5, 1: 0.55, 2: 0.61, 3: 0.82, 4: 0.51, 5: 0.35, 6: 0.58, 7: 0.66, 8: 0.93, 9: 0.86},
         'H3': {0: 0.42, 1: 0.51, 2: 0.86, 3: 0.59, 4: 0.46, 5: 0.71, 6: 0.58, 7: 0.72, 8: 0.53, 9: 0.92},
         'H4': {0: 0.89, 1: 0.87, 2: 0.04, 3: 0.64, 4: 0.44, 5: 0.05, 6: 0.33, 7: 0.93, 8: 0.08, 9: 0.9},
         'H5': {0: 0.92, 1: 0.75, 2: 0.13, 3: 0.85, 4: 0.51, 5: 0.15, 6: 0.38, 7: 0.92, 8: 0.36, 9: 0.76},
         'chirality': {0: 'Left', 1: 'Left', 2: 'Left', 3: 'Left', 4: 'Left', 5: 'Right', 6: 'Right', 7: 'Right', 8: 'Right', 9: 'Right'},
         'image': {0: 'image_0', 1: 'image_1', 2: 'image_2', 3: 'image_3', 4: 'image_4', 5: 'image_0', 6: 'image_1', 7: 'image_2', 8: 'image_3', 9: 'image_4'}})


     H1    H2    H3    H4    H5 chirality    image
0  0.55  0.50  0.42  0.89  0.92      Left  image_0
1  0.56  0.55  0.51  0.87  0.75      Left  image_1
2  0.46  0.61  0.86  0.04  0.13      Left  image_2
3  0.93  0.82  0.59  0.64  0.85      Left  image_3
4  0.74  0.51  0.46  0.44  0.51      Left  image_4
5  0.35  0.35  0.71  0.05  0.15     Right  image_0
6  0.75  0.58  0.58  0.33  0.38     Right  image_1
7  0.86  0.66  0.72  0.93  0.92     Right  image_2
8  0.81  0.93  0.53  0.08  0.36     Right  image_3
9  0.88  0.86  0.92  0.90  0.76     Right  image_4



# This is what I WANT to do.. but seaborn requires and x and y parameter. 
fig, ax = plt.subplots(figsize=(15,6))
sns.set_theme(style="whitegrid")
ax = sns.violinplot(ax=ax, 
                    data=df, 
                    hue='chirality', 
                    split=True)

I've tried many different approaches but I just can't seem to do it. In the attempt above I get ValueError: Cannot use 'hue' without 'x' and 'y' I don't even know what I could set those as despite trying various things and reshaping my dataframe even further. I think I want x as a list of labels, y as the likelihood values and hue to specify L/R. Any help is appreciated!

CodePudding user response：

Seaborn works easiest with a dataframe in

Here is another example, using the iris dataset, converting it to long form to show split violin plots of each combination of two species:

import matplotlib.pyplot as plt
import seaborn as sns

iris = sns.load_dataset('iris')
iris_long = iris.melt(id_vars='species')
iris_long['variable'] = iris_long['variable'].apply(lambda s: s.replace('_', '\n'))
sns.set_style('darkgrid')
fig, axs = plt.subplots(ncols=3, figsize=(12, 4), sharey=True)
palette = {'setosa': 'crimson', 'versicolor': 'cornflowerblue', 'virginica': 'limegreen'}
for excluded, ax in zip(iris.species.unique(), axs):
    sns.violinplot(ax=ax, data=iris_long[iris_long['species'] != excluded],
                   x='variable', y='value', hue='species', palette=palette, split=True)
    ax.set(xlabel='', ylabel='')
plt.tight_layout()
plt.show()