Grouping boxplots in seaborn when input is a DataFrame

As the other answers note, the boxplot function is limited to plotting a single "layer" of boxplots, and the groupby parameter only has an effect when the input is a Series and you have a second variable you want to use to bin the observations into each box..

However, you can accomplish what I think you're hoping for with the factorplot function, using kind="box". But, you'll first have to "melt" the sample dataframe into what is called long-form or "tidy" format where each column is a variable and each row is an observation:

df_long = pd.melt(df, "b", var_name="a", value_name="c") 

Then it's very simple to plot:

sns.factorplot("a", hue="b", y="c", data=df_long, kind="box") 

Seaborn's groupby function takes Series not DataFrames, that's why it's not working.

As a work around, you can do this :

fig, ax = plt.subplots(1,2, sharey=True) for i, grp in enumerate(df.filter(regex="a").groupby(by=df.b)): sns.boxplot(grp[1], ax=ax[i]) 

it gives :

Note that df.filter(regex="a") is equivalent to df[['a1','a2', 'a3', 'a4']]

 a1 a2 a3 a4 0 2 4 5 6 1 4 5 6 7 2 5 4 5 5 3 10 4 7 8 4 9 3 4 6 5 3 3 4 4 

Hope this helps

It isn't really any better than the answer you linked, but I think the way to achieve this in seaborn is using the FacetGrid feature, as the groupby parameter is only defined for Series passed to the boxplot function.

Here's some code - the pd.melt is necessary because (as best I can tell) the facet mapping can only take individual columns as parameters, so the data need to be turned into a 'long' format.

g = sns.FacetGrid(pd.melt(df, id_vars='b'), col='b') g.map(sns.boxplot, 'value', 'variable') 

It's not adding a lot to this conversation, but after struggling with this for longer than warranted (the actual clusters are unusable), I thought I would add my implementation as another example. It's got a superimposed scatterplot (because of how annoying my dataset is), shows melt using indices, and some aesthetic tweaks. I hope this is useful for someone.

output_graph

Here it is without using column headers (I saw a different thread that wanted to know how to do this using indices):

combined_array: ndarray = np.concatenate([dbscan_output.data, dbscan_output.labels.reshape(-1, 1)], axis=1) cluster_data_df: DataFrame = DataFrame(combined_array) if you want to use labelled columns: column_names: List[str] = list(outcome_variable_names) column_names.append('cluster') cluster_data_df.set_axis(column_names, axis='columns', inplace=True) graph_data: DataFrame = pd.melt( frame=cluster_data_df, id_vars=['cluster'], # value_vars is an optional param - by default it uses columns except the id vars, but I've included it as an example # value_vars=['outcome_var_1', 'outcome_var_2', 'outcome_var_3', 'outcome_var_4', 'outcome_var_5', 'outcome_var_6'] var_name='psychometric_test', value_name='standard deviations from the mean' ) 

The resulting dataframe (rows = sample_n x variable_n (in my case 1626 x 6 = 9756)):

If you want to use indices with melt:

graph_data: DataFrame = pd.melt( frame=cluster_data_df, id_vars=cluster_data_df.columns[-1], # value_vars=cluster_data_df.columns[:-1], var_name='psychometric_test', value_name='standard deviations from the mean' ) 

And here's the graphing code: (Done with column headings - just note that y-axis=value_name, x-axis = var_name, hue = id_vars):

# plot graph grouped by cluster sns.set_theme(style="ticks") fig = plt.figure(figsize=(10, 10)) fig.set(font_scale=1.2) fig.set_style("white") # create boxplot fig.ax = sns.boxplot(y='standard deviations from the mean', x='psychometric_test', hue='cluster', showfliers=False, data=graph_data) # set box alpha: for patch in fig.ax.artists: r, g, b, a = patch.get_facecolor() patch.set_facecolor((r, g, b, .2)) # create scatterplot fig.ax = sns.stripplot(y='standard deviations from the mean', x='psychometric_test', hue='cluster', data=graph_data, dodge=True, alpha=.25, zorder=1) # customise legend: cluster_n: int = dbscan_output.n_clusters ## create list with legend text i = 0 cluster_info: Dict[int, int] = dbscan_output.cluster_sizes # custom method legend_labels: List[str] = [] while i < cluster_n: label: str = f"cluster {i+1}, n = {cluster_info[i]}" legend_labels.append(label) i += 1 if -1 in cluster_info.keys(): cluster_n += 1 label: str = f"Unclustered, n = {cluster_info[-1]}" legend_labels.insert(0, label) ## fetch existing handles and legends (each tuple will have 2*cluster number -> 1 for each boxplot cluster, 1 for each scatterplot cluster, so I will remove the first half) handles, labels = fig.ax.get_legend_handles_labels() index: int = int(cluster_n*(-1)) labels = legend_labels plt.legend(handles[index:], labels[0:]) plt.xticks(rotation=45) plt.show() asds 

Just a note: Most of my time was spent debugging the melt function. I predominantly got the error "*only integer scalar arrays can be converted to a scalar index with 1D numpy indices array*". My output required me to concatenate my outcome variable value table and the clusters (DBSCAN), and I'd put extra square brackets around the cluster array in the concat method. So I had a column where each value was an invisible List[int], rather than a plain int. It's pretty niche, but maybe it'll help someone.

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