non-categorical dataframe to categorical data for seaborn plotting boxplots, swarmplots, stripplots etc

Question

Im running into a confusion with categorical data plots, which is probably because I dont really understand the concept.

I have a dataframe:

      A         B         C
 0  1.438161 -0.210454 -1.983704
 1 -0.283780 -0.371773  0.017580
 2  0.552564 -0.610548  0.257276
 3  1.931332  0.649179 -1.349062
 4  1.656010 -1.373263  1.333079
 5  0.944862 -0.657849  1.526811

which I can easily plot as a boxplot for each column using seaborn:

sns.boxplot(df)

However swarmplots, stripplots dont work, I guess because categorical data is needed?

     value   indx       
   1.438161    A
  -0.283780    A
       ...
   0.552564    B
   1.931332    B
       ...
   1.656010    C
   0.944862    C

Is there a very easy quick way to do this, that Im not aware of?

https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.swarmplot.html

Answer 1

I think you need parameter data:

sns.boxplot(data=df)

Docs:

data : DataFrame, array, or list of arrays, optional

Dataset for plotting. If x and y are absent, this is interpreted as wide-form. Otherwise it is expected to be long-form.

Answer 2

IIUC, you can use melt to convert one of the variables into categorical format to aid in the plotting of swarmplots and stripplots.

In [3]: df_sns = pd.melt(df, value_vars=['A', 'B', 'C'])

In [4]: df_sns
Out[4]: 
   variable     value
0         A  1.438161
1         A -0.283780
2         A  0.552564
3         A  1.931332
4         A  1.656010
5         A  0.944862
6         B -0.210454
7         B -0.371773
8         B -0.610548
9         B  0.649179
10        B -1.373263
11        B -0.657849
12        C -1.983704
13        C  0.017580
14        C  0.257276
15        C -1.349062
16        C  1.333079
17        C  1.526811

In [5]: sns.swarmplot(x='variable', y='value', data=df_sns)
Out[5]: <matplotlib.axes._subplots.AxesSubplot at 0x268db2a6e10>