Data formatting for grouped boxplot using seaborn or matplotlib

Question

I have 3 dataframes where column names and number of rows are exactly the same in all 3 data frames. I want to plot all the columns from all three dataframes as a grouped boxplot into one image using seaborn or matplotlib. But I am having difficulties in combining and formating the data so that I can plot them as grouped box plot.

df=

          A         B         C         D  E  F  G         H         I  J
0  0.031810  0.000556  0.007798  0.000741  0  0  0  0.000180  0.002105  0
1  0.028687  0.000571  0.009356  0.000000  0  0  0  0.000183  0.001250  0
2  0.029635  0.001111  0.009121  0.000000  0  0  0  0.000194  0.001111  0
3  0.030579  0.002424  0.007672  0.000000  0  0  0  0.000194  0.001176  0
4  0.028544  0.002667  0.007973  0.000000  0  0  0  0.000179  0.001333  0
5  0.027286  0.003226  0.006881  0.000000  0  0  0  0.000196  0.001111  0
6  0.031597  0.003030  0.006695  0.000000  0  0  0  0.000180  0.002353  0
7  0.034226  0.003030  0.010804  0.000667  0  0  0  0.000179  0.003333  0
8  0.035105  0.002941  0.010176  0.000645  0  0  0  0.000364  0.003529  0
9  0.035171  0.003125  0.012666  0.001250  0  0  0  0.000612  0.005556  0

df1 =

          A         B         C         D  E  F  G         H         I  J
0  0.034898  0.003750  0.014091  0.001290  0  0  0  0.001488  0.005333  0
1  0.042847  0.003243  0.011559  0.000625  0  0  0  0.002272  0.010769  0
2  0.046087  0.005455  0.013101  0.000588  0  0  0  0.002147  0.008750  0
3  0.042719  0.003684  0.010496  0.001333  0  0  0  0.002627  0.004444  0
4  0.042410  0.004211  0.011580  0.000645  0  0  0  0.003007  0.006250  0
5  0.044515  0.003500  0.013990  0.000000  0  0  0  0.003954  0.007000  0
6  0.046062  0.004865  0.013278  0.000714  0  0  0  0.004035  0.011111  0
7  0.043666  0.004444  0.013460  0.000625  0  0  0  0.003826  0.010000  0
8  0.039888  0.006857  0.014351  0.000690  0  0  0  0.004314  0.011474  0
9  0.048203  0.006667  0.016338  0.000741  0  0  0  0.005294  0.013603  0

df3 =

          A         B         C         D  E  F  G         H         I  J
0  0.048576  0.006471  0.020130  0.002667  0  0  0  0.005536  0.015179  0
1  0.056270  0.007179  0.021519  0.001429  0  0  0  0.005524  0.012333  0
2  0.054020  0.008235  0.024464  0.001538  0  0  0  0.005926  0.010445  0
3  0.047297  0.008649  0.026650  0.002198  0  0  0  0.005870  0.010000  0
4  0.049347  0.009412  0.022808  0.002838  0  0  0  0.006541  0.012222  0
5  0.052026  0.010000  0.019935  0.002714  0  0  0  0.005062  0.012222  0
6  0.055124  0.010625  0.022950  0.003499  0  0  0  0.005954  0.008964  0
7  0.044411  0.010909  0.019129  0.005709  0  0  0  0.005209  0.007222  0
8  0.047697  0.010270  0.017234  0.008800  0  0  0  0.004808  0.008355  0
9  0.048562  0.010857  0.020219  0.008504  0  0  0  0.005665  0.004862  0

I can do single boxplots by using the following:

g = sns.boxplot(data=df, color = 'white', fliersize=1, linewidth=2, meanline = True, showmeans=True)

But how to get all three in one figure seems a bit difficult. I see I need to re-arrange the whole data and use hue in order to get every thing from combined data frame, but how exactly should I format the data is a question. Any help?

Quang Hoang · Accepted Answer · 2019-10-25 14:26:08Z

1

You can do all in one sns.boxplot run by concatenate the dataframes and passing hue:

tmp = (pd.concat([d.assign(data=i)                       # assign adds the column `data` with values i
                    for i,d in enumerate([df,df1,df3])]  # enumerate gives you a generator of pairs (0,df), (1,df1), (2,df2)
                )
         .melt(id_vars='data')                           # melt basically turns `id_vars` columns into index, 
                                                         # and stacks other columns
      )

sns.boxplot(data=tmp, x='variable', hue='data', y='value')

Output:

edited Oct 25, 2019 at 14:26

answered Oct 25, 2019 at 13:52

Quang Hoang

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2 Comments

HT121 Over a year ago

Hi Quang, Thanks for your answer and it works like a charm. May I also ask you to add some details on how you are approaching this just to understand your code?

Quang Hoang Over a year ago

@HT121 see update. You can print tmp to see the structure.

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