'Could not interpret input' error with Seaborn when plotting groupbys

The reason for the exception you are getting is that Program becomes an index of the dataframes df_mean and df_count after your group_by operation.

If you wanted to get the factorplot from df_mean, an easy solution is to add the index as a column,

In [7]:df_mean['Program'] = df_mean.indexIn [8]:%matplotlib inlineimport seaborn as snssns.factorplot(x='Program', y='Value', data=df_mean)

However you could even more simply let factorplot do the calculations for you,

sns.factorplot(x='Program', y='Value', data=df)

You'll obtain the same result.Hope it helps.

EDIT after comments

Indeed you make a very good point about the parameter as_index; by default it is set to True, and in that case Program becomes part of the index, as in your question.

In [14]:df_mean = df.groupby('Program', as_index=True).mean().sort(['Value'], ascending=False)[['Value']]df_meanOut[14]:        ValueProgram prog3   45prog2   40prog1   20

Just to be clear, this way Program is not column anymore, but it becomes the index. the trick df_mean['Program'] = df_mean.index actually keeps the index as it is, and adds a new column for the index, so that Program is duplicated now.

In [15]:df_mean['Program'] = df_mean.indexdf_meanOut[15]:        Value   ProgramProgram     prog3   45  prog3prog2   40  prog2prog1   20  prog1

However, if you set as_index to False, you get Program as a column, plus a new autoincrement index,

In [16]:df_mean = df.groupby('Program', as_index=False).mean().sort(['Value'], ascending=False)[['Program', 'Value']]df_meanOut[16]:    Program Value2   prog3   451   prog2   400   prog1   20

This way you could feed it directly to seaborn. Still, you could use df and get the same result.

Hope it helps.