I have a multi-indexed DataFrame
with names attached to the column levels. I'd like to be able to easily shuffle the columns around so that they match the order specified by the user. Since this is down the pipeline, I'm not able to use this recommended solution and order them properly at creation time.
I have a data table that looks (something) like
Experiment BASE IWWGCW IWWGDW
Lead Time 24 48 24 48 24 48
2010-11-27 12:00:00 0.997 0.991 0.998 0.990 0.998 0.990
2010-11-28 12:00:00 0.998 0.987 0.997 0.990 0.997 0.990
2010-11-29 12:00:00 0.997 0.992 0.997 0.992 0.997 0.992
2010-11-30 12:00:00 0.997 0.987 0.997 0.987 0.997 0.987
2010-12-01 12:00:00 0.996 0.986 0.996 0.986 0.996 0.986
I want to take in a list like ['IWWGCW', 'IWWGDW', 'BASE']
and reorder this to be:
Experiment IWWGCW IWWGDW BASE
Lead Time 24 48 24 48 24 48
2010-11-27 12:00:00 0.998 0.990 0.998 0.990 0.997 0.991
2010-11-28 12:00:00 0.997 0.990 0.997 0.990 0.998 0.987
2010-11-29 12:00:00 0.997 0.992 0.997 0.992 0.997 0.992
2010-11-30 12:00:00 0.997 0.987 0.997 0.987 0.997 0.987
2010-12-01 12:00:00 0.996 0.986 0.996 0.986 0.996 0.986
with the caveat that I don't always know at what level "Experiment" will be. I tried (where df
is the multi-indexed frame shown above)
df2 = df.reindex_axis(['IWWGCW', 'IWWGDW', 'BASE'], axis=1, level='Experiment')
but that didn't seem to work - it completed successfully, but the DataFrame that was returned had its column order unchanged.
My workaround is to have a function like:
def reorder_columns(frame, column_name, new_order):
"""Shuffle the specified columns of the frame to match new_order."""
index_level = frame.columns.names.index(column_name)
new_position = lambda t: new_order.index(t[index_level])
new_index = sorted(frame.columns, key=new_position)
new_frame = frame.reindex_axis(new_index, axis=1)
return new_frame
where reorder_columns(df, 'Experiment', ['IWWGCW', 'IWWGDW', 'BASE'])
does what I expect but it feels like I'm doing extra work. Is there an easier way to do this?
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