0

week and month are both dataframes, filtered from a Master based on based on month and week values like week 18 and "May" I have run pivot_table function on them, after which I want to rename level 1 column names and then drop the level 0 column names. both the dataframes week and month have gone through same operation the statement df = df.droplevel(0,axis=1) does not change the df in anyway, what m I doing wrong

for df in (week, month):
        
df.columns.set_levels(['a','b','c','d'],level=1,inplace=True)
        
df = df.droplevel(0,axis=1)
RJ Adriaansen
  • 9,131
  • 2
  • 12
  • 26
Rebel_09
  • 13
  • 5
  • Could you add a self-contained example that reproduces your problem (see [How to create a Minimal, Reproducible Example](https://stackoverflow.com/help/minimal-reproducible-example) and [How to make good reproducible pandas examples](https://stackoverflow.com/q/20109391/14311263))? – Timus May 08 '22 at 10:57
  • You can't change a dataframe in a for loop like that, as you are actually just changing variable `df` each time, not `week` and `month`: https://stackoverflow.com/questions/19290762/cant-modify-list-elements-in-a-loop/19290848#19290848 There are definitely ways of doing this, but do you need to do this in a loop? – Rawson May 08 '22 at 17:18

1 Answers1

0

You can't change a dataframe in a for loop like that, as you are actually just changing variable df each time, not week and month: see here. There are definitely ways of doing this, but do you need to do this in a loop?

Here is one way of completing this (I have changed df to x, and changed your list of dataframes to list of variable names as strings), by using globals():

for x in ["week", "month"]:
    globals()[x].columns.set_levels(['a','b','c','d'],level=1,inplace=True)
    globals()[x] = globals()[x].droplevel(0,axis=1)
Rawson
  • 2,637
  • 1
  • 5
  • 14