Change Order Of Columns In Pandas Dataframes In A Loop

I have a number of pandas.Dataframe objects and want to reorder the columns of all of them in a for loop, but it's not working. What I have is: import numpy as np import pandas as

Solution 1:

I've spent a while on it, it actually gave me a nice puzzle. It works this way, because in your first loop you modify the existing objects, but in the second loop you actually create new objects and overwrite the old ones; by that the list dfs loses its references to df1 and df2. If you want the code to work in the way that after second loop you'd like to see the changes applied to df1 and df2, you can only use methods, that operate on the original dataframe and do not require overwriting. I'm not convinced that my way is the optimal one, but that's what I mean:

import numpy as np
import pandas as pd

df1 = pd.DataFrame(np.random.rand(5, 5))
df2 = pd.DataFrame(np.random.rand(5, 5))

dfs = [ df1, df2 ]

fordfin dfs:
    df.columns = [ 'a', 'b', 'c', 'd', 'e' ]

fordfin dfs:
    forcin ['e', 'd', 'c', 'b', 'a']:
        df.insert(df.shape[1],c+'_new',df[c])
    #df.drop(['e', 'd', 'c', 'b', 'a'], axis=1)
    forcin [ 'a', 'b', 'c', 'd', 'e' ]:
        del df[c]
    df.columns = ['e', 'd', 'c', 'b', 'a']

Then calling df1 prints:

           e           d           c           ba00.5508850.8795570.2026260.2188670.26605710.3440120.7670830.1396420.6851410.55938520.2716890.2473220.7496760.9031620.68038930.6436750.3176810.2172230.7761920.66554240.4804410.9818500.5583030.7805690.484447

Solution 2:

Use enumerate and remember to assign back into your list:

for i, dfin enumerate(dfs):
    dfs[i] = df[['e', 'd', 'c', 'b', 'a']]