1# setting up a dummy dataframe
2raw_data = {'name': ['Willard Morris', 'Al Jennings', 'Omar Mullins', 'Spencer McDaniel'],
3        'age': [20, 19, 22, 21],
4        'favorite_color': ['blue', 'red', 'yellow', "green"],
5        'grade': [88, 92, 95, 70]}
6df = pd.DataFrame(raw_data, index = ['Willard Morris', 'Al Jennings', 'Omar Mullins', 'Spencer McDaniel'])
7df
8
9#now 'age' will appear at the end of our df
10df = df[['favorite_color','grade','name','age']]
11df.head()

1# Get column list in ['item1','item2','item3'] format 
2df.columns
3# [0]output: 
4Index(['item1','item2','item3'], dtype='object')
5
6# Copy just the list portion of the output and rearrange the columns 
7cols = ['item3','item1','item2']
8
9# Resave dataframe using new column order
10df = df[cols]
11

1In [39]: df
2Out[39]: 
3          0         1         2         3         4  mean
40  0.172742  0.915661  0.043387  0.712833  0.190717     1
51  0.128186  0.424771  0.590779  0.771080  0.617472     1
62  0.125709  0.085894  0.989798  0.829491  0.155563     1
73  0.742578  0.104061  0.299708  0.616751  0.951802     1
84  0.721118  0.528156  0.421360  0.105886  0.322311     1
95  0.900878  0.082047  0.224656  0.195162  0.736652     1
106  0.897832  0.558108  0.318016  0.586563  0.507564     1
117  0.027178  0.375183  0.930248  0.921786  0.337060     1
128  0.763028  0.182905  0.931756  0.110675  0.423398     1
139  0.848996  0.310562  0.140873  0.304561  0.417808     1
14
15In [40]: df = df[['mean', 4,3,2,1]]
16