I have the following DataFrame (df):
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(10, 5))
Here's a way to move one existing column that will modify the existing data frame in place.
my_column = df.pop('column name')
df.insert(3, my_column.name, my_column)
Just type the column name you want to change, and set the index for the new location.
def change_column_order(df, col_name, index):
    cols = df.columns.tolist()
    cols.remove(col_name)
    cols.insert(index, col_name)
    return df[cols]
For your case, this would be like:
df = change_column_order(df, 'mean', 0)
Just flipping helps often.
df[df.columns[::-1]]
Or just shuffle for a look.
import random
cols = list(df.columns)
random.shuffle(cols)
df[cols]
You could also do something like this:
df = df[['mean', '0', '1', '2', '3']]
You can get the list of columns with:
cols = list(df.columns.values)
The output will produce:
['0', '1', '2', '3', 'mean']
...which is then easy to rearrange manually before dropping it into the first function
The Most simple way
Suppose you have df with columns A B C,
you can just df.reindex(['B','C','A'],axis=1)
You can use reindex which can be used for both axis:
df
#           0         1         2         3         4      mean
# 0  0.943825  0.202490  0.071908  0.452985  0.678397  0.469921
# 1  0.745569  0.103029  0.268984  0.663710  0.037813  0.363821
# 2  0.693016  0.621525  0.031589  0.956703  0.118434  0.484254
# 3  0.284922  0.527293  0.791596  0.243768  0.629102  0.495336
# 4  0.354870  0.113014  0.326395  0.656415  0.172445  0.324628
# 5  0.815584  0.532382  0.195437  0.829670  0.019001  0.478415
# 6  0.944587  0.068690  0.811771  0.006846  0.698785  0.506136
# 7  0.595077  0.437571  0.023520  0.772187  0.862554  0.538182
# 8  0.700771  0.413958  0.097996  0.355228  0.656919  0.444974
# 9  0.263138  0.906283  0.121386  0.624336  0.859904  0.555009
df.reindex(['mean', *range(5)], axis=1)
#        mean         0         1         2         3         4
# 0  0.469921  0.943825  0.202490  0.071908  0.452985  0.678397
# 1  0.363821  0.745569  0.103029  0.268984  0.663710  0.037813
# 2  0.484254  0.693016  0.621525  0.031589  0.956703  0.118434
# 3  0.495336  0.284922  0.527293  0.791596  0.243768  0.629102
# 4  0.324628  0.354870  0.113014  0.326395  0.656415  0.172445
# 5  0.478415  0.815584  0.532382  0.195437  0.829670  0.019001
# 6  0.506136  0.944587  0.068690  0.811771  0.006846  0.698785
# 7  0.538182  0.595077  0.437571  0.023520  0.772187  0.862554
# 8  0.444974  0.700771  0.413958  0.097996  0.355228  0.656919
# 9  0.555009  0.263138  0.906283  0.121386  0.624336  0.859904