Does the order of columns in the DataFrame matter?

[new_to_python]

Thanks. I came across the following example:

# Example 2

In [194]: lefth = pd.DataFrame({'key1': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'], 
     ...:                       'key2': [2000, 2001, 2002, 2001, 2002], 
     ...:                       'data': np.arange(5.)})  
In [196]: lefth                                                                                           
Out[196]: 
     key1  key2  data
0    Ohio  2000   0.0
1    Ohio  2001   1.0
2    Ohio  2002   2.0
3  Nevada  2001   3.0
4  Nevada  2002   4.0

As indicated above, on my machine the columns are listed as key1, key2 and data which seems to be according to the order I entered the columns in the pd.DataFrame command. However, the person who made this example has the columns displayed as data followed by key1 and key2 using the same command. How come? I don't quite remember well but I think somebody mentioned that depending on the version python is used, the columns could be arranged differently. Is this true?

Does that mean it is always better to access the columns by names because the order of columns could be arranged differently for unknown reason and people could obtain different results or even errors when using the index-based access method?