pandas how to swap or reorder columns

Question

I know that there are ways to swap the column order in python pandas. Let say I have this example dataset:

import pandas as pd    
employee = {'EmployeeID' : [0,1,2],
     'FirstName' : ['a','b','c'],
     'LastName' : ['a','b','c'],
     'MiddleName' : ['a','b', None],
     'Contact' : ['(M) 133-245-3123', '(F)[email protected]', '(F)312-533-2442 [email protected]']}

df = pd.DataFrame(employee)

The one basic way to do would be:

neworder = ['EmployeeID','FirstName','MiddleName','LastName','Contact']
df=df.reindex(columns=neworder)

However, as you can see, I only want to swap two columns. It was doable just because there are only 4 column, but what if I have like 100 columns? what would be an effective way to swap or reorder columns?

There might be 2 cases:

1. when you just want 2 columns swapped.
2. when you want 3 columns reordered. (I am pretty sure that this case can be applied to more than 3 columns.)

Solution 1

Two column Swapping

cols = list(df.columns)
a, b = cols.index('LastName'), cols.index('MiddleName')
cols[b], cols[a] = cols[a], cols[b]
df = df[cols]

Reorder column Swapping (2 swaps)

cols = list(df.columns)
a, b, c, d = cols.index('LastName'), cols.index('MiddleName'), cols.index('Contact'), cols.index('EmployeeID')
cols[a], cols[b], cols[c], cols[d] = cols[b], cols[a], cols[d], cols[c]
df = df[cols]

Swapping Multiple

Now it comes down to how you can play with list slices -

cols = list(df.columns)
cols = cols[1::2] + cols[::2]
df = df[cols]

Solution 2

Say your current order of column is [b,c,d,a] and you want to order it into [a,b,c,d], you could do it this way:

new_df = old_df[['a', 'b', 'c', 'd']]

Solution 3

When faced with same problem at larger scale, I came across a very elegant solution at this link: http://www.datasciencemadesimple.com/re-arrange-or-re-order-the-column-of-dataframe-in-pandas-python-2/ under the heading "Rearrange the column of dataframe by column position in pandas python".

Basically if you have the column order as a list, you can read that in as the new column order.

##### Rearrange the column of dataframe by column position in pandas python

df2=df1[df1.columns[[3,2,1,0]]]
print(df2)

In my case, I had a pre-calculated column linkage that determined the new order I wanted. If this order was defined as an array in L, then:

a_L_order = a[a.columns[L]]

Solution 4

If you want to have a fixed list of columns at the beginning, you could do something like

cols = ['EmployeeID','FirstName','MiddleName','LastName']
df = df[cols + [c for c in df.columns if c not in cols]]

This will put these 4 columns first and leave the rest untouched (without any duplicate column).

View more solutions

Comments

[naccode]

Can this be achieved 'inplace'?

[RWL01]

This definitely wins for simplicity.

[Jeremy K.]

For any R users looking for the equivalent of tidyverse's everything() command, this is what you're looking for.

[MikeB2019x]

You don't need to create a new dataframe, you can just assign: old_df = old_df[['a', 'b', 'c', 'd']].