Difference between df.reindex() and df.set_index() methods in pandas

Just to add, the undo to set_index would be reset_index method (more or less):

df = pd.DataFrame({'a': [1, 2],'b': [3, 4]})
print (df)

df.set_index('a', inplace=True)
print(df)

df.reset_index(inplace=True, drop=False)
print(df)

---

   a  b
0  1  3
1  2  4
   b
a   
1  3
2  4
   a  b
0  1  3
1  2  4

You can see the difference on a simple example. Let's consider this dataframe:

df = pd.DataFrame({'a': [1, 2],'b': [3, 4]})
print (df)
   a  b
0  1  3
1  2  4

Indexes are then 0 and 1

If you use set_index with the column 'a' then the indexes are 1 and 2. If you do df.set_index('a').loc[1,'b'], you will get 3.

Now if you want to use reindex with the same indexes 1 and 2 such as df.reindex([1,2]), you will get 4.0 when you do df.reindex([1,2]).loc[1,'b']

What happend is that set_index has replaced the previous indexes (0,1) with (1,2) (values from column 'a') without touching the order of values in the column 'b'

df.set_index('a')
   b
a   
1  3
2  4

while reindex change the indexes but keeps the values in column 'b' associated to the indexes in the original df

df.reindex(df.a.values).drop('a',1) # equivalent to df.reindex(df.a.values).drop('a',1)
     b
1  4.0
2  NaN
# drop('a',1) is just to not care about column a in my example

Finally, reindex change the order of indexes without changing the values of the row associated to each index, while set_index will change the indexes with the values of a column, without touching the order of the other values in the dataframe