Difference Between Df.reindex() And Df.set_index() Methods In Pandas

I was confused by this, which is very simple but I didn't immediately find the answer on StackOverflow: df.set_index('xcol') makes the column 'xcol' become the index (when it is a

Solution 1:

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')
   ba1324

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
14.02  NaN
# drop('a',1) is just tonot 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

Solution 2:

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)

---

ab013124ba1324ab013124

Solution 3:

Besides great answer from Ben. T, I would like to give one more example of how they are different when you use reindex and set_index to an index column

import pandas as pd
import numpy as np
testdf = pd.DataFrame({'a': [1, 3, 2],'b': [3, 5, 4],'c': [5, 7, 6]})

print(testdf)
print(testdf.set_index(np.random.permutation(testdf.index)))
print(testdf.reindex(np.random.permutation(testdf.index)))

Output:

• With set_index, when index column (the first column) is shuffled, the order of other columns are kept intact
• With reindex, the order of rows are changed accordingly to the shuffle of index column.

ab  c
013513572246ab  c
113523570246ab  c
224613570135