Swapping rows within the same pandas dataframe

Question

I'm trying to swap the rows within the same DataFrame in pandas.

I've tried running

a = pd.DataFrame(data = [[1,2],[3,4]], index=range(2), columns = ['A', 'B'])
b, c = a.iloc[0], a.iloc[1]
a.iloc[0], a.iloc[1] = c, b

but I just end up with both the rows showing the values for the second row (3,4).

Even the variables b and c are now both assigned to 3 and 4 even though I did not assign them again. Am I doing something wrong?

score 18 · Accepted Answer · answered Oct 23 '17 at 13:52

18

Use a temporary varaible to store the value using .copy(), because you are changing the values while assigning them on chain i.e. Unless you use copy the data will be changed directly.

a = pd.DataFrame(data = [[1,2],[3,4]], index=range(2), columns = ['A', 'B'])
b, c = a.iloc[0], a.iloc[1]


temp = a.iloc[0].copy()
a.iloc[0] = c
a.iloc[1] = temp

Or you can directly use copy like

a = pd.DataFrame(data = [[1,2],[3,4]], index=range(2), columns = ['A', 'B'])
b, c = a.iloc[0].copy(), a.iloc[1].copy()
a.iloc[0],a.iloc[1] = c,b

answered Oct 23 '17 at 13:52

Bharath M Shetty

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Oh yes this solved the problem. Many thanks! I'm still unsure about when values are on a chain. Would you say that it would be safer to just always use the `.copy()` method when I am assigning new variables? – Zac Oct 23 '17 at 14:39
1

Yes by default they hold the reference to main object . Whatever changes you do will reflect on main object. So u need copy. – Bharath M Shetty Oct 23 '17 at 14:44

score 12 · Answer 2 · answered Nov 15 '20 at 06:10

12

The accepted answer does not make changes the index name.

If you only want to alter the order of rows you should use dataframe.reindex(arraylike). Notice that the index has changed.

answered Nov 15 '20 at 06:10

Chuan

429
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Yes, using `.reindex()` is the one. – Dendi Handian Sep 08 '21 at 09:44

score 5 · Answer 3 · edited Apr 26 '21 at 07:24

5

In this way, it can be extrapolated to more complex situations:

a = pd.DataFrame(data = [[1,2],[3,4]], index=range(2), columns = ['A', 'B'])
rows = a.index.to_list()
# Move the last row to the first index
rows = rows[-1:]+rows[:-1]
a=a.loc[rows]

edited Apr 26 '21 at 07:24

Seneo

87
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11

answered Mar 01 '19 at 04:46

Leonardo Nunes

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score 3 · Answer 4 · edited Nov 02 '21 at 14:37

3

df = pd.DataFrame(data = [[1,2],[4,5],[6,7]], index=['a','b','c'], columns = ['A', 'B'])

df

df.reindex(['a','c','b'])

edited Nov 02 '21 at 14:37

Prageeth Jayathissa

1,798
1
10
16

answered Mar 02 '21 at 12:31

Saurabh Kumar

129
1
8

Swapping rows within the same pandas dataframe

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