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Given an existing Dataframe that is indexed.
>>> df = pd.DataFrame(np.random.randn(10, 5),columns=['a', 'b', 'c', 'd', 'e'])
>>> df
a b c d e
0 -0.131666 -0.315019 0.306728 -0.642224 -0.294562
1 0.769310 -1.277065 0.735549 -0.900214 -1.826320
2 -1.561325 -0.155571 0.544697 0.275880 -0.451564
3 0.612561 -0.540457 2.390871 -2.699741 0.534807
4 -1.504476 -2.113726 0.785208 -1.037256 -0.292959
5 0.467429 1.327839 -1.666649 1.144189 0.322896
6 -0.306556 1.668364 0.036508 0.596452 0.066755
7 -1.689779 1.469891 -0.068087 -1.113231 0.382235
8 0.028250 -2.145618 0.555973 -0.473131 -0.638056
9 0.633408 -0.791857 0.933033 1.485575 -0.021429
>>> df.set_index("a")
b c d e
a
-0.131666 -0.315019 0.306728 -0.642224 -0.294562
0.769310 -1.277065 0.735549 -0.900214 -1.826320
-1.561325 -0.155571 0.544697 0.275880 -0.451564
0.612561 -0.540457 2.390871 -2.699741 0.534807
-1.504476 -2.113726 0.785208 -1.037256 -0.292959
0.467429 1.327839 -1.666649 1.144189 0.322896
-0.306556 1.668364 0.036508 0.596452 0.066755
-1.689779 1.469891 -0.068087 -1.113231 0.382235
0.028250 -2.145618 0.555973 -0.473131 -0.638056
0.633408 -0.791857 0.933033 1.485575 -0.021429
How to move the 3rd row to the first row?
That says, expected result:
b c d e
a
-1.561325 -0.155571 0.544697 0.275880 -0.451564
-0.131666 -0.315019 0.306728 -0.642224 -0.294562
0.769310 -1.277065 0.735549 -0.900214 -1.826320
0.612561 -0.540457 2.390871 -2.699741 0.534807
-1.504476 -2.113726 0.785208 -1.037256 -0.292959
0.467429 1.327839 -1.666649 1.144189 0.322896
-0.306556 1.668364 0.036508 0.596452 0.066755
-1.689779 1.469891 -0.068087 -1.113231 0.382235
0.028250 -2.145618 0.555973 -0.473131 -0.638056
0.633408 -0.791857 0.933033 1.485575 -0.021429
Now the original first row should become the second row.
831
shift() If you want to shift your column or subtract the column value with the previous row value from the DataFrame, you can do it by using the shift() function. It consists of a scalar parameter called period, which is responsible for showing the number of shifts to be made over the desired axis.
To move the third row to the first, you can create an index moving the target row to the first element. I use a conditional list comprehension to join by lists. Then, just use iloc to select the desired index rows. if desired, you can also reset your index.
Pandas DataFrame: transpose() functionThe transpose() function is used to transpose index and columns. Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. If True, the underlying data is copied. Otherwise (default), no copy is made if possible.
To move the third row to the first, you can create an index moving the target row to the first element. I use a conditional list comprehension to join by lists.
Then, just use iloc to select the desired index rows.
np.random.seed(0)
df = pd.DataFrame(np.random.randn(5, 3),columns=['a', 'b', 'c'])
>>> df
a b c
0 1.764052 0.400157 0.978738
1 2.240893 1.867558 -0.977278
2 0.950088 -0.151357 -0.103219
3 0.410599 0.144044 1.454274
4 0.761038 0.121675 0.443863
target_row = 2
# Move target row to first element of list.
idx = [target_row] + [i for i in range(len(df)) if i != target_row]
>>> df.iloc[idx]
a b c
2 0.950088 -0.151357 -0.103219
0 1.764052 0.400157 0.978738
1 2.240893 1.867558 -0.977278
3 0.410599 0.144044 1.454274
4 0.761038 0.121675 0.443863
if desired, you can also reset your index.
>>> df.iloc[idx].reset_index(drop=True)
a b c
0 0.950088 -0.151357 -0.103219
1 1.764052 0.400157 0.978738
2 2.240893 1.867558 -0.977278
3 0.410599 0.144044 1.454274
4 0.761038 0.121675 0.443863
Alternatively, you can just reindex the list using idx:
>>> df.reindex(idx)
a b c
2 0.950088 -0.151357 -0.103219
0 1.764052 0.400157 0.978738
1 2.240893 1.867558 -0.977278
3 0.410599 0.144044 1.454274
4 0.761038 0.121675 0.443863
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