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?
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
.copy() method when I am assigning new variables?
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.
.reindex() is the one.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]
df = pd.DataFrame(data = [[1,2],[4,5],[6,7]], index=['a','b','c'], columns = ['A', 'B'])
df
df.reindex(['a','c','b'])
If you have more rows to swap and the above answers (df.reindex or temp variables) might not be practical, in that case, I would recommend df.rename .sort_index instead.
However, please note that your original index will be destroyed using this method.
a = pd.DataFrame(data = [[1,2],[3,4]], index=range(2), columns = ['A', 'B'])
a.rename({0:1,1:0}).sort_index()
# A B
# 0 3 4
# 1 1 2
