How to remove the extra row (or column) after transpose() in Pandas

Question

After using transpose on a dataframe there is always an extra row as a remainder from the initial dataframe's index for example:

import pandas as pd

df = pd.DataFrame({'fruit':['apple','banana'],'number':[3,5]})
df
    fruit  number
0   apple       3
1  banana       5
df.transpose()
        0       1
fruit   apple  banana
number      3       5

Even when i have no index:

df.reset_index(drop = True, inplace = True)
df
    fruit  number
0   apple       3
1  banana       5

df.transpose()
        0       1
fruit   apple  banana
number      3       5

The problem is that when I save the dataframe to a csv file by:

df.to_csv(f)

this extra row stays at the top and I have to remove it manually every time.

Also this doesn't work:

 df.to_csv(f, index = None)

because the old index is no longer considered an index (just another row...).

It also happened when I transposed the other way around and I got an extra column which i could not remove.

Any tips?

Answer 1

I had the same problem, I solved it by reseting index before doing the transpose. I mean df.set_index('fruit').transpose():

import pandas as pd

df = pd.DataFrame({'fruit':['apple','banana'],'number':[3,5]})
df
    fruit   number
0   apple   3
1   banana  5

And df.set_index('fruit').transpose() gives:

fruit   apple   banana
number  3       5

Answer 2

Instead of removing the extra index, why don't try setting the new index that you want and then use slicing ?

step 1: Set the new index you want:
df.columns = df.iloc[0]
step 2: Create a new dataframe removing extra row.
df_new = df[1:]