I have a Dataframe that is composed by 3760 rows. I want to split it in 10 parts of equal lenght and then use each new array as a column for a new DataFrame.
A way that I found to do this is:
alfa = np.array_split(dff, 10)
caa = pd.concat([alfa[0].reset_index(drop=True), alfa[1].reset_index(drop=True), alfa[2].reset_index(drop=True), alfa[3].reset_index(drop=True),
alfa[4].reset_index(drop=True), alfa[5].reset_index(drop=True), alfa[6].reset_index(drop=True), alfa[7].reset_index(drop=True),
alfa[8].reset_index(drop=True), alfa[9].reset_index(drop=True)], axis=1)
Not very cool, not very efficient.
Then I tried
teta = pd.concat(np.array_split(dff, 10), axis=1, ignore_index=True)
But it doesn't work as I wanted since it gives me this:

I assume that is because the ignore_index works on the axis 1
Is there a better way to do it?