Create empty Dataframe with same dimensions as another?

Question

I have searched a bit, but can not find a good answer. I want to create an empty dataframe with same dimensions as another dataframe so I can add new columns. Today I create an empty dataframe filled with zeroes, and then I delete the zero column. I hope there is a better way, but can not find the answer. Can someone help me?

I do like this today and it works, but it is very ugly.

df_copy = pandas.DataFrame(numpy.zeros(len(df_original.index)))  df_copy = df_copy.drop([0],axis=1)  

And now I can add new columns as I process data. So basically I want an empty dataframe with same dimensions as another dataframe.

df_copy["price"] = pricesList df_copy["size"] = sizesList 

EDIT: Another closely related question: how do I create an empty Dataframe with dimensions mxn? I have got the answer below how to create an empty dataframe with dimensions 1xn, which is by setting the index. But how do I create an empty nxm dataframe filled with zeroes? The reason I am asking, is because I suspect(?) it is faster to create a zero filled dataframe, and then replace each element as needed. The alternative is to create an empty dataframe with dimensions 1xn and then add columns as needed - which I am told is slow. So it might be faster to create an empty dataframe with nxm dimensions and then replace elements as needed (by copying a list to each column). Say a column has 100 rows, and I create a sublist with 25 rows, so I just copy this list to the correct subcolumn, and repeat. This is faster than adding a new column?

Answer 1

Creating an empty dataframe with the same index and columns as another dataframe:

import pandas as pd df_copy = pd.DataFrame().reindex_like(df_original) 

Answer 2

For anyone coming to this page looking to create a dataframe of same columns, same dtypes, and no rows:

import pandas as pd df_copy = df_original.iloc[:0,:].copy()