Is there a way to copy only the structure (not the data) of a Pandas DataFrame?

Question

I received a DataFrame from somewhere and want to create another DataFrame with the same number and names of columns and rows (indexes). For example, suppose that the original data frame was created as

import pandas as pd
df1 = pd.DataFrame([[11,12],[21,22]], columns=['c1','c2'], index=['i1','i2'])

I copied the structure by explicitly defining the columns and names:

df2 = pd.DataFrame(columns=df1.columns, index=df1.index)    

I don't want to copy the data, otherwise I could just write df2 = df1.copy(). In other words, after df2 being created it must contain only NaN elements:

In [1]: df1
Out[1]: 
    c1  c2
i1  11  12
i2  21  22

In [2]: df2
Out[2]: 
     c1   c2
i1  NaN  NaN
i2  NaN  NaN

Is there a more idiomatic way of doing it?

Answer 1

That's a job for reindex_like. Start with the original:

df1 = pd.DataFrame([[11, 12], [21, 22]], columns=['c1', 'c2'], index=['i1', 'i2'])

Construct an empty DataFrame and reindex it like df1:

pd.DataFrame().reindex_like(df1)
Out: 
    c1  c2
i1 NaN NaN
i2 NaN NaN   

Answer 2

In version 0.18 of pandas, the DataFrame constructor has no options for creating a dataframe like another dataframe with NaN instead of the values.

The code you use df2 = pd.DataFrame(columns=df1.columns, index=df1.index) is the most logical way, the only way to improve on it is to spell out even more what you are doing is to add data=None, so that other coders directly see that you intentionally leave out the data from this new DataFrame you are creating.

TLDR: So my suggestion is:

Explicit is better than implicit

df2 = pd.DataFrame(data=None, columns=df1.columns, index=df1.index)

Very much like yours, but more spelled out.

Answer 3

Not exactly answering this question, but a similar one for people coming here via a search engine

My case was creating a copy of the data frame without data and without index. One can achieve this by doing the following. This will maintain the dtypes of the columns.

empty_copy = df.drop(df.index)