Pandas operates more like data.frame in this regard. You can check this using the memory_profiler package; here's an example of its use in the Jupyter notebook:

First define a program that will test this:

%%file df_memprofile.py
import numpy as np
import pandas as pd

def foo():
    x = np.random.rand(1000000, 5)
    y = pd.DataFrame(x, columns=list('abcde'))
    y.rename(columns = {'e': 'f'}, inplace=True)
    return y

Then load the memory profiler and run + profile the function

%load_ext memory_profiler
from df_memprofile import foo
%mprun -f foo foo()

I get the following output:

Filename: /Users/jakevdp/df_memprofile.py

Line #    Mem usage    Increment   Line Contents
================================================
     4     66.1 MiB     66.1 MiB   def foo():
     5    104.2 MiB     38.2 MiB       x = np.random.rand(1000000, 5)
     6    104.4 MiB      0.2 MiB       y = pd.DataFrame(x, columns=list('abcde'))
     7    142.6 MiB     38.2 MiB       y.rename(columns = {'e': 'f'}, inplace=True)
     8    142.6 MiB      0.0 MiB       return y

You can see a couple things:

1. when y is created, it is just a light wrapper around the original array: i.e. no data is copied.

2. When the column in y is renamed, it results in duplication of the entire data array in memory (it's the same 38MB increment as when x is created in the first place).

So, unless I'm missing something, it appears that Pandas operates more like R's dataframes than R's data tables.

Edit: Note that rename() has an argument copy that controls this behavior, and defaults to True. For example, using this:

y.rename(columns = {'e': 'f'}, inplace=True, copy=False)

... results in an inplace operation without copying data.

Alternatively, you can modify the columns attribute directly:

y.columns = ['a', 'b', 'c', 'd', 'f']