Here's a comparison of the different methods - sys.getsizeof(df) is simplest.

For this example, df is a dataframe with 814 rows, 11 columns (2 ints, 9 objects) - read from a 427kb shapefile

sys.getsizeof(df)

>>> import sys
>>> sys.getsizeof(df)
(gives results in bytes)
462456

df.memory_usage()

>>> df.memory_usage()
...
(lists each column at 8 bytes/row)

>>> df.memory_usage().sum()
71712
(roughly rows * cols * 8 bytes)

>>> df.memory_usage(deep=True)
(lists each column's full memory usage)

>>> df.memory_usage(deep=True).sum()
(gives results in bytes)
462432

df.info()

Prints dataframe info to stdout. Technically these are kibibytes (KiB), not kilobytes - as the docstring says, "Memory usage is shown in human-readable units (base-2 representation)." So to get bytes would multiply by 1024, e.g. 451.6 KiB = 462,438 bytes.

>>> df.info()
...
memory usage: 70.0+ KB

>>> df.info(memory_usage='deep')
...
memory usage: 451.6 KB