Checking whether data frame is copy or view in Pandas

I've elaborated on this example with pandas 1.0.1. There's not only a boolean _is_view attribute, but also _is_copy which can be None or a reference to the original DataFrame:

df = pd.DataFrame([[1,2,3,4],[5,6,7,8]], index = ['row1','row2'], 
        columns = ['a','b','c','d'])
df2 = df.iloc[0:2, :]
df3 = df.loc[df['a'] == 1, :]

# df is neither copy nor view
df._is_view, df._is_copy
Out[1]: (False, None)

# df2 is a view AND a copy
df2._is_view, df2._is_copy
Out[2]: (True, <weakref at 0x00000236635C2228; to 'DataFrame' at 0x00000236635DAA58>)

# df3 is not a view, but a copy
df3._is_view, df3._is_copy
Out[3]: (False, <weakref at 0x00000236635C2228; to 'DataFrame' at 0x00000236635DAA58>)

So checking these two attributes should tell you not only if you're dealing with a view or not, but also if you have a copy or an "original" DataFrame.

See also this thread for a discussion explaining why you can't always predict whether your code will return a view or not.

Answers from HYRY and Marius in comments!

One can check either by:

• testing equivalence of the values.base attribute rather than the values attribute, as in:

  df.values.base is df2.values.base instead of df.values is df2.values.

• or using the (admittedly internal) _is_view attribute (df2._is_view is True).

Thanks everyone!