["python","pandas","chained-assignment"]
Checking whether data frame is copy or view in Pandas

Is there an easy way to check whether two data frames are different copies or views of the same underlying data that doesn't involve manipulations? I'm trying to get a grip on when each is generated, and given how idiosyncratic the rules seem to be, I'd like an easy way to test. For example, I thought "id(df.values)" would be stable across views, but they don't seem to be: # Make two data frames that are views of same data. df = pd.DataFrame([[1,2,3,4],[5,6,7,8]], index = ['row1','row2'], columns = ['a','b','c','d']) df2 = df.iloc[0:2,:] # Demonstrate they are views: df.iloc[0,0] = 99 df2.iloc[0,0] Out[70]: 99 # Now try and compare the id on values attribute # Different despite being views! id(df.values) Out[71]: 4753564496 id(df2.values) Out[72]: 4753603728 # And we can of course compare df and df2 df is df2 Out[73]: False Other answers I've looked up that try to give rules, but don't seem consistent, and also don't answer this question of how to test: What rules does Pandas use to generate a view vs a copy? Pandas: Subindexing dataframes: Copies vs views Understanding pandas dataframe indexing Re-assignment in Pandas: Copy or view? And of course: - http://pandas.pydata.org/pandas-docs/stable/indexing.html#returning-a-view-versus-a-copy UPDATE: Comments below seem to answer the question -- looking at the df.values.base attribute rather than df.values attribute does it, as does a reference to the df._is_copy attribute (though the latter is probably very bad form since it's an internal).
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).