Checking whether data frame is copy or view in Pandas
PythonPandasChained AssignmentPython Problem Overview
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:
-
https://stackoverflow.com/questions/23296282/what-rules-does-pandas-use-to-generate-a-view-vs-a-copy
-
https://stackoverflow.com/questions/17960511/pandas-subindexing-dataframes-copies-vs-views
-
https://stackoverflow.com/questions/14192741/understanding-pandas-dataframe-indexing
-
https://stackoverflow.com/questions/22537112/re-assignment-in-pandas-copy-or-view
And of course:
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).
Python Solutions
Solution 1 - Python
Answers from HYRY and Marius in comments!
One can check either by:
-
testing equivalence of the
values.base
attribute rather than thevalues
attribute, as in:df.values.base is df2.values.base
instead ofdf.values is df2.values
. -
or using the (admittedly internal)
_is_view
attribute (df2._is_view
isTrue
).
Thanks everyone!
Solution 2 - Python
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.
Solution 3 - Python
You might trace the memory your pandas/python environment is consuming, and, on the assumption that a copy will utilise more memory than a view, be able to decide one way or another.
I believe there are libraries out there that will present the memory usage within the python environment itself - e.g. Heapy/Guppy.
There ought to be a metric you can apply that takes a baseline picture of memory usage prior to creating the object under inspection, then another picture afterwards. Comparison of the two memory maps (assuming nothing else has been created and we can isolate the change is due to the new object) should provide an idea of whether a view or copy has been produced.
We'd need to get an idea of the different memory profiles of each type of implementation, but some experimentation should yield results.