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API / CoW: constructing DataFrame from DataFrame/BlockManager creates lazy copy #51239

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Merged
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Feb 26, 2023
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API / CoW: constructing DataFrame from DataFrame/BlockManager creates lazy copy #51239

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692af75
ENH: Keep track of references in DataFrame constructor for manager of df
phofl Feb 8, 2023
9d8902f
Merge branch 'main' into cons_df
phofl Feb 10, 2023
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Update v2.0.0.rst
phofl Feb 10, 2023
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Merge remote-tracking branch 'upstream/main' into cons_df
phofl Feb 10, 2023
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Adjust whatsnew
phofl Feb 15, 2023
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Adjust whatsnew
phofl Feb 15, 2023
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Merge branch 'main' into cons_df
phofl Feb 15, 2023
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Merge branch 'main' into cons_df
phofl Feb 25, 2023
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4 changes: 4 additions & 0 deletions doc/source/whatsnew/v2.0.0.rst
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Expand Up @@ -246,6 +246,10 @@ Copy-on-Write improvements
a modification to the data happens) when constructing a Series from an existing
Series with the default of ``copy=False`` (:issue:`50471`)

- The :class:`DataFrame` constructor will now create a lazy copy (deferring the copy until
a modification to the data happens) when constructing from an existing
:class:`DataFrame` with the default of ``copy=False`` (:issue:`51239`)

- The :class:`DataFrame` constructor, when constructing a DataFrame from a dictionary
of Series objects and specifying ``copy=False``, will now use a lazy copy
of those Series objects for the columns of the DataFrame (:issue:`50777`)
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2 changes: 2 additions & 0 deletions pandas/core/frame.py
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Expand Up @@ -656,6 +656,8 @@ def __init__(
data = data.copy(deep=False)

if isinstance(data, (BlockManager, ArrayManager)):
if using_copy_on_write():
data = data.copy(deep=False)
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The previous PR only did this for DataFrame, not for BlockManager.

We have many places where we have the pattern of new_data = self._mgr.<something>; self._constructor(new_data). In those cases, I think in theory the manager method should already have taken care of the references, and so an additional shallow copy is not needed.

But, it should also be harmless (except for a bit of overheead), since those intermediate manager / blocks will go out of scope?

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I wanted to do this also for a manager, but this caused all sorts of problems because we kept the Manager alive.

Yeah this is a safety net for something like

new_data = self._mgr

if something:  # False
    ...
return self._constructor(new_data)

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Yes if they are a true intermediate manager they go out of scope immediately. But if we forgot performing a shallow copy for some reason, this catches this

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One more thing here, do you know what is the overhead of this shallow copy? Because our "fastpath" for DataFrame creation goes through here

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Small test (not using this branch):

In [97]: df = pd.DataFrame({'a': [1, 2, 3], 'b': [.1, .2, .3]})

In [98]: mgr = df._mgr

In [100]: %timeit mgr.copy(deep=False)
4.1 µs ± 47.4 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

In [102]: %timeit pd.DataFrame(mgr)
1.47 µs ± 35.7 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

So in relative terms, it's not an insignificant change .. Not fully sure how important this is in real-world cases though.

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Yeah it's not totally cheap, it's from 1 to 3 on my machine.

We could keep it out of here if you prefer and investigate other methods of keeping the reference here?

# first check if a Manager is passed without any other arguments
# -> use fastpath (without checking Manager type)
if index is None and columns is None and dtype is None and not copy:
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19 changes: 19 additions & 0 deletions pandas/tests/copy_view/test_constructors.py
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Expand Up @@ -82,6 +82,25 @@ def test_series_from_series_with_reindex(using_copy_on_write):
assert not result._mgr.blocks[0].refs.has_reference()


@pytest.mark.parametrize("func", [lambda x: x, lambda x: x._mgr])
@pytest.mark.parametrize("columns", [None, ["a"]])
def test_dataframe_constructor_mgr_or_df(using_copy_on_write, columns, func):
df = DataFrame({"a": [1, 2, 3]})
df_orig = df.copy()

new_df = DataFrame(func(df))

assert np.shares_memory(get_array(df, "a"), get_array(new_df, "a"))
new_df.iloc[0] = 100

if using_copy_on_write:
assert not np.shares_memory(get_array(df, "a"), get_array(new_df, "a"))
tm.assert_frame_equal(df, df_orig)
else:
assert np.shares_memory(get_array(df, "a"), get_array(new_df, "a"))
tm.assert_frame_equal(df, new_df)


@pytest.mark.parametrize("dtype", [None, "int64", "Int64"])
@pytest.mark.parametrize("index", [None, [0, 1, 2]])
@pytest.mark.parametrize("columns", [None, ["a", "b"], ["a", "b", "c"]])
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