8000 PERF: SparseDataFrame._init_dict uses intermediary dict, not DataFrame by kernc · Pull Request #16883 · pandas-dev/pandas · GitHub
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PERF: SparseDataFrame._init_dict uses intermediary dict, not DataFrame #16883

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Merged
merged 9 commits into from
Jul 17, 2017
Merged

PERF: SparseDataFrame._init_dict uses intermediary dict, not DataFrame #16883

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e785961
PERF: SparseDataFrame._init_dict uses intermediary dict, not DataFrame
kernc Jul 11, 2017
caf3a36
add whatsnew entry
kernc Jul 11, 2017
31d9b28
fixup! PERF: SparseDataFrame._init_dict uses intermediary dict, not D…
kernc Jul 11, 2017
b55b1a2
fixup! PERF: SparseDataFrame._init_dict uses intermediary dict, not D…
kernc Jul 12, 2017
7053de5
fixup! PERF: SparseDataFrame._init_dict uses intermediary dict, not D…
kernc Jul 13, 2017
83d8140
xfail one more test
kernc Jul 13, 2017
e0b468f
fixup! xfail one more test
kernc Jul 13, 2017
f41b490
add asv benchmarks
kernc Jul 13, 2017
0a98ac9
fixup! add whatsnew entry
kernc Jul 17, 2017
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PERF: SparseDataFrame._init_dict uses intermediary dict, not DataFrame
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@kernc
kernc committed Jul 11, 2017
commit e785961c2d81c8b8224a5784a87339f00f9c0971
25 changes: 16 additions & 9 deletions pandas/core/sparse/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,10 @@

from pandas.core.dtypes.missing import isnull, notnull
from pandas.core.dtypes.cast import maybe_upcast, find_common_type
from pandas.core.dtypes.common import _ensure_platform_int, is_scipy_sparse
from pandas.core.dtypes.common import (
_ensure_platform_int, is_scipy_sparse,
is_float,
)

from pandas.core.common import _try_sort
from pandas.compat.numpy import function as nv
Expand Down Expand Up @@ -143,7 +146,7 @@ def _init_dict(self, data, index, columns, dtype=None):
sp_maker = lambda x: SparseArray(x, kind=self._default_kind,
fill_value=self._default_fill_value,
copy=True, dtype=dtype)
sdict = DataFrame()
sdict = {}
for k, v in compat.iteritems(data):
if isinstance(v, Series):
# Force alignment, no copy necessary
Expand All @@ -159,15 +162,12 @@ def _init_dict(self, data, index, columns, dtype=None):
v = [v.get(i, nan) for i in index]

v = sp_maker(v)
sdict[k] = v
sdict[_nan_to_np_nan(k)] = v

# TODO: figure out how to handle this case, all nan's?
# add in any other columns we want to have (completeness)
nan_vec = np.empty(len(index))
nan_vec.fill(nan)
for c in columns:
if c not in sdict:
sdict[c] = sp_maker(nan_vec)
nan_arr = sp_maker(np.full(len(index), np.nan))
sdict.update((c, nan_arr) for c in columns if c not in sdict)

return to_manager(sdict, columns, index)

Expand Down Expand Up @@ -846,6 +846,13 @@ def applymap(self, func):
return self.apply(lambda x: lmap(func, x))


def _nan_to_np_nan(value):
"""Normalize nan values to singleton np.NaN object so that when NaNs are
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use isnull

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I had wrongly assumed raw numpy was, as is often the case, faster.

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no, why do you need this at all?

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@kernc kernc Jul 12, 2017 •
edited
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Because one of the tests (test_nan_columnname, #8822) apparently sets a float('nan') (is not np.nan) as a valid column name, and {float('nan'): 'foo'}[np.nan] is a KeyError. I didn't know what else to do. DataFrame apparently handles all cases of nan specially; it seemed easiest to force singleton for dict.

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have a look thru how DataFrame handles this in the init_dict routines

don't want to be reinventing the wheel here

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@kernc kernc Jul 12, 2017 •
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how DataFrame handles this

Incorrectly in the sense that:

>>> df = pd.DataFrame({np.nan: [1, 2]})
>>> df[np.nan]   # Arguably expectedly, nan matches nan
0    1
1    2
Name: nan, dtype: int64

>>> df = pd.DataFrame({np.nan: [1, 2], 2: [2, 3]}, columns=[np.nan, 2])
>>> df   # nan from dict didn't match the nan from ensured Float64Index
  NaN    2.0
0  NaN     2
1  NaN     3

Nans are tricky because it generally holds nan != nan. But I guess more often than not, this leads to confusion when nan is expected to be a single, catch-all bin.

How can I improve on the above singleton approach?

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can you point to the test that is failing. I don't want to address this in this PR. This is non-trivial and needs to be common code. ok with xfailing those tests (and making an issue)

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The existing failing test for sparse is pandas.tests.sparse.test_frame.TestSparseDataFrame.test_nan_columnname.
Issue #8822 has some discussion. nan in indexes are supported and have valid uses (e.g. #3729).
So xfailing that test in this PR ...

used as dict keys, getitem works.
"""
return np.nan if is_float(value) and np.isnan(value) else value


def to_manager(sdf, columns, index):
""" create and return the block manager from a dataframe of series,
columns, index
Expand All @@ -855,7 +862,7 @@ def to_manager(sdf, columns, index):
axes = [_ensure_index(columns), _ensure_index(index)]

return create_block_manager_from_arrays(
[sdf[c] for c in columns], columns, axes)
[sdf[_nan_to_np_nan(c)] for c in columns], columns, axes)


def stack_sparse_frame(frame):
Expand Down
0