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Apr 2, 2023
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CoW: Switch to copy=False everywhere for Series constructor #52068

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20f4d0b
CoW: Switch to copy=False everywhere for Series constructor
phofl Mar 18, 2023
ef64f09
Remove
phofl Apr 2, 2023
7baacd4
Merge remote-tracking branch 'upstream/main' into cow_series_copy_fal…
phofl Apr 2, 2023
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6 changes: 3 additions & 3 deletions pandas/core/algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -838,7 +838,7 @@ def value_counts(
if bins is not None:
from pandas.core.reshape.tile import cut

values = Series(values)
values = Series(values, copy=False)
try:
ii = cut(values, bins, include_lowest=True)
except TypeError as err:
Expand All @@ -861,7 +861,7 @@ def value_counts(
else:
if is_extension_array_dtype(values):
# handle Categorical and sparse,
result = Series(values)._values.value_counts(dropna=dropna)
result = Series(values, copy=False)._values.value_counts(dropna=dropna)
result.name = name
result.index.name = index_name
counts = result._values
Expand Down Expand Up @@ -893,7 +893,7 @@ def value_counts(
idx = idx.astype(object)
idx.name = index_name

result = Series(counts, index=idx, name=name)
result = Series(counts, index=idx, name=name, copy=False)

if sort:
result = result.sort_values(ascending=ascending)
Expand Down
2 changes: 1 addition & 1 deletion pandas/core/arrays/_mixins.py
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Expand Up @@ -447,7 +447,7 @@ def value_counts(self, dropna: bool = True) -> Series:

index_arr = self._from_backing_data(np.asarray(result.index._data))
index = Index(index_arr, name=result.index.name)
return Series(result._values, index=index, name=result.name)
return Series(result._values, index=index, name=result.name, copy=False)

def _quantile(
self,
Expand Down
2 changes: 1 addition & 1 deletion pandas/core/arrays/arrow/array.py
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Expand Up @@ -1124,7 +1124,7 @@ def value_counts(self, dropna: bool = True) -> Series:

index = Index(type(self)(values))

return Series(counts, index=index, name="count")
return Series(counts, index=index, name="count", copy=False)

@classmethod
def _concat_same_type(cls, to_concat) -> Self:
Expand Down
12 changes: 8 additions & 4 deletions pandas/core/arrays/categorical.py
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Expand Up @@ -1501,7 +1501,9 @@ def value_counts(self, dropna: bool = True) -> Series:
ix = coerce_indexer_dtype(ix, self.dtype.categories)
ix = self._from_backing_data(ix)

return Series(count, index=CategoricalIndex(ix), dtype="int64", name="count")
return Series(
count, index=CategoricalIndex(ix), dtype="int64", name="count", copy=False
)

# error: Argument 2 of "_empty" is incompatible with supertype
# "NDArrayBackedExtensionArray"; supertype defines the argument type as
Expand Down Expand Up @@ -1759,7 +1761,9 @@ def _values_for_rank(self):
# reorder the categories (so rank can use the float codes)
# instead of passing an object array to rank
values = np.array(
self.rename_categories(Series(self.categories).rank().values)
self.rename_categories(
Series(self.categories, copy=False).rank().values
)
)
return values

Expand Down Expand Up @@ -2504,15 +2508,15 @@ def codes(self) -> Series:
"""
from pandas import Series

return Series(self._parent.codes, index=self._index)
return Series(self._parent.codes, index=self._index, copy=False)
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This should maybe not be a copy=False? (modifying the series should never mutate those codes)

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Good point


def _delegate_method(self, name, *args, **kwargs):
from pandas import Series

method = getattr(self._parent, name)
res = method(*args, **kwargs)
if res is not None:
return Series(res, index=self._index, name=self._name)
return Series(res, index=self._index, name=self._name, copy=False)
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We are sure that none of the methods where this is used give a view? (didn't check in detail where this is used, just wondering)

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I think so, but not 100%. Lets leave this out for now



# utility routines
Expand Down
4 changes: 2 additions & 2 deletions pandas/core/arrays/masked.py
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Original file line number Diff line number Diff line change
Expand Up @@ -995,7 +995,7 @@ def value_counts(self, dropna: bool = True) -> Series:
)

if dropna:
res = Series(value_counts, index=keys, name="count")
res = Series(value_counts, index=keys, name="count", copy=False)
res.index = res.index.astype(self.dtype)
res = res.astype("Int64")
return res
Expand All @@ -1011,7 +1011,7 @@ def value_counts(self, dropna: bool = True) -> Series:
mask = np.zeros(len(counts), dtype="bool")
counts_array = IntegerArray(counts, mask)

return Series(counts_array, index=index, name="count")
return Series(counts_array, index=index, name="count", copy=False)

@doc(ExtensionArray.equals)
def equals(self, other) -> bool:
Expand Down
1 change: 1 addition & 0 deletions pandas/core/arrays/sparse/accessor.py
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Expand Up @@ -219,6 +219,7 @@ def to_dense(self) -> Series:
self._parent.array.to_dense(),
index=self._parent.index,
name=self._parent.name,
copy=False,
)


Expand Down
2 changes: 1 addition & 1 deletion pandas/core/arrays/sparse/array.py
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Expand Up @@ -886,7 +886,7 @@ def value_counts(self, dropna: bool = True) -> Series:
index = Index(keys)
else:
index = keys
return Series(counts, index=index)
return Series(counts, index=index, copy=False)

# --------
# Indexing
Expand Down
2 changes: 1 addition & 1 deletion pandas/core/arrays/sparse/scipy_sparse.py
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Expand Up @@ -195,7 +195,7 @@ def coo_to_sparse_series(
from pandas import SparseDtype

try:
ser = Series(A.data, MultiIndex.from_arrays((A.row, A.col)))
ser = Series(A.data, MultiIndex.from_arrays((A.row, A.col)), copy=False)
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Should this keyword get exposed to the user (and with a default of copy=True) ?

I would say this is a similar case as Series(ndarray) where we decided to change the default to True but give the ability for power uses to specify copy=False

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The astype below creates a copy as far as I understand, so copy=False here should be good.

except AttributeError as err:
raise TypeError(
f"Expected coo_matrix. Got {type(A).__name__} instead."
Expand Down
2 changes: 1 addition & 1 deletion pandas/core/groupby/generic.py
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Expand Up @@ -707,7 +707,7 @@ def value_counts(
llab = lambda lab, inc: lab[inc]
else:
# lab is a Categorical with categories an IntervalIndex
cat_ser = cut(Series(val), bins, include_lowest=True)
cat_ser = cut(Series(val, copy=False), bins, include_lowest=True)
cat_obj = cast("Categorical", cat_ser._values)
lev = cat_obj.categories
lab = lev.take(
Expand Down
2 changes: 1 addition & 1 deletion pandas/core/groupby/groupby.py
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Expand Up @@ -1486,7 +1486,7 @@ def _agg_py_fallback(

if values.ndim == 1:
# For DataFrameGroupBy we only get here with ExtensionArray
ser = Series(values)
ser = Series(values, copy=False)
else:
# We only get here with values.dtype == object
# TODO: special case not needed with ArrayManager
Expand Down
10 changes: 6 additions & 4 deletions pandas/core/indexes/accessors.py
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Expand Up @@ -102,7 +102,9 @@ def _delegate_property_get(self, name):
else:
index = self._parent.index
# return the result as a Series, which is by definition a copy
result = Series(result, index=index, name=self.name).__finalize__(self._parent)
result = Series(result, index=index, name=self.name, copy=False).__finalize__(
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The comment just above should be updated then. But also wondering if we have properties that can be a view, in which case we shouldn't necessarily do copy=False? (but rather attach a ref to self._parent)

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Looking through the datetimelike properties/methods, I think theoretically things like round/ceil/floor could return a view (if nothing needs to be rounded), but that's not an optimization we currently have I think.
Also tz_convert/tz_localize could theoretically return a view (but in practice also do not do that, from a quick test)

In [41]: s = pd.Series(pd.date_range("2012-01-01", periods=3, tz="UTC"))

In [43]: s._values._ndarray
Out[43]: 
array(['2012-01-01T00:00:00.000000000', '2012-01-02T00:00:00.000000000',
       '2012-01-03T00:00:00.000000000'], dtype='datetime64[ns]')

In [44]: s2 = s.dt.tz_convert("Europe/Brussels")

In [45]: np.shares_memory(s._values._ndarray, s2._values._ndarray)
Out[45]: False

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Actually they return views if it's a no-op. So will remove the copy=False for now

self._parent
)

# setting this object will show a SettingWithCopyWarning/Error
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Sidenote: seeing this here, this might also be a case to look into if we can raise a "chained assignment" error

result._is_copy = (
Expand Down Expand Up @@ -130,9 +132,9 @@ def _delegate_method(self, name, *args, **kwargs):
if not is_list_like(result):
return result

result = Series(result, index=self._parent.index, name=self.name).__finalize__(
self._parent
)
result = Series(
result, index=self._parent.index, name=self.name, copy=False
).__finalize__(self._parent)

# setting this object will show a SettingWithCopyWarning/Error
result._is_copy = (
Expand Down
4 changes: 2 additions & 2 deletions pandas/core/reshape/encoding.py
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Expand Up @@ -239,7 +239,7 @@ def _get_dummies_1d(
from pandas.core.reshape.concat import concat

# Series avoids inconsistent NaN handling
codes, levels = factorize_from_iterable(Series(data))
codes, levels = factorize_from_iterable(Series(data, copy=False))

if dtype is None:
dtype = np.dtype(bool)
Expand Down Expand Up @@ -313,7 +313,7 @@ def get_empty_frame(data) -> DataFrame:
fill_value=fill_value,
dtype=dtype,
)
sparse_series.append(Series(data=sarr, index=index, name=col))
sparse_series.append(Series(data=sarr, index=index, name=col, copy=False))

return concat(sparse_series, axis=1, copy=False)

Expand Down
4 changes: 2 additions & 2 deletions pandas/core/strings/accessor.py
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Expand Up @@ -379,7 +379,7 @@ def _get_series_list(self, others):
if isinstance(others, ABCSeries):
return [others]
elif isinstance(others, ABCIndex):
return [Series(others._values, index=idx, dtype=others.dtype)]
return [Series(others, index=idx, dtype=others.dtype)]
elif isinstance(others, ABCDataFrame):
return [others[x] for x in others]
elif isinstance(others, np.ndarray) and others.ndim == 2:
Expand Down Expand Up @@ -634,7 +634,7 @@ def cat(
else:
dtype = self._orig.dtype
res_ser = Series(
result, dtype=dtype, index=data.index, name=self._orig.name
result, dtype=dtype, index=data.index, name=self._orig.name, copy=False
)
out = res_ser.__finalize__(self._orig, method="str_cat")
return out
Expand Down
2 changes: 1 addition & 1 deletion pandas/core/strings/object_array.py
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Expand Up @@ -377,7 +377,7 @@ def _str_get_dummies(self, sep: str = "|"):
arr = sep + arr.astype(str) + sep

tags: set[str] = set()
for ts in Series(arr).str.split(sep):
for ts in Series(arr, copy=False).str.split(sep):
tags.update(ts)
tags2 = sorted(tags - {""})

Expand Down
2 changes: 1 addition & 1 deletion pandas/core/tools/datetimes.py
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Expand Up @@ -247,7 +247,7 @@ def _maybe_cache(
cache_dates = convert_listlike(unique_dates, format)
# GH#45319
try:
cache_array = Series(cache_dates, index=unique_dates)
cache_array = Series(cache_dates, index=unique_dates, copy=False)
except OutOfBoundsDatetime:
return cache_array
# GH#39882 and GH#35888 in case of None and NaT we get duplicates
Expand Down
4 changes: 2 additions & 2 deletions pandas/core/window/ewm.py
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Expand Up @@ -733,7 +733,7 @@ def cov_func(x, y):
self.ignore_na,
bias,
)
return Series(result, index=x.index, name=x.name)
return Series(result, index=x.index, name=x.name, copy=False)

return self._apply_pairwise(
self._selected_obj, other, pairwise, cov_func, numeric_only
Expand Down Expand Up @@ -810,7 +810,7 @@ def _cov(X, Y):
x_var = _cov(x_array, x_array)
y_var = _cov(y_array, y_array)
result = cov / zsqrt(x_var * y_var)
return Series(result, index=x.index, name=x.name)
return Series(result, index=x.index, name=x.name, copy=False)

return self._apply_pairwise(
self._selected_obj, other, pairwise, cov_func, numeric_only
Expand Down
8 changes: 4 additions & 4 deletions pandas/core/window/rolling.py
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Expand Up @@ -384,7 +384,7 @@ def _insert_on_column(self, result: DataFrame, obj: DataFrame) -> None:

if self.on is not None and not self._on.equals(obj.index):
name = self._on.name
extra_col = Series(self._on, index=self.obj.index, name=name)
extra_col = Series(self._on, index=self.obj.index, name=name, copy=False)
if name in result.columns:
# TODO: sure we want to overwrite results?
result[name] = extra_col
Expand Down Expand Up @@ -1418,7 +1418,7 @@ def _generate_cython_apply_func(
def apply_func(values, begin, end, min_periods, raw=raw):
if not raw:
# GH 45912
values = Series(values, index=self._on)
values = Series(values, index=self._on, copy=False)
return window_func(values, begin, end, min_periods)

return apply_func
Expand Down Expand Up @@ -1675,7 +1675,7 @@ def cov_func(x, y):
notna(x_array + y_array).astype(np.float64), start, end, 0
)
result = (mean_x_y - mean_x * mean_y) * (count_x_y / (count_x_y - ddof))
return Series(result, index=x.index, name=x.name)
return Series(result, index=x.index, name=x.name, copy=False)

return self._apply_pairwise(
self._selected_obj, other, pairwise, cov_func, numeric_only
Expand Down Expand Up @@ -1732,7 +1732,7 @@ def corr_func(x, y):
)
denominator = (x_var * y_var) ** 0.5
result = numerator / denominator
return Series(result, index=x.index, name=x.name)
return Series(result, index=x.index, name=x.name, copy=False)

return self._apply_pairwise(
self._selected_obj, other, pairwise, corr_func, numeric_only
Expand Down
4 changes: 2 additions & 2 deletions pandas/io/stata.py
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Expand Up @@ -1989,7 +1989,7 @@ def _do_convert_categoricals(
# TODO: if we get a non-copying rename_categories, use that
cat_data = cat_data.rename_categories(categories)
except ValueError as err:
vc = Series(categories).value_counts()
vc = Series(categories, copy=False).value_counts()
repeated_cats = list(vc.index[vc > 1])
repeats = "-" * 80 + "\n" + "\n".join(repeated_cats)
# GH 25772
Expand All @@ -2006,7 +2006,7 @@ def _do_convert_categoricals(
"""
raise ValueError(msg) from err
# TODO: is the next line needed above in the data(...) method?
cat_series = Series(cat_data, index=data.index)
cat_series = Series(cat_data, index=data.index, copy=False)
cat_converted_data.append((col, cat_series))
else:
cat_converted_data.append((col, data[col]))
Expand Down
2 changes: 1 addition & 1 deletion pandas/plotting/_matplotlib/boxplot.py
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Expand Up @@ -289,7 +289,7 @@ def _grouped_plot_by_column(
ax_values.append(re_plotf)
ax.grid(grid)

result = pd.Series(ax_values, index=columns)
result = pd.Series(ax_values, index=columns, copy=False)

# Return axes in multiplot case, maybe revisit later # 985
if return_type is None:
Expand Down