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REF: Clean up concat statefullness and validation #57933

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merged 4 commits into from
Mar 20, 2024
Merged

REF: Clean up concat statefullness and validation #57933

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9396388
REF: Clean up concat statefullness and validation
mroeschke Mar 19, 2024
45c0fc1
Merge remote-tracking branch 'upstream/main' into ref/concat/validate
mroeschke Mar 20, 2024
3b0c9fb
Use DataFrame again
mroeschke Mar 20, 2024
33610e9
Ignore false positive mypy?
mroeschke Mar 20, 2024
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229 changes: 110 additions & 119 deletions pandas/core/reshape/concat.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,10 +17,7 @@

from pandas.util._decorators import cache_readonly

from pandas.core.dtypes.common import (
is_bool,
is_iterator,
)
from pandas.core.dtypes.common import is_bool
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.generic import (
ABCDataFrame,
Expand Down Expand Up @@ -423,11 +420,12 @@ def __init__(
self.ignore_index = ignore_index
self.verify_integrity = verify_integrity

objs, keys = self._clean_keys_and_objs(objs, keys)
objs, keys, ndims = _clean_keys_and_objs(objs, keys)

# figure out what our result ndim is going to be
ndims = self._get_ndims(objs)
sample, objs = self._get_sample_object(objs, ndims, keys, names, levels)
# select an object to be our result reference
sample, objs = _get_sample_object(
objs, ndims, keys, names, levels, self.intersect
)

# Standardize axis parameter to int
if sample.ndim == 1:
Expand Down Expand Up @@ -458,100 +456,6 @@ def __init__(
self.names = names or getattr(keys, "names", None)
self.levels = levels

def _get_ndims(self, objs: list[Series | DataFrame]) -> set[int]:
# figure out what our result ndim is going to be
ndims = set()
for obj in objs:
if not isinstance(obj, (ABCSeries, ABCDataFrame)):
msg = (
f"cannot concatenate object of type '{type(obj)}'; "
"only Series and DataFrame objs are valid"
)
raise TypeError(msg)

ndims.add(obj.ndim)
return ndims

def _clean_keys_and_objs(
self,
objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
keys,
) -> tuple[list[Series | DataFrame], Index | None]:
if isinstance(objs, abc.Mapping):
if keys is None:
keys = list(objs.keys())
objs_list = [objs[k] for k in keys]
else:
objs_list = list(objs)

if len(objs_list) == 0:
raise ValueError("No objects to concatenate")

if keys is None:
objs_list = list(com.not_none(*objs_list))
else:
# GH#1649
key_indices = []
clean_objs = []
if is_iterator(keys):
keys = list(keys)
if len(keys) != len(objs_list):
# GH#43485
raise ValueError(
f"The length of the keys ({len(keys)}) must match "
f"the length of the objects to concatenate ({len(objs_list)})"
)
for i, obj in enumerate(objs_list):
if obj is not None:
key_indices.append(i)
clean_objs.append(obj)
objs_list = clean_objs

if not isinstance(keys, Index):
keys = Index(keys)

if len(key_indices) < len(keys):
keys = keys.take(key_indices)

if len(objs_list) == 0:
raise ValueError("All objects passed were None")

return objs_list, keys

def _get_sample_object(
self,
objs: list[Series | DataFrame],
ndims: set[int],
keys,
names,
levels,
) -> tuple[Series | DataFrame, list[Series | DataFrame]]:
# get the sample
# want the highest ndim that we have, and must be non-empty
# unless all objs are empty
sample: Series | DataFrame | None = None
if len(ndims) > 1:
max_ndim = max(ndims)
for obj in objs:
if obj.ndim == max_ndim and np.sum(obj.shape):
sample = obj
break

else:
# filter out the empties if we have not multi-index possibilities
# note to keep empty Series as it affect to result columns / name
non_empties = [obj for obj in objs if sum(obj.shape) > 0 or obj.ndim == 1]

if len(non_empties) and (
keys is None and names is None and levels is None and not self.intersect
):
objs = non_empties
sample = objs[0]

if sample is None:
sample = objs[0]
return sample, objs

def _sanitize_mixed_ndim(
self,
objs: list[Series | DataFrame],
Expand Down Expand Up @@ -664,29 +568,24 @@ def get_result(self):
out = sample._constructor_from_mgr(new_data, axes=new_data.axes)
return out.__finalize__(self, method="concat")

def _get_result_dim(self) -> int:
if self._is_series and self.bm_axis == 1:
return 2
else:
return self.objs[0].ndim

@cache_readonly
def new_axes(self) -> list[Index]:
ndim = self._get_result_dim()
if self._is_series and self.bm_axis == 1:
ndim = 2
else:
ndim = self.objs[0].ndim
return [
self._get_concat_axis if i == self.bm_axis else self._get_comb_axis(i)
self._get_concat_axis
if i == self.bm_axis
else get_objs_combined_axis(
self.objs,
axis=self.objs[0]._get_block_manager_axis(i),
intersect=self.intersect,
sort=self.sort,
)
for i in range(ndim)
]

def _get_comb_axis(self, i: AxisInt) -> Index:
data_axis = self.objs[0]._get_block_manager_axis(i)
return get_objs_combined_axis(
self.objs,
axis=data_axis,
intersect=self.intersect,
sort=self.sort,
)

@cache_readonly
def _get_concat_axis(self) -> Index:
"""
Expand Down Expand Up @@ -747,6 +646,98 @@ def _maybe_check_integrity(self, concat_index: Index) -> None:
raise ValueError(f"Indexes have overlapping values: {overlap}")


def _clean_keys_and_objs(
objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
keys,
) -> tuple[list[Series | DataFrame], Index | None, set[int]]:
"""
Returns
-------
clean_objs : list[Series | DataFrame]
LIst of DataFrame and Series with Nones removed.
keys : Index | None
None if keys was None
Index if objs was a Mapping or keys was not None. Filtered where objs was None.
ndim : set[int]
Unique .ndim attribute of obj encountered.
"""
if isinstance(objs, abc.Mapping):
if keys is None:
keys = objs.keys()
objs_list = [objs[k] for k in keys]
else:
objs_list = list(objs)

if len(objs_list) == 0:
raise ValueError("No objects to concatenate")

if keys is not None:
if not isinstance(keys, Index):
keys = Index(keys)
if len(keys) != len(objs_list):
# GH#43485
raise ValueError(
f"The length of the keys ({len(keys)}) must match "
f"the length of the objects to concatenate ({len(objs_list)})"
)

# GH#1649
key_indices = []
clean_objs = []
ndims = set()
for i, obj in enumerate(objs_list):
if obj is None:
continue
elif isinstance(obj, (ABCSeries, ABCDataFrame)):
key_indices.append(i)
clean_objs.append(obj)
ndims.add(obj.ndim)
else:
msg = (
f"cannot concatenate object of type '{type(obj)}'; "
"only Series and DataFrame objs are valid"
)
raise TypeError(msg)

if keys is not None and len(key_indices) < len(keys):
keys = keys.take(key_indices)

if len(clean_objs) == 0:
raise ValueError("All objects passed were None")

return clean_objs, keys, ndims


def _get_sample_object(
objs: list[Series | DataFrame],
ndims: set[int],
keys,
names,
levels,
intersect: bool,
) -> tuple[Series | DataFrame, list[Series | DataFrame]]:
# get the sample
# want the highest ndim that we have, and must be non-empty
# unless all objs are empty
if len(ndims) > 1:
max_ndim = max(ndims)
for obj in objs:
if obj.ndim == max_ndim and sum(obj.shape): # type: ignore[arg-type]
return obj, objs
elif keys is None and names is None and levels is None and not intersect:
# filter out the empties if we have not multi-index possibilities
# note to keep empty Series as it affect to result columns / name
if ndims.pop() == 2:
non_empties = [obj for obj in objs if sum(obj.shape)]
else:
non_empties = objs

if len(non_empties):
return non_empties[0], non_empties

return objs[0], objs


def _concat_indexes(indexes) -> Index:
return indexes[0].append(indexes[1:])

Expand Down