def __init__(self, index: MultiIndex, level=-1, constructor=None):
if constructor is None:
constructor = DataFrame
self.constructor = constructor
self.index = index.remove_unused_levels()
self.level = self.index._get_level_number(level)
# when index includes `nan`, need to lift levels/strides by 1
self.lift = 1 if -1 in self.index.codes[self.level] else 0
# Note: the "pop" below alters these in-place.
self.new_index_levels = list(self.index.levels)
self.new_index_names = list(self.index.names)
self.removed_name = self.new_index_names.pop(self.level)
self.removed_level = self.new_index_levels.pop(self.level)
self.removed_level_full = index.levels[self.level]
# Bug fix GH 20601
# If the data frame is too big, the number of unique index combination
# will cause int32 overflow on windows environments.
# We want to check and raise an error before this happens
num_rows = np.max([index_level.size for index_level in self.new_index_levels])
num_columns = self.removed_level.size
# GH20601: This forces an overflow if the number of cells is too high.
num_cells = np.multiply(num_rows, num_columns, dtype=np.int32)
if num_rows > 0 and num_columns > 0 and num_cells <= 0:
raise ValueError("Unstacked DataFrame is too big, causing int32 overflow")
self._make_selectors()
def __init__(self, index: MultiIndex, level=-1, constructor=None):
if constructor is None:
constructor = DataFrame
self.constructor = constructor
self.index = index.remove_unused_levels()
self.level = self.index._get_level_number(level)
# when index includes `nan`, need to lift levels/strides by 1
self.lift = 1 if -1 in self.index.codes[self.level] else 0
# Note: the "pop" below alters these in-place.
self.new_index_levels = list(self.index.levels)
self.new_index_names = list(self.index.names)
self.removed_name = self.new_index_names.pop(self.level)
self.removed_level = self.new_index_levels.pop(self.level)
self.removed_level_full = index.levels[self.level]
# Bug fix GH 20601
# If the data frame is too big, the number of unique index combination
# will cause int32 overflow on windows environments.
# We want to check and raise an error before this happens
num_rows = np.max(
[index_level.size for index_level in self.new_index_levels])
num_columns = self.removed_level.size
# GH20601: This forces an overflow if the number of cells is too high.
num_cells = num_rows * num_columns
# GH 26314: Previous ValueError raised was too restrictive for many users.
if num_cells > np.iinfo(np.int32).max:
warnings.warn(
f"The following operation may generate {num_cells} cells "
f"in the resulting pandas object.",
PerformanceWarning,
)
self._make_selectors()
