STY: Use strict arg in zip() in pandas/core/reshape

pandas/core/reshape/concat.py

@@ -840,7 +840,7 @@ def _make_concat_multiindex(indexes, keys, levels=None, names=None) -> MultiInde
  if (levels is None and isinstance(keys[0], tuple)) or (
  levels is not None and len(levels) > 1
  ):
- zipped = list(zip(*keys))
+ zipped = list(zip(*keys, strict=True))
  if names is None:
  names = [None] * len(zipped)
 
@@ -866,13 +866,13 @@ def _make_concat_multiindex(indexes, keys, levels=None, names=None) -> MultiInde
  # things are potentially different sizes, so compute the exact codes
  # for each level and pass those to MultiIndex.from_arrays
 
- for hlevel, level in zip(zipped, levels):
+ for hlevel, level in zip(zipped, levels, strict=True):
  to_concat = []
  if isinstance(hlevel, Index) and hlevel.equals(level):
  lens = [len(idx) for idx in indexes]
  codes_list.append(np.repeat(np.arange(len(hlevel)), lens))
  else:
- for key, index in zip(hlevel, indexes):
+ for key, index in zip(hlevel, indexes, strict=True):
  # Find matching codes, include matching nan values as equal.
  mask = (isna(level) & isna(key)) | (level == key)
  if not mask.any():
@@ -922,7 +922,7 @@ def _make_concat_multiindex(indexes, keys, levels=None, names=None) -> MultiInde
 
  # do something a bit more speedy
 
- for hlevel, level in zip(zipped, levels):
+ for hlevel, level in zip(zipped, levels, strict=True):
  hlevel_index = ensure_index(hlevel)
  mapped = level.get_indexer(hlevel_index)
 

pandas/core/reshape/encoding.py

@@ -209,7 +209,9 @@ def check_len(item, name: str) -> None:
  # columns to prepend to result.
  with_dummies = [data.select_dtypes(exclude=dtypes_to_encode)]
 
- for col, pre, sep in zip(data_to_encode.items(), prefix, prefix_sep):
+ for col, pre, sep in zip(
+ data_to_encode.items(), prefix, prefix_sep, strict=False
+ ):
  # col is (column_name, column), use just column data here
  dummy = _get_dummies_1d(
  col[1],
@@ -323,15 +325,15 @@ def get_empty_frame(data) -> DataFrame:
  codes = codes[mask]
  n_idx = np.arange(N)[mask]
 
- for ndx, code in zip(n_idx, codes):
+ for ndx, code in zip(n_idx, codes, strict=True):
  sp_indices[code].append(ndx)
 
  if drop_first:
  # remove first categorical level to avoid perfect collinearity
  # GH12042
  sp_indices = sp_indices[1:]
  dummy_cols = dummy_cols[1:]
- for col, ixs in zip(dummy_cols, sp_indices):
+ for col, ixs in zip(dummy_cols, sp_indices, strict=True):
  sarr = SparseArray(
  np.ones(len(ixs), dtype=dtype),
  sparse_index=IntIndex(N, ixs),
@@ -535,7 +537,11 @@ def from_dummies(
  raise ValueError(len_msg)
  elif isinstance(default_category, Hashable):
  default_category = dict(
- zip(variables_slice, [default_category] * len(variables_slice))
+ zip(
+ variables_slice,
+ [default_category] * len(variables_slice),
+ strict=True,
+ )
  )
  else:
  raise TypeError(

pandas/core/reshape/melt.py

@@ -199,7 +199,7 @@ def melt(
  missing = idx == -1
  if missing.any():
  missing_labels = [
- lab for lab, not_found in zip(labels, missing) if not_found
+ lab for lab, not_found in zip(labels, missing, strict=True) if not_found
  ]
  raise KeyError(
  "The following id_vars or value_vars are not present in "

pandas/core/reshape/merge.py

@@ -1242,7 +1242,7 @@ def _maybe_restore_index_levels(self, result: DataFrame) -> None:
  """
  names_to_restore = []
  for name, left_key, right_key in zip(
- self.join_names, self.left_on, self.right_on
+ self.join_names, self.left_on, self.right_on, strict=True
  ):
  if (
  # Argument 1 to "_is_level_reference" of "NDFrame" has incompatible
@@ -1275,7 +1275,7 @@ def _maybe_add_join_keys(
 
  assert all(isinstance(x, _known) for x in self.left_join_keys)
 
- keys = zip(self.join_names, self.left_on, self.right_on)
+ keys = zip(self.join_names, self.left_on, self.right_on, strict=True)
  for i, (name, lname, rname) in enumerate(keys):
  if not _should_fill(lname, rname):
  continue
@@ -1584,7 +1584,7 @@ def _get_merge_keys(
 
  # ugh, spaghetti re #733
  if _any(self.left_on) and _any(self.right_on):
- for lk, rk in zip(self.left_on, self.right_on):
+ for lk, rk in zip(self.left_on, self.right_on, strict=True):
  lk = extract_array(lk, extract_numpy=True)
  rk = extract_array(rk, extract_numpy=True)
  if is_lkey(lk):
@@ -1647,7 +1647,7 @@ def _get_merge_keys(
  right_keys = [
  lev._values.take(lev_codes)
  for lev, lev_codes in zip(
- self.right.index.levels, self.right.index.codes
+ self.right.index.levels, self.right.index.codes, strict=True
  )
  ]
  else:
@@ -1669,7 +1669,7 @@ def _get_merge_keys(
  left_keys = [
  lev._values.take(lev_codes)
  for lev, lev_codes in zip(
- self.left.index.levels, self.left.index.codes
+ self.left.index.levels, self.left.index.codes, strict=True
  )
  ]
  else:
@@ -1686,7 +1686,7 @@ def _maybe_coerce_merge_keys(self) -> None:
  # or if we have object and integer dtypes
 
  for lk, rk, name in zip(
- self.left_join_keys, self.right_join_keys, self.join_names
+ self.left_join_keys, self.right_join_keys, self.join_names, strict=True
  ):
  if (len(lk) and not len(rk)) or (not len(lk) and len(rk)):
  continue
@@ -2058,7 +2058,7 @@ def get_join_indexers(
  _factorize_keys(left_keys[n], right_keys[n], sort=sort)
  for n in range(len(left_keys))
  )
- zipped = zip(*mapped)
+ zipped = zip(*mapped, strict=True)
  llab, rlab, shape = (list(x) for x in zipped)
 
  # get flat i8 keys from label lists
@@ -2443,7 +2443,7 @@ def _check_dtype_match(left: ArrayLike, right: ArrayLike, i: int) -> None:
  raise MergeError(msg)
 
  # validate index types are the same
- for i, (lk, rk) in enumerate(zip(left_join_keys, right_join_keys)):
+ for i, (lk, rk) in enumerate(zip(left_join_keys, right_join_keys, strict=True)):
  _check_dtype_match(lk, rk, i)
 
  if self.left_index:
@@ -2628,7 +2628,7 @@ def _get_multiindex_indexer(
  _factorize_keys(index.levels[n]._values, join_keys[n], sort=sort)
  for n in range(index.nlevels)
  )
- zipped = zip(*mapped)
+ zipped = zip(*mapped, strict=True)
  rcodes, lcodes, shape = (list(x) for x in zipped)
  if sort:
  rcodes = list(map(np.take, rcodes, index.codes))

pandas/core/reshape/pivot.py

@@ -1098,8 +1098,8 @@ def crosstab(
  from pandas import DataFrame
 
  data = {
- **dict(zip(unique_rownames, index)),
- **dict(zip(unique_colnames, columns)),
+ **dict(zip(unique_rownames, index, strict=True)),
+ **dict(zip(unique_colnames, columns, strict=True)),
  }
  df = DataFrame(data, index=common_idx)
 

pandas/core/reshape/reshape.py

@@ -696,7 +696,9 @@ def stack_factorize(index):
  levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
  )
  else:
- levels, (ilab, clab) = zip(*map(stack_factorize, (frame.index, frame.columns)))
+ levels, (ilab, clab) = zip(
+ *map(stack_factorize, (frame.index, frame.columns)), strict=True
+ )
  codes = ilab.repeat(K), np.tile(clab, N).ravel()
  new_index = MultiIndex(
  levels=levels,
@@ -778,21 +780,21 @@ def _stack_multi_column_index(columns: MultiIndex) -> MultiIndex | Index:
 
  levs = (
  [lev[c] if c >= 0 else None for c in codes]
- for lev, codes in zip(columns.levels[:-1], columns.codes[:-1])
+ for lev, codes in zip(columns.levels[:-1], columns.codes[:-1], strict=False)
  )
 
  # Remove duplicate tuples in the MultiIndex.
- tuples = zip(*levs)
+ tuples = zip(*levs, strict=True)
  unique_tuples = (key for key, _ in itertools.groupby(tuples))
- new_levs = zip(*unique_tuples)
+ new_levs = zip(*unique_tuples, strict=True)
 
  # The dtype of each level must be explicitly set to avoid inferring the wrong type.
  # See GH-36991.
  return MultiIndex.from_arrays(
  [
  # Not all indices can accept None values.
  Index(new_lev, dtype=lev.dtype) if None not in new_lev else new_lev
- for new_lev, lev in zip(new_levs, columns.levels)
+ for new_lev, lev in zip(new_levs, columns.levels, strict=False)
  ],
  names=columns.names[:-1],
  )

pyproject.toml

@@ -453,12 +453,6 @@ exclude = [
 "pandas/core/groupby/grouper.py" = ["B905"]
 "pandas/core/groupby/ops.py" = ["B905"]
 "pandas/core/methods/to_dict.py" = ["B905"]
-"pandas/core/reshape/concat.py" = ["B905"]
-"pandas/core/reshape/encoding.py" = ["B905"]
-"pandas/core/reshape/melt.py" = ["B905"]
-"pandas/core/reshape/merge.py" = ["B905"]
-"pandas/core/reshape/pivot.py" = ["B905"]
-"pandas/core/reshape/reshape.py" = ["B905"]
 "pandas/core/window/rolling.py" = ["B905"]
 "pandas/_testing/asserters.py" = ["B905"]
 "pandas/_testing/_warnings.py" = ["B905"]