Merge branch 'issue-60550-fix-v2' of https://github.com/Abhibhav2003/pandas-Abhibhav into issue-60550-fix-v2

Author: Abhibhav2003

doc/source/getting_started/overview.rst

@@ -174,3 +174,4 @@ License
 -------
 
 .. literalinclude:: ../../../LICENSE
+   :language: none

doc/source/whatsnew/v3.0.0.rst

@@ -694,8 +694,10 @@ Interval
 Indexing
 ^^^^^^^^
 - Bug in :meth:`DataFrame.__getitem__` returning modified columns when called with ``slice`` in Python 3.12 (:issue:`57500`)
+- Bug in :meth:`DataFrame.__getitem__` when slicing a :class:`DataFrame` with many rows raised an ``OverflowError`` (:issue:`59531`)
 - Bug in :meth:`DataFrame.from_records` throwing a ``ValueError`` when passed an empty list in ``index`` (:issue:`58594`)
 - Bug in :meth:`DataFrame.loc` with inconsistent behavior of loc-set with 2 given indexes to Series (:issue:`59933`)
+- Bug in :meth:`Index.get_indexer` and similar methods when ``NaN`` is located at or after position 128 (:issue:`58924`)
 - Bug in :meth:`MultiIndex.insert` when a new value inserted to a datetime-like level gets cast to ``NaT`` and fails indexing (:issue:`60388`)
 - Bug in printing :attr:`Index.names` and :attr:`MultiIndex.levels` would not escape single quotes (:issue:`60190`)
 
@@ -712,7 +714,7 @@ MultiIndex
 - :func:`MultiIndex.get_level_values` accessing a :class:`DatetimeIndex` does not carry the frequency attribute along (:issue:`58327`, :issue:`57949`)
 - Bug in :class:`DataFrame` arithmetic operations in case of unaligned MultiIndex columns (:issue:`60498`)
 - Bug in :class:`DataFrame` arithmetic operations with :class:`Series` in case of unaligned MultiIndex (:issue:`61009`)
--
+- Bug in :meth:`MultiIndex.from_tuples` causing wrong output with input of type tuples having NaN values (:issue:`60695`, :issue:`60988`)
 
 I/O
 ^^^
@@ -787,6 +789,7 @@ Reshaping
 - Bug in :meth:`DataFrame.pivot_table` incorrectly subaggregating results when called without an ``index`` argument (:issue:`58722`)
 - Bug in :meth:`DataFrame.stack` with the new implementation where ``ValueError`` is raised when ``level=[]`` (:issue:`60740`)
 - Bug in :meth:`DataFrame.unstack` producing incorrect results when manipulating empty :class:`DataFrame` with an :class:`ExtentionDtype` (:issue:`59123`)
+- Bug in :meth:`concat` where concatenating DataFrame and Series with ``ignore_index = True`` drops the series name (:issue:`60723`, :issue:`56257`)
 
 Sparse
 ^^^^^^

pandas/_libs/hashtable.pxd

@@ -41,7 +41,7 @@ cdef class HashTable:
 
 cdef class UInt64HashTable(HashTable):
     cdef kh_uint64_t *table
-    cdef int64_t na_position
+    cdef Py_ssize_t na_position
     cdef bint uses_mask
 
     cpdef get_item(self, uint64_t val)
@@ -51,7 +51,7 @@ cdef class UInt64HashTable(HashTable):
 
 cdef class Int64HashTable(HashTable):
     cdef kh_int64_t *table
-    cdef int64_t na_position
+    cdef Py_ssize_t na_position
     cdef bint uses_mask
 
     cpdef get_item(self, int64_t val)
@@ -61,7 +61,7 @@ cdef class Int64HashTable(HashTable):
 
 cdef class UInt32HashTable(HashTable):
     cdef kh_uint32_t *table
-    cdef int64_t na_position
+    cdef Py_ssize_t na_position
     cdef bint uses_mask
 
     cpdef get_item(self, uint32_t val)
@@ -71,7 +71,7 @@ cdef class UInt32HashTable(HashTable):
 
 cdef class Int32HashTable(HashTable):
     cdef kh_int32_t *table
-    cdef int64_t na_position
+    cdef Py_ssize_t na_position
     cdef bint uses_mask
 
     cpdef get_item(self, int32_t val)
@@ -81,7 +81,7 @@ cdef class Int32HashTable(HashTable):
 
 cdef class UInt16HashTable(HashTable):
     cdef kh_uint16_t *table
-    cdef int64_t na_position
+    cdef Py_ssize_t na_position
     cdef bint uses_mask
 
     cpdef get_item(self, uint16_t val)
@@ -91,7 +91,7 @@ cdef class UInt16HashTable(HashTable):
 
 cdef class Int16HashTable(HashTable):
     cdef kh_int16_t *table
-    cdef int64_t na_position
+    cdef Py_ssize_t na_position
     cdef bint uses_mask
 
     cpdef get_item(self, int16_t val)
@@ -101,7 +101,7 @@ cdef class Int16HashTable(HashTable):
 
 cdef class UInt8HashTable(HashTable):
     cdef kh_uint8_t *table
-    cdef int64_t na_position
+    cdef Py_ssize_t na_position
     cdef bint uses_mask
 
     cpdef get_item(self, uint8_t val)
@@ -111,7 +111,7 @@ cdef class UInt8HashTable(HashTable):
 
 cdef class Int8HashTable(HashTable):
     cdef kh_int8_t *table
-    cdef int64_t na_position
+    cdef Py_ssize_t na_position
     cdef bint uses_mask
 
     cpdef get_item(self, int8_t val)
@@ -121,7 +121,7 @@ cdef class Int8HashTable(HashTable):
 
 cdef class Float64HashTable(HashTable):
     cdef kh_float64_t *table
-    cdef int64_t na_position
+    cdef Py_ssize_t na_position
     cdef bint uses_mask
 
     cpdef get_item(self, float64_t val)
@@ -131,7 +131,7 @@ cdef class Float64HashTable(HashTable):
 
 cdef class Float32HashTable(HashTable):
     cdef kh_float32_t *table
-    cdef int64_t na_position
+    cdef Py_ssize_t na_position
     cdef bint uses_mask
 
     cpdef get_item(self, float32_t val)
@@ -141,7 +141,7 @@ cdef class Float32HashTable(HashTable):
 
 cdef class Complex64HashTable(HashTable):
     cdef kh_complex64_t *table
-    cdef int64_t na_position
+    cdef Py_ssize_t na_position
     cdef bint uses_mask
 
     cpdef get_item(self, complex64_t val)
@@ -151,7 +151,7 @@ cdef class Complex64HashTable(HashTable):
 
 cdef class Complex128HashTable(HashTable):
     cdef kh_complex128_t *table
-    cdef int64_t na_position
+    cdef Py_ssize_t na_position
     cdef bint uses_mask
 
     cpdef get_item(self, complex128_t val)

pandas/_libs/hashtable_class_helper.pxi.in

@@ -535,7 +535,7 @@ cdef class {{name}}HashTable(HashTable):
             int ret = 0
             {{c_type}} val
             khiter_t k
-            int8_t na_position = self.na_position
+            Py_ssize_t na_position = self.na_position
 
         if self.uses_mask and mask is None:
             raise NotImplementedError  # pragma: no cover
@@ -567,7 +567,7 @@ cdef class {{name}}HashTable(HashTable):
             Int64Vector self_locs = Int64Vector()
             Int64VectorData *l
             Int64VectorData *sl
-            int8_t na_position = self.na_position
+            Py_ssize_t na_position = self.na_position
 
         l = &locs.data
         sl = &self_locs.data
@@ -609,7 +609,7 @@ cdef class {{name}}HashTable(HashTable):
             {{c_type}} val
             khiter_t k
             intp_t[::1] locs = np.empty(n, dtype=np.intp)
-            int8_t na_position = self.na_position
+            Py_ssize_t na_position = self.na_position
 
         if self.uses_mask and mask is None:
             raise NotImplementedError  # pragma: no cover

pandas/_libs/lib.pyx

@@ -502,7 +502,7 @@ def has_only_ints_or_nan(const floating[:] arr) -> bool:
     return True
 
 
-def maybe_indices_to_slice(ndarray[intp_t, ndim=1] indices, int max_len):
+def maybe_indices_to_slice(ndarray[intp_t, ndim=1] indices, intp_t max_len):
     cdef:
         Py_ssize_t i, n = len(indices)
         intp_t k, vstart, vlast, v

pandas/_libs/tslibs/timedeltas.pyx

@@ -1740,7 +1740,8 @@ cdef class _Timedelta(timedelta):
         Format the Timedelta as ISO 8601 Duration.
 
         ``P[n]Y[n]M[n]DT[n]H[n]M[n]S``, where the ``[n]`` s are replaced by the
-        values. See https://en.wikipedia.org/wiki/ISO_8601#Durations.
+        values. See Wikipedia:
+        `ISO 8601 § Durations <https://en.wikipedia.org/wiki/ISO_8601#Durations>`_.
 
         Returns
         -------

pandas/_libs/tslibs/timestamps.pyx

@@ -1309,7 +1309,7 @@ cdef class _Timestamp(ABCTimestamp):
         By default, the fractional part is omitted if self.microsecond == 0
         and self._nanosecond == 0.
 
-        If self.tzinfo is not None, the UTC offset is also attached, giving
+        If self.tzinfo is not None, the UTC offset is also attached,
         giving a full format of 'YYYY-MM-DD HH:MM:SS.mmmmmmnnn+HH:MM'.
 
         Parameters

pandas/core/algorithms.py

@@ -1647,6 +1647,8 @@ def map_array(
         If the function returns a tuple with more than one element
         a MultiIndex will be returned.
     """
+    from pandas import Index
+
     if na_action not in (None, "ignore"):
         msg = f"na_action must either be 'ignore' or None, {na_action} was passed"
         raise ValueError(msg)
@@ -1676,6 +1678,10 @@ def map_array(
 
             if len(mapper) == 0:
                 mapper = Series(mapper, dtype=np.float64)
+            elif isinstance(mapper, dict):
+                mapper = Series(
+                    mapper.values(), index=Index(mapper.keys(), tupleize_cols=False)
+                )
             else:
                 mapper = Series(mapper)
 

pandas/core/frame.py

@@ -10286,7 +10286,9 @@ def apply(
         either the DataFrame's index (``axis=0``) or the DataFrame's columns
         (``axis=1``). By default (``result_type=None``), the final return type
         is inferred from the return type of the applied function. Otherwise,
-        it depends on the `result_type` argument.
+        it depends on the `result_type` argument. The return type of the applied
+        function is inferred based on the first computed result obtained after
+        applying the function to a Series object.
 
         Parameters
         ----------

pandas/core/indexes/multi.py

@@ -9,6 +9,7 @@
     Sequence,
 )
 from functools import wraps
+from itertools import zip_longest
 from sys import getsizeof
 from typing import (
     TYPE_CHECKING,
@@ -588,7 +589,7 @@ def from_tuples(
         elif isinstance(tuples, list):
             arrays = list(lib.to_object_array_tuples(tuples).T)
         else:
-            arrs = zip(*tuples)
+            arrs = zip_longest(*tuples, fillvalue=np.nan)
             arrays = cast(list[Sequence[Hashable]], arrs)
 
         return cls.from_arrays(arrays, sortorder=sortorder, names=names)

pandas/core/methods/describe.py

@@ -345,14 +345,14 @@ def _refine_percentiles(
     percentiles : list-like of numbers, optional
         The percentiles to include in the output.
     """
-    # Fix for issue #60550 :
+
     from pandas import Series
 
     if percentiles is None:
         return np.array([0.25, 0.5, 0.75])
 
-    # Fix for issue #60550 :
-    if isinstance(percentiles, (list, np.ndarray, Series)) and len(percentiles) == 0:
+    # Handling empty list , empty numpy array and empty Series :
+    elif isinstance(percentiles, (list, np.ndarray, Series)) and len(percentiles) == 0:
         return np.array([])
 
     # explicit conversion of `percentiles` to list

pandas/core/reshape/concat.py

@@ -477,18 +477,23 @@ def _sanitize_mixed_ndim(
 
         else:
             name = getattr(obj, "name", None)
+            rename_columns = False
             if ignore_index or name is None:
                 if axis == 1:
                     # doing a row-wise concatenation so need everything
                     # to line up
-                    name = 0
+                    if name is None:
+                        name = 0
+                        rename_columns = True
                 else:
                     # doing a column-wise concatenation so need series
                     # to have unique names
-                    name = current_column
-                    current_column += 1
+                    if name is None:
+                        rename_columns = True
+                        name = current_column
+                        current_column += 1
                 obj = sample._constructor(obj, copy=False)
-                if isinstance(obj, ABCDataFrame):
+                if isinstance(obj, ABCDataFrame) and rename_columns:
                     obj.columns = range(name, name + 1, 1)
             else:
                 obj = sample._constructor({name: obj}, copy=False)

pandas/tests/indexes/multi/test_constructors.py

@@ -410,6 +410,19 @@ def test_from_tuples_with_tuple_label():
     tm.assert_frame_equal(expected, result)
 
 
+@pytest.mark.parametrize(
+    "keys, expected",
+    [
+        ((("l1",), ("l1", "l2")), (("l1", np.nan), ("l1", "l2"))),
+        ((("l1", "l2"), ("l1",)), (("l1", "l2"), ("l1", np.nan))),
+    ],
+)
+def test_from_tuples_with_various_tuple_lengths(keys, expected):
+    # GH 60695
+    idx = MultiIndex.from_tuples(keys)
+    assert tuple(idx) == expected
+
+
 # ----------------------------------------------------------------------------
 # from_product
 # ----------------------------------------------------------------------------

pandas/tests/libs/test_hashtable.py

@@ -149,18 +149,19 @@ def test_map_locations(self, table_type, dtype, writable):
     def test_map_locations_mask(self, table_type, dtype, writable):
         if table_type == ht.PyObjectHashTable:
             pytest.skip("Mask not supported for object")
-        N = 3
+        N = 129  # must be > 128 to test GH#58924
         table = table_type(uses_mask=True)
         keys = (np.arange(N) + N).astype(dtype)
         keys.flags.writeable = writable
-        table.map_locations(keys, np.array([False, False, True]))
+        mask = np.concatenate([np.repeat(False, N - 1), [True]], axis=0)
+        table.map_locations(keys, mask)
         for i in range(N - 1):
             assert table.get_item(keys[i]) == i
 
         with pytest.raises(KeyError, match=re.escape(str(keys[N - 1]))):
             table.get_item(keys[N - 1])
 
-        assert table.get_na() == 2
+        assert table.get_na() == N - 1
 
     def test_lookup(self, table_type, dtype, writable):
         N = 3