BUG: Avoid RangeIndex conversion in read_csv if dtype is specified (p…

…andas-dev#59316)

* BUG: Avoid RangeIndex conversion in read_csv if dtype is specified

* Undo change

* Typing

pandas/io/parsers/base_parser.py

@@ -4,6 +4,7 @@
 from copy import copy
 import csv
 from enum import Enum
+import itertools
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -271,7 +272,7 @@ def _maybe_make_multi_index_columns(
 
     @final
     def _make_index(
-        self, data, alldata, columns, indexnamerow: list[Scalar] | None = None
+        self, alldata, columns, indexnamerow: list[Scalar] | None = None
     ) -> tuple[Index | None, Sequence[Hashable] | MultiIndex]:
         index: Index | None
         if isinstance(self.index_col, list) and len(self.index_col):
@@ -326,7 +327,11 @@ def _agg_index(self, index) -> Index:
         converters = self._clean_mapping(self.converters)
         clean_dtypes = self._clean_mapping(self.dtype)
 
-        for i, arr in enumerate(index):
+        if self.index_names is not None:
+            names: Iterable = self.index_names
+        else:
+            names = itertools.cycle([None])
+        for i, (arr, name) in enumerate(zip(index, names)):
             if self._should_parse_dates(i):
                 arr = date_converter(
                     arr,
@@ -369,12 +374,17 @@ def _agg_index(self, index) -> Index:
             arr, _ = self._infer_types(
                 arr, col_na_values | col_na_fvalues, cast_type is None, try_num_bool
             )
-            arrays.append(arr)
-
-        names = self.index_names
-        index = ensure_index_from_sequences(arrays, names)
+            if cast_type is not None:
+                # Don't perform RangeIndex inference
+                idx = Index(arr, name=name, dtype=cast_type)
+            else:
+                idx = ensure_index_from_sequences([arr], [name])
+            arrays.append(idx)
 
-        return index
+        if len(arrays) == 1:
+            return arrays[0]
+        else:
+            return MultiIndex.from_arrays(arrays)
 
     @final
     def _set_noconvert_dtype_columns(
@@ -704,12 +714,11 @@ def _get_empty_meta(
         dtype_dict: defaultdict[Hashable, Any]
         if not is_dict_like(dtype):
             # if dtype == None, default will be object.
-            default_dtype = dtype or object
-            dtype_dict = defaultdict(lambda: default_dtype)
+            dtype_dict = defaultdict(lambda: dtype)
         else:
             dtype = cast(dict, dtype)
             dtype_dict = defaultdict(
-                lambda: object,
+                lambda: None,
                 {columns[k] if is_integer(k) else k: v for k, v in dtype.items()},
             )
 
@@ -726,8 +735,14 @@ def _get_empty_meta(
         if (index_col is None or index_col is False) or index_names is None:
             index = default_index(0)
         else:
-            data = [Series([], dtype=dtype_dict[name]) for name in index_names]
-            index = ensure_index_from_sequences(data, names=index_names)
+            # TODO: We could return default_index(0) if dtype_dict[name] is None
+            data = [
+                Index([], name=name, dtype=dtype_dict[name]) for name in index_names
+            ]
+            if len(data) == 1:
+                index = data[0]
+            else:
+                index = MultiIndex.from_arrays(data)
             index_col.sort()
 
             for i, n in enumerate(index_col):

pandas/io/parsers/c_parser_wrapper.py

@@ -338,7 +338,7 @@ def read(
             data = {k: v for k, (i, v) in zip(names, data_tups)}
 
             date_data = self._do_date_conversions(names, data)
-            index, column_names = self._make_index(date_data, alldata, names)
+            index, column_names = self._make_index(alldata, names)
 
         return index, column_names, date_data
 

pandas/io/parsers/python_parser.py

@@ -312,9 +312,7 @@ def read(
         conv_data = self._convert_data(data)
         conv_data = self._do_date_conversions(columns, conv_data)
 
-        index, result_columns = self._make_index(
-            conv_data, alldata, columns, indexnamerow
-        )
+        index, result_columns = self._make_index(alldata, columns, indexnamerow)
 
         return index, result_columns, conv_data
 

pandas/tests/io/parser/dtypes/test_dtypes_basic.py

@@ -29,6 +29,8 @@
     "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
 )
 
+xfail_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")
+
 
 @pytest.mark.parametrize("dtype", [str, object])
 @pytest.mark.parametrize("check_orig", [True, False])
@@ -614,6 +616,7 @@ def test_string_inference_object_dtype(all_parsers, dtype):
     tm.assert_frame_equal(result, expected)
 
 
+@xfail_pyarrow
 def test_accurate_parsing_of_large_integers(all_parsers):
     # GH#52505
     data = """SYMBOL,MOMENT,ID,ID_DEAL
@@ -624,7 +627,7 @@ def test_accurate_parsing_of_large_integers(all_parsers):
 AMZN,20230301181139587,2023552585717889759,2023552585717263360
 MSFT,20230301181139587,2023552585717889863,2023552585717263361
 NVDA,20230301181139587,2023552585717889827,2023552585717263361"""
-    orders = pd.read_csv(StringIO(data), dtype={"ID_DEAL": pd.Int64Dtype()})
+    orders = all_parsers.read_csv(StringIO(data), dtype={"ID_DEAL": pd.Int64Dtype()})
     assert len(orders.loc[orders["ID_DEAL"] == 2023552585717263358, "ID_DEAL"]) == 1
     assert len(orders.loc[orders["ID_DEAL"] == 2023552585717263359, "ID_DEAL"]) == 1
     assert len(orders.loc[orders["ID_DEAL"] == 2023552585717263360, "ID_DEAL"]) == 2
@@ -646,3 +649,16 @@ def test_dtypes_with_usecols(all_parsers):
         values = ["1", "4"]
     expected = DataFrame({"a": pd.Series(values, dtype=object), "c": [3, 6]})
     tm.assert_frame_equal(result, expected)
+
+
+def test_index_col_with_dtype_no_rangeindex(all_parsers):
+    data = StringIO("345.5,519.5,0\n519.5,726.5,1")
+    result = all_parsers.read_csv(
+        data,
+        header=None,
+        names=["start", "stop", "bin_id"],
+        dtype={"start": np.float32, "stop": np.float32, "bin_id": np.uint32},
+        index_col="bin_id",
+    ).index
+    expected = pd.Index([0, 1], dtype=np.uint32, name="bin_id")
+    tm.assert_index_equal(result, expected)