reverted changes

Author: debnathshoham

pandas/io/parsers/base_parser.py

@@ -532,9 +532,6 @@ def _convert_to_ndarrays(
             conv_f = None if converters is None else converters.get(c, None)
             if isinstance(dtypes, dict):
                 cast_type = dtypes.get(c, None)
-                if cast_type is None and c.split(".")[-1].isnumeric:
-                    orig_c = ".".join(c.split(".")[:-1])
-                    cast_type = dtypes.get(orig_c, None)
             else:
                 # single dtype or None
                 cast_type = dtypes

pandas/io/parsers/python_parser.py

@@ -4,6 +4,7 @@
     abc,
     defaultdict,
 )
+from copy import copy
 import csv
 from io import StringIO
 import re
@@ -25,6 +26,7 @@
 )
 
 from pandas.core.dtypes.common import is_integer
+from pandas.core.dtypes.inference import is_dict_like
 
 from pandas.io.parsers.base_parser import (
     ParserBase,
@@ -80,7 +82,7 @@ def __init__(self, f: FilePathOrBuffer | list, **kwds):
         self.verbose = kwds["verbose"]
         self.converters = kwds["converters"]
 
-        self.dtype = kwds["dtype"]
+        self.dtype = copy(kwds["dtype"])
         self.thousands = kwds["thousands"]
         self.decimal = kwds["decimal"]
 
@@ -416,14 +418,21 @@ def _infer_columns(self):
                     counts: DefaultDict = defaultdict(int)
 
                     for i, col in enumerate(this_columns):
+                        old_col = col
                         cur_count = counts[col]
 
                         if cur_count > 0:
                             while cur_count > 0:
                                 counts[col] = cur_count + 1
                                 col = f"{col}.{cur_count}"
                                 cur_count = counts[col]
-
+                            if (
+                                self.dtype is not None
+                                and is_dict_like(self.dtype)
+                                and self.dtype.get(old_col) is not None
+                                and self.dtype.get(col) is None
+                            ):
+                                self.dtype.update({col: self.dtype.get(old_col)})
                         this_columns[i] = col
                         counts[col] = cur_count + 1
                 elif have_mi_columns: