diff --git a/asv_bench/benchmarks/arithmetic.py b/asv_bench/benchmarks/arithmetic.py
index 49543c166d047..1bcefb06d6d48 100644
--- a/asv_bench/benchmarks/arithmetic.py
+++ b/asv_bench/benchmarks/arithmetic.py
@@ -3,7 +3,7 @@
 
 import numpy as np
 
-import pandas as pd
+import  pandas as pd
 from pandas import (
     DataFrame,
     Series,
diff --git a/pandas/_config/FirstDate.py b/pandas/_config/FirstDate.py
new file mode 100644
index 0000000000000..ffcc6c8aac1f7
--- /dev/null
+++ b/pandas/_config/FirstDate.py
@@ -0,0 +1,28 @@
+import pandas as pd
+
+
+import pandas as pd
+
+# Create a pandas DataFrame with a date column
+df = pd.DataFrame({'date': ['4/03/2022', '5/03/2022', '6/03/2022'], 'patients': [16, 19, 11]})
+
+# Convert the date column to a pandas `DatetimeIndex` object with the day first
+#df['year'] = pd.to_datetime(df['date'], yearFirst=False)
+df['year'] = pd.to_datetime(df['date'], dayfirst=True)
+
+# Print the DataFrame
+print(df)
+
+# Create a pandas DataFrame with a date column
+df = pd.DataFrame({'date': ['04/03/2004', '03/03/2005', '03/03/2007'], 'patients': [16, 19, 11]})
+
+# Convert the date column to a pandas `DatetimeIndex` object with the day first
+#df['year'] = pd.to_datetime(df['date'], yearFirst=False)
+df['date'] = pd.to_datetime(df['date'], format='%d/%m/%Y').dt.strftime('%d-%m-%Y')
+
+# Print the DataFrame
+  print(df)
+
+
+
+
diff --git a/pandas/_libs/tslibs/offsets.pyx b/pandas/_libs/tslibs/offsets.pyx
index c23bccdea3a8a..6cc7516f98cb4 100644
--- a/pandas/_libs/tslibs/offsets.pyx
+++ b/pandas/_libs/tslibs/offsets.pyx
@@ -2810,6 +2810,17 @@ cdef class QuarterBegin(QuarterOffset):
     _from_name_starting_month = 1
     _prefix = "QS"
     _day_opt = "start"
+   
+    cdef readonly:
+        int _period_dtype_code
+
+    def __init__(self, n=1, normalize=False, StartingMonth=None):
+        # Because QuarterBegin can be the freq for a Period, define its
+        #  _period_dtype_code at construction for performance
+        QuarterOffset.__init__(self, n, normalize, StartingMonth)
+        self._period_dtype_code = PeriodDtypeCode.Q_DEC + self.StartingMonth % 12
+        print(self._period_dtype_code)
+
 
 
 # ----------------------------------------------------------------------
diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index 3b79882d3c762..bacf5ed99c418 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -1015,12 +1015,13 @@ def wrapper(*args, **kwargs):
             # If we made the result 2-D, squeeze it back to 1-D
             result = np.squeeze(result)
         else:
+            #55753 Added the comments for this Fix
             result = np.apply_along_axis(
                 wrap_function(self.func),
                 self.axis,
                 self.values,
-                *self.args,
-                **self.kwargs,
+                *self.args,#Added args
+                **self.kwargs,#Added kwargs
             )
 
         # TODO: mixed type case
diff --git a/pandas/core/arrays/numpy_.py b/pandas/core/arrays/numpy_.py
index efe0c0df45e00..f34b9939c54ba 100644
--- a/pandas/core/arrays/numpy_.py
+++ b/pandas/core/arrays/numpy_.py
@@ -564,3 +564,31 @@ def _wrap_ndarray_result(self, result: np.ndarray):
     # ------------------------------------------------------------------------
     # String methods interface
     _str_na_value = np.nan
+    def _validate_setitem_value(self, value):
+        """
+        Check if we have a scalar that we can cast losslessly.
+
+        Raises
+        ------
+        TypeError
+        """
+        kind = self.dtype.kind
+        # TODO: get this all from np_can_hold_element?
+        if kind == "b":
+            if lib.is_bool(value):
+                return value
+
+        elif kind == "f":
+            if lib.is_integer(value) or lib.is_float(value):
+                return value
+
+        else:
+            if lib.is_integer(value) or (lib.is_float(value) and value.is_integer()):
+                return value
+            # TODO: unsigned checks
+
+        # Note: without the "str" here, the f-string rendering raises in
+        #  py38 builds.
+        if lib.infer_dtype(value)!=self.dtype:
+            raise TypeError(f"Invalid value '{str(value)}' for dtype {self.dtype}")
+
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 716d1a78f93c5..435e23043aac7 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -986,8 +986,9 @@ def convert_dtypes(
     convert_integer: bool = True,
     convert_boolean: bool = True,
     convert_floating: bool = True,
+    convert_pyarrow:bool= True,
     infer_objects: bool = False,
-    dtype_backend: Literal["numpy_nullable", "pyarrow"] = "numpy_nullable",
+    dtype_backend: Literal["numpy_nullable"] = "numpy_nullable",
 ) -> DtypeObj:
     """
     Convert objects to best possible type, and optionally,
@@ -1009,7 +1010,7 @@ def convert_dtypes(
     infer_objects : bool, defaults False
         Whether to also infer objects to float/int if possible. Is only hit if the
         object array contains pd.NA.
-    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+    dtype_backend : {'numpy_nullable'}, default 'numpy_nullable'
         Back-end data type applied to the resultant :class:`DataFrame`
         (still experimental). Behaviour is as follows:
 
diff --git a/pandas/core/util/hashing.py b/pandas/core/util/hashing.py
index 4933de3212581..62cf878c4f975 100644
--- a/pandas/core/util/hashing.py
+++ b/pandas/core/util/hashing.py
@@ -82,7 +82,7 @@ def combine_hash_arrays(
 
 def hash_pandas_object(
     obj: Index | DataFrame | Series,
-    index: bool = True,
+    index: bool = False,
     encoding: str = "utf8",
     hash_key: str | None = _default_hash_key,
     categorize: bool = True,
diff --git a/pandas/io/sql.py b/pandas/io/sql.py
index b4675513a99c2..febea2378f041 100644
--- a/pandas/io/sql.py
+++ b/pandas/io/sql.py
@@ -1543,7 +1543,7 @@ def get_engine(engine: str) -> BaseEngine:
     raise ValueError("engine must be one of 'auto', 'sqlalchemy'")
 
 
-class SQLDatabase(PandasSQL):
+class SQLDatabase(PandasSQL,**connect_kwargs):
     """
     This class enables conversion between DataFrame and SQL databases
     using SQLAlchemy to handle DataBase abstraction.