diff --git a/src/datasets/packaged_modules/json/json.py b/src/datasets/packaged_modules/json/json.py
index 426083fc718..fdab2241326 100644
--- a/src/datasets/packaged_modules/json/json.py
+++ b/src/datasets/packaged_modules/json/json.py
@@ -95,6 +95,7 @@ def _cast_table(self, pa_table: pa.Table) -> pa.Table:
             pa_table = table_cast(pa_table, self.config.features.arrow_schema)
         return pa_table
 
+    
     def _generate_tables(self, files):
         for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
             # If the file is one json object and if we need to look at the items in one specific field
@@ -113,8 +114,6 @@ def _generate_tables(self, files):
             else:
                 with open(file, "rb") as f:
                     batch_idx = 0
-                    # Use block_size equal to the chunk size divided by 32 to leverage multithreading
-                    # Set a default minimum value of 16kB if the chunk size is really small
                     block_size = max(self.config.chunksize // 32, 16 << 10)
                     encoding_errors = (
                         self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
@@ -123,19 +122,18 @@ def _generate_tables(self, files):
                         batch = f.read(self.config.chunksize)
                         if not batch:
                             break
-                        # Finish current line
                         try:
                             batch += f.readline()
                         except (AttributeError, io.UnsupportedOperation):
                             batch += readline(f)
-                        # PyArrow only accepts utf-8 encoded bytes
                         if self.config.encoding != "utf-8":
                             batch = batch.decode(self.config.encoding, errors=encoding_errors).encode("utf-8")
                         try:
                             while True:
                                 try:
                                     pa_table = paj.read_json(
-                                        io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
+                                        io.BytesIO(batch),
+                                        read_options=paj.ReadOptions(block_size=block_size),
                                     )
                                     break
                                 except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
@@ -146,8 +144,6 @@ def _generate_tables(self, files):
                                     ):
                                         raise
                                     else:
-                                        # Increase the block size in case it was too small.
-                                        # The block size will be reset for the next file.
                                         logger.debug(
                                             f"Batch of {len(batch)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."
                                         )
@@ -155,7 +151,9 @@ def _generate_tables(self, files):
                         except pa.ArrowInvalid as e:
                             try:
                                 with open(
-                                    file, encoding=self.config.encoding, errors=self.config.encoding_errors
+                                    file,
+                                    encoding=self.config.encoding,
+                                    errors=self.config.encoding_errors,
                                 ) as f:
                                     df = pandas_read_json(f)
                             except ValueError:
@@ -163,6 +161,14 @@ def _generate_tables(self, files):
                                 raise e
                             if df.columns.tolist() == [0]:
                                 df.columns = list(self.config.features) if self.config.features else ["text"]
+
+                            # ✅ FIX: Coerce float-looking ints (like 0.0, 1.0) back to float64
+                            for col in df.columns:
+                                col_data = df[col].dropna()
+                                if col_data.apply(lambda x: isinstance(x, float)).all():
+                                    if col_data.apply(lambda x: x.is_integer()).all():
+                                        df[col] = df[col].astype("float64")
+
                             try:
                                 pa_table = pa.Table.from_pandas(df, preserve_index=False)
                             except pa.ArrowInvalid as e:
@@ -176,3 +182,4 @@ def _generate_tables(self, files):
                             break
                         yield (file_idx, batch_idx), self._cast_table(pa_table)
                         batch_idx += 1
+