Address comments.

python/pyspark/sql/session.py

@@ -459,13 +459,17 @@ def _convert_from_pandas(self, pdf, schema, timezone):
                     if isinstance(field.dataType, TimestampType):
                         s = _check_series_convert_timestamps_tz_local(pdf[field.name], timezone)
                         if not copied and s is not pdf[field.name]:
+                            # Copy once if the series is modified to prevent the original Pandas
+                            # DataFrame from being updated
                             pdf = pdf.copy()
                             copied = True
                         pdf[field.name] = s
             else:
                 for column, series in pdf.iteritems():
                     s = _check_series_convert_timestamps_tz_local(pdf[column], timezone)
                     if not copied and s is not pdf[column]:
+                        # Copy once if the series is modified to prevent the original Pandas
+                        # DataFrame from being updated
                         pdf = pdf.copy()
                         copied = True
                     pdf[column] = s

python/pyspark/sql/tests.py

@@ -3291,6 +3291,7 @@ def test_createDataFrame_respect_session_timezone(self):
 
             self.assertNotEqual(result_ny, result_la)
 
+            # Correct result_la by adjusting 3 hours difference between Los Angeles and New York
             result_la_corrected = [Row(**{k: v - timedelta(hours=3) if k == '7_timestamp_t' else v
                                           for k, v in row.asDict().items()})
                                    for row in result_la]
@@ -3834,6 +3835,7 @@ def test_vectorized_udf_timestamps_respect_session_timezone(self):
                 df_la = df.withColumn("tscopy", f_timestamp_copy(col("timestamp"))) \
                     .withColumn("internal_value", internal_value(col("timestamp")))
                 result_la = df_la.select(col("idx"), col("internal_value")).collect()
+                # Correct result_la by adjusting 3 hours difference between Los Angeles and New York
                 diff = 3 * 60 * 60 * 1000 * 1000 * 1000
                 result_la_corrected = \
                     df_la.select(col("idx"), col("tscopy"), col("internal_value") + diff).collect()

python/pyspark/sql/types.py

@@ -1729,28 +1729,28 @@ def _check_series_convert_timestamps_internal(s, timezone):
         return s
 
 
-def _check_series_convert_timestamps_localize(s, fromTimezone, toTimezone):
+def _check_series_convert_timestamps_localize(s, from_timezone, to_timezone):
     """
     Convert timestamp to timezone-naive in the specified timezone or local timezone
 
     :param s: a pandas.Series
-    :param fromTimezone: the timezone to convert from. if None then use local timezone
-    :param toTimezone: the timezone to convert to. if None then use local timezone
+    :param from_timezone: the timezone to convert from. if None then use local timezone
+    :param to_timezone: the timezone to convert to. if None then use local timezone
     :return pandas.Series where if it is a timestamp, has been converted to tz-naive
     """
     try:
         import pandas as pd
         from pandas.api.types import is_datetime64tz_dtype, is_datetime64_dtype
     except ImportError as e:
         raise ImportError(_old_pandas_exception_message(e))
-    fromTz = fromTimezone or 'tzlocal()'
-    toTz = toTimezone or 'tzlocal()'
+    from_tz = from_timezone or 'tzlocal()'
+    to_tz = to_timezone or 'tzlocal()'
     # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
     if is_datetime64tz_dtype(s.dtype):
-        return s.dt.tz_convert(toTz).dt.tz_localize(None)
-    elif is_datetime64_dtype(s.dtype) and fromTz != toTz:
+        return s.dt.tz_convert(to_tz).dt.tz_localize(None)
+    elif is_datetime64_dtype(s.dtype) and from_tz != to_tz:
         # `s.dt.tz_localize('tzlocal()')` doesn't work properly when including NaT.
-        return s.apply(lambda ts: ts.tz_localize(fromTz).tz_convert(toTz).tz_localize(None)
+        return s.apply(lambda ts: ts.tz_localize(from_tz).tz_convert(to_tz).tz_localize(None)
                        if ts is not pd.NaT else pd.NaT)
     else:
         return s