diff --git a/python/pyspark/pandas/series.py b/python/pyspark/pandas/series.py
index f7f8c89ab2783..d295d366b869e 100644
--- a/python/pyspark/pandas/series.py
+++ b/python/pyspark/pandas/series.py
@@ -21,6 +21,7 @@
 import datetime
 import re
 import inspect
+import math
 import warnings
 from collections.abc import Mapping
 from functools import partial, reduce
@@ -116,6 +117,7 @@
     verify_temp_column_name,
     SPARK_CONF_ARROW_ENABLED,
     log_advice,
+    is_ansi_mode_enabled,
 )
 from pyspark.pandas.datetimes import DatetimeMethods
 from pyspark.pandas.spark.accessors import SparkSeriesMethods
@@ -3367,15 +3369,39 @@ def autocorr(self, lag: int = 1) -> float:
         scol = self.spark.column
         if lag == 0:
             corr = sdf.select(F.corr(scol, scol)).head()[0]
+            return np.nan if corr is None else corr
         else:
             lag_scol = F.lag(scol, lag).over(Window.orderBy(NATURAL_ORDER_COLUMN_NAME))
             lag_col_name = verify_temp_column_name(sdf, "__autocorr_lag_tmp_col__")
-            corr = (
-                sdf.withColumn(lag_col_name, lag_scol)
-                .select(F.corr(scol, F.col(lag_col_name)))
-                .head()[0]
-            )
-        return np.nan if corr is None else corr
+
+            spark_session = self._internal.spark_frame.sparkSession
+            if is_ansi_mode_enabled(spark_session):
+                sdf = sdf.withColumn(lag_col_name, lag_scol)
+                sdf_mean = sdf.select(
+                    F.avg(scol).alias("mean_x"),
+                    F.avg(F.col(lag_col_name)).alias("mean_y"),
+                ).collect()[0]
+
+                mean_x = sdf_mean["mean_x"]
+                mean_y = sdf_mean["mean_y"]
+
+                corr_expr = F.try_divide(
+                    F.avg((scol - mean_x) * (F.col(lag_col_name) - mean_y)),
+                    F.stddev_samp(scol) * F.stddev_samp(F.col(lag_col_name)),
+                )
+
+                corr = sdf.select(corr_expr.alias("corr")).head()[0]
+                if corr is None or math.isnan(corr) or abs(corr) < 1e-12:
+                    return np.nan
+                return corr
+            else:
+                corr = (
+                    sdf.withColumn(lag_col_name, lag_scol)
+                    .select(F.corr(scol, F.col(lag_col_name)))
+                    .head()[0]
+                )
+
+                return np.nan if corr is None else corr
 
     def corr(
         self, other: "Series", method: str = "pearson", min_periods: Optional[int] = None