diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 6bb7da6b2edb..3ced81427397 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -36,6 +36,7 @@
 from pyspark.sql.types import StringType, DataType
 # Keep UserDefinedFunction import for backwards compatible import; moved in SPARK-22409
 from pyspark.sql.udf import UserDefinedFunction, _create_udf
+from pyspark.sql.utils import to_str
 
 # Note to developers: all of PySpark functions here take string as column names whenever possible.
 # Namely, if columns are referred as arguments, they can be always both Column or string,
@@ -114,6 +115,10 @@ def _():
     _.__doc__ = 'Window function: ' + doc
     return _
 
+
+def _options_to_str(options):
+    return {key: to_str(value) for (key, value) in options.items()}
+
 _lit_doc = """
     Creates a :class:`Column` of literal value.
 
@@ -2343,7 +2348,7 @@ def from_json(col, schema, options={}):
         schema = schema.json()
     elif isinstance(schema, Column):
         schema = _to_java_column(schema)
-    jc = sc._jvm.functions.from_json(_to_java_column(col), schema, options)
+    jc = sc._jvm.functions.from_json(_to_java_column(col), schema, _options_to_str(options))
     return Column(jc)
 
 
@@ -2384,7 +2389,7 @@ def to_json(col, options={}):
     """
 
     sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.to_json(_to_java_column(col), options)
+    jc = sc._jvm.functions.to_json(_to_java_column(col), _options_to_str(options))
     return Column(jc)
 
 
@@ -2415,7 +2420,7 @@ def schema_of_json(json, options={}):
         raise TypeError("schema argument should be a column or string")
 
     sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.schema_of_json(col, options)
+    jc = sc._jvm.functions.schema_of_json(col, _options_to_str(options))
     return Column(jc)
 
 
@@ -2442,7 +2447,7 @@ def schema_of_csv(csv, options={}):
         raise TypeError("schema argument should be a column or string")
 
     sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.schema_of_csv(col, options)
+    jc = sc._jvm.functions.schema_of_csv(col, _options_to_str(options))
     return Column(jc)
 
 
@@ -2464,7 +2469,7 @@ def to_csv(col, options={}):
     """
 
     sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.to_csv(_to_java_column(col), options)
+    jc = sc._jvm.functions.to_csv(_to_java_column(col), _options_to_str(options))
     return Column(jc)
 
 
@@ -2775,6 +2780,11 @@ def from_csv(col, schema, options={}):
     >>> value = data[0][0]
     >>> df.select(from_csv(df.value, schema_of_csv(value)).alias("csv")).collect()
     [Row(csv=Row(_c0=1, _c1=2, _c2=3))]
+    >>> data = [("   abc",)]
+    >>> df = spark.createDataFrame(data, ("value",))
+    >>> options = {'ignoreLeadingWhiteSpace': True}
+    >>> df.select(from_csv(df.value, "s string", options).alias("csv")).collect()
+    [Row(csv=Row(s=u'abc'))]
     """
 
     sc = SparkContext._active_spark_context
@@ -2785,7 +2795,7 @@ def from_csv(col, schema, options={}):
     else:
         raise TypeError("schema argument should be a column or string")
 
-    jc = sc._jvm.functions.from_csv(_to_java_column(col), schema, options)
+    jc = sc._jvm.functions.from_csv(_to_java_column(col), schema, _options_to_str(options))
     return Column(jc)
 
 
diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index aa5bf635d187..7596b02b227b 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -27,23 +27,11 @@
 from pyspark.sql.column import _to_seq
 from pyspark.sql.types import *
 from pyspark.sql import utils
+from pyspark.sql.utils import to_str
 
 __all__ = ["DataFrameReader", "DataFrameWriter"]
 
 
-def to_str(value):
-    """
-    A wrapper over str(), but converts bool values to lower case strings.
-    If None is given, just returns None, instead of converting it to string "None".
-    """
-    if isinstance(value, bool):
-        return str(value).lower()
-    elif value is None:
-        return value
-    else:
-        return str(value)
-
-
 class OptionUtils(object):
 
     def _set_opts(self, schema=None, **options):
diff --git a/python/pyspark/sql/utils.py b/python/pyspark/sql/utils.py
index ca5e85bb3a9b..c30cc1482750 100644
--- a/python/pyspark/sql/utils.py
+++ b/python/pyspark/sql/utils.py
@@ -207,3 +207,16 @@ def call(self, jdf, batch_id):
 
     class Java:
         implements = ['org.apache.spark.sql.execution.streaming.sources.PythonForeachBatchFunction']
+
+
+def to_str(value):
+    """
+    A wrapper over str(), but converts bool values to lower case strings.
+    If None is given, just returns None, instead of converting it to string "None".
+    """
+    if isinstance(value, bool):
+        return str(value).lower()
+    elif value is None:
+        return value
+    else:
+        return str(value)