diff --git a/CHANGELOG.md b/CHANGELOG.md
index 42ce0be0..4bdbfef6 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,9 @@
 ## 1.13.0 [unreleased]
 
+### Bug Fixes
+1. [#164](https://github.com/influxdata/influxdb-client-python/pull/170): Skip DataFrame rows without data - all fields are nan. 
+
+
 ## 1.12.0 [2020-10-30]
 
 1. [#163](https://github.com/influxdata/influxdb-client-python/pull/163): Added support for Python 3.9
diff --git a/influxdb_client/client/write/dataframe_serializer.py b/influxdb_client/client/write/dataframe_serializer.py
index 952b5052..6abbddce 100644
--- a/influxdb_client/client/write/dataframe_serializer.py
+++ b/influxdb_client/client/write/dataframe_serializer.py
@@ -37,6 +37,14 @@ def _itertuples(data_frame):
     return zip(data_frame.index, *cols)
 
 
+def _is_nan(x):
+    return x != x
+
+
+def _any_not_nan(p, indexes):
+    return any(map(lambda inx: not _is_nan(p[inx]), indexes))
+
+
 def data_frame_to_list_of_points(data_frame, point_settings, **kwargs):
     """Serialize DataFrame into LineProtocols."""
     from ...extras import pd, np
@@ -61,6 +69,7 @@ def data_frame_to_list_of_points(data_frame, point_settings, **kwargs):
 
     tags = []
     fields = []
+    fields_indexes = []
     keys = []
 
     if point_settings.defaultTags:
@@ -73,14 +82,18 @@ def data_frame_to_list_of_points(data_frame, point_settings, **kwargs):
         keys.append(key.translate(_ESCAPE_KEY))
         key_format = f'{{keys[{index}]}}'
 
+        index_value = index + 1
         if key in data_frame_tag_columns:
-            tags.append({'key': key, 'value': f"{key_format}={{str(p[{index + 1}]).translate(_ESCAPE_KEY)}}"})
+            tags.append({'key': key, 'value': f"{key_format}={{str(p[{index_value}]).translate(_ESCAPE_KEY)}}"})
         elif issubclass(value.type, np.integer):
-            fields.append(f"{key_format}={{p[{index + 1}]}}i")
+            fields.append(f"{key_format}={{p[{index_value}]}}i")
+            fields_indexes.append(index_value)
         elif issubclass(value.type, (np.float, np.bool_)):
-            fields.append(f"{key_format}={{p[{index + 1}]}}")
+            fields.append(f"{key_format}={{p[{index_value}]}}")
+            fields_indexes.append(index_value)
         else:
-            fields.append(f"{key_format}=\"{{str(p[{index + 1}]).translate(_ESCAPE_STRING)}}\"")
+            fields.append(f"{key_format}=\"{{str(p[{index_value}]).translate(_ESCAPE_STRING)}}\"")
+            fields_indexes.append(index_value)
 
     tags.sort(key=lambda x: x['key'])
     tags = ','.join(map(lambda y: y['value'], tags))
@@ -100,7 +113,7 @@ def data_frame_to_list_of_points(data_frame, point_settings, **kwargs):
     if isnull.any():
         rep = _replace(data_frame)
         lp = (reduce(lambda a, b: re.sub(*b, a), rep, f(p))
-              for p in _itertuples(data_frame))
+              for p in filter(lambda x: _any_not_nan(x, fields_indexes), _itertuples(data_frame)))
         return list(lp)
     else:
         return list(map(f, _itertuples(data_frame)))
diff --git a/tests/test_WriteApiDataFrame.py b/tests/test_WriteApiDataFrame.py
index 7b190462..63de0c82 100644
--- a/tests/test_WriteApiDataFrame.py
+++ b/tests/test_WriteApiDataFrame.py
@@ -99,9 +99,10 @@ def test_write_nan(self):
         data_frame = pd.DataFrame(data=[[3.1955, np.nan, 20.514305, np.nan],
                                         [5.7310, np.nan, 23.328710, np.nan],
                                         [np.nan, 3.138664, np.nan, 20.755026],
-                                        [5.7310, 5.139563, 23.328710, 19.791240]],
+                                        [5.7310, 5.139563, 23.328710, 19.791240],
+                                        [np.nan, np.nan, np.nan, np.nan]],
                                   index=[now, now + timedelta(minutes=30), now + timedelta(minutes=60),
-                                         now + timedelta(minutes=90)],
+                                         now + timedelta(minutes=90), now + timedelta(minutes=120)],
                                   columns=["actual_kw_price", "forecast_kw_price", "actual_general_use",
                                            "forecast_general_use"])