diff --git a/sdc/datatypes/hpat_pandas_series_functions.py b/sdc/datatypes/hpat_pandas_series_functions.py
index 658d6460c..146572e24 100644
--- a/sdc/datatypes/hpat_pandas_series_functions.py
+++ b/sdc/datatypes/hpat_pandas_series_functions.py
@@ -4933,14 +4933,22 @@ def hpat_pandas_series_dropna(self, axis=0, inplace=False):
     if not (inplace is False or isinstance(inplace, types.Omitted)):
         ty_checker.raise_exc(inplace, 'bool', 'inplace')
 
-    def hpat_pandas_series_dropna_impl(self, axis=0, inplace=False):
-        # generate Series index if needed by using SeriesType.index (i.e. not self._index)
-        na_data_arr = sdc.hiframes.api.get_nan_mask(self._data)
-        data = self._data[~na_data_arr]
-        index = self.index[~na_data_arr]
-        return pandas.Series(data, index, self._name)
+    if isinstance(self.data.dtype, types.Number) and isinstance(self.index, (types.Number, types.NoneType)):
+        def hpat_pandas_series_dropna_impl(self, axis=0, inplace=False):
+            index = self.index
+            return numpy_like.dropna(self._data, index, self._name)
+
+        return hpat_pandas_series_dropna_impl
+
+    else:
+        def hpat_pandas_series_dropna_str_impl(self, axis=0, inplace=False):
+            # generate Series index if needed by using SeriesType.index (i.e. not self._index)
+            na_data_arr = sdc.hiframes.api.get_nan_mask(self._data)
+            data = self._data[~na_data_arr]
+            index = self.index[~na_data_arr]
+            return pandas.Series(data, index, self._name)
 
-    return hpat_pandas_series_dropna_impl
+        return hpat_pandas_series_dropna_str_impl
 
 
 @sdc_overload_method(SeriesType, 'fillna')
diff --git a/sdc/functions/numpy_like.py b/sdc/functions/numpy_like.py
index cbdb43904..266520c13 100644
--- a/sdc/functions/numpy_like.py
+++ b/sdc/functions/numpy_like.py
@@ -33,6 +33,7 @@
 
 import numba
 import numpy
+import pandas
 import numpy as np
 
 from numba import types, jit, prange, numpy_support, literally
@@ -43,6 +44,7 @@
 from sdc.utilities.sdc_typing_utils import TypeChecker
 from sdc.str_arr_ext import (StringArrayType, pre_alloc_string_array, get_utf8_size, str_arr_is_na)
 from sdc.utilities.utils import sdc_overload, sdc_register_jitable
+from sdc.utilities.prange_utils import parallel_chunks
 
 
 def astype(self, dtype):
@@ -475,6 +477,49 @@ def nanprod_impl(a):
     return nanprod_impl
 
 
+def dropna(arr, idx, name):
+    pass
+
+
+@sdc_overload(dropna)
+def dropna_overload(arr, idx, name):
+    dtype = arr.dtype
+    dtype_idx = idx.dtype
+    isnan = get_isnan(dtype)
+
+    def dropna_impl(arr, idx, name):
+        chunks = parallel_chunks(len(arr))
+        arr_len = numpy.empty(len(chunks), dtype=numpy.int64)
+        length = 0
+
+        for i in prange(len(chunks)):
+            chunk = chunks[i]
+            res = 0
+            for j in range(chunk.start, chunk.stop):
+                if not isnan(arr[j]):
+                    res += 1
+            length += res
+            arr_len[i] = res
+
+        result_data = numpy.empty(shape=length, dtype=dtype)
+        result_index = numpy.empty(shape=length, dtype=dtype_idx)
+        for i in prange(len(chunks)):
+            chunk = chunks[i]
+            new_start = int(sum(arr_len[0:i]))
+            new_stop = new_start + arr_len[i]
+            current_pos = new_start
+
+            for j in range(chunk.start, chunk.stop):
+                if not isnan(arr[j]):
+                    result_data[current_pos] = arr[j]
+                    result_index[current_pos] = idx[j]
+                    current_pos += 1
+
+        return pandas.Series(result_data, result_index, name)
+
+    return dropna_impl
+
+
 def nanmean(a):
     pass