diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index 1be599e..8c0f2fe 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -13,6 +13,6 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v2
         with:
-          python-version: 3.6
+          python-version: 3.8
       - name: Lint via pre-commit checks
         uses: pre-commit/action@v2.0.0
diff --git a/.github/workflows/xskillscore_installs.yml b/.github/workflows/xskillscore_installs.yml
index 68dfc61..4dc3d9d 100644
--- a/.github/workflows/xskillscore_installs.yml
+++ b/.github/workflows/xskillscore_installs.yml
@@ -17,7 +17,7 @@ jobs:
     - name: Setup python
       uses: actions/setup-python@v2
       with:
-        python-version: 3.6
+        python-version: 3.8
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index e0b8757..211bb5b 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -34,6 +34,8 @@ Internal Changes
 - Added Python 3.7 and Python 3.8 to the CI. Use the latest version of Python 3
   for development. (:issue:`21`, :pr:`189`). `Aaron Spring`_
 - Lint with the latest black. (:issue:`179`, :pr:`191`). `Ray Bell`_
+- Update mape algorithm from scikit-learn v0.24.0 and test against it.
+  (:issue:`160`, :pr:`230`) `Ray Bell`_
 
 
 xskillscore v0.0.18 (2020-09-23)
diff --git a/ci/doc.yml b/ci/doc.yml
index afbdf91..ee45765 100644
--- a/ci/doc.yml
+++ b/ci/doc.yml
@@ -2,7 +2,7 @@ name: xskillscore-docs
 channels:
   - conda-forge
 dependencies:
-  - python=3.6
+  - python=3.8
   - bottleneck
   - doc8
   - importlib_metadata
diff --git a/xskillscore/core/deterministic.py b/xskillscore/core/deterministic.py
index aafe46a..a8958eb 100644
--- a/xskillscore/core/deterministic.py
+++ b/xskillscore/core/deterministic.py
@@ -1046,12 +1046,13 @@ def mape(a, b, dim=None, weights=None, skipna=False, keep_attrs=False):
     .. math::
         \\mathrm{MAPE} = \\frac{1}{n} \\sum_{i=1}^{n}
                          \\frac{\\vert a_{i} - b_{i} \\vert}
-                               {\\vert a_{i} \\vert}
+                               {max(\epsilon, \\vert a_{i} \\vert)}
 
     .. note::
         The percent error is calculated in reference to ``a``. Percent
         error is reported as decimal percent. I.e., a value of 1 is
-        100%.
+        100%. :math:`\epsilon` is an arbitrary small yet strictly positive
+        number to avoid undefined results when ``a`` is zero.
 
     Parameters
     ----------
@@ -1078,6 +1079,10 @@ def mape(a, b, dim=None, weights=None, skipna=False, keep_attrs=False):
     xarray.Dataset or xarray.DataArray
         Mean Absolute Percentage Error.
 
+    See Also
+    --------
+    sklearn.metrics.mean_absolute_percentage_error
+
     References
     ----------
     https://en.wikipedia.org/wiki/Mean_absolute_percentage_error
diff --git a/xskillscore/core/np_deterministic.py b/xskillscore/core/np_deterministic.py
index e6fb19a..ae736ac 100644
--- a/xskillscore/core/np_deterministic.py
+++ b/xskillscore/core/np_deterministic.py
@@ -652,7 +652,7 @@ def _mape(a, b, weights, axis, skipna):
     .. math::
         \\mathrm{MAPE} = \\frac{1}{n} \\sum_{i=1}^{n}
                          \\frac{\\vert a_{i} - b_{i} \\vert}
-                               {\\vert a_{i} \\vert}
+                               {max(\epsilon, \\vert a_{i} \\vert)}
 
     Parameters
     ----------
@@ -679,6 +679,13 @@ def _mape(a, b, weights, axis, skipna):
     Percent error is reported as decimal percent. I.e., a value of
     1 is 100%.
 
+    \epsilon is an arbitrary small yet strictly positive number to avoid
+    undefined results when ``a`` is zero.
+
+    See Also
+    --------
+    sklearn.metrics.mean_absolute_percentage_error
+
     References
     ----------
     https://en.wikipedia.org/wiki/Mean_absolute_percentage_error
@@ -687,8 +694,8 @@ def _mape(a, b, weights, axis, skipna):
     if skipna:
         a, b, weights = _match_nans(a, b, weights)
     weights = _check_weights(weights)
-    # replace divided by 0 with nan
-    mape = np.absolute(a - b) / np.absolute(np.where(a != 0, a, np.nan))
+    epsilon = np.finfo(np.float64).eps
+    mape = np.absolute(a - b) / np.maximum(np.absolute(a), epsilon)
     if weights is not None:
         return sumfunc(mape * weights, axis=axis) / sumfunc(weights, axis=axis)
     else:

diff --git a/.github/workflows/xskillscore_testing.yml b/.github/workflows/xskillscore_testing.yml
index 4ed6d27..0508ea9 100644
--- a/.github/workflows/xskillscore_testing.yml
+++ b/.github/workflows/xskillscore_testing.yml
@@ -60,7 +60,7 @@ jobs:
           channels: conda-forge
           mamba-version: '*'
           activate-environment: xskillscore-docs-notebooks
-          python-version: 3.6
+          python-version: 3.8
       - name: Set up conda environment
         run: |
           mamba env update -f ci/docs_notebooks.yml
diff --git a/xskillscore/tests/conftest.py b/xskillscore/tests/conftest.py
index 059f3e2..9998ae9 100644
--- a/xskillscore/tests/conftest.py
+++ b/xskillscore/tests/conftest.py
@@ -68,6 +68,13 @@ def b_nan(b):
     return b.copy().where(b < 0.5)
 
 
+# with zeros
+@pytest.fixture
+def a_with_zeros(a):
+    """Zeros"""
+    return a.copy().where(a < 0.5, 0)
+
+
 # dask
 @pytest.fixture
 def a_dask(a):
@@ -116,6 +123,12 @@ def b_1d_nan(a_1d_nan):
     return b
 
 
+@pytest.fixture
+def a_1d_with_zeros(a_with_zeros):
+    """Timeseries of a with zeros"""
+    return a_with_zeros.isel(lon=0, lat=0, drop=True)
+
+
 # weights
 @pytest.fixture
 def weights(a):
diff --git a/xskillscore/tests/test_metric_results_accurate.py b/xskillscore/tests/test_metric_results_accurate.py
index a00e3d4..54f1481 100644
--- a/xskillscore/tests/test_metric_results_accurate.py
+++ b/xskillscore/tests/test_metric_results_accurate.py
@@ -2,7 +2,12 @@ import numpy as np
 import pytest
 import sklearn.metrics
 from scipy.stats import pearsonr, spearmanr
-from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+from sklearn.metrics import (
+    mean_absolute_error,
+    mean_absolute_percentage_error,
+    mean_squared_error,
+    r2_score,
+)
 
 import xskillscore as xs
 from xskillscore.core.deterministic import (
@@ -23,6 +28,11 @@ xs_skl_metrics = [
     (r2, r2_score),
     (mse, mean_squared_error),
     (mae, mean_absolute_error),
+    (mape, mean_absolute_percentage_error),
+]
+
+xs_skl_metrics_with_zeros = [
+    (mape, mean_absolute_percentage_error),
 ]
 
 xs_scipy_metrics = [
@@ -34,7 +44,6 @@ xs_scipy_metrics = [
 
 
 xs_np_metrics = [
-    (mape, lambda x, y: np.mean(np.abs((x - y) / x))),
     (me, lambda x, y: np.mean(x - y)),
     (smape, lambda x, y: 1 / len(x) * np.sum(np.abs(y - x) / (np.abs(x) + np.abs(y)))),
 ]
@@ -69,6 +78,15 @@ def test_xs_same_as_skl_same_name(a_1d, b_1d, request):
     assert np.allclose(actual, expected)
 
 
+@pytest.mark.parametrize("xs_skl_metrics", xs_skl_metrics_with_zeros)
+def test_xs_same_as_skl_with_zeros(a_1d_with_zeros, b_1d, xs_skl_metrics):
+    """Tests xskillscore metric is same as scikit-learn metric."""
+    xs_metric, skl_metric = xs_skl_metrics
+    actual = xs_metric(a_1d_with_zeros, b_1d, "time")
+    expected = skl_metric(a_1d_with_zeros, b_1d)
+    assert np.allclose(actual, expected)
+
+
 @pytest.mark.parametrize("xs_scipy_metrics", xs_scipy_metrics)
 def test_xs_same_as_scipy(a_1d, b_1d, xs_scipy_metrics):
     """Tests xskillscore metric is same as scipy metric."""

Add MAPE in See Also and in testing from sklearn
`sklearn` has MAPE now in their master branch (https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/_regression.py#L197). Once released, we can test against this and put it in "See Also" for MAPE.
xskillscore/tests/test_metric_results_accurate.py::test_xs_same_as_skl_with_zeros[xs_skl_metrics0]
xskillscore/tests/test_metric_results_accurate.py::test_xs_same_as_skl[xs_skl_metrics0] xskillscore/tests/test_metric_results_accurate.py::test_xs_same_as_skl[xs_skl_metrics1] xskillscore/tests/test_metric_results_accurate.py::test_xs_same_as_skl[xs_skl_metrics2] xskillscore/tests/test_metric_results_accurate.py::test_xs_same_as_skl[xs_skl_metrics3] xskillscore/tests/test_metric_results_accurate.py::test_xs_same_as_skl_rmse[False] xskillscore/tests/test_metric_results_accurate.py::test_xs_same_as_scipy[xs_scipy_metrics0] xskillscore/tests/test_metric_results_accurate.py::test_xs_same_as_scipy[xs_scipy_metrics1] xskillscore/tests/test_metric_results_accurate.py::test_xs_same_as_scipy[xs_scipy_metrics2] xskillscore/tests/test_metric_results_accurate.py::test_xs_same_as_scipy[xs_scipy_metrics3] xskillscore/tests/test_metric_results_accurate.py::test_mape_same_as_numpy[xs_np_metrics0] xskillscore/tests/test_metric_results_accurate.py::test_mape_same_as_numpy[xs_np_metrics1]