diff --git a/Makefile b/Makefile
index e2a65aa..c57faf7 100644
--- a/Makefile
+++ b/Makefile
@@ -32,7 +32,7 @@ coverage:
 	rm -r $(TESTDIR)
 
 pep8:
-	flake8 $(PROJECT) setup.py
+	flake8 $(PROJECT) setup.py examples
 
 lint:
 	pylint $(PROJECT) setup.py
diff --git a/examples/scipygridder.py b/examples/scipygridder.py
new file mode 100644
index 0000000..4531d15
--- /dev/null
+++ b/examples/scipygridder.py
@@ -0,0 +1,84 @@
+"""
+Gridding with Scipy
+===================
+
+Scipy offers a range of interpolation methods in :mod:`scipy.interpolate` and 3
+specifically for 2D data (linear, nearest neighbors, and bicubic). Verde offers
+an interface for these 3 scipy interpolators in :class:`verde.ScipyGridder`.
+
+All of these interpolations work on Cartesian data, so if we want to grid
+geographic data (like our Baja California bathymetry) we need to project them
+into a Cartesian system. We'll use
+`pyproj <https://github.com/jswhit/pyproj>`__ to calculate a Mercator
+projection for the data.
+
+For convenience, Verde still allows us to make geographic grids by passing the
+``projection`` argument to :meth:`verde.ScipyGridder.grid` and the like. When
+doing so, the grid will be generated using geographic coordinates which will be
+projected prior to interpolation.
+"""
+import matplotlib.pyplot as plt
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+import pyproj
+import numpy as np
+import verde as vd
+
+# We'll test this on the Baja California shipborne bathymetry data
+data = vd.datasets.fetch_baja_bathymetry()
+
+# Before gridding, we need to decimate the data to avoid aliasing because of
+# the oversampling along the ship tracks. We'll use a blocked median with 5
+# arc-minute blocks.
+spacing = 5/60
+lon, lat, bathymetry = vd.block_reduce(data.longitude, data.latitude,
+                                       data.bathymetry_m, reduction=np.median,
+                                       spacing=spacing)
+
+# Project the data using pyproj so that we can use it as input for the gridder.
+# We'll set the latitude of true scale to the mean latitude of the data.
+projection = pyproj.Proj(proj='merc', lat_ts=data.latitude.mean())
+easting, northing = projection(lon, lat)
+
+# Now we can set up a gridder for the decimated data
+grd = vd.ScipyGridder(method='cubic').fit(easting, northing, bathymetry)
+print("Gridder used:", grd)
+
+# Get the grid region in geographic coordinates
+region = vd.get_region(data.longitude, data.latitude)
+print("Data region:", region)
+
+# The 'grid' method can still make a geographic grid if we pass in a projection
+# function that converts lon, lat into the easting, northing coordinates that
+# we used in 'fit'. This can be any function that takes lon, lat and returns x,
+# y. In our case, it'll be the 'projection' variable that we created above.
+# We'll also set the names of the grid dimensions and the name the data
+# variable in our grid (the default would be 'scalars', which isn't very
+# informative).
+grid = grd.grid(region=region, spacing=spacing, projection=projection,
+                dims=['latitude', 'longitude'], data_names=['bathymetry_m'])
+print("Generated geographic grid:")
+print(grid)
+
+# Cartopy requires setting the coordinate reference system (CRS) of the
+# original data through the transform argument. Their docs say to use
+# PlateCarree to represent geographic data.
+crs = ccrs.PlateCarree()
+
+plt.figure(figsize=(7, 6))
+# Make a Mercator map of our gridded bathymetry
+ax = plt.axes(projection=ccrs.Mercator())
+ax.set_title("Gridded Bathymetry Using Scipy", pad=25)
+# Plot the gridded bathymetry
+pc = ax.pcolormesh(grid.longitude, grid.latitude, grid.bathymetry_m,
+                   transform=crs, vmax=0)
+cb = plt.colorbar(pc, pad=0.08)
+cb.set_label('bathymetry [m]')
+# Plot the locations of the decimated data
+ax.plot(lon, lat, '.k', markersize=0.5, transform=crs)
+# Plot the land as a solid color
+ax.add_feature(cfeature.LAND, edgecolor='black', zorder=2)
+ax.set_extent(region, crs=crs)
+ax.gridlines(draw_labels=True)
+plt.tight_layout()
+plt.show()
diff --git a/verde/__init__.py b/verde/__init__.py
index 6c4e8f6..505fb9c 100644
--- a/verde/__init__.py
+++ b/verde/__init__.py
@@ -5,7 +5,7 @@ from ._version import get_versions as _get_versions
 
 # Import functions/classes to make the API
 from .utils import scatter_points, grid_coordinates, profile_coordinates, \
-    block_reduce, block_region, inside
+    block_reduce, block_region, inside, get_region
 from . import datasets
 from .scipy_bridge import ScipyGridder
 
diff --git a/verde/base/gridder.py b/verde/base/gridder.py
index 58b7ad1..5789398 100644
--- a/verde/base/gridder.py
+++ b/verde/base/gridder.py
@@ -212,7 +212,7 @@ class BaseGridder(BaseEstimator):
         raise NotImplementedError()
 
     def grid(self, region=None, shape=None, spacing=None, adjust='spacing',
-             dims=None, data_names=None):
+             dims=None, data_names=None, projection=None):
         """
         Interpolate the data onto a regular grid.
 
@@ -278,7 +278,10 @@ class BaseGridder(BaseEstimator):
         region = get_region(self, region)
         easting, northing = grid_coordinates(region, shape=shape,
                                              spacing=spacing, adjust=adjust)
-        data = check_data(self.predict(easting, northing))
+        if projection is None:
+            data = check_data(self.predict(easting, northing))
+        else:
+            data = check_data(self.predict(*projection(easting, northing)))
         coords = {dims[1]: easting[0, :], dims[0]: northing[:, 0]}
         attrs = {'metadata': 'Generated by {}'.format(repr(self))}
         data_vars = {name: (dims, value, attrs)
@@ -286,7 +289,7 @@ class BaseGridder(BaseEstimator):
         return xr.Dataset(data_vars, coords=coords, attrs=attrs)
 
     def scatter(self, region=None, size=300, random_state=0, dims=None,
-                data_names=None):
+                data_names=None, projection=None):
         """
         Interpolate values onto a random scatter of points.
 
@@ -331,17 +334,16 @@ class BaseGridder(BaseEstimator):
         data_names = get_data_names(self, data_names)
         region = get_region(self, region)
         east, north = scatter_points(region, size, random_state)
-        data = check_data(self.predict(east, north))
-        column_names = [dim for dim in dims]
-        column_names.extend(data_names)
-        columns = [north, east]
-        columns.extend(data)
-        table = pd.DataFrame(
-            {name: value for name, value in zip(column_names, columns)},
-            columns=column_names)
-        return table
-
-    def profile(self, point1, point2, size, dims=None, data_names=None):
+        if projection is None:
+            data = check_data(self.predict(east, north))
+        else:
+            data = check_data(self.predict(*projection(east, north)))
+        columns = [(dims[0], north), (dims[1], east)]
+        columns.extend(zip(data_names, data))
+        return pd.DataFrame(dict(columns), columns=[c[0] for c in columns])
+
+    def profile(self, point1, point2, size, dims=None, data_names=None,
+                projection=None):
         """
         Interpolate data along a profile between two points.
 
@@ -388,13 +390,10 @@ class BaseGridder(BaseEstimator):
         data_names = get_data_names(self, data_names)
         east, north, distances = profile_coordinates(
             point1, point2, size, coordinate_system=coordsys)
-        data = check_data(self.predict(east, north))
-        column_names = [dim for dim in dims]
-        column_names.append('distance')
-        column_names.extend(data_names)
-        columns = [north, east, distances]
-        columns.extend(data)
-        table = pd.DataFrame(
-            {name: value for name, value in zip(column_names, columns)},
-            columns=column_names)
-        return table
+        if projection is None:
+            data = check_data(self.predict(east, north))
+        else:
+            data = check_data(self.predict(*projection(east, north)))
+        columns = [(dims[0], north), (dims[1], east), ('distance', distances)]
+        columns.extend(zip(data_names, data))
+        return pd.DataFrame(dict(columns), columns=[c[0] for c in columns])

diff --git a/verde/tests/test_base.py b/verde/tests/test_base.py
index 98ed528..d39c4b1 100644
--- a/verde/tests/test_base.py
+++ b/verde/tests/test_base.py
@@ -1,4 +1,4 @@
-# pylint: disable=unused-argument
+# pylint: disable=unused-argument,too-many-locals
 """
 Test the base classes and their utility functions.
 """
@@ -8,7 +8,7 @@ import numpy.testing as npt
 
 from ..base import BaseGridder
 from ..base.gridder import get_dims, get_data_names, get_region
-from .. import grid_coordinates
+from .. import grid_coordinates, scatter_points
 
 
 def test_get_dims():
@@ -95,46 +95,89 @@ def test_get_region():
     assert get_region(grd, region=(1, 2, 3, 4)) == (1, 2, 3, 4)
 
 
-def test_basegridder():
-    "Test basic functionality of BaseGridder"
+class PolyGridder(BaseGridder):
+    "A test gridder"
 
-    with pytest.raises(NotImplementedError):
-        BaseGridder().predict(None, None)
+    def __init__(self, degree=1):
+        self.degree = degree
 
-    class TestGridder(BaseGridder):
-        "A test gridder"
+    def fit(self, easting, northing, data):
+        "Fit an easting polynomial"
+        ndata = data.size
+        nparams = self.degree + 1
+        jac = np.zeros((ndata, nparams))
+        for j in range(nparams):
+            jac[:, j] = easting.ravel()**j
+        self.coefs_ = np.linalg.solve(jac.T.dot(jac), jac.T.dot(data.ravel()))
+        return self
 
-        def __init__(self, constant=0):
-            self.constant = constant
+    def predict(self, easting, northing):
+        "Predict the data"
+        return np.sum(cof*easting**deg for deg, cof in enumerate(self.coefs_))
 
-        def fit(self, easting, northing, data):
-            "Get the data mean"
-            self.mean_ = data.mean()
-            return self
 
-        def predict(self, easting, northing):
-            "Predict the data mean"
-            return np.ones_like(easting)*self.mean_ + self.constant
+def test_basegridder():
+    "Test basic functionality of BaseGridder"
 
-    grd = TestGridder()
-    assert repr(grd) == 'TestGridder(constant=0)'
-    grd.constant = 1000
-    assert repr(grd) == 'TestGridder(constant=1000)'
+    with pytest.raises(NotImplementedError):
+        BaseGridder().predict(None, None)
+
+    grd = PolyGridder()
+    assert repr(grd) == 'PolyGridder(degree=1)'
+    grd.degree = 2
+    assert repr(grd) == 'PolyGridder(degree=2)'
 
     region = (0, 10, -10, -5)
     shape = (50, 30)
-    east, north = grid_coordinates(region, shape)
-    data = np.ones_like(east)
-    grd = TestGridder().fit(east, north, data)
+    angular, linear = 2, 100
+    east, north = scatter_points(region, 1000, random_state=0)
+    data = angular*east + linear
+    grd = PolyGridder().fit(east, north, data)
 
     with pytest.raises(ValueError):
-        # A region should be given because it hasn't been assigned by
-        # TestGridder
+        # A region should be given because it hasn't been assigned
         grd.grid()
 
+    east_true, north_true = grid_coordinates(region, shape)
+    data_true = angular*east_true + linear
     grid = grd.grid(region, shape)
-    npt.assert_allclose(grid.scalars.values, data)
-    npt.assert_allclose(grid.easting.values, east[0, :])
-    npt.assert_allclose(grid.northing.values, north[:, 0])
-    npt.assert_allclose(grd.scatter(region, 100).scalars, 1)
-    npt.assert_allclose(grd.profile((0, 100), (20, 10), 100).scalars, 1)
+
+    npt.assert_allclose(grd.coefs_, [linear, angular])
+    npt.assert_allclose(grid.scalars.values, data_true)
+    npt.assert_allclose(grid.easting.values, east_true[0, :])
+    npt.assert_allclose(grid.northing.values, north_true[:, 0])
+    npt.assert_allclose(grd.scatter(region, 1000, random_state=0).scalars,
+                        data)
+    npt.assert_allclose(grd.profile((0, 0), (10, 0), 30).scalars,
+                        angular*east_true[0, :] + linear)
+
+
+def test_basegridder_projection():
+    "Test basic functionality of BaseGridder when passing in a projection"
+
+    region = (0, 10, -10, -5)
+    shape = (50, 30)
+    angular, linear = 2, 100
+    east, north = scatter_points(region, 1000, random_state=0)
+    data = angular*east + linear
+    east_true, north_true = grid_coordinates(region, shape)
+    data_true = angular*east_true + linear
+    grd = PolyGridder().fit(east, north, data)
+
+    # Lets say we want to specify the region for a grid using a coordinate
+    # system that is lon/2, lat/2.
+    def proj(lon, lat):
+        "Project from the new coordinates to the original"
+        return (lon*2, lat*2)
+
+    proj_region = [i/2 for i in region]
+    grid = grd.grid(proj_region, shape, projection=proj)
+    scat = grd.scatter(proj_region, 1000, random_state=0, projection=proj)
+    prof = grd.profile((0, 0), (5, 0), 30, projection=proj)
+
+    npt.assert_allclose(grd.coefs_, [linear, angular])
+    npt.assert_allclose(grid.scalars.values, data_true)
+    npt.assert_allclose(grid.easting.values, east_true[0, :]/2)
+    npt.assert_allclose(grid.northing.values, north_true[:, 0]/2)
+    npt.assert_allclose(scat.scalars, data)
+    npt.assert_allclose(prof.scalars, angular*east_true[0, :] + linear)

Option to project coordinates before gridding
The methods in `BaseGridder` should take an optional `projection(lon, lat) -> east, north` argument to project coordinates before passing them to `predict`. This allows Cartesian gridders to create geographic grids without a lot of work. The grid output should still be in the original coordinates. Can take pyproj projections as well with this interface.
verde/tests/test_base.py::test_basegridder_projection
verde/tests/test_base.py::test_get_dims verde/tests/test_base.py::test_get_dims_fails verde/tests/test_base.py::test_get_data_names verde/tests/test_base.py::test_get_data_names_fails verde/tests/test_base.py::test_get_region