diff --git a/.travis.yml b/.travis.yml
index a21d4d94413..fbc01b4815d 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -60,8 +60,8 @@ script:
   - python --version
   - python -OO -c "import xarray"
   - if [[ "$CONDA_ENV" == "docs" ]]; then
-      conda install -c conda-forge --override-channels sphinx sphinx_rtd_theme sphinx-gallery numpydoc "gdal>2.2.4";
-      sphinx-build -n -j auto -b html -d _build/doctrees doc _build/html;
+      cd doc;
+      sphinx-build -n -j auto -b html -d _build/doctrees . _build/html;
     elif [[ "$CONDA_ENV" == "lint" ]]; then
       pycodestyle xarray ;
     elif [[ "$CONDA_ENV" == "py36-hypothesis" ]]; then
diff --git a/ci/requirements-py36.yml b/ci/requirements-py36.yml
index 0ed6dd78c3a..7523b14608b 100644
--- a/ci/requirements-py36.yml
+++ b/ci/requirements-py36.yml
@@ -26,8 +26,8 @@ dependencies:
   - pseudonetcdf>=3.0.1
   - eccodes
   - cdms2
-  # - pynio  # xref #2683
-  # - iris>=1.10    # xref #2683
+# - pynio  # xref #2683
+# - iris>=1.10  # xref #2683
   - pydap
   - lxml
   - pip:
diff --git a/doc/environment.yml b/doc/environment.yml
index ca4f622cd38..f4d1f4e9008 100644
--- a/doc/environment.yml
+++ b/doc/environment.yml
@@ -2,22 +2,25 @@ name: xarray-docs
 channels:
   - conda-forge
 dependencies:
-  - python=3.6
-  - numpy=1.14.5
+  - python=3.7
+  - numpy=1.16.0
   - pandas=0.23.3
-  - scipy=1.1.0
-  - matplotlib=2.2.2
+  - scipy=1.2.0
+  - matplotlib=3.0.2
   - seaborn=0.9.0
-  - dask=0.18.2
-  - ipython=6.4.0
-  - netCDF4=1.4.0
-  - cartopy=0.16.0
-  - rasterio=1.0.1
+  - dask=1.1.0
+  - ipython=7.2.0
+  - netCDF4=1.4.2
+  - cartopy=0.17.0
+  - rasterio=1.0.13
   - zarr=2.2.0
-  - iris=2.1.0
-  - flake8=3.5.0
+  - iris=2.2.0
+  - flake8=3.6.0
   - cftime=1.0.3.4
-  - bottleneck=1.2
-  - sphinx=1.7.6
+  - bottleneck=1.2.1
+  - sphinx=1.8.2
   - numpydoc=0.8.0
   - sphinx-gallery=0.2.0
+  - pillow=5.4.1
+  - sphinx_rtd_theme=0.4.2
+  - mock=2.0.0
diff --git a/doc/examples/multidimensional-coords.rst b/doc/examples/multidimensional-coords.rst
index 7c86f897a24..a5084043977 100644
--- a/doc/examples/multidimensional-coords.rst
+++ b/doc/examples/multidimensional-coords.rst
@@ -78,9 +78,8 @@ grid, we can take advantage of xarray's ability to apply
     ax = plt.axes(projection=ccrs.PlateCarree());
     ds.Tair[0].plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(),
                                x='xc', y='yc', add_colorbar=False);
-    ax.coastlines();
     @savefig xarray_multidimensional_coords_12_0.png width=100%
-    plt.tight_layout();
+    ax.coastlines();
 
 Multidimensional Groupby
 ------------------------
diff --git a/doc/pandas.rst b/doc/pandas.rst
index fc20d161e05..1538fced648 100644
--- a/doc/pandas.rst
+++ b/doc/pandas.rst
@@ -14,7 +14,7 @@ __ http://pandas.pydata.org/pandas-docs/stable/visualization.html
 __ http://stanford.edu/~mwaskom/software/seaborn/
 
 .. ipython:: python
-   :suppress:
+    :suppress:
 
     import numpy as np
     import pandas as pd
@@ -93,7 +93,6 @@ DataFrames:
 
     s = ds['foo'].to_series()
     s
-
     # or equivalently, with Series.to_xarray()
     xr.DataArray.from_series(s)
 
@@ -173,11 +172,10 @@ So you can represent a Panel, in two ways:
 Let's take a look:
 
 .. ipython:: python
-   :okwarning:
+    :okwarning:
 
     panel = pd.Panel(np.random.rand(2, 3, 4), items=list('ab'), major_axis=list('mno'),
                      minor_axis=pd.date_range(start='2000', periods=4, name='date'))
-
     panel
 
 As a DataArray:
diff --git a/doc/time-series.rst b/doc/time-series.rst
index 1ced1ac30f6..32c6b581aa4 100644
--- a/doc/time-series.rst
+++ b/doc/time-series.rst
@@ -15,7 +15,6 @@ core functionality.
     import numpy as np
     import pandas as pd
     import xarray as xr
-
     np.random.seed(123456)
 
 Creating datetime64 data
@@ -241,7 +240,6 @@ coordinate with dates from a no-leap calendar and a
 
    from itertools import product
    from cftime import DatetimeNoLeap
-   
    dates = [DatetimeNoLeap(year, month, 1) for year, month in
             product(range(1, 3), range(1, 13))]
    da = xr.DataArray(np.arange(24), coords=[dates], dims=['time'], name='foo')