diff --git a/.travis.yml b/.travis.yml
index 620cffbb6674..f667ad969b4a 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -22,6 +22,8 @@ matrix:
         # pyflakes doesn't respect "# noqa" and cannot ignore files, so filter out six.py
         - PYFLAKES_NODOCTEST=1 pyflakes scipy benchmarks/benchmarks | grep -E -v 'unable to detect undefined names|assigned to but never used|imported but unused|redefinition of unused|may be undefined, or defined from star imports' | grep -E -v 'scipy/_lib/six.py' > test.out; cat test.out; test \! -s test.out
         - pycodestyle scipy benchmarks/benchmarks
+        - |
+          grep -rlIP '[^\x00-\x7F]' scipy | grep '\.pyx\?' | sort > unicode.out; grep -rlI '# -\*- coding: \(utf-8\|latin-1\) -\*-' scipy | grep '\.pyx\?' | sort > coding.out; comm -23 unicode.out coding.out > test_code.out; cat test_code.out;  test \! -s test_code.out
     - python: 3.7
       dist: xenial # travis-ci/travis-ci/issues/9815
       sudo: true
diff --git a/scipy/io/__init__.py b/scipy/io/__init__.py
index 64a7aa55d399..e991144a3bd8 100644
--- a/scipy/io/__init__.py
+++ b/scipy/io/__init__.py
@@ -1,5 +1,5 @@
-# -*- encoding:utf-8 -*-
-"""
+# -*- coding: utf-8 -*-
+u"""
 ==================================
 Input and output (:mod:`scipy.io`)
 ==================================
diff --git a/scipy/stats/_continuous_distns.py b/scipy/stats/_continuous_distns.py
index 44c05bb7d67f..bada811a2b6f 100644
--- a/scipy/stats/_continuous_distns.py
+++ b/scipy/stats/_continuous_distns.py
@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 #
 # Author:  Travis Oliphant  2002-2011 with contributions from
 #          SciPy Developers 2004-2011
@@ -3342,7 +3343,7 @@ class norminvgauss_gen(rv_continuous):
 
     O. Barndorff-Nielsen, "Normal Inverse Gaussian Distributions and Stochastic
     Volatility Modelling", Scandinavian Journal of Statistics, Vol. 24,
-    pp. 1–13, 1997.
+    pp. 1-13, 1997.
 
     %(example)s
 
@@ -3379,7 +3380,7 @@ def _stats(self, a, b):
 
 
 class invweibull_gen(rv_continuous):
-    r"""An inverted Weibull continuous random variable.
+    u"""An inverted Weibull continuous random variable.
 
     This distribution is also known as the Fréchet distribution or the
     type II extreme value distribution.
@@ -3392,7 +3393,7 @@ class invweibull_gen(rv_continuous):
 
     .. math::
 
-        f(x, c) = c x^{-c-1} \exp(-x^{-c})
+        f(x, c) = c x^{-c-1} \\exp(-x^{-c})
 
     for :math:`x > 0`, :math:`c > 0`.
 
@@ -4002,9 +4003,9 @@ def _cdf(self, x, alpha, beta):
                 data_out[data_mask] = np.array([levy_stable._cdf_single_value_zolotarev(_x, _alpha, _beta)
                             for _x, _alpha, _beta in data_subset]).reshape(len(data_subset), 1)
             else:
-                warnings.warn('FFT method is considered experimental for ' +
-                              'cumulative distribution function ' +
-                              'evaluations. Use Zolotarev’s method instead).',
+                warnings.warn(u'FFT method is considered experimental for ' +
+                              u'cumulative distribution function ' +
+                              u'evaluations. Use Zolotarev’s method instead).',
                               RuntimeWarning)
                 _alpha, _beta = pair
                 _x = data_subset[:,(0,)]
diff --git a/scipy/stats/_stats_mstats_common.py b/scipy/stats/_stats_mstats_common.py
index e86c8287b487..8c17b7095386 100644
--- a/scipy/stats/_stats_mstats_common.py
+++ b/scipy/stats/_stats_mstats_common.py
@@ -11,6 +11,7 @@
                                                    'rvalue', 'pvalue',
                                                    'stderr'))
 
+
 def linregress(x, y=None):
     """
     Calculate a linear least-squares regression for two sets of measurements.
@@ -327,7 +328,7 @@ def siegelslopes(y, x=None, method="hierarchical"):
 
     .. [2] A. Stein and M. Werman, "Finding the repeated median regression
            line", Proceedings of the Third Annual ACM-SIAM Symposium on
-           Discrete Algorithms, pp. 409–413, 1992.
+           Discrete Algorithms, pp. 409-413, 1992.
 
     Examples
     --------