fixups

Author: rhshadrach

doc/source/user_guide/basics.rst

@@ -2063,6 +2063,7 @@ or a passed ``Series``), then it will be preserved in DataFrame operations. Furt
 different numeric dtypes will **NOT** be combined. The following example will give you a taste.
 
 .. ipython:: python
+   :okwarning:
 
    df1 = pd.DataFrame(np.random.randn(8, 1), columns=["A"], dtype="float32")
    df1

doc/source/user_guide/enhancingperf.rst

@@ -171,6 +171,7 @@ can be improved by passing an ``np.ndarray``.
    In [4]: %%cython
       ...: cimport numpy as np
       ...: import numpy as np
+      ...: np.import_array()
       ...: cdef double f_typed(double x) except? -2:
       ...:     return x * (x - 1)
       ...: cpdef double integrate_f_typed(double a, double b, int N):
@@ -225,6 +226,7 @@ and ``wraparound`` checks can yield more performance.
       ...: cimport cython
       ...: cimport numpy as np
       ...: import numpy as np
+      ...: np.import_array()
       ...: cdef np.float64_t f_typed(np.float64_t x) except? -2:
       ...:     return x * (x - 1)
       ...: cpdef np.float64_t integrate_f_typed(np.float64_t a, np.float64_t b, np.int64_t N):

doc/source/whatsnew/v0.11.0.rst

@@ -73,6 +73,7 @@ Dtypes
 Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passed (either directly via the ``dtype`` keyword, a passed ``ndarray``, or a passed ``Series``, then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will **NOT** be combined. The following example will give you a taste.
 
 .. ipython:: python
+   :okwarning:
 
    df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')
    df1