Code review changes

Changes suggested by @WillAyd

Signed-off-by: Michael Tiemann <[email protected]>

pandas/_libs/hashtable_class_helper.pxi.in

@@ -1128,7 +1128,6 @@ cdef class StringHashTable(HashTable):
         use_na_value = na_value is not None
 
         # assign pointers and pre-filter out missing (if ignore_na)
-        keep_rval_refs = []
         vecs = <const char **>malloc(n * sizeof(char *))
         for i in range(n):
             val = values[i]

pandas/core/nanops.py

@@ -998,28 +998,28 @@ def nanvar(
         values = values.copy()
         np.putmask(values, mask, 0)
 
-    # xref GH10242
-    # Compute variance via two-pass algorithm, which is stable against
-    # cancellation errors and relatively accurate for small numbers of
-    # observations.
-    #
-    # See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance...
     if values.dtype.kind == "c":
+        # xref GH10242
+        # Compute variance via two-pass algorithm, which is stable against
+        # cancellation errors and relatively accurate for small numbers of
+        # observations.
+        #
+        # See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance...
+        # ...but also,
+        # see https://numpy.org/doc/stable/reference/generated/numpy.nanvar.html#numpy-nanvar
+        # which explains why computing the variance of complex numbers
+        # requires first normalizing the complex differences to magnitudes
         avg = _ensure_numeric(values.sum(axis=axis, dtype=values.dtype)) / count
-    else:
-        avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
-    if axis is not None:
-        avg = np.expand_dims(avg, axis)
-    # ...but also,
-    # see https://numpy.org/doc/stable/reference/generated/numpy.nanvar.html#numpy-nanvar
-    # which explains why computing the variance of complex numbers
-    # requires first normalizing the complex differences to magnitudes
-    if values.dtype.kind == "c":
+        if axis is not None:
+            avg = np.expand_dims(avg, axis)
         deltas = _ensure_numeric(avg - values)
         avg_re = np.real(deltas)
         avg_im = np.imag(deltas)
         sqr = avg_re**2 + avg_im**2
     else:
+        avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
+        if axis is not None:
+            avg = np.expand_dims(avg, axis)
         sqr = _ensure_numeric((avg - values) ** 2)
 
     if mask is not None: