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BLD: use tempita for hashtable #13815

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Merged
merged 1 commit into from
Jul 28, 2016
Merged

BLD: use tempita for hashtable #13815

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6 changes: 3 additions & 3 deletions pandas/core/algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -413,7 +413,7 @@ def _value_counts_arraylike(values, dropna=True):
freq = values.freq

values = values.view(np.int64)
keys, counts = htable.value_count_scalar64(values, dropna)
keys, counts = htable.value_count_int64(values, dropna)

if dropna:
msk = keys != iNaT
Expand All @@ -434,10 +434,10 @@ def _value_counts_arraylike(values, dropna=True):

elif is_integer_dtype(dtype):
values = _ensure_int64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
keys, counts = htable.value_count_int64(values, dropna)
elif is_float_dtype(dtype):
values = _ensure_float64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
keys, counts = htable.value_count_float64(values, dropna)
else:
values = _ensure_object(values)
mask = isnull(values)
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2 changes: 1 addition & 1 deletion pandas/core/groupby.py
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Expand Up @@ -4399,7 +4399,7 @@ def _groupby_indices(values):
# bit better than factorizing again
reverse = dict(enumerate(values.categories))
codes = values.codes.astype('int64')
_, counts = _hash.value_count_scalar64(codes, False)
_, counts = _hash.value_count_int64(codes, False)
else:
reverse, codes, counts = _algos.group_labels(
_values_from_object(_ensure_object(values)))
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