def _value_counts_arraylike(values, dropna=True):
is_datetimetz = com.is_datetimetz(values)
is_period = (isinstance(values, gt.ABCPeriodIndex)
or com.is_period_arraylike(values))
orig = values
from pandas.core.series import Series
values = Series(values).values
dtype = values.dtype
if com.is_datetime_or_timedelta_dtype(dtype) or is_period:
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.period import PeriodIndex
if is_period:
values = PeriodIndex(values)
freq = values.freq
values = values.view(np.int64)
keys, counts = htable.value_count_scalar64(values, dropna)
if dropna:
msk = keys != iNaT
keys, counts = keys[msk], counts[msk]
# convert the keys back to the dtype we came in
keys = keys.astype(dtype)
# dtype handling
if is_datetimetz:
if isinstance(orig, gt.ABCDatetimeIndex):
tz = orig.tz
else:
tz = orig.dt.tz
keys = DatetimeIndex._simple_new(keys, tz=tz)
if is_period:
keys = PeriodIndex._simple_new(keys, freq=freq)
elif com.is_integer_dtype(dtype):
values = com._ensure_int64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
elif com.is_float_dtype(dtype):
values = com._ensure_float64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
else:
values = com._ensure_object(values)
mask = com.isnull(values)
keys, counts = htable.value_count_object(values, mask)
if not dropna and mask.any():
keys = np.insert(keys, 0, np.NaN)
counts = np.insert(counts, 0, mask.sum())
return keys, counts
def _value_counts_arraylike(values, dropna=True):
is_datetimetz = com.is_datetimetz(values)
is_period = (isinstance(values, gt.ABCPeriodIndex) or
com.is_period_arraylike(values))
orig = values
from pandas.core.series import Series
values = Series(values).values
dtype = values.dtype
if com.is_datetime_or_timedelta_dtype(dtype) or is_period:
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.period import PeriodIndex
if is_period:
values = PeriodIndex(values)
freq = values.freq
values = values.view(np.int64)
keys, counts = htable.value_count_scalar64(values, dropna)
if dropna:
msk = keys != iNaT
keys, counts = keys[msk], counts[msk]
# convert the keys back to the dtype we came in
keys = keys.astype(dtype)
# dtype handling
if is_datetimetz:
if isinstance(orig, gt.ABCDatetimeIndex):
tz = orig.tz
else:
tz = orig.dt.tz
keys = DatetimeIndex._simple_new(keys, tz=tz)
if is_period:
keys = PeriodIndex._simple_new(keys, freq=freq)
elif com.is_integer_dtype(dtype):
values = com._ensure_int64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
elif com.is_float_dtype(dtype):
values = com._ensure_float64(values)
keys, counts = htable.value_count_scalar64(values, dropna)
else:
values = com._ensure_object(values)
mask = com.isnull(values)
keys, counts = htable.value_count_object(values, mask)
if not dropna and mask.any():
keys = np.insert(keys, 0, np.NaN)
counts = np.insert(counts, 0, mask.sum())
return keys, counts
def _value_counts_arraylike(values, dropna=True):
is_datetimetz_type = is_datetimetz(values)
is_period_type = (is_period_dtype(values) or
is_period_arraylike(values))
orig = values
from pandas.core.series import Series
values = Series(values).values
dtype = values.dtype
if needs_i8_conversion(dtype) or is_period_type:
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.period import PeriodIndex
if is_period_type:
# values may be an object
values = PeriodIndex(values)
freq = values.freq
values = values.view(np.int64)
keys, counts = htable.value_count_int64(values, dropna)
if dropna:
msk = keys != iNaT
keys, counts = keys[msk], counts[msk]
# convert the keys back to the dtype we came in
keys = keys.astype(dtype)
# dtype handling
if is_datetimetz_type:
keys = DatetimeIndex._simple_new(keys, tz=orig.dtype.tz)
if is_period_type:
keys = PeriodIndex._simple_new(keys, freq=freq)
elif is_integer_dtype(dtype):
values = _ensure_int64(values)
keys, counts = htable.value_count_int64(values, dropna)
elif is_float_dtype(dtype):
values = _ensure_float64(values)
keys, counts = htable.value_count_float64(values, dropna)
else:
values = _ensure_object(values)
mask = isnull(values)
keys, counts = htable.value_count_object(values, mask)
if not dropna and mask.any():
keys = np.insert(keys, 0, np.NaN)
counts = np.insert(counts, 0, mask.sum())
return keys, counts
