def nanmin(values, axis=None, skipna=True):
values, mask, dtype = _get_values(values, skipna, fill_value_typ='+inf')
# numpy 1.6.1 workaround in Python 3.x
if (values.dtype == np.object_ and compat.PY3):
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(builtins.min, apply_ax, values)
else:
try:
result = builtins.min(values)
except:
result = np.nan
else:
if ((axis is not None and values.shape[axis] == 0)
or values.size == 0):
try:
result = com.ensure_float(values.sum(axis))
result.fill(np.nan)
except:
result = np.nan
else:
result = values.min(axis)
result = _wrap_results(result, dtype)
return _maybe_null_out(result, axis, mask)
def nanmin(values, axis=None, skipna=True):
values, mask, dtype, dtype_max = _get_values(values,
skipna,
fill_value_typ='+inf')
# numpy 1.6.1 workaround in Python 3.x
if is_object_dtype(values) and compat.PY3:
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(builtins.min, apply_ax, values)
else:
try:
result = builtins.min(values)
except:
result = np.nan
else:
if ((axis is not None and values.shape[axis] == 0)
or values.size == 0):
try:
result = ensure_float(values.sum(axis, dtype=dtype_max))
result.fill(np.nan)
except:
result = np.nan
else:
result = values.min(axis)
result = _wrap_results(result, dtype)
return _maybe_null_out(result, axis, mask)
def nanmin(values, axis=None, skipna=True):
values, mask, dtype = _get_values(values, skipna, fill_value_typ='+inf')
# numpy 1.6.1 workaround in Python 3.x
if (values.dtype == np.object_
and sys.version_info[0] >= 3): # pragma: no cover
if values.ndim > 1:
apply_ax = axis if axis is not None else 0
result = np.apply_along_axis(builtins.min, apply_ax, values)
else:
result = builtins.min(values)
else:
if ((axis is not None and values.shape[axis] == 0)
or values.size == 0):
result = com.ensure_float(values.sum(axis))
result.fill(np.nan)
else:
result = values.min(axis)
result = _wrap_results(result, dtype)
return _maybe_null_out(result, axis, mask)
