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)