def meta_nonempty_object(x):
"""Create a nonempty pandas object from the given metadata.
Returns a pandas DataFrame, Series, or Index that contains two rows
of fake data.
"""
if is_scalar(x):
return _nonempty_scalar(x)
else:
raise TypeError(
"Expected Pandas-like Index, Series, DataFrame, or scalar, "
f"got {typename(type(x))}")
def make_meta_object(x, index=None):
"""Create an empty cudf object containing the desired metadata.
Parameters
----------
x : dict, tuple, list, cudf.Series, cudf.DataFrame, cudf.Index,
dtype, scalar
To create a DataFrame, provide a `dict` mapping of `{name: dtype}`, or
an iterable of `(name, dtype)` tuples. To create a `Series`, provide a
tuple of `(name, dtype)`. If a cudf object, names, dtypes, and index
should match the desired output. If a dtype or scalar, a scalar of the
same dtype is returned.
index : cudf.Index, optional
Any cudf index to use in the metadata. If none provided, a
`RangeIndex` will be used.
Examples
--------
>>> make_meta([('a', 'i8'), ('b', 'O')])
Empty DataFrame
Columns: [a, b]
Index: []
>>> make_meta(('a', 'f8'))
Series([], Name: a, dtype: float64)
>>> make_meta('i8')
1
"""
if hasattr(x, "_meta"):
return x._meta
elif is_arraylike(x) and x.shape:
return x[:0]
if index is not None:
index = make_meta(index)
if isinstance(x, dict):
return cudf.DataFrame(
{c: _empty_series(c, d, index=index)
for (c, d) in x.items()},
index=index,
)
if isinstance(x, tuple) and len(x) == 2:
return _empty_series(x[0], x[1], index=index)
elif isinstance(x, (list, tuple)):
if not all(isinstance(i, tuple) and len(i) == 2 for i in x):
raise ValueError(
f"Expected iterable of tuples of (name, dtype), got {x}")
return cudf.DataFrame(
{c: _empty_series(c, d, index=index)
for (c, d) in x},
columns=[c for c, d in x],
index=index,
)
elif not hasattr(x, "dtype") and x is not None:
# could be a string, a dtype object, or a python type. Skip `None`,
# because it is implictly converted to `dtype('f8')`, which we don't
# want here.
try:
dtype = np.dtype(x)
return _scalar_from_dtype(dtype)
except Exception:
# Continue on to next check
pass
if is_scalar(x):
return _nonempty_scalar(x)
raise TypeError(f"Don't know how to create metadata from {x}")
def make_meta_pandas_datetime_tz(x, index=None):
return _nonempty_scalar(x)