def test_is_dataframe_like():
df = cudf.DataFrame({'x': [1, 2, 3]})
assert is_dataframe_like(df)
assert is_series_like(df.x)
assert is_index_like(df.index)
assert not is_dataframe_like(df.x)
assert not is_series_like(df)
assert not is_index_like(df)
def test_is_dataframe_like(monkeypatch, frame_value_counts):
# When we drop support for pandas 1.0, this compat check can
# be dropped
if frame_value_counts:
monkeypatch.setattr(pd.DataFrame, "value_counts", lambda x: None, raising=False)
df = pd.DataFrame({"x": [1, 2, 3]})
ddf = dd.from_pandas(df, npartitions=1)
assert is_dataframe_like(df)
assert is_dataframe_like(ddf)
assert not is_dataframe_like(df.x)
assert not is_dataframe_like(ddf.x)
assert not is_dataframe_like(df.index)
assert not is_dataframe_like(ddf.index)
assert not is_dataframe_like(pd.DataFrame)
assert not is_series_like(df)
assert not is_series_like(ddf)
assert is_series_like(df.x)
assert is_series_like(ddf.x)
assert not is_series_like(df.index)
assert not is_series_like(ddf.index)
assert not is_series_like(pd.Series)
assert not is_index_like(df)
assert not is_index_like(ddf)
assert not is_index_like(df.x)
assert not is_index_like(ddf.x)
assert is_index_like(df.index)
assert is_index_like(ddf.index)
assert not is_index_like(pd.Index)
# The following checks support of class wrappers, which
# requires the comparions of `x.__class__` instead of `type(x)`
class DataFrameWrapper:
__class__ = pd.DataFrame
wrap = DataFrameWrapper()
wrap.dtypes = None
wrap.columns = None
assert is_dataframe_like(wrap)
class SeriesWrapper:
__class__ = pd.Series
wrap = SeriesWrapper()
wrap.dtype = None
wrap.name = None
assert is_series_like(wrap)
class IndexWrapper:
__class__ = pd.Index
wrap = IndexWrapper()
wrap.dtype = None
wrap.name = None
assert is_index_like(wrap)
def test_is_dataframe_like():
df = pd.DataFrame({'x': [1, 2, 3]})
assert is_dataframe_like(df)
assert not is_dataframe_like(df.x)
assert not is_dataframe_like(df.index)
assert not is_series_like(df)
assert is_series_like(df.x)
assert not is_series_like(df.index)
assert not is_index_like(df)
assert not is_index_like(df.x)
assert is_index_like(df.index)
ddf = dd.from_pandas(df, npartitions=1)
assert is_dataframe_like(ddf)
def _maybe_partial_time_string(index, indexer):
"""
Convert indexer for partial string selection
if data has DatetimeIndex/PeriodIndex
"""
# do not pass dd.Index
assert is_index_like(index)
if not isinstance(index, (pd.DatetimeIndex, pd.PeriodIndex)):
return indexer
if PANDAS_GT_130:
kind_option = {}
else:
kind_option = {"kind": "loc"}
if isinstance(indexer, slice):
if isinstance(indexer.start, str):
start = index._maybe_cast_slice_bound(indexer.start, "left", **kind_option)
else:
start = indexer.start
if isinstance(indexer.stop, str):
stop = index._maybe_cast_slice_bound(indexer.stop, "right", **kind_option)
else:
stop = indexer.stop
return slice(start, stop)
elif isinstance(indexer, str):
start = index._maybe_cast_slice_bound(indexer, "left", **kind_option)
stop = index._maybe_cast_slice_bound(indexer, "right", **kind_option)
return slice(min(start, stop), max(start, stop))
return indexer
def unique(x, series_name=None):
out = x.unique()
# out can be either an np.ndarray or may already be a series
# like object. When out is an np.ndarray, it must be wrapped.
if not (is_series_like(out) or is_index_like(out)):
out = pd.Series(out, name=series_name)
return out
def _monotonic_chunk(x, prop):
if x.empty:
# if input is empty, return empty df for chunk
data = None
else:
data = x if is_index_like(x) else x.iloc
data = [[getattr(x, prop), data[0], data[-1]]]
return pd.DataFrame(data=data, columns=["monotonic", "first", "last"])
def test_is_dataframe_like(monkeypatch, frame_value_counts):
# When we drop support for pandas 1.0, this compat check can
# be dropped
if frame_value_counts:
monkeypatch.setattr(pd.DataFrame,
"value_counts",
lambda x: None,
raising=False)
df = pd.DataFrame({"x": [1, 2, 3]})
ddf = dd.from_pandas(df, npartitions=1)
assert is_dataframe_like(df)
assert is_dataframe_like(ddf)
assert not is_dataframe_like(df.x)
assert not is_dataframe_like(ddf.x)
assert not is_dataframe_like(df.index)
assert not is_dataframe_like(ddf.index)
assert not is_dataframe_like(pd.DataFrame)
assert not is_series_like(df)
assert not is_series_like(ddf)
assert is_series_like(df.x)
assert is_series_like(ddf.x)
assert not is_series_like(df.index)
assert not is_series_like(ddf.index)
assert not is_series_like(pd.Series)
assert not is_index_like(df)
assert not is_index_like(ddf)
assert not is_index_like(df.x)
assert not is_index_like(ddf.x)
assert is_index_like(df.index)
assert is_index_like(ddf.index)
assert not is_index_like(pd.Index)
def test_is_dataframe_like():
df = pd.DataFrame({'x': [1, 2, 3]})
ddf = dd.from_pandas(df, npartitions=1)
assert is_dataframe_like(df)
assert is_dataframe_like(ddf)
assert not is_dataframe_like(df.x)
assert not is_dataframe_like(ddf.x)
assert not is_dataframe_like(df.index)
assert not is_dataframe_like(ddf.index)
assert not is_dataframe_like(pd.DataFrame)
assert not is_series_like(df)
assert not is_series_like(ddf)
assert is_series_like(df.x)
assert is_series_like(ddf.x)
assert not is_series_like(df.index)
assert not is_series_like(ddf.index)
assert not is_series_like(pd.Series)
assert not is_index_like(df)
assert not is_index_like(ddf)
assert not is_index_like(df.x)
assert not is_index_like(ddf.x)
assert is_index_like(df.index)
assert is_index_like(ddf.index)
assert not is_index_like(pd.Index)
