def _getitem_tuple_arg(self, arg):
from cudf.dataframe.dataframe import DataFrame
from cudf.dataframe.index import as_index
columns = self._get_column_selection(arg[1])
df = DataFrame()
for col in columns:
df.add_column(name=col, data=self._df[col].loc[arg[0]])
if df.shape[0] == 1: # we have a single row
if isinstance(arg[0], slice):
df.index = as_index(arg[0].start)
else:
df.index = as_index(arg[0])
return df
def _getitem_tuple_arg(self, arg):
from cudf.dataframe.dataframe import DataFrame
from cudf.dataframe.index import as_index
from cudf.utils.cudautils import arange
from cudf import MultiIndex
# Step 1: Gather columns
if isinstance(self._df.columns, MultiIndex):
columns_df = self._df.columns._get_column_major(self._df, arg[1])
else:
columns = self._get_column_selection(arg[1])
columns_df = DataFrame()
for col in columns:
columns_df.add_column(name=col, data=self._df[col])
# Step 2: Gather rows
if isinstance(columns_df.index, MultiIndex):
return columns_df.index._get_row_major(columns_df, arg[0])
else:
if isinstance(self._df.columns, MultiIndex):
if isinstance(arg[0], slice):
start, stop, step = arg[0].indices(len(columns_df))
indices = arange(start, stop, step)
df = columns_df.take(indices)
else:
df = columns_df.take(arg[0])
else:
df = DataFrame()
for col in columns_df.columns:
df[col] = columns_df[col].loc[arg[0]]
# Step 3: Gather index
if df.shape[0] == 1: # we have a single row
if isinstance(arg[0], slice):
start = arg[0].start
if start is None:
start = self._df.index[0]
df.index = as_index(start)
else:
df.index = as_index(arg[0])
# Step 4: Downcast
if self._can_downcast_to_series(df, arg):
return self._downcast_to_series(df, arg)
return df
def _getitem_tuple_arg(self, arg):
from cudf import MultiIndex
from cudf.dataframe.dataframe import DataFrame
from cudf.dataframe.dataframe import Series
from cudf.dataframe.index import as_index
# Iloc Step 1:
# Gather the columns specified by the second tuple arg
columns = self._get_column_selection(arg[1])
if isinstance(self._df.columns, MultiIndex):
columns_df = self._df.columns._get_column_major(self._df, arg[1])
if (
len(columns_df) == 0
and len(columns_df.columns) == 0
and not isinstance(arg[0], slice)
):
result = Series([], name=arg[0])
result._index = columns_df.columns.copy(deep=False)
return result
else:
if isinstance(arg[0], slice):
columns_df = DataFrame()
for col in columns:
columns_df.add_column(name=col, data=self._df[col])
columns_df._index = self._df._index
else:
columns_df = self._df._columns_view(columns)
# Iloc Step 2:
# Gather the rows specified by the first tuple arg
if isinstance(columns_df.index, MultiIndex):
df = columns_df.index._get_row_major(columns_df, arg[0])
if (len(df) == 1 and len(columns_df) >= 1) and not (
isinstance(arg[0], slice) or isinstance(arg[1], slice)
):
# Pandas returns a numpy scalar in this case
return df[0]
if self._can_downcast_to_series(df, arg):
return self._downcast_to_series(df, arg)
return df
else:
df = DataFrame()
for key, col in columns_df._cols.items():
df[key] = col.iloc[arg[0]]
df.columns = columns_df.columns
# Iloc Step 3:
# Reindex
if df.shape[0] == 1: # we have a single row without an index
if isinstance(arg[0], slice):
start = arg[0].start
if start is None:
start = 0
df.index = as_index(self._df.index[start])
else:
df.index = as_index(self._df.index[arg[0]])
# Iloc Step 4:
# Downcast
if self._can_downcast_to_series(df, arg):
if isinstance(df.columns, MultiIndex):
if len(df) > 0 and not (
isinstance(arg[0], slice) or isinstance(arg[1], slice)
):
return list(df._cols.values())[0][0]
elif df.shape[1] > 1:
result = self._downcast_to_series(df, arg)
result.index = df.columns
return result
elif not isinstance(arg[0], slice):
result_series = list(df._cols.values())[0]
result_series.index = df.columns
result_series.name = arg[0]
return result_series
else:
return list(df._cols.values())[0]
return self._downcast_to_series(df, arg)
if df.shape[0] == 0 and df.shape[1] == 0:
from cudf.dataframe.index import RangeIndex
slice_len = arg[0].stop or len(self._df)
start, stop, step = arg[0].indices(slice_len)
df._index = RangeIndex(start, stop)
return df