def _loc_to_iloc(self, arg):
from cudf.dataframe.series import Series
from cudf.dataframe.index import Index
if isinstance(
arg, (list, np.ndarray, pd.Series, range, Index, DeviceNDArray)
):
if len(arg) == 0:
arg = Series(np.array([], dtype="int32"))
else:
arg = Series(arg)
if isinstance(arg, Series):
if arg.dtype in [np.bool, np.bool_]:
return arg
else:
return indices_from_labels(self._sr, arg)
elif is_scalar(arg):
found_index = self._sr.index.find_label_range(arg, None)[0]
return found_index
elif isinstance(arg, slice):
start_index, stop_index = self._sr.index.find_label_range(
arg.start, arg.stop
)
return slice(start_index, stop_index, arg.step)
else:
raise NotImplementedError(
".loc not implemented for label type {}".format(
type(arg).__name__
)
)
def __getitem__(self, arg):
from cudf.dataframe.series import Series
from cudf.dataframe.index import Index
if isinstance(
arg,
(list, np.ndarray, pd.Series, range, Index, DeviceNDArray)):
if len(arg) == 0:
arg = Series(np.array([], dtype='int32'))
else:
arg = Series(arg)
if isinstance(arg, Series):
if arg.dtype in [np.bool, np.bool_]:
return self._sr.iloc[arg]
# To do this efficiently we need a solution to
# https://github.com/rapidsai/cudf/issues/1087
out = Series([],
dtype=self._sr.dtype,
index=self._sr.index.__class__([]))
for s in arg:
out = out.append(self._sr.loc[s:s], ignore_index=False)
return out
elif is_single_value(arg):
found_index = self._sr.index.find_label_range(arg, None)[0]
return self._sr.iloc[found_index]
elif isinstance(arg, slice):
start_index, stop_index = self._sr.index.find_label_range(
arg.start, arg.stop)
return self._sr.iloc[start_index:stop_index:arg.step]
else:
raise NotImplementedError(
".loc not implemented for label type {}".format(
type(arg).__name__))
def _compute_validity_mask(self, index, row_tuple, max_length):
""" Computes the valid set of indices of values in the lookup
"""
from cudf import DataFrame
from cudf import Series
from cudf import concat
from cudf.utils.cudautils import arange
lookup = DataFrame()
for idx, row in enumerate(row_tuple):
if row == slice(None):
continue
lookup[index._source_data.columns[idx]] = Series(row)
data_table = concat(
[
index._source_data,
DataFrame({"idx": Series(arange(len(index._source_data)))}),
],
axis=1,
)
result = lookup.merge(data_table)["idx"]
# Avoid computing levels unless the result of the merge is empty,
# which suggests that a KeyError should be raised.
if len(result) == 0:
for idx, row in enumerate(row_tuple):
if row == slice(None):
continue
if row not in index.levels[idx]:
raise KeyError(row)
return result
def normalize_chunks(self, size, chunks):
if isinstance(chunks, six.integer_types):
# *chunks* is the chunksize
return cudautils.arange(0, size, chunks)
else:
# *chunks* is an array of chunk leading offset
chunks = Series(chunks)
return chunks.to_gpu_array()
def codes(self):
from cudf.dataframe.series import Series
data = self._parent.data
if self._parent.has_null_mask:
mask = self._parent.mask
null_count = self._parent.null_count
return Series.from_masked_array(data=data.mem, mask=mask.mem,
null_count=null_count)
else:
return Series(data)
def _get_column_major(self, df, row_tuple):
from cudf import Series
from cudf import DataFrame
valid_indices = self._get_valid_indices_by_tuple(
df.columns, row_tuple, len(df._cols)
)
result = df._take_columns(valid_indices)
if isinstance(row_tuple, (numbers.Number, slice)):
row_tuple = [row_tuple]
if len(result) == 0 and len(result.columns) == 0:
result_columns = df.columns.copy(deep=False)
clear_codes = DataFrame()
for name in df.columns.names:
clear_codes[name] = Series([])
result_columns._codes = clear_codes
result_columns._source_data = clear_codes
result.columns = result_columns
elif len(row_tuple) < len(self.levels) and (
not slice(None) in row_tuple
and not isinstance(row_tuple[0], slice)
):
columns = self._popn(len(row_tuple))
result.columns = columns.take(valid_indices)
else:
result.columns = self.take(valid_indices)
if len(result.columns.levels) == 1:
columns = []
for code in result.columns.codes[result.columns.codes.columns[0]]:
columns.append(result.columns.levels[0][code])
name = result.columns.names[0]
result.columns = as_index(columns, name=name)
return result
def concat(objs, ignore_index=False):
"""Concatenate DataFrames, Series, or Indices row-wise.
Parameters
----------
objs : list of DataFrame, Series, or Index
ignore_index : bool
Set True to ignore the index of the *objs* and provide a
default range index instead.
Returns
-------
A new object of like type with rows from each object in ``objs``.
"""
if not objs:
raise ValueError("Need at least one object to concatenate")
# no-op for single object
if len(objs) == 1:
return objs[0]
typs = set(type(o) for o in objs)
if len(typs) > 1:
raise ValueError("`concat` expects all objects to be of the same "
"type. Got mix of %r." % [t.__name__ for t in typs])
typ = list(typs)[0]
if typ is DataFrame:
return DataFrame._concat(objs, ignore_index=ignore_index)
elif typ is Series:
return Series._concat(objs)
elif issubclass(typ, Index):
return Index._concat(objs)
else:
raise ValueError("Unknown type %r" % typ)
def _get_row_major(self, df, row_tuple):
valid_indices = self._compute_validity_mask(df, row_tuple)
from cudf import Series
result = df.take(Series(valid_indices))
# Build new index - INDEX based MultiIndex
# ---------------
from cudf import DataFrame
out_index = DataFrame()
# Select the last n-k columns where n is the number of source
# levels and k is the length of the indexing tuple
for k in range(len(row_tuple), len(df.index.levels)):
out_index.add_column(df.index.names[k],
df.index.codes[df.index.codes.columns[k]])
# If there's only one column remaining in the output index, convert
# it into a StringIndex and name the final index values according
# to the proper codes.
if len(out_index.columns) == 1:
out_index = []
for val in result.index.codes[result.index.codes.columns[len(result.index.codes.columns)-1]]: # noqa: E501
out_index.append(result.index.levels[
len(result.index.codes.columns)-1][val])
# TODO: Warning! The final index column could be arbitrarily
# ordered integers, not Strings, so we need to check for that
# dtype and produce a GenericIndex instead of a StringIndex
out_index = StringIndex(out_index)
out_index.name = result.index.names[len(result.index.names)-1]
result.index = out_index
else:
# Otherwise pop the leftmost levels, names, and codes from the
# source index until it has the correct number of columns (n-k)
if(len(out_index.columns)) > 0:
result.reset_index(drop=True)
result.index = result.index._popn(len(row_tuple))
return result
def _get_valid_indices_by_tuple(self, index, row_tuple, max_length):
from cudf.utils.cudautils import arange
from cudf import Series
# Instructions for Slicing
# if tuple, get first and last elements of tuple
# if open beginning tuple, get 0 to highest valid_index
# if open ending tuple, get highest valid_index to len()
# if not open end or beginning, get range lowest beginning index
# to highest ending index
if isinstance(row_tuple, slice):
if (
isinstance(row_tuple.start, numbers.Number)
or isinstance(row_tuple.stop, numbers.Number)
or row_tuple == slice(None)
):
stop = row_tuple.stop or max_length
start, stop, step = row_tuple.indices(stop)
return arange(start, stop, step)
start_values = self._compute_validity_mask(
index, row_tuple.start, max_length
)
stop_values = self._compute_validity_mask(
index, row_tuple.stop, max_length
)
return Series(arange(start_values.min(), stop_values.max() + 1))
elif isinstance(row_tuple, numbers.Number):
return row_tuple
return self._compute_validity_mask(index, row_tuple, max_length)
def _apply_op(self, fn, other=None):
from cudf.dataframe.series import Series
idx_series = Series(self)
op = getattr(idx_series, fn)
if other is not None:
return as_index(op(other))
else:
return as_index(op())
def _sortjoin(self, other, how='left', return_indexers=False):
"""Join with another column.
When the column is a index, set *return_indexers* to obtain
the indices for shuffling the remaining columns.
"""
from cudf.dataframe.series import Series
if not self.is_type_equivalent(other):
raise TypeError('*other* is not compatible')
lkey, largsort = self.sort_by_values(True)
rkey, rargsort = other.sort_by_values(True)
with _gdf.apply_join(
[lkey], [rkey], how=how, method='sort') as (lidx, ridx):
if lidx.size > 0:
raw_index = cudautils.gather_joined_index(
lkey.to_gpu_array(),
rkey.to_gpu_array(),
lidx,
ridx,
)
buf_index = Buffer(raw_index)
else:
buf_index = Buffer.null(dtype=self.dtype)
joined_index = lkey.replace(data=buf_index)
if return_indexers:
def gather(idxrange, idx):
mask = (Series(idx) != -1).as_mask()
return idxrange.take(idx).set_mask(mask).fillna(-1)
if len(joined_index) > 0:
indexers = (
gather(Series(largsort), lidx),
gather(Series(rargsort), ridx),
)
else:
indexers = (
Series(Buffer.null(dtype=np.intp)),
Series(Buffer.null(dtype=np.intp))
)
return joined_index, indexers
else:
return joined_index
def wrapper(*args, **kwargs):
ret = getattr(self._parent._data, attr)(*args, **kwargs)
if isinstance(ret, nvstrings.nvstrings):
ret = Series(
columnops.as_column(ret),
index=self._index,
name=self._parent.name,
)
return ret
def concat(objs, axis=0, ignore_index=False, sort=None):
"""Concatenate DataFrames, Series, or Indices row-wise.
Parameters
----------
objs : list of DataFrame, Series, or Index
axis : concatenation axis, 0 - index, 1 - columns
ignore_index : bool
Set True to ignore the index of the *objs* and provide a
default range index instead.
Returns
-------
A new object of like type with rows from each object in ``objs``.
"""
if sort not in (None, False):
raise NotImplementedError("sort parameter is not yet supported")
if not objs:
raise ValueError("Need at least one object to concatenate")
# no-op for single object
if len(objs) == 1:
return objs[0]
typs = set(type(o) for o in objs)
allowed_typs = {Series, DataFrame}
# when axis is 1 (column) we can concat with Series and Dataframes
if axis == 1:
assert typs.issubset(allowed_typs)
df = DataFrame()
for idx, o in enumerate(objs):
if isinstance(o, Series):
name = o.name
if o.name is None:
# pandas uses 0-offset
name = idx - 1
df[name] = o
else:
for col in o.columns:
df[col] = o[col]
return df
if len(typs) > 1:
raise ValueError("`concat` expects all objects to be of the same "
"type. Got mix of %r." % [t.__name__ for t in typs])
typ = list(typs)[0]
if typ is DataFrame:
return DataFrame._concat(objs, axis=axis, ignore_index=ignore_index)
elif typ is Series:
return Series._concat(objs, axis=axis)
elif issubclass(typ, Index):
return Index._concat(objs)
else:
raise ValueError("Unknown type %r" % typ)
def _get_row_major(self, df, row_tuple):
from cudf import Series
valid_indices = self._get_valid_indices_by_tuple(
df.index, row_tuple, len(df.index)
)
indices = Series(valid_indices)
result = df.take(indices)
final = self._index_and_downcast(result, result.index, row_tuple)
return final
def _group_dataframe(self, df, levels):
"""Group dataframe.
The output dataframe has the same number of rows as the input
dataframe. The rows are shuffled so that the groups are moved
together in ascending order based on the multi-level index.
Parameters
----------
df : DataFrame
levels : list[str]
Column names for the multi-level index.
Returns
-------
(df, segs) : namedtuple
* df : DataFrame
The grouped dataframe.
* segs : Series.
Group starting index.
"""
if len(df) == 0:
# Groupby on empty dataframe
return _dfsegs_pack(df=df, segs=Buffer(np.asarray([])))
# Prepare dataframe
orig_df = df.copy()
df = df.loc[:, levels].reset_index(drop=True)
df = df.to_frame() if isinstance(df, Series) else df
rowid_column = '__cudf.groupby.rowid'
df[rowid_column] = df.index.as_column()
col_order = list(levels)
# Perform grouping
df, segs, markers = self._group_first_level(col_order[0],
rowid_column, df)
rowidcol = df[rowid_column]
sorted_keys = [Series(df.index.as_column())]
del df
more_keys, reordering_indices, segs = self._group_inner_levels(
col_order[1:], rowidcol, segs,
markers=markers)
sorted_keys.extend(more_keys)
valcols = [k for k in orig_df.columns if k not in levels]
# Prepare output
# All key columns are already sorted
out_df = DataFrame()
for k, sr in zip(levels, sorted_keys):
out_df[k] = sr
# Shuffle the value columns
self._group_shuffle(orig_df.loc[:, valcols],
reordering_indices, out_df)
return _dfsegs_pack(df=out_df, segs=segs)
def __init__(self, levels=None, codes=None, labels=None, names=None,
**kwargs):
self.names = names
column_names = []
if labels:
warnings.warn("the 'labels' keyword is deprecated, use 'codes' "
"instead", FutureWarning)
if labels and not codes:
codes = labels
# early termination enables lazy evaluation of codes
if 'source_data' in kwargs:
self._source_data = kwargs['source_data']
self._codes = codes
self._levels = levels
self.names = self._source_data.columns
return
# name setup
if isinstance(names, (Sequence,
pd.core.indexes.frozen.FrozenNDArray,
pd.core.indexes.frozen.FrozenList)):
if sum(x is None for x in names) > 1:
column_names = list(range(len(codes)))
else:
column_names = names
elif names is None:
column_names = list(range(len(codes)))
else:
column_names = names
if len(levels) == 0:
raise ValueError('Must pass non-zero number of levels/codes')
import cudf
if not isinstance(codes, cudf.dataframe.dataframe.DataFrame) and\
not isinstance(codes[0], (Sequence,
pd.core.indexes.frozen.FrozenNDArray)):
raise TypeError('Codes is not a Sequence of sequences')
if not isinstance(codes, cudf.dataframe.dataframe.DataFrame):
self._codes = cudf.dataframe.dataframe.DataFrame()
for idx, code in enumerate(codes):
code = np.array(code)
self._codes.add_column(column_names[idx],
columnops.as_column(code))
else:
self._codes = codes
# converting levels to numpy array will produce a Float64Index
# (on empty levels)for levels mimicking the behavior of Pandas
self._levels = np.array([Series(level).to_array() for level in levels])
self._validate_levels_and_codes(self._levels, self._codes)
self.name = None
self.names = names
def lower(self):
"""
Convert strings in the Series/Index to lowercase.
Returns
-------
Series/Index of str dtype
A copy of the object with all strings converted to lowercase.
"""
from cudf.dataframe import Series
return Series(self._parent.data.lower(), index=self._index)
def searchsorted(self, value, side="left"):
"""Find indices where elements should be inserted to maintain order
Parameters
----------
value : Column
Column of values to search for
side : str {‘left’, ‘right’} optional
If ‘left’, the index of the first suitable location found is given.
If ‘right’, return the last such index
Returns
-------
An index series of insertion points with the same shape as value
"""
from cudf.dataframe.series import Series
idx_series = Series(self, name=self.name)
result = idx_series.searchsorted(value, side)
return as_index(result)
def _hashjoin(self, other, how='left', return_indexers=False):
from cudf.dataframe.series import Series
if not self.is_type_equivalent(other):
raise TypeError('*other* is not compatible')
with _gdf.apply_join(
[self], [other], how=how, method='hash') as (lidx, ridx):
if lidx.size > 0:
raw_index = cudautils.gather_joined_index(
self.to_gpu_array(),
other.to_gpu_array(),
lidx,
ridx,
)
buf_index = Buffer(raw_index)
else:
buf_index = Buffer.null(dtype=self.dtype)
joined_index = self.replace(data=buf_index)
if return_indexers:
def gather(idxrange, idx):
mask = (Series(idx) != -1).as_mask()
return idxrange.take(idx).set_mask(mask).fillna(-1)
if len(joined_index) > 0:
indexers = (
gather(Series(range(0, len(self))), lidx),
gather(Series(range(0, len(other))), ridx),
)
else:
indexers = (
Series(Buffer.null(dtype=np.intp)),
Series(Buffer.null(dtype=np.intp))
)
return joined_index, indexers
else:
return joined_index
def _group_inner_levels(self, columns, rowidcol, segs, markers):
"""Group the second and onwards level.
Parameters
----------
columns : sequence[str]
Group keys. The order is important.
rowid_column : str
The name of the special column with the original rowid.
It's internally used to determine the shuffling order.
df : DataFrame
The dataframe being grouped.
segs : Series
First level group begin offsets.
Returns
-------
(sorted_keys, reordering_indices, segments)
- sorted_keys : list[Series]
List of sorted key columns.
Column order is same as arg *columns*.
- reordering_indices : device array
The indices to gather on to shuffle the dataframe
into the grouped seqence.
- segments : Series
Group begin offsets.
"""
dsegs = segs.astype(dtype=np.int32).data.mem
sorted_keys = []
plan_cache = {}
for col in columns:
# Shuffle the key column according to the previous groups
srkeys = self._df[col].take(rowidcol.to_gpu_array(),
ignore_index=True)
# Segmented sort on the key
shuf = Column(Buffer(cudautils.arange(len(srkeys))))
cache_key = (len(srkeys), srkeys.dtype, shuf.dtype)
plan = plan_cache.get(cache_key)
plan = apply_segsort(srkeys._column, shuf, dsegs, plan=plan)
plan_cache[cache_key] = plan
sorted_keys.append(srkeys) # keep sorted key cols
# Determine segments
dsegs, markers = cudautils.find_segments(srkeys.to_gpu_array(),
dsegs,
markers=markers)
# Shuffle
rowidcol = rowidcol.take(shuf.to_gpu_array(), ignore_index=True)
reordering_indices = rowidcol.to_gpu_array()
return sorted_keys, reordering_indices, Series(dsegs)
def _to_frame(self):
from cudf import DataFrame
# for each column of codes
# replace column with mapping from integers to levels
df = self.codes.copy(deep=False)
for idx, column in enumerate(df.columns):
# use merge as a replace fn
level = DataFrame({'idx': Series(cudautils.arange(len(
self.levels[idx]),
dtype=df[column].dtype)),
'level': self.levels[idx]})
code = DataFrame({'idx': df[column]})
df[column] = code.merge(level).level
return df
def apply(self, method):
gpu_out = numba.cuda.device_array_like(self.gpu_in)
kernel = get_ewm_kernel(method)
kernel[(self.number_of_blocks,),
(self.number_of_threads,),
0,
self.shared_buffer_size * 8](self.gpu_in,
gpu_out,
self.window,
self.span,
self.array_len,
self.thread_tile,
self.min_periods)
return Series(gpu_out)
def _compute_levels_and_codes(self):
levels = []
from cudf import DataFrame
codes = DataFrame()
names = []
# Note: This is an O(N^2) solution using gpu masking
# to compute new codes for the MultiIndex. There may be
# a faster solution that could be executed on gpu at the same
# time the groupby is calculated.
for by in self._source_data.columns:
if len(self._source_data[by]) > 0:
level = self._source_data[by].unique()
replaced = self._source_data[by].replace(
level, Series(range(len(level))))
else:
level = np.array([])
replaced = np.array([])
levels.append(level)
codes[by] = Series(replaced, dtype="int32")
names.append(by)
self._levels = levels
self._codes = codes
self.names = names
def cat(self, others=None, sep=None, na_rep=None):
"""
Concatenate strings in the Series/Index with given separator.
If *others* is specified, this function concatenates the Series/Index
and elements of others element-wise. If others is not passed, then all
values in the Series/Index are concatenated into a single string with
a given sep.
Parameters
----------
others : Series or List of str
Strings to be appended.
The number of strings must match size() of this instance.
This must be either a Series of string dtype or a Python
list of strings.
sep : str
If specified, this separator will be appended to each string
before appending the others.
na_rep : str
This character will take the place of any null strings
(not empty strings) in either list.
- If `na_rep` is None, and `others` is None, missing values in
the Series/Index are omitted from the result.
- If `na_rep` is None, and `others` is not None, a row
containing a missing value in any of the columns (before
concatenation) will have a missing value in the result.
Returns
-------
concat : str or Series/Index of str dtype
If `others` is None, `str` is returned, otherwise a `Series/Index`
(same type as caller) of str dtype is returned.
"""
from cudf.dataframe import Series, Index
if isinstance(others, (Series, Index)):
assert others.dtype == np.dtype('object')
others = others.data
out = Series(self._parent.data.cat(others=others,
sep=sep,
na_rep=na_rep),
index=self._index)
if len(out) == 1 and others is None:
out = out[0]
return out
def __getattr__(self, attr, *args, **kwargs):
from cudf.dataframe.series import Series
if hasattr(self._parent._data, attr):
passed_attr = getattr(self._parent._data, attr)
if callable(passed_attr):
def wrapper(*args, **kwargs):
return getattr(self._parent._data, attr)(*args, **kwargs)
if isinstance(wrapper, nvstrings.nvstrings):
wrapper = Series(columnops.as_column(wrapper),
index=self._index)
return wrapper
else:
return passed_attr
else:
raise AttributeError(attr)
def apply_multiindex_or_single_index(self, result):
if len(result) == 0:
final_result = DataFrame()
for col in result.columns:
if col not in self._by:
final_result[col] = result[col]
if len(self._by) == 1 or len(final_result.columns) == 0:
dtype = 'float64' if len(self._by) == 1 else 'object'
name = self._by[0] if len(self._by) == 1 else None
from cudf.dataframe.index import GenericIndex
index = GenericIndex(Series([], dtype=dtype))
index.name = name
final_result.index = index
else:
mi = MultiIndex(source_data=result[self._by])
mi.names = self._by
final_result.index = mi
if len(final_result.columns) == 1 and hasattr(self, "_gotattr"):
final_series = Series([], name=final_result.columns[0])
final_series.index = final_result.index
return final_series
return final_result
if len(self._by) == 1:
from cudf.dataframe import index
idx = index.as_index(result[self._by[0]])
idx.name = self._by[0]
result = result.drop(idx.name)
if idx.name == self._LEVEL_0_INDEX_NAME:
idx.name = self._original_index_name
result = result.set_index(idx)
return result
else:
multi_index = MultiIndex(source_data=result[self._by])
final_result = DataFrame()
for col in result.columns:
if col not in self._by:
final_result[col] = result[col]
if len(final_result.columns) == 1 and hasattr(self, "_gotattr"):
final_series = Series(final_result[final_result.columns[0]])
final_series.name = final_result.columns[0]
final_series.index = multi_index
return final_series
return final_result.set_index(multi_index)
def from_dlpack(pycapsule_obj):
"""Converts from a DLPack tensor to a cuDF object.
DLPack is an open-source memory tensor structure:
`dmlc/dlpack <https://github.com/dmlc/dlpack>`_.
This function takes a PyCapsule object which contains a pointer to
a DLPack tensor as input, and returns a cuDF object. This function deep
copies the data in the DLPack tensor into a cuDF object.
Parameters
----------
pycapsule_obj : PyCapsule
Input DLPack tensor pointer which is encapsulated in a PyCapsule
object.
Returns
-------
A cuDF DataFrame or Series depending on if the input DLPack tensor is 1D
or 2D.
"""
try:
res, valids = cpp_dlpack.from_dlpack(pycapsule_obj)
except GDFError as err:
if str(err) == "b'GDF_DATASET_EMPTY'":
raise ValueError(
"Cannot create a cuDF Object from a DLPack tensor of 0 size")
else:
raise err
cols = []
for idx in range(len(valids)):
mask = None
if valids[idx]:
mask = Buffer(valids[idx])
cols.append(
columnops.build_column(Buffer(res[idx]),
dtype=res[idx].dtype,
mask=mask))
if len(cols) == 1:
return Series(cols[0])
else:
df = DataFrame()
for idx, col in enumerate(cols):
df[idx] = col
return df
def extract(self, pat, flags=0, expand=True):
"""
Extract capture groups in the regex `pat` as columns in a DataFrame.
For each subject string in the Series, extract groups from the first
match of regular expression `pat`.
Parameters
----------
pat : str
Regular expression pattern with capturing groups.
expand : bool, default True
If True, return DataFrame with on column per capture group.
If False, return a Series/Index if there is one capture group or
DataFrame if there are multiple capture groups.
Returns
-------
DataFrame or Series/Index
A DataFrame with one row for each subject string, and one column
for each group. If `expand=False` and `pat` has only one capture
group, then return a Series/Index.
Notes
-----
The `flags` parameter is not yet supported and will raise a
NotImplementedError if anything other than the default value is passed.
"""
if flags != 0:
raise NotImplementedError("`flags` parameter is not yet supported")
from cudf.dataframe import DataFrame, Series
out = self._parent.data.extract(pat)
if len(out) == 1 and expand is False:
return Series(
out[0],
index=self._index
)
else:
out_df = DataFrame(index=self._index)
for idx, val in enumerate(out):
out_df[idx] = val
return out_df
def len(self):
"""
Computes the length of each element in the Series/Index.
Returns
-------
Series or Index of int: A Series or Index of integer values
indicating the length of each element in the Series or Index.
"""
from cudf.dataframe.series import Series
out_dev_arr = rmm.device_array(len(self._parent), dtype='int32')
ptr = get_ctype_ptr(out_dev_arr)
self._parent.data.len(ptr)
mask = None
if self._parent.null_count > 0:
mask = self._parent.mask
column = columnops.build_column(Buffer(out_dev_arr),
np.dtype('int32'),
mask=mask)
return Series(column, index=self._index)
def _group_first_level(self, col, rowid_column, df):
"""Group first level *col* of *df*
Parameters
----------
col : str
Name of the first group key column.
df : DataFrame
The dataframe being grouped.
Returns
-------
(df, segs)
- df : DataFrame
Sorted by *col- * index
- segs : Series
Group begin offsets
"""
df = df.loc[:, [col, rowid_column]]
df = df.set_index(col).sort_index()
segs, markers = df.index._find_segments()
return df, Series(segs), markers
