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 : {0/'index', 1/'columns'}, default 0
The axis to concatenate along.
ignore_index : bool, default False
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}
param_axis = _axis_map.get(axis, None)
if param_axis is None:
raise ValueError(
'`axis` must be 0 / "index" or 1 / "columns", got: {0}'.format(
param_axis))
else:
axis = param_axis
# 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 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 : {0/'index', 1/'columns'}, default 0
The axis to concatenate along.
ignore_index : bool, default False
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")
objs = [obj for obj in objs if obj is not None]
# Return for single object
if len(objs) == 1:
return objs[0]
if len(objs) == 0:
raise ValueError("All objects passed were None")
typs = set(type(o) for o in objs)
allowed_typs = {Series, DataFrame}
param_axis = _axis_map.get(axis, None)
if param_axis is None:
raise ValueError(
'`axis` must be 0 / "index" or 1 / "columns", got: {0}'.format(
param_axis
)
)
else:
axis = param_axis
# when axis is 1 (column) we can concat with Series and Dataframes
if axis == 1:
assert typs.issubset(allowed_typs)
df = DataFrame()
sr_name = 0
for idx, o in enumerate(objs):
if isinstance(o, Series):
name = o.name
if name is None:
name = sr_name
sr_name += 1
objs[idx] = o.to_frame(name=name)
for idx, o in enumerate(objs):
if idx == 0:
df.index = o.index
for col in o._data.names:
if col in df._data:
raise NotImplementedError(
"A Column with duplicate name found: {0}, cuDF\
doesn't support having multiple columns with\
same names yet.".format(
col
)
)
df[col] = o._data[col]
result_columns = objs[0].columns
for o in objs[1:]:
result_columns = result_columns.append(o.columns)
df.columns = result_columns
return df
typ = list(typs)[0]
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 typ is cudf.MultiIndex:
return cudf.MultiIndex._concat(objs)
elif issubclass(typ, Index):
return Index._concat(objs)
else:
raise ValueError("Unknown type %r" % typ)
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 : {0/'index', 1/'columns'}, default 0
The axis to concatenate along.
ignore_index : bool, default False
Set True to ignore the index of the *objs* and provide a
default range index instead.
sort : bool, default False
Sort non-concatenation axis if it is not already aligned.
Returns
-------
A new object of like type with rows from each object in ``objs``.
Examples
--------
Combine two ``Series``.
>>> import cudf
>>> s1 = cudf.Series(['a', 'b'])
>>> s2 = cudf.Series(['c', 'd'])
>>> s1
0 a
1 b
dtype: object
>>> s2
0 c
1 d
dtype: object
>>> cudf.concat([s1, s2])
0 a
1 b
0 c
1 d
dtype: object
Clear the existing index and reset it in the
result by setting the ``ignore_index`` option to ``True``.
>>> cudf.concat([s1, s2], ignore_index=True)
0 a
1 b
2 c
3 d
dtype: object
Combine two DataFrame objects with identical columns.
>>> df1 = cudf.DataFrame([['a', 1], ['b', 2]],
... columns=['letter', 'number'])
>>> df1
letter number
0 a 1
1 b 2
>>> df2 = cudf.DataFrame([['c', 3], ['d', 4]],
... columns=['letter', 'number'])
>>> df2
letter number
0 c 3
1 d 4
>>> cudf.concat([df1, df2])
letter number
0 a 1
1 b 2
0 c 3
1 d 4
Combine DataFrame objects with overlapping columns and return
everything. Columns outside the intersection will
be filled with ``null`` values.
>>> df3 = cudf.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
... columns=['letter', 'number', 'animal'])
>>> df3
letter number animal
0 c 3 cat
1 d 4 dog
>>> cudf.concat([df1, df3], sort=False)
letter number animal
0 a 1 None
1 b 2 None
0 c 3 cat
1 d 4 dog
Combine ``DataFrame`` objects horizontally along the
x axis by passing in ``axis=1``.
>>> df4 = cudf.DataFrame([['bird', 'polly'], ['monkey', 'george']],
... columns=['animal', 'name'])
>>> df4
animal name
0 bird polly
1 monkey george
>>> cudf.concat([df1, df4], axis=1)
letter number animal name
0 a 1 bird polly
1 b 2 monkey george
"""
if not objs:
raise ValueError("No objects to concatenate")
objs = [obj for obj in objs if obj is not None]
# Return for single object
if len(objs) == 1:
if ignore_index:
result = cudf.DataFrame(
data=objs[0]._data.copy(deep=True),
index=cudf.RangeIndex(len(objs[0])),
)
else:
result = objs[0].copy()
return result
if len(objs) == 0:
raise ValueError("All objects passed were None")
# Retrieve the base types of `objs`. In order to support sub-types
# and object wrappers, we use `isinstance()` instead of comparing
# types directly
typs = set()
for o in objs:
if isinstance(o, cudf.MultiIndex):
typs.add(cudf.MultiIndex)
if issubclass(type(o), Index):
typs.add(type(o))
elif isinstance(o, DataFrame):
typs.add(DataFrame)
elif isinstance(o, Series):
typs.add(Series)
else:
raise ValueError(f"cannot concatenate object of type {type(o)}")
allowed_typs = {Series, DataFrame}
param_axis = _axis_map.get(axis, None)
if param_axis is None:
raise ValueError(
'`axis` must be 0 / "index" or 1 / "columns", got: {0}'.format(
param_axis
)
)
else:
axis = param_axis
# when axis is 1 (column) we can concat with Series and Dataframes
if axis == 1:
assert typs.issubset(allowed_typs)
df = DataFrame()
_normalize_series_and_dataframe(objs, axis=axis)
objs, match_index = _align_objs(objs)
for idx, o in enumerate(objs):
if not ignore_index and idx == 0:
df.index = o.index
for col in o._data.names:
if col in df._data:
raise NotImplementedError(
"A Column with duplicate name found: {0}, cuDF\
doesn't support having multiple columns with\
same names yet.".format(
col
)
)
df[col] = o._data[col]
result_columns = objs[0].columns
for o in objs[1:]:
result_columns = result_columns.append(o.columns)
df.columns = result_columns.unique()
if ignore_index:
df.index = None
return df
elif not match_index:
return df.sort_index()
else:
return df
typ = list(typs)[0]
if len(typs) > 1:
if allowed_typs == typs:
# This block of code will run when `objs` has
# both Series & DataFrame kind of inputs.
_normalize_series_and_dataframe(objs, axis=axis)
typ = DataFrame
else:
raise ValueError(
"`concat` cannot concatenate objects of "
"types: %r." % sorted([t.__name__ for t in typs])
)
if typ is DataFrame:
objs = [obj for obj in objs if obj.shape != (0, 0)]
if len(objs) == 0:
# If objs is empty, that indicates all of
# objs are empty dataframes.
return cudf.DataFrame()
elif len(objs) == 1:
if ignore_index:
result = cudf.DataFrame(
data=objs[0]._data.copy(deep=True),
index=cudf.RangeIndex(len(objs[0])),
)
else:
result = objs[0].copy()
return result
else:
return DataFrame._concat(
objs, axis=axis, ignore_index=ignore_index, sort=sort
)
elif typ is Series:
return Series._concat(
objs, axis=axis, index=None if ignore_index else True
)
elif typ is cudf.MultiIndex:
return cudf.MultiIndex._concat(objs)
elif issubclass(typ, Index):
return Index._concat(objs)
else:
raise ValueError(f"cannot concatenate object of type {typ}")