cudf.concat#

cudf.concat(objs, axis=0, join='outer', ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=None)[source]#

Concatenate DataFrames, Series, or Indices row-wise.

Parameters:

objslist or dictionary of DataFrame, Series, or Index
axis{0/’index’, 1/’columns’}, default 0: The axis to concatenate along. axis=1 must be passed if a dictionary is passed.
join{‘inner’, ‘outer’}, default ‘outer’: How to handle indexes on other axis (or axes).
ignore_indexbool, default False: Set True to ignore the index of the objs and provide a default range index instead.
keyssequence, default None: If multiple levels passed, should contain tuples. Construct hierarchical index using the passed keys as the outermost level. Currently not supported.
levelslist of sequences, default None: Specific levels (unique values) to use for constructing a MultiIndex. Otherwise they will be inferred from the keys. Currently not supported.
nameslist, default None: Names for the levels in the resulting hierarchical index. Currently not supported.
verify_integritybool, default False: Check whether the new concatenated axis contains duplicates. This can be very expensive relative to the actual data concatenation. Currently not supported.
sortbool, 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   <NA>
1      b       2   <NA>
0      c       3    cat
1      d       4    dog

Combine DataFrame objects with overlapping columns and return only those that are shared by passing inner to the join keyword argument.

>>> cudf.concat([df1, df3], join="inner")
  letter  number
0      a       1
1      b       2
0      c       3
1      d       4

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

Combine a dictionary of DataFrame objects horizontally:

>>> d = {'first': df1, 'second': df2}
>>> cudf.concat(d, axis=1)
  first           second
  letter  number  letter  number
0      a       1       c       3
1      b       2       d       4