bigframes.pandas.concat#

bigframes.pandas.concat(objs: Iterable[bigframes.series.Series], *, axis: Literal['index', 0] = 0, join='outer', ignore_index=False) → bigframes.series.Series[source]#

bigframes.pandas.concat(objs: Iterable[bigframes.dataframe.DataFrame], *, axis: Literal['index', 0] = 0, join='outer', ignore_index=False) → bigframes.dataframe.DataFrame

bigframes.pandas.concat(objs: Iterable[bigframes.dataframe.DataFrame | bigframes.series.Series], *, axis: Literal['columns', 1], join='outer', ignore_index=False) → bigframes.dataframe.DataFrame

bigframes.pandas.concat(objs: Iterable[bigframes.dataframe.DataFrame | bigframes.series.Series], *, axis=0, join='outer', ignore_index=False) → bigframes.dataframe.DataFrame | bigframes.series.Series

Concatenate BigQuery DataFrames objects along a particular axis.

Allows optional set logic along the other axes.

Can also add a layer of hierarchical indexing on the concatenation axis, which may be useful if the labels are the same (or overlapping) on the passed axis number.

Note

It is not recommended to build DataFrames by adding single rows in a for loop. Build a list of rows and make a DataFrame in a single concat.

Examples:

>>> import bigframes.pandas as pd
>>> pd.options.display.progress_bar = None

Combine two Series.

>>> s1 = pd.Series(['a', 'b'])
>>> s2 = pd.Series(['c', 'd'])
>>> pd.concat([s1, s2])
0    a
1    b
0    c
1    d
dtype: string

Clear the existing index and reset it in the result by setting the ignore_index option to True.

>>> pd.concat([s1, s2], ignore_index=True)
0    a
1    b
2    c
3    d
dtype: string

Combine two DataFrame objects with identical columns.

>>> df1 = pd.DataFrame([['a', 1], ['b', 2]],
...                    columns=['letter', 'number'])
>>> df1
  letter  number
0      a       1
1      b       2

[2 rows x 2 columns]
>>> df2 = pd.DataFrame([['c', 3], ['d', 4]],
...                    columns=['letter', 'number'])
>>> df2
  letter  number
0      c       3
1      d       4

[2 rows x 2 columns]
>>> pd.concat([df1, df2])
  letter  number
0      a       1
1      b       2
0      c       3
1      d       4

[4 rows x 2 columns]

Combine DataFrame objects with overlapping columns and return everything. Columns outside the intersection will be filled with NaN values.

>>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
...                    columns=['letter', 'number', 'animal'])
>>> df3
  letter  number animal
0      c       3    cat
1      d       4    dog

[2 rows x 3 columns]
>>> pd.concat([df1, df3])
  letter  number animal
0      a       1   <NA>
1      b       2   <NA>
0      c       3    cat
1      d       4    dog

[4 rows x 3 columns]

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

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

[4 rows x 2 columns]

Parameters:

• objs (list of objects) Objects to concatenate. Any None objects will be dropped silently unless they are all None in which case a ValueError will be raised.

• axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to concatenate along.

• join ({'inner', 'outer'}, default 'outer') How to handle indexes on other axis (or axes).

• ignore_index (bool, default False) If True, do not use the index values along the concatenation axis. The resulting axis will be labeled 0, , n - 1. This is useful if you are concatenating objects where the concatenation axis does not have meaningful indexing information. Note the index values on the other axes are still respected in the join.

Returns:

When concatenating all Series along the index (axis=0), a Series is returned. When objs contains at least one DataFrame, a DataFrame is returned.

Return type:

object, type of objs