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) onthe passed axis number.

Note

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

Examples:

>>>importbigframes.pandasaspd>>>pd.options.display.progress_bar=None

Combine twoSeries.

>>>s1=pd.Series(['a','b'])>>>s2=pd.Series(['c','d'])>>>pd.concat([s1,s2])0    a1    b0    c1    ddtype: string

Clear the existing index and reset it in the resultby setting theignore_index option toTrue.

>>>pd.concat([s1,s2],ignore_index=True)0    a1    b2    c3    ddtype: string

Combine twoDataFrame objects with identical columns.

>>>df1=pd.DataFrame([['a',1],['b',2]],...columns=['letter','number'])>>>df1  letter  number0      a       11      b       2[2 rows x 2 columns]>>>df2=pd.DataFrame([['c',3],['d',4]],...columns=['letter','number'])>>>df2  letter  number0      c       31      d       4[2 rows x 2 columns]>>>pd.concat([df1,df2])  letter  number0      a       11      b       20      c       31      d       4[4 rows x 2 columns]

CombineDataFrame objects with overlapping columnsand return everything. Columns outside the intersection willbe filled withNaN values.

>>>df3=pd.DataFrame([['c',3,'cat'],['d',4,'dog']],...columns=['letter','number','animal'])>>>df3  letter  number animal0      c       3    cat1      d       4    dog[2 rows x 3 columns]>>>pd.concat([df1,df3])  letter  number animal0      a       1   <NA>1      b       2   <NA>0      c       3    cat1      d       4    dog[4 rows x 3 columns]

CombineDataFrame objects with overlapping columnsand return only those that are shared by passinginner tothejoin keyword argument.

>>>pd.concat([df1,df3],join="inner")  letter  number0      a       11      b       20      c       31      d       4[4 rows x 2 columns]
Parameters:

  • objs (list ofobjects) – Objects to concatenate. Any None objects will be dropped silently unlessthey 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. Theresulting axis will be labeled 0, …, n - 1. This is useful if you areconcatenating objects where the concatenation axis does not havemeaningful indexing information. Note the index values on the otheraxes are still respected in the join.

Returns:

When concatenating allSeries along the index (axis=0), aSeries is returned. Whenobjs contains at least oneDataFrame, aDataFrame is returned.

Return type:

object,type of objs

On this page

This Page