pandas.HDFStore.append#

HDFStore.append(key,value,format=None,axes=None,index=True,append=True,complib=None,complevel=None,columns=None,min_itemsize=None,nan_rep=None,chunksize=None,expectedrows=None,dropna=None,data_columns=None,encoding=None,errors='strict')[source]#

Append to Table in file.

Node must already exist and be Table format.

Parameters:

keystr

value{Series, DataFrame}

format‘table’ is the default

Format to use when storing object in HDFStore. Value can be one of:

'table'

Table format. Write as a PyTables Table structure which may performworse but allow more flexible operations like searching / selectingsubsets of the data.

indexbool, default True

Write DataFrame index as a column.

appendbool, default True

Append the input data to the existing.

data_columnslist of columns, or True, default None

List of columns to create as indexed data columns for on-diskqueries, or True to use all columns. By default only the axesof the object are indexed. Seehere.

min_itemsizedict of columns that specify minimum str sizes

nan_repstr to use as str nan representation

chunksizesize to chunk the writing

expectedrowsexpected TOTAL row size of this table

encodingdefault None, provide an encoding for str

dropnabool, default False, optional

Do not write an ALL nan row to the store settableby the option ‘io.hdf.dropna_table’.

Notes

Doesnot check if data being appended overlaps with existingdata in the table, so be careful

Examples

>>>df1=pd.DataFrame([[1,2],[3,4]],columns=['A','B'])>>>store=pd.HDFStore("store.h5",'w')>>>store.put('data',df1,format='table')>>>df2=pd.DataFrame([[5,6],[7,8]],columns=['A','B'])>>>store.append('data',df2)>>>store.close()   A  B0  1  21  3  40  5  61  7  8