HDFStoreがHDF5 python pandasデータフレームを更新しました

Question

私はという2つのデータフレームを持っています。これはpd.HDFStoreオブジェクトに格納され、もう1つはデータフレームに追加されます。

store = pd.HDFStore('dataframe_store.h5') 

df1 = pd.DataFrame(np.empty((100, 5))) 
df2 = pd.DataFrame(np.empty((100, 5))) 

store['df1'] = df1 

事実、私は

store['df1'] = df1.append(df2) 

...に等しくなるように最終的な結果を希望私はむしろ完全に新しいデータフレームとHDFStoreオブジェクトを上書きし、格納されたdf1にdf2を追加したいと思い。これは可能ですか？

さらに、次のコードを実行すると、私はValueError can only append to Tablesを返します...これはなぜですか？

the docsパー

df = pd.DataFrame(np.empty((1000, 5))) 
df2 = pd.DataFrame(np.empty((1000, 5))) 

store = pd.HDFStore('store.h5') 

store['df'] = df 

store.append('df', df2) 

Answer 1

（私の強調）：

HDFStore supports another PyTables format on disk, the table format. Conceptually a table is shaped very much like a DataFrame, with rows and columns. A table may be appended to in the same or other sessions. In addition, delete & query type operations are supported. This format is specified by format='table' or format='t' to append or put or to_hdf

New in version 0.13.

This format can be set as an option as well pd.set_option('io.hdf.default_format','table') to enable put/append/to_hdf to by default store in the table format.

In [361]: store = pd.HDFStore('store.h5') 

In [362]: df1 = df[0:4] 

In [363]: df2 = df[4:] 

# append data (creates a table automatically) 
In [364]: store.append('df', df1) 

In [365]: store.append('df', df2) 

In [366]: store 
Out[366]: 
<class 'pandas.io.pytables.HDFStore'> 
File path: store.h5 

# select the entire object 
In [367]: store.select('df') 
Out[367]: 
        A   B   C 
2000-01-01 0.887163 0.859588 -0.636524 
2000-01-02 0.015696 -2.242685 1.150036 
2000-01-03 0.991946 0.953324 -2.021255 
2000-01-04 -0.334077 0.002118 0.405453 
2000-01-05 0.289092 1.321158 -1.546906 
2000-01-06 -0.202646 -0.655969 0.193421 
2000-01-07 0.553439 1.318152 -0.469305 
2000-01-08 0.675554 -1.817027 -0.183109 

# the type of stored data 
In [368]: store.root.df._v_attrs.pandas_type 
Out[368]: 'frame_table' 

Note: You can also create a table by passing format='table' or format='t' to a put operation.