python - HDFStore 更新存储的 HDF5 python pandas 数据帧

Question

我有两个数据帧，df1 存储在 pd.HDFStore 对象中，另一个要附加到数据帧。

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

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

store['df1'] = df1

实际上，我希望最终结果等于...

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

我想将 df2 附加到存储的 df1，而不是用新数据帧完全覆盖 HDFStore 对象。这可能吗？

此外，当我运行以下代码时，我返回 ValueError can only append to Tables...这是为什么？

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

根据 the docs (我的重点):

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.