Amelio Vazquez-Reina Amelio Vazquez-Reina - 1 year ago 193
Python Question

Iteratively writing to HDF5 Stores in Pandas

Pandas has the following examples for how to store

in HDF5 files:

Prepare some data:

In [1142]: store = HDFStore('store.h5')

In [1143]: index = date_range('1/1/2000', periods=8)

In [1144]: s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])

In [1145]: df = DataFrame(randn(8, 3), index=index,
......: columns=['A', 'B', 'C'])

In [1146]: wp = Panel(randn(2, 5, 4), items=['Item1', 'Item2'],
......: major_axis=date_range('1/1/2000', periods=5),
......: minor_axis=['A', 'B', 'C', 'D'])

Save it in a store:

In [1147]: store['s'] = s

In [1148]: store['df'] = df

In [1149]: store['wp'] = wp

Inspect what's in the store:

In [1150]: store
<class ''>
File path: store.h5
/df frame (shape->[8,3])
/s series (shape->[5])
/wp wide (shape->[2,5,4])

Close the store:

In [1151]: store.close()


  1. In the code above, when is the data actually written to disk?

  2. Say I want to add thousands of large dataframes living in
    files to a single
    file. I would need to load them and add them to the
    file one by one since I cannot afford to have them all in memory at once as they would take too much memory. Is this possible with HDF5? What would be the correct way to do it?

  3. The Pandas documentation says the following:

    "These stores are not appendable once written (though you simply
    remove them and rewrite). Nor are they queryable; they must be
    retrieved in their entirety."

    What does it mean by not appendable nor queryable? Also, shouldn't it say once closed instead of written?

Answer Source
  1. As soon as the statement is exectued, eg store['df'] = df. The close just closes the actual file (which will be closed for you if the process exists, but will print a warning message)

  2. Read the section

    It is generally not a good idea to put a LOT of nodes in an .h5 file. You probably want to append and create a smaller number of nodes.

    You can just iterate thru your .csv and store/append them one by one. Something like:

    for f in files:
      df = pd.read_csv(f)

    Would be one way (creating a separate node for each file)

  3. Not appendable - once you write it, you can only retrieve it all at once, e.g. you cannot select a sub-section

    If you have a table, then you can do things like:


    which is like a database query, only getting part of the data

    Thus, a store is not appendable, nor queryable, while a table is both.

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