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added pd as namespace #12268

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84fc7bf
added pd as namespace
dacoex Feb 9, 2016
01dcacd
added more namespace
Feb 9, 2016
dfac360
added warning for retrieving from groups
Feb 9, 2016
ef48a31
Revert "added warning for retrieving from groups"
Feb 10, 2016
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32 changes: 24 additions & 8 deletions doc/source/io.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -1399,7 +1399,7 @@ Writing to a file, with a date index and a date column
dfj2['date'] = Timestamp('20130101')
dfj2['ints'] = list(range(5))
dfj2['bools'] = True
dfj2.index = date_range('20130101', periods=5)
dfj2.index = pd.date_range('20130101', periods=5)
dfj2.to_json('test.json')
open('test.json').read()

Expand Down Expand Up @@ -2553,7 +2553,7 @@ for some advanced strategies

.. ipython:: python

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

Objects can be written to the file just like adding key-value pairs to a
Expand All @@ -2562,7 +2562,7 @@ dict:
.. ipython:: python

np.random.seed(1234)
index = date_range('1/1/2000', periods=8)
index = pd.date_range('1/1/2000', periods=8)
s = Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])
df = DataFrame(randn(8, 3), index=index,
columns=['A', 'B', 'C'])
Expand Down Expand Up @@ -2611,7 +2611,7 @@ Closing a Store, Context Manager

# Working with, and automatically closing the store with the context
# manager
with HDFStore('store.h5') as store:
with pd.HDFStore('store.h5') as store:
store.keys()

.. ipython:: python
Expand Down Expand Up @@ -2754,7 +2754,7 @@ enable ``put/append/to_hdf`` to by default store in the ``table`` format.

.. ipython:: python

store = HDFStore('store.h5')
store = pd.HDFStore('store.h5')
df1 = df[0:4]
df2 = df[4:]

Expand Down Expand Up @@ -2801,6 +2801,22 @@ everything in the sub-store and BELOW, so be *careful*.

.. _io.hdf5-types:

.. warning:: Hierarchical keys cannot be retrieved as dotted (attribute) access as described above for items stored under root node.
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This belongs in a separate PR --- or I would consider renaming this one to make it obvious that this is the big change here.

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Either way, what would you prefer?

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I'd opt for another PR, it just keeps things easier if PRs are smaller and about one thing only.


.. ipython:: python

store.foo.bar.bah
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this sectionneeds to be in a separete PR

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I thought with was already merged. So here you go.

AttributeError: 'HDFStore' object has no attribute 'foo'

store.root.foo.bar.bah
/foo/bar/bah (Group) ''
children := ['block0_items' (Array), 'axis1' (Array), 'axis0' (Array), 'block0_values' (Array)]






Storing Types
'''''''''''''

Expand Down Expand Up @@ -3364,7 +3380,7 @@ Compression for all objects within the file

.. code-block:: python

store_compressed = HDFStore('store_compressed.h5', complevel=9, complib='blosc')
store_compressed = pd.HDFStore('store_compressed.h5', complevel=9, complib='blosc')

Or on-the-fly compression (this only applies to tables). You can turn
off file compression for a specific table by passing ``complevel=0``
Expand Down Expand Up @@ -3573,7 +3589,7 @@ It is possible to write an ``HDFStore`` object that can easily be imported into
index=range(100))
df_for_r.head()

store_export = HDFStore('export.h5')
store_export = pd.HDFStore('export.h5')
store_export.append('df_for_r', df_for_r, data_columns=df_dc.columns)
store_export

Expand Down Expand Up @@ -3662,7 +3678,7 @@ number of options, please see the docstring.
.. ipython:: python

# a legacy store
legacy_store = HDFStore(legacy_file_path,'r')
legacy_store = pd.HDFStore(legacy_file_path,'r')
legacy_store

# copy (and return the new handle)
Expand Down