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ARROW-13476: [Doc][Python] Switch ipc/io doc to use context managers #10957

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ARROW-13476: [Doc][Python] Switch ipc/io doc to use context managers #10957

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904af30
Switch ipc doc to use context managers
amol- Aug 18, 2021
9a37536
Migrate other examples to context managers
amol- Aug 18, 2021
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9 changes: 4 additions & 5 deletions docs/source/python/extending_types.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -111,15 +111,14 @@ IPC protocol::

>>> batch = pa.RecordBatch.from_arrays([arr], ["ext"])
>>> sink = pa.BufferOutputStream()
>>> writer = pa.RecordBatchStreamWriter(sink, batch.schema)
>>> writer.write_batch(batch)
>>> writer.close()
>>> with pa.RecordBatchStreamWriter(sink, batch.schema) as writer:
... writer.write_batch(batch)
>>> buf = sink.getvalue()

and then reading it back yields the proper type::

>>> reader = pa.ipc.open_stream(buf)
>>> result = reader.read_all()
>>> with pa.ipc.open_stream(buf) as reader:
... result = reader.read_all()
>>> result.column('ext').type
UuidType(extension<arrow.py_extension_type>)

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44 changes: 23 additions & 21 deletions docs/source/python/ipc.rst
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Expand Up @@ -65,21 +65,20 @@ this one can be created with :func:`~pyarrow.ipc.new_stream`:
.. ipython:: python

sink = pa.BufferOutputStream()
writer = pa.ipc.new_stream(sink, batch.schema)

with pa.ipc.new_stream(sink, batch.schema) as writer:
for i in range(5):
writer.write_batch(batch)

Here we used an in-memory Arrow buffer stream, but this could have been a
socket or some other IO sink.
Here we used an in-memory Arrow buffer stream (``sink``),
but this could have been a socket or some other IO sink.

When creating the ``StreamWriter``, we pass the schema, since the schema
(column names and types) must be the same for all of the batches sent in this
particular stream. Now we can do:

.. ipython:: python

for i in range(5):
writer.write_batch(batch)
writer.close()

buf = sink.getvalue()
buf.size

Expand All @@ -89,10 +88,11 @@ convenience function ``pyarrow.ipc.open_stream``:

.. ipython:: python

reader = pa.ipc.open_stream(buf)
reader.schema

batches = [b for b in reader]
with pa.ipc.open_stream(buf) as reader:
schema = reader.schema
batches = [b for b in reader]
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@amol- amol- Aug 18, 2021 •
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Took me a while to get around this one, if you wonder why this block is indented to more spaces than the other blocks, it's to work around what seemed like a bug in ipython directive.
When indented normally the second line was parsed as outside of the with block

In [0]: with pa.ipc.open_stream(buf) as reader:
   ...:    schema = reader.schema
In [1]: batches = [b for b in reader]

instead of

In [0]: with pa.ipc.open_stream(buf) as reader:
   ...:       schema = reader.schema
   ...:       batches = [b for b in reader]

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@pitrou pitrou Aug 19, 2021

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Uh.


schema
len(batches)

We can check the returned batches are the same as the original input:
Expand All @@ -115,11 +115,10 @@ The :class:`~pyarrow.RecordBatchFileWriter` has the same API as
.. ipython:: python

sink = pa.BufferOutputStream()
writer = pa.ipc.new_file(sink, batch.schema)

for i in range(10):
writer.write_batch(batch)
writer.close()

with pa.ipc.new_file(sink, batch.schema) as writer:
for i in range(10):
writer.write_batch(batch)

buf = sink.getvalue()
buf.size
Expand All @@ -131,15 +130,16 @@ operations. We can also use the :func:`~pyarrow.ipc.open_file` method to open a

.. ipython:: python

reader = pa.ipc.open_file(buf)
with pa.ipc.open_file(buf) as reader:
num_record_batches = reader.num_record_batches
b = reader.get_batch(3)

Because we have access to the entire payload, we know the number of record
batches in the file, and can read any at random:
batches in the file, and can read any at random.

.. ipython:: python

reader.num_record_batches
b = reader.get_batch(3)
num_record_batches
b.equals(batch)

Reading from Stream and File Format for pandas
Expand All @@ -151,7 +151,9 @@ DataFrame output:

.. ipython:: python

df = pa.ipc.open_file(buf).read_pandas()
with pa.ipc.open_file(buf) as reader:
df = reader.read_pandas()

df[:5]

Efficiently Writing and Reading Arrow Data
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15 changes: 3 additions & 12 deletions docs/source/python/parquet.rst
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Expand Up @@ -208,22 +208,13 @@ We can similarly write a Parquet file with multiple row groups by using

.. ipython:: python

writer = pq.ParquetWriter('example2.parquet', table.schema)
for i in range(3):
writer.write_table(table)
writer.close()
with pq.ParquetWriter('example2.parquet', table.schema) as writer:
for i in range(3):
writer.write_table(table)

pf2 = pq.ParquetFile('example2.parquet')
pf2.num_row_groups

Alternatively python ``with`` syntax can also be use:

.. ipython:: python

with pq.ParquetWriter('example3.parquet', table.schema) as writer:
for i in range(3):
writer.write_table(table)

Inspecting the Parquet File Metadata
------------------------------------

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10 changes: 4 additions & 6 deletions docs/source/python/plasma.rst
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Expand Up @@ -360,9 +360,8 @@ size of the Plasma object.
# is done to determine the size of buffer to request from the object store.
object_id = plasma.ObjectID(np.random.bytes(20))
mock_sink = pa.MockOutputStream()
stream_writer = pa.RecordBatchStreamWriter(mock_sink, record_batch.schema)
stream_writer.write_batch(record_batch)
stream_writer.close()
with pa.RecordBatchStreamWriter(mock_sink, record_batch.schema) as stream_writer:
stream_writer.write_batch(record_batch)
data_size = mock_sink.size()
buf = client.create(object_id, data_size)

Expand All @@ -372,9 +371,8 @@ The DataFrame can now be written to the buffer as follows.

# Write the PyArrow RecordBatch to Plasma
stream = pa.FixedSizeBufferWriter(buf)
stream_writer = pa.RecordBatchStreamWriter(stream, record_batch.schema)
stream_writer.write_batch(record_batch)
stream_writer.close()
with pa.RecordBatchStreamWriter(stream, record_batch.schema) as stream_writer:
stream_writer.write_batch(record_batch)

Finally, seal the finished object for use by all clients:

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