amol- commented on a change in pull request #1:
URL: https://github.com/apache/arrow-cookbook/pull/1#discussion_r676499072
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File path: python/source/io.rst
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@@ -0,0 +1,423 @@
+========================
+Reading and Writing Data
+========================
+
+Recipes related to reading and writing data from disk using
+Apache Arrow.
+
+.. contents::
+
+Write a Parquet file
+====================
+
+.. testsetup::
+
+ import numpy as np
+ import pyarrow as pa
+
+ arr = pa.array(np.arange(100))
+
+Given an array with all numbers from 0 to 100
+
+.. testcode::
+
+ print(f"{arr[0]} .. {arr[-1]}")
+
+.. testoutput::
+
+ 0 .. 99
+
+To write it to a Parquet file, as Parquet is a columnar format,
+we must create a :class:`pyarrow.Table` out of it,
+so that we get a table of a single column which can then be
+written to a Parquet file.
+
+.. testcode::
+
+ table = pa.Table.from_arrays([arr], names=["col1"])
+
+Once we have a table, it can be written to a Parquet File
+using the functions provided by the ``pyarrow.parquet`` module
+
+.. testcode::
+
+ import pyarrow.parquet as pq
+
+ pq.write_table(table, "example.parquet", compression=None)
+
+Reading a Parquet file
+======================
+
+Given a Parquet file, it can be read back to a :class:`pyarrow.Table`
+by using :func:`pyarrow.parquet.read_table` function
+
+.. testcode::
+
+ import pyarrow.parquet as pq
+
+ table = pq.read_table("example.parquet")
+
+The resulting table will contain the same columns that existed in
+the parquet file as :class:`ChunkedArray`
+
+.. testcode::
+
+ print(table)
+
+ col1 = table["col1"]
+ print(f"{type(col1).__name__} = {col1[0]} .. {col1[-1]}")
+
+.. testoutput::
+
+ pyarrow.Table
+ col1: int64
+ ChunkedArray = 0 .. 99
Review comment:
It was actually one of the original design goals of the cookbook, while
it's true that it might require more maintenance cost when the repr output
changes, it's a minor effort (you just need to copy the new output into the old
`.. testoutput`) but that will guarantee us that if there is _any_ change in
arrow in terms of behaviours the recipes will always reflect exactly what arrow
is going to do/answer.
We plan to setup a nightly job to verify the cookbook against the latest
arrow master so that we catch any change and can update it.
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