[Python] Add example for pa.Table.from_pylist (#226)
diff --git a/python/source/create.rst b/python/source/create.rst
index 9571a29..0b416d3 100644
--- a/python/source/create.rst
+++ b/python/source/create.rst
@@ -42,7 +42,7 @@
import numpy as np
- array = pa.array([1, 2, 3, 4, 5],
+ array = pa.array([1, 2, 3, 4, 5],
mask=np.array([True, False, True, False, True]))
print(array)
@@ -104,7 +104,7 @@
Arrow allows fast zero copy creation of arrow arrays
from numpy and pandas arrays and series, but it's also
-possible to create Arrow Arrays and Tables from
+possible to create Arrow Arrays and Tables from
plain Python structures.
The :func:`pyarrow.table` function allows creation of Tables
@@ -136,6 +136,33 @@
:func:`pyarrow.array` for conversion to Arrow arrays,
and will benefit from zero copy behaviour when possible.
+The :meth:`pyarrow.Table.from_pylist` method allows the creation
+of Tables from python lists of row dicts. Types are inferred if a
+schema is not explicitly passed.
+
+.. testcode::
+
+ import pyarrow as pa
+
+ table = pa.Table.from_pylist([
+ {"col1": 1, "col2": "a"},
+ {"col1": 2, "col2": "b"},
+ {"col1": 3, "col2": "c"},
+ {"col1": 4, "col2": "d"},
+ {"col1": 5, "col2": "e"}
+ ])
+
+ print(table)
+
+.. testoutput::
+
+ pyarrow.Table
+ col1: int64
+ col2: string
+ ----
+ col1: [[1,2,3,4,5]]
+ col2: [["a","b","c","d","e"]]
+
Creating Record Batches
=======================
@@ -153,7 +180,7 @@
pa.array([2, 4, 6, 8, 10])
], names=["odd", "even"])
-Multiple batches can be combined into a table using
+Multiple batches can be combined into a table using
:meth:`pyarrow.Table.from_batches`
.. testcode::
@@ -178,7 +205,7 @@
odd: [[1,3,5,7,9],[11,13,15,17,19]]
even: [[2,4,6,8,10],[12,14,16,18,20]]
-Equally, :class:`pyarrow.Table` can be converted to a list of
+Equally, :class:`pyarrow.Table` can be converted to a list of
:class:`pyarrow.RecordBatch` using the :meth:`pyarrow.Table.to_batches`
method
@@ -230,7 +257,7 @@
]
If you already know the categories and indices then you can skip the encode
-step and directly create the ``DictionaryArray`` using
+step and directly create the ``DictionaryArray`` using
:meth:`pyarrow.DictionaryArray.from_arrays`
.. testcode::
