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Incorporated changes from style suggestions

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Harish Kesava Rao
2018-12-18 07:01:38 -06:00
parent fd2a8f3e8f
commit 3cf750bea1
1 changed files with 12 additions and 12 deletions
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docs/scenarios/serialization.rst
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@@ -21,7 +21,7 @@ Flat vs. Nested data
******************** ********************
Before beginning to serialize data, it is important to identify or decide how the Before beginning to serialize data, it is important to identify or decide how the
data needs to be structured during data serialization - flat or nested. data should to be structured during data serialization - flat or nested.
The differences in the two styles are shown in the below examples. The differences in the two styles are shown in the below examples.
Flat style: Flat style:
@@ -42,7 +42,7 @@ Nested style:
For more reading on the two styles, please see the discussion on For more reading on the two styles, please see the discussion on
`Python mailing list <https://mail.python.org/pipermail/python-list/2010-October/590762.html>`__, `Python mailing list <https://mail.python.org/pipermail/python-list/2010-October/590762.html>`__,
`IETF mailing list <https://www.ietf.org/mail-archive/web/json/current/msg03739.html>`__ and `IETF mailing list <https://www.ietf.org/mail-archive/web/json/current/msg03739.html>`__ and
`here <https://softwareengineering.stackexchange.com/questions/350623/flat-or-nested-json-for-hierarchal-data>`__. `in stackexchange <https://softwareengineering.stackexchange.com/questions/350623/flat-or-nested-json-for-hierarchal-data>`__.
**************** ****************
Serializing Text Serializing Text
@@ -57,7 +57,7 @@ If the data to be serialized is located in a file and contains flat data, Python
repr repr
---- ----
The repr method in Python takes a single object parameter and returns a printable representation of the input The repr method in Python takes a single object parameter and returns a printable representation of the input:
.. code-block:: python .. code-block:: python
@@ -79,7 +79,7 @@ ast.literal_eval
---------------- ----------------
The literal_eval method safely parses and evaluates an expression for a Python datatype. The literal_eval method safely parses and evaluates an expression for a Python datatype.
Supported data types are: strings, numbers, tuples, lists, dicts, booleans and None. Supported data types are: strings, numbers, tuples, lists, dicts, booleans, and None.
.. code-block:: python .. code-block:: python
@@ -114,8 +114,8 @@ Simple example for writing:
writer.writerows(iterable) writer.writerows(iterable)
The module's contents, functions and examples can be found The module's contents, functions, and examples can be found
`here <https://docs.python.org/3/library/csv.html>`__. `in the Python documentation <https://docs.python.org/3/library/csv.html>`__.
================== ==================
YAML (nested data) YAML (nested data)
@@ -178,7 +178,7 @@ Example:
root = tree.getroot() root = tree.getroot()
More documentation on using the `xml.dom` and `xml.sax` packages can be found More documentation on using the `xml.dom` and `xml.sax` packages can be found
`here <https://docs.python.org/3/library/xml.html>`__. `in the Python XML library documentation <https://docs.python.org/3/library/xml.html>`__.
******* *******
@@ -186,21 +186,21 @@ Binary
******* *******
======================= =======================
Numpy Array (flat data) NumPy Array (flat data)
======================= =======================
Python's Numpy array can be used to serialize and deserialize data to and from byte representation. Python's NumPy array can be used to serialize and deserialize data to and from byte representation.
Example: Example:
.. code-block:: python .. code-block:: python
import numpy as np import NumPy as np
# Converting Numpy array to byte format # Converting NumPy array to byte format
byte_output = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]).tobytes() byte_output = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]).tobytes()
# Converting byte format back to Numpy array # Converting byte format back to NumPy array
array_format = np.frombuffer(byte_output) array_format = np.frombuffer(byte_output)