@@ -21,7 +21,7 @@ Flat vs. Nested data
2121********************
2222
2323Before beginning to serialize data, it is important to identify or decide how the
24- data needs to be structured during data serialization - flat or nested.
24+ data should to be structured during data serialization - flat or nested.
2525The differences in the two styles are shown in the below examples.
2626
2727Flat style:
@@ -42,7 +42,7 @@ Nested style:
4242 For more reading on the two styles, please see the discussion on
4343`Python mailing list <https://mail.python.org/pipermail/python-list/2010-October/590762.html >`__,
4444`IETF mailing list <https://www.ietf.org/mail-archive/web/json/current/msg03739.html >`__ and
45- `here <https://softwareengineering.stackexchange.com/questions/350623/flat-or-nested-json-for-hierarchal-data >`__.
45+ `in stackexchange <https://softwareengineering.stackexchange.com/questions/350623/flat-or-nested-json-for-hierarchal-data >`__.
4646
4747****************
4848Serializing Text
@@ -57,7 +57,7 @@ If the data to be serialized is located in a file and contains flat data, Python
5757repr
5858----
5959
60- The repr method in Python takes a single object parameter and returns a printable representation of the input
60+ The repr method in Python takes a single object parameter and returns a printable representation of the input:
6161
6262.. code-block :: python
6363
@@ -79,7 +79,7 @@ ast.literal_eval
7979----------------
8080
8181The literal_eval method safely parses and evaluates an expression for a Python datatype.
82- Supported data types are: strings, numbers, tuples, lists, dicts, booleans and None.
82+ Supported data types are: strings, numbers, tuples, lists, dicts, booleans, and None.
8383
8484.. code-block :: python
8585
@@ -114,8 +114,8 @@ Simple example for writing:
114114 writer.writerows(iterable)
115115
116116
117- The module's contents, functions and examples can be found
118- `here <https://docs.python.org/3/library/csv.html >`__.
117+ The module's contents, functions, and examples can be found
118+ `in the Python documentation <https://docs.python.org/3/library/csv.html >`__.
119119
120120==================
121121YAML (nested data)
@@ -178,29 +178,29 @@ Example:
178178 root = tree.getroot()
179179
180180 More documentation on using the `xml.dom ` and `xml.sax ` packages can be found
181- `here <https://docs.python.org/3/library/xml.html >`__.
181+ `in the Python XML library documentation <https://docs.python.org/3/library/xml.html >`__.
182182
183183
184184*******
185185Binary
186186*******
187187
188188=======================
189- Numpy Array (flat data)
189+ NumPy Array (flat data)
190190=======================
191191
192- Python's Numpy array can be used to serialize and deserialize data to and from byte representation.
192+ Python's NumPy array can be used to serialize and deserialize data to and from byte representation.
193193
194194Example:
195195
196196.. code-block :: python
197197
198- import numpy as np
198+ import NumPy as np
199199
200- # Converting Numpy array to byte format
200+ # Converting NumPy array to byte format
201201 byte_output = np.array([ [1 , 2 , 3 ], [4 , 5 , 6 ], [7 , 8 , 9 ] ]).tobytes()
202202
203- # Converting byte format back to Numpy array
203+ # Converting byte format back to NumPy array
204204 array_format = np.frombuffer(byte_output)
205205
206206
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