@@ -74,8 +74,9 @@ The repr method in Python takes a single object parameter and returns a printabl
7474 # write content to files using repr
7575 with open (' /tmp/file.py' ) as f:f.write(repr (a))
7676
77+
7778 ast.literal_eval
78- ________________
79+ ----------------
7980
8081The literal_eval method safely parses and evaluates an expression for a Python datatype.
8182Supported data types are: strings, numbers, tuples, lists, dicts, booleans and None.
@@ -95,6 +96,7 @@ Simple example for reading:
9596
9697.. code-block :: python
9798
99+ # Reading CSV content from a file
98100 import csv
99101 with open (' /tmp/file.csv' , newline = ' ' ) as f:
100102 reader = csv.reader(f)
@@ -105,6 +107,7 @@ Simple example for writing:
105107
106108.. code-block :: python
107109
110+ # Writing CSV content to a file
108111 import csv
109112 with open (' /temp/file.csv' , ' w' , newline = ' ' ) as f:
110113 writer = csv.writer(f)
@@ -123,6 +126,7 @@ structures in Python. One such example is below.
123126
124127.. code-block :: python
125128
129+ # Reading YAML content from a file using the load method
126130 import yaml
127131 with open (' /tmp/file.yaml' , ' r' , newline = ' ' ) as f:
128132 try :
@@ -144,22 +148,66 @@ Reading:
144148
145149.. code-block :: python
146150
151+ # Reading JSON content from a file
147152 import json
148153 with open (' /tmp/file.json' , ' r' ) as f:
149- data = json.dump (f)
154+ data = json.load (f)
150155
151156 Writing:
152157
153158.. code-block :: python
154159
160+ # writing JSON content to a file using the dump method
155161 import json
156162 with open (' /tmp/file.json' , ' w' ) as f:
157163 json.dump(data, f, sort_keys = True )
158164
165+ =================
166+ XML (nested data)
167+ =================
168+
169+ XML parsing in Python is possible using the `xml ` package.
170+
171+ Example:
172+
173+ .. code-block :: python
174+
175+ # reading XML content from a file
176+ import xml.etree.ElementTree as ET
177+ tree = ET .parse(' country_data.xml' )
178+ root = tree.getroot()
179+
180+ More documentation on using the `xml.dom ` and `xml.sax ` packages can be found
181+ `here <https://docs.python.org/3/library/xml.html >`__.
182+
183+
184+ *******
185+ Binary
186+ *******
187+
188+ =======================
189+ Numpy Array (flat data)
190+ =======================
159191
160- ******
161- Pickle
162- ******
192+ Python's Numpy array can be used to serialize and deserialize data to and from byte representation.
193+
194+ Example:
195+
196+ .. code-block :: python
197+
198+ import numpy as np
199+
200+ # Converting Numpy array to byte format
201+ byte_output = np.array([ [1 , 2 , 3 ], [4 , 5 , 6 ], [7 , 8 , 9 ] ]).tobytes()
202+
203+ # Converting byte format back to Numpy array
204+ array_format = np.frombuffer(byte_output)
205+
206+
207+
208+ ====================
209+ Pickle (nested data)
210+ ====================
163211
164212The native data serialization module for Python is called `Pickle
165213<https://docs.python.org/2/library/pickle.html> `_.
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