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Harish Kesava Rao

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docs/scenarios
- serialization.rst

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`‎docs/scenarios/serialization.rst`

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Original file line number	Diff line number	Diff line change
`@@ -74,8 +74,9 @@ The repr method in Python takes a single object parameter and returns a printabl`
`74`	`74`	`# write content to files using repr`
`75`	`75`	`withopen('/tmp/file.py')as f:f.write(repr(a))`
`76`	`76`
	`77`	`+`
`77`	`78`	`ast.literal_eval`
`78`		`-________________`
	`79`	`+----------------`
`79`	`80`
`80`	`81`	`The literal_eval method safely parses and evaluates an expression for a Python datatype.`
`81`	`82`	`Supported data types are: strings, numbers, tuples, lists, dicts, booleans and None.`
`@@ -95,6 +96,7 @@ Simple example for reading:`
`95`	`96`
`96`	`97`	`..code-block::python`
`97`	`98`
	`99`	`+# Reading CSV content from a file`
`98`	`100`	`import csv`
`99`	`101`	`withopen('/tmp/file.csv',newline='')as f:`
`100`	`102`	`reader= csv.reader(f)`
`@@ -105,6 +107,7 @@ Simple example for writing:`
`105`	`107`
`106`	`108`	`..code-block::python`
`107`	`109`
	`110`	`+# Writing CSV content to a file`
`108`	`111`	`import csv`
`109`	`112`	`withopen('/temp/file.csv','w',newline='')as f:`
`110`	`113`	`writer= csv.writer(f)`
`@@ -123,6 +126,7 @@ structures in Python. One such example is below.`
`123`	`126`
`124`	`127`	`..code-block::python`
`125`	`128`
	`129`	`+# Reading YAML content from a file using the load method`
`126`	`130`	`import yaml`
`127`	`131`	`withopen('/tmp/file.yaml','r',newline='')as f:`
`128`	`132`	`try:`
`@@ -144,22 +148,66 @@ Reading:`
`144`	`148`
`145`	`149`	`..code-block::python`
`146`	`150`
	`151`	`+# Reading JSON content from a file`
`147`	`152`	`import json`
`148`	`153`	`withopen('/tmp/file.json','r')as f:`
`149`		`- data= json.dump(f)`
	`154`	`+ data= json.load(f)`
`150`	`155`
`151`	`156`	`Writing:`
`152`	`157`
`153`	`158`	`..code-block::python`
`154`	`159`
	`160`	`+# writing JSON content to a file using the dump method`
`155`	`161`	`import json`
`156`	`162`	`withopen('/tmp/file.json','w')as f:`
`157`	`163`	`json.dump(data, f,sort_keys=True)`
`158`	`164`
	`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.ElementTreeasET`
	`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`	`+=======================`
`159`	`191`
`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 numpyas 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`	`+====================`
`163`	`211`
`164`	`212`	The native data serialization module for Python is called `Pickle
`165`	`213`	<https://docs.python.org/2/library/pickle.html>`_.

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