World's Best AI Learning Platform with profoundly Demanding Certification Programs
Designed by IITians, only for AI Learners.
Download our e-book of Introduction To Python
Related Blog
Top Discussion
Exception Type: JSONDecodeError at /update/ Exception Value: Expecting value: line 1 column 1 (char 0) How can I write Python code to change a date string from "mm/dd/yy hh: mm" format to "YYYY-MM-DD HH: mm" format? How to leave/exit/deactivate a Python virtualenvironment Change the hyperparameter means ? Why is my f1_scores different when i calculate them manually vs output by sklearn.metrics What is the method for assigning a non-scalar value to a PyTorch tensor? Find the Largest and smallest items in a collection. How Python interpreter Works? Join Discussion
Top Courses
Master's In Artificial Intelligence Job Guarantee Program
4.5 (1,292 Ratings)
559 Learners
Webinars
Python Data Persistence - Plistlib Module
Neha Kumawat
a year ago
The plist format is mainly used by MAC OS X. These files are basically XML documents. They store and retrieve properties of an object. Python library contains plist module, that is used to read and write 'property list' files (they usually have .plist' extension).
The plistlib module is more or less similar to other serialization libraries in the sense, it also provides dumps() and loads() functions for string representation of Python objects and load() and dump() functions for disk operation.
Following dictionary object maintains property (key) and corresponding value −
proplist = {
"name" : "Ganesh",
"designation":"manager",
"dept":"accts",
"salary" : {"basic":12000, "da":4000, "hra":800}
}
In order to write these properties in a disk file, we call dump() function in plist module.
import plistlib
fileName=open('salary.plist','wb')
plistlib.dump(proplist, fileName)
fileName.close()
Conversely, to read back the property values, use load() function as follows −
fp= open('salary.plist', 'rb')
pl = plistlib.load(fp)
print(pl)
Like the Blog, then Share it with your friends and colleagues to make this AI community stronger.
To learn more about nuances of Artificial Intelligence, Python Programming, Deep Learning, Data Science and Machine Learning, visit our insideAIML blog page.
Keep Learning. Keep Growing.