Why pickling is important?

Sulochana Kamshetty

9 months ago

Pickling Is Important
Pickling Is Important
Welcome to the new content from InsideAIML. Let's understand the new concept of python i.e pickling and unpickling in python. Why it is used & also the purpose of the pickling in our python programming world.
Python pickle module is used for serializing and de-serializing python object structures.
Let us understand what does this exactly mean and what it does with.
Python works with objects (list, dictionaries, etc.) into byte streams (0s and 1s) is called pickling or serialization or flattening, modelling etc..
In python everyone use this pickling model like the object created for the class, list, variables etc or any kind of datatypes  for pickling
The object should be classified in machine learning    for pickling on train dataset..
The pickling we do serialize and unpickling does de-serialize back to the python objects
Important note:-  Only after importing pickle module we can do pickling and unpickling.
Importing pickle can be done using the following command
Import pickle
mydict = {1:'6',2:'3',3:'h'}
pick_out = open("dict.pickle","wb")
pickle.dump(mydict,pick_out)
pick_out.close()
Output:- 
In the above code, list – “mydict” contains four elements (1:'6',2:'3',3:'h'). We open the file in “wb” mode instead of “w” as all the operations are done using bytes in the current working directory. A new file named “dict.pickle” is created, which converts the mydict data in the byte stream.
Let's understand what is unpickling?
Unpickle a simple list
import pickle
pickle_off = open ("dict.pickle", "rb")
emp = pickle.load (pickle_off)
print(emp)
print(emp[2])


output:- {1:'6', 2: '3', 3: 'h'}

3
 On running above scripts, you can see your dict.pickle data again as output.
If we are working on larger/massive dataset then every time you cannot run all the program which may also create a tariff in our work so to avoid such issues we use pickling for better and faster program
Try with these simple codes for better understanding
Most Use cases of Pickling:
1) It is mostly used when the data is huge and stored in memory for the pickling process and we can work back where the last program was leftover with.
2) Storing python objects in a database.
3) Converting an arbitrary python object to a string so that it can be used as a dictionary key (e.g. for caching & memorization).
For more such blogs/courses on data science, machine learning, artificial intelligence and emerging new technologies do visit us at InsideAIML.
Thanks for reading…
Happy Learning…   

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