For a data scientist, sets of data are always important and widely used as dictionaries, data frames, etc. While they work upon a program writing, it becomes a crucial need for them to save data. By saving data, it becomes helpful to store and use them while writing a program or while sending them to a person, who is on the other end. Understanding this peculiar need for data storage, Python provides an eminent feature for its users to serialize data.
Serialization refers to the process of converting the object or the data structures into a format where they can be stored or obtained later. Since the data is transformed and stored in another format, it provides the feature of restoring and deserializing the original data from the serialized format. In addition to the data conversion, serialization even provides the opportunity to reduce the data size so that it can fit into the required disk space or bandwidth.
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With the effective methods of serialization and deserialization, which are commonly known as pickling and unpickling; Python provides an easier interface of data conversion.
If an application requires a nominal amount of data persistency, then picking is the better option to be used. Pickling helps in saving the data on the disk, and thus you can re-work it whenever needed. Pickling is the best choice while working on algorithms related to machine learning; there will be great time management since there will not be any rewrites or instructions to the model.
Pickling can be performed for certain data types such as Booleans, integers, floats, complex numbers, lists, tuples, strings, dictionaries with pickable objects, and sets. However, to pickle these data types, it may be necessary to pickle the functions and classes.
In Python, pickle is protocol specific, hence data cannot be used across various programming languages; in other words, no cross-language compatibility. Similarly, it works the same for different Python versions. You cannot unpickle a file in the XYZ version, which was picked in a Python ABC version; doing so may unnecessarily execute malicious code.
In the Pickle module, the data format is Python-specific. Therefore, it is essential to write the required code while performing the serializing or deserializing. The Python function which is used for serializing an object hierarchy is dumps(), and the function which is used for de-serializing is loads().
Protocols act as the conventions for deconstructing and constructing the objects in Python. There are about 5 protocols used in pickling. When a higher protocol version is used, you will need the recent version of Python for obtaining the highly compatible and readable pickle.
However, in order to serialize the data which is completely designed with the fundamentals objects of Python in the fastest way, it is recommended for preferring the marshal module. The module helps with the function to read and write a Python value into a binary format.
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Marshal module provides the feature of object serialization which is much similar to that of the pickle module. Though the method doesn’t provide help for data maintenance and transmission of Python objects, it helps the interpreter to do read and write operations to the compiled versions of Python modules. The marshal module is popularly known as internal object serialization due to its varying data format usage. Since the data format keeps on changing, it fails in proving compatible across the Python versions. This reason makes the marshal module to be known as Internal Python Object Serialization.
The Marshal module also defines load() and dump() functions to read and write the marshaled objects.
1. dump(): It supports objects with standard data types; and by marshaling these Python objects, it returns a similar byte object.
2. loads(): By using the function, once can convert a byte object to a corresponding Python object. If the conversion fails to provide the required Python object, then it raises the TypeError or ValueError.
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The shelf is another module type from Python’s standard library. The module type is simpler and known as a powerful tool to maintain data storage when there isn’t any need to obtain a relational database solution. The files that are stored in Shelve replicate the DBM database and accept the string-type objects, and values that are of pickable objects.
Effectively, the Python object persistence or the modules of shelve have three classes, namely:
Below you can find the information about the three classes:
|Shelf||It is a base class, initialized with the dict-like object and used for implementing shelf.|
|BsdDbShelf||It is the subclass of the Shelf class. The dict-like object should support first(), previous(), next(), set location(), and last() methods while passing through its constructor.|
|DbfilenameShelf||It is another subclass of Shelf. It doesn’t accept dict object as a parameter, rather it accepts the filename for its constructor.|
However, defining the open() function in the Shelve module will effectively return a DbfilenameShelf. Also, it is easiest for obtaining a Shelf object.
Below find the functions of dumps() and loads().
1. Dumps(): The function helps in converting a Python object into JSON format.
2. Loads(): The function helps in the conversion of JSON string back into a Python object.
JSON Module is further classified into two different classes, i.e. JSONEncoder and JSONDecoder.
The object of JSONEncoder is an encoder for Python data structures. The below table illustrates the conversion of Python data type to their corresponding JSON type.
Python Data type
Corresponding JSON type
int, float, int- & float-derived Enums
The object of JSONDEcoder helps to decode a JSON string back to its Python data structure; find the below table as reference:
Serialization is the practice that simplifies the data storage methods of a data scientist. Python Serialization is one of the best features which eases the interface of data conversion. Pickling and unpickling, which are popularly known for serialization and deserialization are effective ways to transform and store data in another format or vice-versa.
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Anjaneyulu Naini is working as a Content contributor for Mindmajix. He has a great understanding of today’s technology and statistical analysis environment, which includes key aspects such as analysis of variance and software,. He is well aware of various technologies such as Python, Artificial Intelligence, Oracle, Business Intelligence, Altrex, etc. Connect with him on LinkedIn and Twitter.
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