{"id":2744,"date":"2021-02-14T06:54:11","date_gmt":"2021-02-14T06:54:11","guid":{"rendered":"https:\/\/www.afternerd.com\/blog\/?p=2744"},"modified":"2021-02-14T06:54:12","modified_gmt":"2021-02-14T06:54:12","slug":"python-pickle","status":"publish","type":"post","link":"https:\/\/www.afternerd.com\/blog\/python-pickle\/","title":{"rendered":"What is Pickling in Python? (In-depth Guide)"},"content":{"rendered":"\n

Pickling<\/em> in Python means the process of serializing a Python object into a byte stream. The pickle module is responsible for the serialization and deserialization of Python objects. What does that mean? well, this is what I am going to answer in this article, so let’s get started.<\/p>\n\n\n\n

First, let’s understand what serialization and deserialization mean?<\/p>\n\n\n\n

Say you have a Python object (for example, a dictionary object) that looks like this:<\/p>\n\n\n\n

employee = {\"name\": \"Bob\", \"age\": 25}<\/code><\/pre>\n\n\n\n
that you want to write to a file so that another Python process can read it later. How can you do that?<\/p>\n\n\n\n
 Well, one option is to write the dictionary as a text file and then read this text file from the other Python program.<\/p>\n\n\n\n
For example, your text file can be formatted in the following manner:<\/p>\n\n\n\n
name:Bob\nage:25<\/code><\/pre>\n\n\n\n
Now, the other Python program can read this file, split each line based on the :<\/span> delimiter and voila. There you go!<\/p>\n\n\n\n
So what’s wrong with this approach?<\/p>\n\n\n\n
I agree with you it is a working solution and it might be OK for some situations.<\/p>\n\n\n\n
However, it is not ideal because of these two reasons:<\/p>\n\n\n\n
  1. Text files take more space<\/em><\/strong> when they are stored on disk. This might be OK for trivial programs, but imagine if you have to send this serialized object to another machine over the network. In this case, having a small payload is crucial or else you might congest the network.<\/li>
  2. The way you formatted your file was arbitrary<\/em><\/strong>. You had to somehow communicate to the other Python program what your “schema” looks like. This doesn’t scale. Ideally, what we need is a well-defined standardized protocol so that any other program can easily and deterministically read your serialized data.<\/li><\/ol>\n\n\n\n
    Another popular standard for serializing data is JSON<\/a>. You probably might have heard of it.<\/p>\n\n\n\n
    \"\"<\/figure><\/div>\n\n\n\n
    JSON is another textual protocol that is widely used, standardized, but doesn’t really solve the issue of being a textual representation, which means it is going to be large in size.<\/p>\n\n\n\n
    This is exactly the problem the pickle<\/em> solves.<\/p>\n\n\n\n
    So what is the use of pickle in Python?<\/h2>\n\n\n\n
    If you want to serialize a Python object, whether to store it on disk or to transfer it over the network, pickle<\/em> is a Python module that helps you serialize<\/em> and deserialize<\/em> Python objects in a binary format (not textual format). This means that the size of your serialized objects will be much more compact than their textual counterparts.<\/p>\n\n\n\n
    How to pickle a Python object?<\/h2>\n\n\n\n
    Here is an example of how to pickle a python dictionary and write it to a file:<\/p>\n\n\n\n
    import pickle\n\ne = {\"name\": \"Bob\", \"age\": 25}\nwith open('employee.pickle', 'wb') as f:\n    pickle.dump(e, f)<\/code><\/pre>\n\n\n\n
    Note the following:<\/p>\n\n\n\n