Python Pickle Module for saving Objects by serialization




This tutorial is going to cover the pickle module, which is a part of your standard library with your installation of Python.

So what is pickling? Pickling is the serializing and de-serializing of python objects to a byte stream. Unpicking is the opposite.

You may hear this methodology called serialization, marshalling or flattening in other languages, but it is pretty much exclusively referred to as pickling in Python. So what does pickling mean, simply?

Pickling is used to store python objects. This means things like lists, dictionaries, class objects, and more.

What are some examples?

Generally, you will find pickling to be most useful with data analysis, where you are performing routine tasks on the data, such as pre-processing. Also, it makes a lot of sense when you're working with Python-specific data types, such as dictionaries.

For example, we use pickling in the NLTK tutorial series to save our trained machine learning algorithm. This is so that, every time we want to use it, we do not need to constantly re-train it, which takes a while.

Instead, we just train the algorithm once, store it to a variable (an object), and then we pickle it. In the case of the NLTK module, generating the classifiers every time was taking 5-15+ minutes. With pickle, it was taking about 5 seconds.

If you have a large dataset, for example, and you're loading that massive data set into memory every time you run the program, it may make a lot of sense to just pickle it, and then load that instead, because it will be far faster, again by 50 - 100x, sometimes far more depending on the size.

Let's show a simple example:

import pickle

example_dict = {1:"6",2:"2",3:"f"}

pickle_out = open("dict.pickle","wb")
pickle.dump(example_dict, pickle_out)
pickle_out.close()

First, import pickle to use it, then we define an example dictionary, which is a Python object.
Next, we open a file (note that we open to write bytes in Python 3+), then we use pickle.dump() to put the dict into opened file, then close.

The above code will save the pickle file for us, now we need to cover how to access the pickled file:

pickle_in = open("dict.pickle","rb")
example_dict = pickle.load(pickle_in)
Open the pickle file
Use pickle.load() to load it to a var.

That's all there is to it, now you can do things like:

print(example_dict)
print(example_dict[3])
This shows that we've retained the dict data-type.

Through saving the serialized object, it's nature is included, so we don't have to worry about loading things "as" strings, dictionaries, lists, etc.


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