Hey Ninjas! Just like how we label the jars in our kitchen so that we remember where precisely something is placed for our later use; similarly, while working with Python, sometimes, when our data is big or complicated, we use a Pickle module, which helps us save all the essential part of the data so we can use the data later on without any complications.
In this blog, we will learn about the Pickle module in Python, how it works, some examples, and its advantages and disadvantages.
What is Pickle in Python?
Python contains a module called Pickle that helps to serialize or deserialize the data in Python. Serialization is the process of converting an object (for example dictionary or list) in Python into a file or binary strings such that it can easily be accessed later.
This process can also be referred to as Pickling. Deserialization is a process of converting serialized binary data into a Python object. This process can also be referred to as unpickling. This helps us to store and send Python data anywhere with ease.
# Serialize (pickle) the data with open('data.pkl', 'wb') as file: pickle.dump(data_to_pickle, file)
# Deserialize (unpickle) the data with open('data.pkl', 'rb') as file: loaded_data = pickle.load(file)
# Display result print(loaded_data)
Output
Explanation
The code begins by importing the pickle module, which provides functions for serializing and deserializing Python objects. The variable data_to_pickle is initialized with a Python dictionary containing sample data. Using the with statement and the 'wb' mode (binary write mode), the code opens a file named 'data.pkl' for writing. It then uses pickle.dump() to serialize (pickle) the data_to_pickle dictionary and save it to the file. Next, the code opens the 'data.pkl' file for reading using the 'rb' mode (binary read mode). It then uses pickle.load() to deserialize (unpickle) the data from the file and store it in the loaded_data variable. Finally, the code prints the loaded_data variable, which now contains the dictionary with the same data that was originally pickled.
Python Pickle Dump
In Python, Pickle Dump is used to convert Python objects which are pickable into files or binary strings.
Firstly, we import the Pickle module usingthe import pickle statement in Python.
To pickle a Python object into a file, we use pickle.dump(object, file).
To pickle a Python object into pickled bytes, we use pickle.dump(object).
Note: If we try to pickle an unpickable object, Python gives PicklingError.
Syntax
pickle.dump(object, file, protocol=None)
object refers to the Python object that is to be pickled.
file: This is the file object where we save the pickled data.
protocol is an optional parameter used to specify the version of the Pickle protocol.
Example of Python Pickle Dump
Let us try to Pickle the Python dictionary using the Pickle dump function.
import pickle
# Python Object
data = {"name": "Rohan", "age": 19}
# Open a file in binary mode to write the serialized data
with open("data.pickle", "wb") as f:
# Serialize and save the Python object to the file
pickle.dump(data, f)
Explanation
In this example, firstly, we created a Python dictionary, then opened a file named data.pickle in binary mode and then used the dump function to pickle the dictionary.
“wb” is used to open the file in binary format for writing.
Python Pickle Load
In Python, Pickle Load is used to convert serialized or pickled data into a Python object.
file is the object which is converted into a Python object previously serialized by the dump function.
The Fix_imports parameter is used to handle the backward compatibility issues.
encoding is an optionalparameter used to specifythe encoding used for decoding byte strings.
Example of Python Pickle Load
Let us try to deserialize the previously pickled object using the Pickle load function.
import pickle
# Open the file in binary mode to read the serialized data
with open("data.pickle", "rb") as f:
# Deserialize and load the Python object from the file
data = pickle.load(f)
print(data)
Output
Explanation
Here in this example, we opened data.pickle file in binary mode and used the load function to unpickle the file.
“rb” is used while opening the file, and it specifies the file should be read in binary format.
Advantages
There are several advantages of using the Pickle module in Python.
Python Pickle is easy to use and beginner friendly. We can easily serialize or deserialize Python objects.
Python Pickle helps us to transfer data between different platforms.
The pickle module can be used on most Python objects.
Python Pickle also provides security while transferring data.
Disadvantages
While there are many advantages of using the Pickle module, there are some disadvantages we need to consider.
The pickle module can only be used for Python objects. It cannot be used for other programming languages.
The Pickled file can be larger, creating problems while storing and transferring data.
Yes, pickle is a default module in Python. It's used for serializing and deserializing Python objects, allowing data to be saved to and loaded from files in a structured format.
Q. Why is pickle called pickle in Python?
The term pickle in Python likely comes from the idea of preserving something, similar to pickling in food preservation. Pickling in Python refers to the process of preserving objects for later use or storage.
Q. How do you pickle a model in Python?
To pickle a model in Python, you use the pickle module. It serializes (converts into a format that can be stored) the model object, allowing you to save it to a file. Later, you can deserialize (load) it from that file to use the model again. This is useful for storing trained machine learning models or other complex objects for future use without retraining or rebuilding.
Q. Why is Python Pickle used?
Python Pickle is used to store and send Python data anywhere with ease.
Q. What are some alternatives to Python Pickle?
Some alternatives to Python Pickle are JSON, YAML, and MessagePack.
Conclusion
This article discusses the topic of Python Pickle. In this blog, we have discussed what Python Pickle is, the main functions in Pickle module, along with its advantages and disadvantages. We hope this blog has helped you enhance your knowledge of Python Pickle. If you want to learn more, then check out our articles.
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