Home Backend Development Python Tutorial Python之pickle: Pickle your data

Python之pickle: Pickle your data

Dec 16, 2016 am 11:52 AM
pickle

1.What is Pickle? What are the advantages of Pickle?

Python provides a standard module called Pickle. This is an amazing module that can express almost any Python object (even some Python code blocks (forms)!) as a string, a process called pickling. Reconstructing an object from a string representation is called unpickling. Objects in the encapsulated state can be stored in files or objects, and can also be transferred between remote machines over the network.

The Pickle module will create a Python language-specific binary format without converting them into strings or writing them into a binary file without using underlying file access operations. You basically don’t need to consider any files. detail.

2.Pickle’s main functions

The two main functions in the Pickle module are dump() and load(). The

dump() function saves the data object to a given file in a specific format.

When the load() function takes out saved objects from the file, pickle knows how to restore these objects to their original format.

The dumps() function performs the same serialization as the dump() function. Instead of accepting a stream object and saving the serialized data to a disk file, this function simply returns the serialized data.
The loads() function performs the same deserialization as the load() function. Instead of accepting a stream object and reading the serialized data from a file, it accepts a str object containing the serialized data and returns the object directly.

cPickle is a faster C language compiled version of pickle.
pickle and cPickle are equivalent to java's serialization and deserialization operations

3. How to use Pickle

import pickle

with open('mydata.pickle','wb') as mysavedata:
pickle.dump({'alice':0,'clio':8},mysavedata)

with open('mydata.pickle','rb') as myrestoredata:
a_dict = pickle.load(myrestoredata)

print a_dict

Result:
{'clio': 8, 'alice': 0}




For more Python pickle: Pickle your data related articles, please pay attention to PHP Chinese website !


Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
2 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
Repo: How To Revive Teammates
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
Hello Kitty Island Adventure: How To Get Giant Seeds
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

How to Use Python to Find the Zipf Distribution of a Text File How to Use Python to Find the Zipf Distribution of a Text File Mar 05, 2025 am 09:58 AM

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

How Do I Use Beautiful Soup to Parse HTML? How Do I Use Beautiful Soup to Parse HTML? Mar 10, 2025 pm 06:54 PM

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

Image Filtering in Python Image Filtering in Python Mar 03, 2025 am 09:44 AM

Dealing with noisy images is a common problem, especially with mobile phone or low-resolution camera photos. This tutorial explores image filtering techniques in Python using OpenCV to tackle this issue. Image Filtering: A Powerful Tool Image filter

How to Work With PDF Documents Using Python How to Work With PDF Documents Using Python Mar 02, 2025 am 09:54 AM

PDF files are popular for their cross-platform compatibility, with content and layout consistent across operating systems, reading devices and software. However, unlike Python processing plain text files, PDF files are binary files with more complex structures and contain elements such as fonts, colors, and images. Fortunately, it is not difficult to process PDF files with Python's external modules. This article will use the PyPDF2 module to demonstrate how to open a PDF file, print a page, and extract text. For the creation and editing of PDF files, please refer to another tutorial from me. Preparation The core lies in using external module PyPDF2. First, install it using pip: pip is P

How to Cache Using Redis in Django Applications How to Cache Using Redis in Django Applications Mar 02, 2025 am 10:10 AM

This tutorial demonstrates how to leverage Redis caching to boost the performance of Python applications, specifically within a Django framework. We'll cover Redis installation, Django configuration, and performance comparisons to highlight the bene

How to Perform Deep Learning with TensorFlow or PyTorch? How to Perform Deep Learning with TensorFlow or PyTorch? Mar 10, 2025 pm 06:52 PM

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

How to Implement Your Own Data Structure in Python How to Implement Your Own Data Structure in Python Mar 03, 2025 am 09:28 AM

This tutorial demonstrates creating a custom pipeline data structure in Python 3, leveraging classes and operator overloading for enhanced functionality. The pipeline's flexibility lies in its ability to apply a series of functions to a data set, ge

Introduction to Parallel and Concurrent Programming in Python Introduction to Parallel and Concurrent Programming in Python Mar 03, 2025 am 10:32 AM

Python, a favorite for data science and processing, offers a rich ecosystem for high-performance computing. However, parallel programming in Python presents unique challenges. This tutorial explores these challenges, focusing on the Global Interprete

See all articles