Home Backend Development Python Tutorial Detailed introduction to string formatting str.format in Python

Detailed introduction to string formatting str.format in Python

Feb 20, 2017 am 10:03 AM

Preface

Python has added a new string formatting method in version 2.6: str.format(). Its basic syntax is to replace the previous %. with {} and :.

Placeholder syntax when formatting:

replacement_field ::= "{" [field_name] ["!" conversion] [":" format_spec] "}"
Copy after login

"Mapping" rules

By position

str.format() Can accept unlimited parameters, and the positions can be out of order:

>>> "{0} {1}".format("hello", "world")
'hello world'
>>> "{} {}".format("hello", "world")
'hello world'
>>> "{1} {0} {1}".format("hello", "world")
'world hello world'
Copy after login

By keyword parameters

When using key parameters, the parameter name needs to be provided in the string:

>>> "I am {name}, age is {age}".format(name="huoty", age=18)
'I am huoty, age is 18'
>>> user = {"name": "huoty", "age": 18}
>>> "I am {name}, age is {age}".format(**user)
'I am huoty, age is 18'
Copy after login

Through object properties

str.format() User properties can be read directly:

>>> class User(object):
...  def __init__(self, name, age):
...   self.name = name
...   self.age = age
...   
...  def __str__(self):
...   return "{self.name}({self.age})".format(self=self)
...  
...  def __repr__(self):
...   return self.__str__()
...  
...
>>> user = User("huoty", 18)
>>> user
huoty(18)
>>> "I am {user.name}, age is {user.age}".format(user=user)
'I am huoty, age is 18'
Copy after login

By subscript

Elements can be accessed by subscripts inside the string that needs to be formatted:

>>> names, ages = ["huoty", "esenich", "anan"], [18, 16, 8]
>>> "I am {0[0]}, age is {1[2]}".format(names, ages)
'I am huoty, age is 8'
>>> users = {"names": ["huoty", "esenich", "anan"], "ages": [18, 16, 8]}
>>> "I am {names[0]}, age is {ages[0]}".format(**users)
Copy after login

Specify conversion

You can specify the conversion type of string:

 conversion ::= "r" | "s" | "a"
Copy after login

Where "!r" corresponds to repr(); "!s" corresponds to str(); "!a" corresponds to ascii(). Example:

>>> "repr() shows quotes: {!r}; str() doesn't: {!s}".format('test1', 'test2')
"repr() shows quotes: 'test1'; str() doesn't: test2"
Copy after login

Format Qualifier

Padding and Alignment

Padding is often used together with alignment. ^, <, > are respectively centered, left-aligned, and right-aligned, followed by width, and the character followed by : can only be one character. If not specified, it will be filled with spaces by default.

>>> "{:>8}".format("181716")
&#39; 181716&#39;
>>> "{:0>8}".format("181716")
&#39;00181716&#39;
>>> "{:->8}".format("181716")
&#39;--181716&#39;
>>> "{:-<8}".format("181716")
&#39;181716--&#39;
>>> "{:-^8}".format("181716")
&#39;-181716-&#39;
>>> "{:-<25}>".format("Here ")
&#39;Here -------------------->&#39;
Copy after login

Floating point precision

Use f to represent the floating point type, and you can add precision control in front of it :

>>> "[ {:.2f} ]".format(321.33345)
&#39;[ 321.33 ]&#39;
>>> "[ {:.1f} ]".format(321.33345)
&#39;[ 321.3 ]&#39;
>>> "[ {:.4f} ]".format(321.33345)
&#39;[ 321.3335 ]&#39;
>>> "[ {:.4f} ]".format(321)
&#39;[ 321.0000 ]&#39;
Copy after login

You can also specify a symbol for floating point numbers, + means + will be displayed before positive numbers, and - will be displayed before negative numbers; (space) means before positive numbers Adding a space and adding -;- before a negative number is consistent with adding nothing ({:f}):

>>> &#39;{:+f}; {:+f}&#39;.format(3.141592657, -3.141592657)
&#39;+3.141593; -3.141593&#39;
>>> &#39;{: f}; {: f}&#39;.format(3.141592657, -3.141592657)
&#39; 3.141593; -3.141593&#39;
>>> &#39;{:f}; {:f}&#39;.format(3.141592657, -3.141592657)
&#39;3.141593; -3.141593&#39;
>>> &#39;{:-f}; {:-f}&#39;.format(3.141592657, -3.141592657)
&#39;3.141593; -3.141593&#39;
>>> &#39;{:+.4f}; {:+.4f}&#39;.format(3.141592657, -3.141592657)
&#39;+3.1416; -3.1416&#39;
Copy after login

Specify the base system

>>> "int: {0:d}; hex: {0:x}; oct: {0:o}; bin: {0:b}".format(18)
&#39;int: 18; hex: 12; oct: 22; bin: 10010&#39;
>>> "int: {0:d}; hex: {0:#x}; oct: {0:#o}; bin: {0:#b}".format(18)
&#39;int: 18; hex: 0x12; oct: 0o22; bin: 0b10010&#39;
Copy after login

Thousand separator

You can use "," as the thousands separator:

>>> &#39;{:,}&#39;.format(1234567890)
&#39;1,234,567,890&#39;
Copy after login

Percent display

>>> "progress: {:.2%}".format(19.88/22)
&#39;progress: 90.36%&#39;
Copy after login

In fact, format Also supports more type symbols:

type ::= "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"
Copy after login

Other tips

Placeholder nesting

Sometimes placeholder nesting is still useful:

>>> &#39;{0:{fill}{align}16}&#39;.format("hello", fill=&#39;*&#39;, align=&#39;^&#39;)
&#39;*****hello******&#39;
>>>
>>> for num in range(5,12):
...  for base in "dXob":
...   print("{0:{width}{base}}".format(num, base=base, width=5), end=&#39; &#39;)
...  print()
...  
...
 5  5  5 101
 6  6  6 110
 7  7  7 111
 8  8 10 1000
 9  9 11 1001
 10  A 12 1010
 11  B 13 1011
Copy after login

As When using the function

, you can not specify the format parameters first, but call it as a function in an unnecessary place:

>>> email_f = "Your email address was {email}".format
>>> print(email_f(email="suodhuoty@gmail.com"))
Your email address was sudohuoty@gmail.com
Copy after login

Escape braces

When you need to use braces in a string, you can use braces to escape:

>>> " The {} set is often represented as { {0} } ".format("empty")
&#39; The empty set is often represented as {0} &#39;
Copy after login

For more detailed introduction to string formatting str.format in Python and related articles, please pay attention to the 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 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

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

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

Serialization and Deserialization of Python Objects: Part 1 Serialization and Deserialization of Python Objects: Part 1 Mar 08, 2025 am 09:39 AM

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

Mathematical Modules in Python: Statistics Mathematical Modules in Python: Statistics Mar 09, 2025 am 11:40 AM

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

See all articles