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Python String Interpolation: 4 Methods (with Code)

• Nov 10, 2023
• 8 Minute Read
• Why Trust Us We uphold a strict editorial policy that emphasizes factual accuracy, relevance, and impartiality. Our content is crafted by top technical writers with deep knowledge in the fields of computer science and data science, ensuring each piece is meticulously reviewed by a team of seasoned editors to guarantee compliance with the highest standards in educational content creation and publishing.
• By Abrar Ahmed

String interpolation is a powerful feature in Python that allows for the dynamic insertion of variables into strings. It enhances code readability, simplifies string formatting, and provides flexibility in generating output. Python offers multiple methods for string interpolation, including the % operator, the str.format() method, f-strings, and template strings. Each method has its own syntax and use cases, but all serve the purpose of achieving efficient and effective string interpolation. We will see the 4 different ways to achieve this in python and with examples also understand how each way works.

What is String Interpolation in Python?

In Python, String Interpolation means placing a placeholder inside a string that can be dynamically changed based on some variable’s value. It is used often when the elements inside a string are not known or could be changing from time to time. It enables the creation of more readable and efficient code by avoiding the need for manual string concatenation or formatting. With string interpolation, variables can be directly embedded within a string, eliminating the need for complex concatenation operations.

Importance of String Interpolation

String interpolation plays a crucial role in Python programming as it enhances code readability, simplifies string manipulation, and improves overall efficiency. By using string interpolation, developers can easily incorporate variables, calculations, and expressions into their strings, resulting in cleaner and more concise code. This feature is especially useful when dealing with dynamic content, such as generating personalized messages or constructing complex queries.

You can check out this article to learn more string formatting techniques: Python String Formatting .

Methods of String Interpolation

There are various methods of implementing string interpolation that we shall discuss below:

1. The % Operator Method

The % formatting in python is perhaps the most naive way of performing string interpolation in python. We simply place, the % sign wherever we expect to put a placeholder and then provide the values of the placeholder after the string

To use the % operator for string interpolation, the following syntax is used:

Let's understand it with an example:

There are some special types of % signs that we can use when we want to let the compiler know what intended value we want to provide in the placeholder. For example, if we want the placeholder to only be replaced by integer values we use the symbol %d.

Similarly, various other symbols can be used for various reasons, some of which are the following:

• %s: when we want the placeholder value to be a string
• %f: when we want the placeholder value to be a floating point decimal like 2.415
• %r: when we want to replace the placeholder with the raw data of a variable
• %x: when the value replacing the placeholder has to be a hexadecimal value
• %o: when the value replacing the placeholder has to be an octal value
• %c: when we want to replace the placeholder with special characters

2. The str.format Method

The str.format() method is a more versatile and preferred way of performing string interpolation in Python. It provides a more readable syntax and allows for greater control over the formatting of the resulting string. Using this method, instead of providing a % sign as a placeholder, we define the placeholder using curly braces { }. The values that we want to replace in the placeholder are passed as arguments to the format function.

To use the str.format() method for string interpolation, the following syntax is used:

An example depicting this is provided below:

We can also provide names to our placeholders for easier readability in our programs. Obviously with too many interpolations to make, we would lose track of which curly brace is getting replaced with what values.

Therefore to name the placeholders, we just provide a name inside the curly brace that we then use while passing the values of the placeholders in the format function.

3. Template

A template is a class inside the string module. A class is a bunch of variables and their associated functions that can be accessed by a single name and a module is a bunch of these classes. How do we do string interpolation using the template class?

The template class allows us to create templates with placeholders in them that we can later use to substitute the placeholder values. You can create a template by using the $ sign as a placeholder and then use the substitute function inside the template class to replace it with placeholder values.

You can have a look at the example below to understand this concept in a better fashion.

4. F-strings (Formatted String Literals)

F strings are perhaps the easiest way of performing string interpolation in python. f strings were introduced after python 3.6 and life has become easier since then. Why do we call it f string? Well, any string on which you want to perform string interpolation using this method has to begin with the letter f before the quotes. F-strings are prefixed with the letter 'f' or 'F' and allow for the direct insertion of variables, expressions, and even function calls within curly braces {}. This eliminates the need for explicit string concatenation or format specifiers.

So if you were to use an f string for wishing hello to every new user, you would write f” Hello ”. But what makes f strings so easy to use? Unlike the other three methods that we saw, you do not have to define the placeholder and their values separately.

You read it right! Using f strings you can directly interpolate values into the string by providing the values in curly brace inside the string.

To use f-strings for string interpolation, the following syntax is used:

Have a look at the example below and it will become crystal clear to you.

String Interpolation vs. Concatenation

String interpolation and string concatenation are both methods used to combine strings and variables. However, they differ in terms of readability, efficiency, and flexibility.

String interpolation, using methods like f-strings or the % operator, offers a more concise and readable syntax for combining strings and variables. It allows for the direct insertion of variables into strings, eliminating the need for explicit concatenation operations. 

On the other hand, string concatenation involves the use of the '+' operator to combine strings and variables. While it is a straightforward method, it can become cumbersome and less readable, especially when dealing with multiple variables or complex formatting requirements.

Benefits of String Interpolation

String interpolation offers several benefits over string concatenation:

• Readability: Interpolated strings are more readable and concise, as the variables are directly inserted into the string.
• Flexibility: Interpolation methods allow for complex expressions, arithmetic operations, and function calls within the string.
• Formatting: Interpolation methods provide format specifiers to control the formatting of interpolated values, such as precision or number formatting.
• Efficiency: Interpolation methods are generally more efficient than concatenation, as they avoid unnecessary string object creation.

String interpolation in Python is a frequent task that requires us to dynamically change the value of a string. It can be achieved in four manners based on the programmers' needs. Strings can be interpolated using the % sign, the format function, the Template class, or using f strings. The % sign is not preferred as it decreases the readability of the program whereas the f strings method is the most preferred as it is easier and increases code readability.

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Python String Interpolation: A Comprehensive Guide

• December 29, 2023 December 29, 2023

Python string interpolation is a powerful feature that allows for dynamic string substitution and reduces repetition in code. It enables developers to insert variable values into string templates, resulting in more readable and efficient code. In this comprehensive guide, we will explore the various techniques available in Python for string interpolation and formatting. We will dive into the four main methods: the string modulo operator, the .format() method, f-strings, and the Template class. Each method has its own advantages and disadvantages, and we will cover them all.

The String Modulo Operator and String Formatting

One of the methods for string interpolation in Python is the string modulo operator (%). This method is concise and easy to read, making it a good choice for code readability. The syntax for string formatting with the modulo operator is as follows:

The string modulo operator uses format indicators to specify the type of substitution to be made. These format indicators include %s for strings, %d for integers, %f for floating-point numbers, and many more. The values to be substituted are placed after the % character outside of the string literal.

For example, consider the following code snippet:

The output of this code would be:

In this example, the %s format indicator is used to convert the variables my_val_1 , my_val_2 , and my_val_1+my_val_2 into strings, which are then substituted into the string template.

The string modulo operator also supports flags that modify the output formatting. For example, the %5d format specifier adds five spaces before the integer value. You can also pad the output with zeros or left-justify the integers using the - flag.

While the string modulo operator is a concise and readable method for string interpolation, it is the least flexible and may not be supported in newer versions of Python. If you are working with an older codebase that uses the string modulo operator, it is important to familiarize yourself with its syntax.

The Python String .format() Method and Positional Arguments

Another method for string interpolation in Python is the .format() method. This method provides more flexibility and power in formatting strings. With the .format() method, you can obtain any string output you need, although it may require more complex code.

The .format() method can be called on any Python string object. It uses replacement fields surrounded by curly braces ({}) to indicate the position of the values to be substituted. These replacement fields can contain the name of a keyword argument or the numeric index of a positional argument.

In this example, the replacement fields {name} and {age} are expanded to display the values of the .format() method’s keyword arguments.

The .format() method also supports format indicators, similar to the string modulo operator. These format indicators can be used to modify the output formatting. For example, you can specify the number of digits after the decimal point for floating-point values.

Python f-strings

Python f-strings, or formatted string literals, provide another way to format strings. F-strings are simpler and more readable than other string formatting techniques, such as the string modulo operator or the .format() method. They also offer better performance.

To use an f-string, you must prefix your string literal with f or F . For example:

In this example, the replacement field includes a Python expression and a date format specifier. The f-string method produces more succinct and readable code compared to the .format() method.

F-strings also support various format indicators that can be used to modify the output formatting. You can refer to Python’s Format Specification Mini-Language documentation for more details on the available format indicators.

Python Template Strings

The Python Template class, part of the String module, provides another method for string interpolation. Template strings offer simpler string substitution compared to other methods, but they do not support string formatting like f-strings, the .format() method, or the string modulo operator. However, they are considered more secure, making them a suitable choice when working with user-generated strings.

To use Template strings, you indicate string substitutions using the $ interpolation character, followed by the name of a dictionary key passed as an argument to the Template class’s substitute() method. The substitute() method requires a dictionary-like object with keys as its argument.

For example:

In this example, the Template class is used to create a string template with a placeholder for the name. The substitute() method is then used to perform the string substitution, using a dictionary-like object as the argument.

Template strings provide readable code, especially when using a single template with various values stored in a dictionary. For example:

In this example, the Template class is used to create a template string with placeholders for the first and last names. The substitute() method is then called within a loop to perform the string substitution for each name dictionary.

Python provides multiple methods for string interpolation and formatting, each with its own advantages and disadvantages. The choice of method depends on the specific use case and the desired level of flexibility and readability. The string modulo operator is a concise and readable method, but it may not be supported in newer versions of Python. The .format() method offers more flexibility and power, but it can be more complex. F-strings provide a combination of formatting power and readability, making them a popular choice. Template strings are simpler and more secure, making them suitable for user-generated strings.

Regardless of the method chosen, Python string interpolation and formatting are powerful tools that can enhance code readability and efficiency. By understanding the different techniques available, developers can choose the method that best suits their needs and create more effective and maintainable code.

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How to perform string interpolation in Python

String interpolation is a technique used in various programming languages to embed expressions or variables within string literals. It allows you to construct strings by combining static text with dynamic content. The resulting string is formed by replacing placeholders or variables with their actual values.

Python offers several ways to perform string interpolation:

• Use f-strings, generally considered the most concise and efficient way.
• Use format() method when you need more flexibility or using Python older than 3.6.
• Use string Template class with custom patterns to handle complex formatting logic.
• Use %-formatting only for historical compatibility.

Each method has its own advantages and disadvantages.

• Using f-strings (formatted string literals), a feature introduced in Python 3.6 that allows for concise and readable string formatting. This method uses f or F as prefix and curly braces {} to indicate placeholders and embed expressions directly inside the string.

There are many different format specifiers, written after a colon ( : ) inside the curly braces that enclose the expression, that can be used with f-strings. They can be used to specify the type, precision, alignment, padding, grouping, and other aspects of the formatting.

• Using format() method , a versatile option available in all Python versions. It allows you to define placeholders in the string using curly braces and pass the values as arguments to the format() method.
• Using %-formatting (older style) , the older method and less readable than f-strings and the format() method. It uses placeholders with a % symbol and format specifiers like s (string), d (integer), and f (float) to define the type of value to be inserted.
• Using string template . This method uses Template class from the string module and dollar signs $ to indicate placeholders and the substitute() method to pass the values.

You might also like

5 Best Ways to Perform String Interpolation in Python

💡 Problem Formulation: String interpolation refers to the insertion of a variable’s value directly into a string. In Python, there are various methods to accomplish this, enhancing both readability and maintainability of the code. Let’s say we have a variable name with the value 'Alice' and we want to create a greeting string such that 'Hello, Alice!' is the desired output. This article demonstrates the top 5 methods to achieve this.

Method 1: Percent (%) Formatting

Percent formatting is one of the oldest methods for string interpolation in Python. It uses a placeholder marked by a ‘%’ character, followed by a letter defining the type of value, such as ‘%s’ for strings.

Here’s an example:

This code snippet uses %s as a placeholder within the string for the name variable. It’s immediately recognizable to those familiar with C’s printf and is still used for its simplicity. However, it’s less flexible and considered outdated compared to newer methods.

Method 2: str.format()

The str.format() method is a versatile way of performing string interpolation. Curly braces ‘{}’ are used as placeholders within the string and can be numbered to insert multiple variables.

In this snippet, the format() method is called on the string, with name passed as a parameter, replacing the ‘{}’ placeholder. It offers advanced formatting options, making it a popular choice for many developers.

Method 3: f-Strings (Formatted String Literals)

F-strings, introduced in Python 3.6, is a modern and concise way to embed expressions inside string literals using curly braces {} and prefixing the string with an ‘f’.

This code utilizes an f-string to embed the variable name directly into the string literal. It’s highly readable, less prone to error, and often performs better than other methods of string formatting.

Method 4: Template Strings

Template Strings are part of the standard library in the string module. They provide a simpler syntax where placeholders are defined by $ and can be a good choice when handling format strings from user input for security reasons.

The Template class is instantiated with the string, and then the substitute() method replaces the $name placeholder with the name variable. It’s a less commonly used method but can be helpful in certain contexts.

Bonus One-Liner Method 5: Concatenation

Though not a form of interpolation per se, concatenation simply combines strings together using the ‘+’ operator. It’s straightforward but can become less manageable with complex string constructions.

This code directly adds multiple string literals and variables using the ‘+’ operator. It’s a simple method that has been around since the early days of Python. However, it can be error-prone and is less efficient for larger strings or numerous variables.

Summary/Discussion

• Method 1: Percent (%) Formatting. Simple and recognizable. Best for quick tasks or legacy code. Less flexible and considered dated.
• Method 2: str.format() . Highly versatile with advanced formatting options. Ideal for complex string formatting, but slightly more verbose than f-strings.
• Method 3: f-Strings (Formatted String Literals). Modern, concise, and efficient. Recommended for Python 3.6+. Provides high readability and performance.
• Method 4: Template Strings. Part of the standard library, offering straightforward syntax and better security for user-generated format strings. Less commonly used.
• Bonus Method 5: Concatenation. Very straightforward method good for simple use cases. Not recommended for complex manipulations due to manageability and performance issues.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.

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Interpolation in Python

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Interpolation in Python refers to the process of estimating unknown values that fall between known values.

This concept is commonly used in data analysis, mathematical modeling, and graphical representations. Python provides several ways to perform interpolation, including the use of libraries like NumPy, SciPy, and pandas, which offer built-in functions and methods for linear and non-linear interpolation.

Understanding Interpolation

At its core, interpolation involves finding a function that passes through a given set of points and then using this function to estimate values at points not originally in the dataset. The simplest form of interpolation is linear interpolation, which assumes that the points can be connected with straight lines, and the value at any intermediate point can be found by linearly extrapolating the line connecting the closest known points.

However, real-world data often requires more sophisticated methods such as polynomial or spline interpolation. These methods provide smoother curves that better fit the data, especially when the relationship between the data points is non-linear.

Here are some examples demonstrating how interpolation can be done in Python:

Example 1: Linear Interpolation Using NumPy

Suppose you have two points and you want to interpolate values between them.

Example 2: Linear Interpolation Using pandas

Pandas can be particularly useful for time series interpolation.

Interpolation is a powerful technique in Python that enables data scientists, researchers, and developers to handle missing data, smooth out datasets, or create models that can predict trends. With Python’s rich set of libraries like NumPy, SciPy, and pandas, users have access to a wide range of interpolation methods to tackle virtually any problem. Whether you’re working on scientific research, financial analysis, or engineering projects, mastering interpolation in Python can significantly enhance your data analysis capabilities.

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In Python, a string is a sequence of characters. For example, "hello" is a string containing a sequence of characters 'h' , 'e' , 'l' , 'l' , and 'o' .

We use single quotes or double quotes to represent a string in Python. For example,

Here, we have created a string variable named string1 . The variable is initialized with the string "Python Programming" .

• Example: Python String

In the above example, we have created string-type variables: name and message with values "Python" and "I love Python" respectively.

Here, we have used double quotes to represent strings, but we can use single quotes too.

• Access String Characters in Python

We can access the characters in a string in three ways.

• Indexing : One way is to treat strings as a list and use index values. For example,
• Negative Indexing : Similar to a list, Python allows negative indexing for its strings. For example,
• Slicing : Access a range of characters in a string by using the slicing operator colon : . For example,

Note : If we try to access an index out of the range or use numbers other than an integer, we will get errors.

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In Python, strings are immutable. That means the characters of a string cannot be changed. For example,

However, we can assign the variable name to a new string. For example,

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We can also create a multiline string in Python. For this, we use triple double quotes """ or triple single quotes ''' . For example,

In the above example, anything inside the enclosing triple quotes is one multiline string.

• Python String Operations

Many operations can be performed with strings, which makes it one of the most used data types in Python.

1. Compare Two Strings

We use the == operator to compare two strings. If two strings are equal, the operator returns True . Otherwise, it returns False . For example,

In the above example,

• str1 and str2 are not equal. Hence, the result is False .
• str1 and str3 are equal. Hence, the result is True .

2. Join Two or More Strings

In Python, we can join (concatenate) two or more strings using the + operator.

In the above example, we have used the + operator to join two strings: greet and name .

• Iterate Through a Python String

We can iterate through a string using a for loop . For example,

• Python String Length

In Python, we use the len() method to find the length of a string. For example,

• String Membership Test

We can test if a substring exists within a string or not, using the keyword in .

• Methods of Python String

Besides those mentioned above, there are various string methods present in Python. Here are some of those methods:

• Escape Sequences in Python

The escape sequence is used to escape some of the characters present inside a string.

Suppose we need to include both a double quote and a single quote inside a string,

Since strings are represented by single or double quotes, the compiler will treat "He said, " as a string. Hence, the above code will cause an error.

To solve this issue, we use the escape character \ in Python.

Here is a list of all the escape sequences supported by Python.

• Python String Formatting (f-Strings)

Python f-Strings makes it easy to print values and variables. For example,

Here, f'{name} is from {country}' is an f-string .

This new formatting syntax is powerful and easy to use. From now on, we will use f-Strings to print strings and variables.

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Strings in python are surrounded by either single quotation marks, or double quotation marks.

'hello' is the same as "hello" .

You can display a string literal with the print() function:

Quotes Inside Quotes

You can use quotes inside a string, as long as they don't match the quotes surrounding the string:

Assign String to a Variable

Assigning a string to a variable is done with the variable name followed by an equal sign and the string:

Multiline Strings

You can assign a multiline string to a variable by using three quotes:

You can use three double quotes:

Or three single quotes:

Note: in the result, the line breaks are inserted at the same position as in the code.

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Strings are Arrays

Like many other popular programming languages, strings in Python are arrays of bytes representing unicode characters.

However, Python does not have a character data type, a single character is simply a string with a length of 1.

Square brackets can be used to access elements of the string.

Get the character at position 1 (remember that the first character has the position 0):

Looping Through a String

Since strings are arrays, we can loop through the characters in a string, with a for loop.

Loop through the letters in the word "banana":

Learn more about For Loops in our Python For Loops chapter.

String Length

To get the length of a string, use the len() function.

The len() function returns the length of a string:

Check String

To check if a certain phrase or character is present in a string, we can use the keyword in .

Check if "free" is present in the following text:

Use it in an if statement:

Print only if "free" is present:

Learn more about If statements in our Python If...Else chapter.

Check if NOT

To check if a certain phrase or character is NOT present in a string, we can use the keyword not in .

Check if "expensive" is NOT present in the following text:

print only if "expensive" is NOT present:

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configparser — Configuration file parser ¶

Source code: Lib/configparser.py

This module provides the ConfigParser class which implements a basic configuration language which provides a structure similar to what’s found in Microsoft Windows INI files. You can use this to write Python programs which can be customized by end users easily.

This library does not interpret or write the value-type prefixes used in the Windows Registry extended version of INI syntax.

TOML is a well-specified format for application configuration files. It is specifically designed to be an improved version of INI.

Support for creating Unix shell-like mini-languages which can also be used for application configuration files.

The json module implements a subset of JavaScript syntax which is sometimes used for configuration, but does not support comments.

Quick Start ¶

Let’s take a very basic configuration file that looks like this:

The structure of INI files is described in the following section . Essentially, the file consists of sections, each of which contains keys with values. configparser classes can read and write such files. Let’s start by creating the above configuration file programmatically.

As you can see, we can treat a config parser much like a dictionary. There are differences, outlined later , but the behavior is very close to what you would expect from a dictionary.

Now that we have created and saved a configuration file, let’s read it back and explore the data it holds.

As we can see above, the API is pretty straightforward. The only bit of magic involves the DEFAULT section which provides default values for all other sections [ 1 ] . Note also that keys in sections are case-insensitive and stored in lowercase [ 1 ] .

It is possible to read several configurations into a single ConfigParser , where the most recently added configuration has the highest priority. Any conflicting keys are taken from the more recent configuration while the previously existing keys are retained.

This behaviour is equivalent to a ConfigParser.read() call with several files passed to the filenames parameter.

Supported Datatypes ¶

Config parsers do not guess datatypes of values in configuration files, always storing them internally as strings. This means that if you need other datatypes, you should convert on your own:

Since this task is so common, config parsers provide a range of handy getter methods to handle integers, floats and booleans. The last one is the most interesting because simply passing the value to bool() would do no good since bool('False') is still True . This is why config parsers also provide getboolean() . This method is case-insensitive and recognizes Boolean values from 'yes' / 'no' , 'on' / 'off' , 'true' / 'false' and '1' / '0' [ 1 ] . For example:

Apart from getboolean() , config parsers also provide equivalent getint() and getfloat() methods. You can register your own converters and customize the provided ones. [ 1 ]

Fallback Values ¶

As with a dictionary, you can use a section’s get() method to provide fallback values:

Please note that default values have precedence over fallback values. For instance, in our example the 'CompressionLevel' key was specified only in the 'DEFAULT' section. If we try to get it from the section 'topsecret.server.example' , we will always get the default, even if we specify a fallback:

One more thing to be aware of is that the parser-level get() method provides a custom, more complex interface, maintained for backwards compatibility. When using this method, a fallback value can be provided via the fallback keyword-only argument:

The same fallback argument can be used with the getint() , getfloat() and getboolean() methods, for example:

Supported INI File Structure ¶

A configuration file consists of sections, each led by a [section] header, followed by key/value entries separated by a specific string ( = or : by default [ 1 ] ). By default, section names are case sensitive but keys are not [ 1 ] . Leading and trailing whitespace is removed from keys and values. Values can be omitted if the parser is configured to allow it [ 1 ] , in which case the key/value delimiter may also be left out. Values can also span multiple lines, as long as they are indented deeper than the first line of the value. Depending on the parser’s mode, blank lines may be treated as parts of multiline values or ignored.

By default, a valid section name can be any string that does not contain ‘\n’. To change this, see ConfigParser.SECTCRE .

The first section name may be omitted if the parser is configured to allow an unnamed top level section with allow_unnamed_section=True . In this case, the keys/values may be retrieved by UNNAMED_SECTION as in config[UNNAMED_SECTION] .

Configuration files may include comments, prefixed by specific characters ( # and ; by default [ 1 ] ). Comments may appear on their own on an otherwise empty line, possibly indented. [ 1 ]

For example:

Unnamed Sections ¶

The name of the first section (or unique) may be omitted and values retrieved by the UNNAMED_SECTION attribute.

Interpolation of values ¶

On top of the core functionality, ConfigParser supports interpolation. This means values can be preprocessed before returning them from get() calls.

The default implementation used by ConfigParser . It enables values to contain format strings which refer to other values in the same section, or values in the special default section [ 1 ] . Additional default values can be provided on initialization.

In the example above, ConfigParser with interpolation set to BasicInterpolation() would resolve %(home_dir)s to the value of home_dir ( /Users in this case). %(my_dir)s in effect would resolve to /Users/lumberjack . All interpolations are done on demand so keys used in the chain of references do not have to be specified in any specific order in the configuration file.

With interpolation set to None , the parser would simply return %(my_dir)s/Pictures as the value of my_pictures and %(home_dir)s/lumberjack as the value of my_dir .

An alternative handler for interpolation which implements a more advanced syntax, used for instance in zc.buildout . Extended interpolation is using ${section:option} to denote a value from a foreign section. Interpolation can span multiple levels. For convenience, if the section: part is omitted, interpolation defaults to the current section (and possibly the default values from the special section).

For example, the configuration specified above with basic interpolation, would look like this with extended interpolation:

Values from other sections can be fetched as well:

Mapping Protocol Access ¶

Added in version 3.2.

Mapping protocol access is a generic name for functionality that enables using custom objects as if they were dictionaries. In case of configparser , the mapping interface implementation is using the parser['section']['option'] notation.

parser['section'] in particular returns a proxy for the section’s data in the parser. This means that the values are not copied but they are taken from the original parser on demand. What’s even more important is that when values are changed on a section proxy, they are actually mutated in the original parser.

configparser objects behave as close to actual dictionaries as possible. The mapping interface is complete and adheres to the MutableMapping ABC. However, there are a few differences that should be taken into account:

By default, all keys in sections are accessible in a case-insensitive manner [ 1 ] . E.g. for option in parser["section"] yields only optionxform ’ed option key names. This means lowercased keys by default. At the same time, for a section that holds the key 'a' , both expressions return True :

All sections include DEFAULTSECT values as well which means that .clear() on a section may not leave the section visibly empty. This is because default values cannot be deleted from the section (because technically they are not there). If they are overridden in the section, deleting causes the default value to be visible again. Trying to delete a default value causes a KeyError .

DEFAULTSECT cannot be removed from the parser:

trying to delete it raises ValueError ,

parser.clear() leaves it intact,

parser.popitem() never returns it.

parser.get(section, option, **kwargs) - the second argument is not a fallback value. Note however that the section-level get() methods are compatible both with the mapping protocol and the classic configparser API.

parser.items() is compatible with the mapping protocol (returns a list of section_name , section_proxy pairs including the DEFAULTSECT). However, this method can also be invoked with arguments: parser.items(section, raw, vars) . The latter call returns a list of option , value pairs for a specified section , with all interpolations expanded (unless raw=True is provided).

The mapping protocol is implemented on top of the existing legacy API so that subclasses overriding the original interface still should have mappings working as expected.

Customizing Parser Behaviour ¶

There are nearly as many INI format variants as there are applications using it. configparser goes a long way to provide support for the largest sensible set of INI styles available. The default functionality is mainly dictated by historical background and it’s very likely that you will want to customize some of the features.

The most common way to change the way a specific config parser works is to use the __init__() options:

defaults , default value: None

This option accepts a dictionary of key-value pairs which will be initially put in the DEFAULT section. This makes for an elegant way to support concise configuration files that don’t specify values which are the same as the documented default.

Hint: if you want to specify default values for a specific section, use read_dict() before you read the actual file.

dict_type , default value: dict

This option has a major impact on how the mapping protocol will behave and how the written configuration files look. With the standard dictionary, every section is stored in the order they were added to the parser. Same goes for options within sections.

An alternative dictionary type can be used for example to sort sections and options on write-back.

Please note: there are ways to add a set of key-value pairs in a single operation. When you use a regular dictionary in those operations, the order of the keys will be ordered. For example:

allow_no_value , default value: False

Some configuration files are known to include settings without values, but which otherwise conform to the syntax supported by configparser . The allow_no_value parameter to the constructor can be used to indicate that such values should be accepted:

delimiters , default value: ('=', ':')

Delimiters are substrings that delimit keys from values within a section. The first occurrence of a delimiting substring on a line is considered a delimiter. This means values (but not keys) can contain the delimiters.

See also the space_around_delimiters argument to ConfigParser.write() .

comment_prefixes , default value: ('#', ';')

inline_comment_prefixes , default value: None

Comment prefixes are strings that indicate the start of a valid comment within a config file. comment_prefixes are used only on otherwise empty lines (optionally indented) whereas inline_comment_prefixes can be used after every valid value (e.g. section names, options and empty lines as well). By default inline comments are disabled and '#' and ';' are used as prefixes for whole line comments.

Changed in version 3.2: In previous versions of configparser behaviour matched comment_prefixes=('#',';') and inline_comment_prefixes=(';',) .

Please note that config parsers don’t support escaping of comment prefixes so using inline_comment_prefixes may prevent users from specifying option values with characters used as comment prefixes. When in doubt, avoid setting inline_comment_prefixes . In any circumstances, the only way of storing comment prefix characters at the beginning of a line in multiline values is to interpolate the prefix, for example:

strict , default value: True

When set to True , the parser will not allow for any section or option duplicates while reading from a single source (using read_file() , read_string() or read_dict() ). It is recommended to use strict parsers in new applications.

Changed in version 3.2: In previous versions of configparser behaviour matched strict=False .

empty_lines_in_values , default value: True

In config parsers, values can span multiple lines as long as they are indented more than the key that holds them. By default parsers also let empty lines to be parts of values. At the same time, keys can be arbitrarily indented themselves to improve readability. In consequence, when configuration files get big and complex, it is easy for the user to lose track of the file structure. Take for instance:

This can be especially problematic for the user to see if she’s using a proportional font to edit the file. That is why when your application does not need values with empty lines, you should consider disallowing them. This will make empty lines split keys every time. In the example above, it would produce two keys, key and this .

default_section , default value: configparser.DEFAULTSECT (that is: "DEFAULT" )

The convention of allowing a special section of default values for other sections or interpolation purposes is a powerful concept of this library, letting users create complex declarative configurations. This section is normally called "DEFAULT" but this can be customized to point to any other valid section name. Some typical values include: "general" or "common" . The name provided is used for recognizing default sections when reading from any source and is used when writing configuration back to a file. Its current value can be retrieved using the parser_instance.default_section attribute and may be modified at runtime (i.e. to convert files from one format to another).

interpolation , default value: configparser.BasicInterpolation

Interpolation behaviour may be customized by providing a custom handler through the interpolation argument. None can be used to turn off interpolation completely, ExtendedInterpolation() provides a more advanced variant inspired by zc.buildout . More on the subject in the dedicated documentation section . RawConfigParser has a default value of None .

converters , default value: not set

Config parsers provide option value getters that perform type conversion. By default getint() , getfloat() , and getboolean() are implemented. Should other getters be desirable, users may define them in a subclass or pass a dictionary where each key is a name of the converter and each value is a callable implementing said conversion. For instance, passing {'decimal': decimal.Decimal} would add getdecimal() on both the parser object and all section proxies. In other words, it will be possible to write both parser_instance.getdecimal('section', 'key', fallback=0) and parser_instance['section'].getdecimal('key', 0) .

If the converter needs to access the state of the parser, it can be implemented as a method on a config parser subclass. If the name of this method starts with get , it will be available on all section proxies, in the dict-compatible form (see the getdecimal() example above).

More advanced customization may be achieved by overriding default values of these parser attributes. The defaults are defined on the classes, so they may be overridden by subclasses or by attribute assignment.

By default when using getboolean() , config parsers consider the following values True : '1' , 'yes' , 'true' , 'on' and the following values False : '0' , 'no' , 'false' , 'off' . You can override this by specifying a custom dictionary of strings and their Boolean outcomes. For example:

Other typical Boolean pairs include accept / reject or enabled / disabled .

This method transforms option names on every read, get, or set operation. The default converts the name to lowercase. This also means that when a configuration file gets written, all keys will be lowercase. Override this method if that’s unsuitable. For example:

The optionxform function transforms option names to a canonical form. This should be an idempotent function: if the name is already in canonical form, it should be returned unchanged.

A compiled regular expression used to parse section headers. The default matches [section] to the name "section" . Whitespace is considered part of the section name, thus [   larch   ] will be read as a section of name "   larch   " . Override this attribute if that’s unsuitable. For example:

While ConfigParser objects also use an OPTCRE attribute for recognizing option lines, it’s not recommended to override it because that would interfere with constructor options allow_no_value and delimiters .

Legacy API Examples ¶

Mainly because of backwards compatibility concerns, configparser provides also a legacy API with explicit get / set methods. While there are valid use cases for the methods outlined below, mapping protocol access is preferred for new projects. The legacy API is at times more advanced, low-level and downright counterintuitive.

An example of writing to a configuration file:

An example of reading the configuration file again:

To get interpolation, use ConfigParser :

Default values are available in both types of ConfigParsers. They are used in interpolation if an option used is not defined elsewhere.

ConfigParser Objects ¶

The main configuration parser. When defaults is given, it is initialized into the dictionary of intrinsic defaults. When dict_type is given, it will be used to create the dictionary objects for the list of sections, for the options within a section, and for the default values.

When delimiters is given, it is used as the set of substrings that divide keys from values. When comment_prefixes is given, it will be used as the set of substrings that prefix comments in otherwise empty lines. Comments can be indented. When inline_comment_prefixes is given, it will be used as the set of substrings that prefix comments in non-empty lines.

When strict is True (the default), the parser won’t allow for any section or option duplicates while reading from a single source (file, string or dictionary), raising DuplicateSectionError or DuplicateOptionError . When empty_lines_in_values is False (default: True ), each empty line marks the end of an option. Otherwise, internal empty lines of a multiline option are kept as part of the value. When allow_no_value is True (default: False ), options without values are accepted; the value held for these is None and they are serialized without the trailing delimiter.

When default_section is given, it specifies the name for the special section holding default values for other sections and interpolation purposes (normally named "DEFAULT" ). This value can be retrieved and changed at runtime using the default_section instance attribute. This won’t re-evaluate an already parsed config file, but will be used when writing parsed settings to a new config file.

Interpolation behaviour may be customized by providing a custom handler through the interpolation argument. None can be used to turn off interpolation completely, ExtendedInterpolation() provides a more advanced variant inspired by zc.buildout . More on the subject in the dedicated documentation section .

All option names used in interpolation will be passed through the optionxform() method just like any other option name reference. For example, using the default implementation of optionxform() (which converts option names to lower case), the values foo %(bar)s and foo %(BAR)s are equivalent.

When converters is given, it should be a dictionary where each key represents the name of a type converter and each value is a callable implementing the conversion from string to the desired datatype. Every converter gets its own corresponding get*() method on the parser object and section proxies.

Changed in version 3.1: The default dict_type is collections.OrderedDict .

Changed in version 3.2: allow_no_value , delimiters , comment_prefixes , strict , empty_lines_in_values , default_section and interpolation were added.

Changed in version 3.5: The converters argument was added.

Changed in version 3.7: The defaults argument is read with read_dict() , providing consistent behavior across the parser: non-string keys and values are implicitly converted to strings.

Changed in version 3.8: The default dict_type is dict , since it now preserves insertion order.

Changed in version 3.13: Raise a MultilineContinuationError when allow_no_value is True , and a key without a value is continued with an indented line.

Return a dictionary containing the instance-wide defaults.

Return a list of the sections available; the default section is not included in the list.

Add a section named section to the instance. If a section by the given name already exists, DuplicateSectionError is raised. If the default section name is passed, ValueError is raised. The name of the section must be a string; if not, TypeError is raised.

Changed in version 3.2: Non-string section names raise TypeError .

Indicates whether the named section is present in the configuration. The default section is not acknowledged.

Return a list of options available in the specified section .

If the given section exists, and contains the given option , return True ; otherwise return False . If the specified section is None or an empty string, DEFAULT is assumed.

Attempt to read and parse an iterable of filenames, returning a list of filenames which were successfully parsed.

If filenames is a string, a bytes object or a path-like object , it is treated as a single filename. If a file named in filenames cannot be opened, that file will be ignored. This is designed so that you can specify an iterable of potential configuration file locations (for example, the current directory, the user’s home directory, and some system-wide directory), and all existing configuration files in the iterable will be read.

If none of the named files exist, the ConfigParser instance will contain an empty dataset. An application which requires initial values to be loaded from a file should load the required file or files using read_file() before calling read() for any optional files:

Changed in version 3.2: Added the encoding parameter. Previously, all files were read using the default encoding for open() .

Changed in version 3.6.1: The filenames parameter accepts a path-like object .

Changed in version 3.7: The filenames parameter accepts a bytes object.

Read and parse configuration data from f which must be an iterable yielding Unicode strings (for example files opened in text mode).

Optional argument source specifies the name of the file being read. If not given and f has a name attribute, that is used for source ; the default is '<???>' .

Added in version 3.2: Replaces readfp() .

Parse configuration data from a string.

Optional argument source specifies a context-specific name of the string passed. If not given, '<string>' is used. This should commonly be a filesystem path or a URL.

Load configuration from any object that provides a dict-like items() method. Keys are section names, values are dictionaries with keys and values that should be present in the section. If the used dictionary type preserves order, sections and their keys will be added in order. Values are automatically converted to strings.

Optional argument source specifies a context-specific name of the dictionary passed. If not given, <dict> is used.

This method can be used to copy state between parsers.

Get an option value for the named section . If vars is provided, it must be a dictionary. The option is looked up in vars (if provided), section , and in DEFAULTSECT in that order. If the key is not found and fallback is provided, it is used as a fallback value. None can be provided as a fallback value.

All the '%' interpolations are expanded in the return values, unless the raw argument is true. Values for interpolation keys are looked up in the same manner as the option.

Changed in version 3.2: Arguments raw , vars and fallback are keyword only to protect users from trying to use the third argument as the fallback fallback (especially when using the mapping protocol).

A convenience method which coerces the option in the specified section to an integer. See get() for explanation of raw , vars and fallback .

A convenience method which coerces the option in the specified section to a floating point number. See get() for explanation of raw , vars and fallback .

A convenience method which coerces the option in the specified section to a Boolean value. Note that the accepted values for the option are '1' , 'yes' , 'true' , and 'on' , which cause this method to return True , and '0' , 'no' , 'false' , and 'off' , which cause it to return False . These string values are checked in a case-insensitive manner. Any other value will cause it to raise ValueError . See get() for explanation of raw , vars and fallback .

When section is not given, return a list of section_name , section_proxy pairs, including DEFAULTSECT.

Otherwise, return a list of name , value pairs for the options in the given section . Optional arguments have the same meaning as for the get() method.

Changed in version 3.8: Items present in vars no longer appear in the result. The previous behaviour mixed actual parser options with variables provided for interpolation.

If the given section exists, set the given option to the specified value; otherwise raise NoSectionError . option and value must be strings; if not, TypeError is raised.

Write a representation of the configuration to the specified file object , which must be opened in text mode (accepting strings). This representation can be parsed by a future read() call. If space_around_delimiters is true, delimiters between keys and values are surrounded by spaces.

Comments in the original configuration file are not preserved when writing the configuration back. What is considered a comment, depends on the given values for comment_prefix and inline_comment_prefix .

Remove the specified option from the specified section . If the section does not exist, raise NoSectionError . If the option existed to be removed, return True ; otherwise return False .

Remove the specified section from the configuration. If the section in fact existed, return True . Otherwise return False .

Transforms the option name option as found in an input file or as passed in by client code to the form that should be used in the internal structures. The default implementation returns a lower-case version of option ; subclasses may override this or client code can set an attribute of this name on instances to affect this behavior.

You don’t need to subclass the parser to use this method, you can also set it on an instance, to a function that takes a string argument and returns a string. Setting it to str , for example, would make option names case sensitive:

Note that when reading configuration files, whitespace around the option names is stripped before optionxform() is called.

A special object representing a section name used to reference the unnamed section (see Unnamed Sections ).

The maximum depth for recursive interpolation for get() when the raw parameter is false. This is relevant only when the default interpolation is used.

RawConfigParser Objects ¶

Legacy variant of the ConfigParser . It has interpolation disabled by default and allows for non-string section names, option names, and values via its unsafe add_section and set methods, as well as the legacy defaults= keyword argument handling.

Consider using ConfigParser instead which checks types of the values to be stored internally. If you don’t want interpolation, you can use ConfigParser(interpolation=None) .

Add a section named section to the instance. If a section by the given name already exists, DuplicateSectionError is raised. If the default section name is passed, ValueError is raised.

Type of section is not checked which lets users create non-string named sections. This behaviour is unsupported and may cause internal errors.

If the given section exists, set the given option to the specified value; otherwise raise NoSectionError . While it is possible to use RawConfigParser (or ConfigParser with raw parameters set to true) for internal storage of non-string values, full functionality (including interpolation and output to files) can only be achieved using string values.

This method lets users assign non-string values to keys internally. This behaviour is unsupported and will cause errors when attempting to write to a file or get it in non-raw mode. Use the mapping protocol API which does not allow such assignments to take place.

Exceptions ¶

Base class for all other configparser exceptions.

Exception raised when a specified section is not found.

Exception raised if add_section() is called with the name of a section that is already present or in strict parsers when a section if found more than once in a single input file, string or dictionary.

Changed in version 3.2: Added the optional source and lineno attributes and parameters to __init__() .

Exception raised by strict parsers if a single option appears twice during reading from a single file, string or dictionary. This catches misspellings and case sensitivity-related errors, e.g. a dictionary may have two keys representing the same case-insensitive configuration key.

Exception raised when a specified option is not found in the specified section.

Base class for exceptions raised when problems occur performing string interpolation.

Exception raised when string interpolation cannot be completed because the number of iterations exceeds MAX_INTERPOLATION_DEPTH . Subclass of InterpolationError .

Exception raised when an option referenced from a value does not exist. Subclass of InterpolationError .

Exception raised when the source text into which substitutions are made does not conform to the required syntax. Subclass of InterpolationError .

Exception raised when attempting to parse a file which has no section headers.

Exception raised when errors occur attempting to parse a file.

Changed in version 3.12: The filename attribute and __init__() constructor argument were removed. They have been available using the name source since 3.2.

Exception raised when a key without a corresponding value is continued with an indented line.

Added in version 3.13.

Table of Contents

• Quick Start
• Supported Datatypes
• Fallback Values
• Supported INI File Structure
• Unnamed Sections
• Interpolation of values
• Mapping Protocol Access
• Customizing Parser Behaviour
• Legacy API Examples
• ConfigParser Objects
• RawConfigParser Objects

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General way to print floats without the .0 part

I’m building SVG code using data interpolation (f-strings and .format), and I have elements (the size of the graph for one) that are internally floats but which are usually integers. But when printing floats, the .0 part is always included. Is there a standard str-interpolation idiom that turns 24.125 into “24.125” but 25.0 into “25” ?

I don’t think there’s a str format code for this (I’m sure someone will correct me if I’m wrong) but you can do this:

f"{float_value:.5f}".rstrip(".0")

The only gotcha here being that 0.0 turns into "" (everything is removed), so you’d need to special-case it somehow. One option would be to always keep the first character. At that point I’d define a function:

You can use the .removesuffix method (Python 3.9+):

Totally forgot (or never really knew) that method existed. The only issue with this is annoying floating-point representations like '0.30000000000000004'

Don’t have time to look up the specification now but I think the ‘g’ format specifier either with or without the alternate mode flag ‘#’ or zero flag ‘0’ can do this…

It does, but can also truncate digits depending on the value, e.g. f"{12325.04:g}" becomes '12325' .

Well, you could do what you suggested, but in 2 steps:

f"{float_value:.5f}".rstrip("0").rstrip(".")

I think this should do it. This will convert both 3.0 and 3. to 3 , but will not change 3.1 which is probably what is desired.

@Gouvernathor I work on a package that provides more control over number → string formatting than the built in format-specification-mini-language does. For those purposes I’m interested: Why exactly do you want to format strings in this way?

You say the elements are internally floats but usually integers. Are the data float-type or integer-type? Or is it a mix of what should be thought of as floats and what should be thought of as integers? If the number is not an integer how are you controlling its display? Are you interested to show a specified number of digits past the decimal point? Do you want to show a specific number of significant figures (bearing in mind # of digits past the decimal point != # of sig figs)?

This is the right way.

It’s just a matter of specifying enough precision:

Er, yes of course, but if you’re looking for a catch-all way to format values in this way, then you don’t want to have to specify the precision every time.

I suppose you’re saying “specify much higher precision than you need”?

The g modifier with no precision passed turns the number to scientific notation above a certain value, so that won’t work for me.

Adding a precision value doesn’t sound like it would stop that.

Because I want to avoid putting floats anywhere without a good reason. Depending on the parameters passed to the function, the values may end up being non-integers but in the canonical case they are integers. Anything getting the data downhill should not get an excuse to upen its calculations to floating point errors. That should take care of your other questions. I am not interested in showing a certain number of decimal points, I trust python to know what that number is, and I want to display the number as precisely as needed - but not more, hence why .0 is not necessary - so no fixed set precision is good in my case.

I don’t think there’s a str format code for this (I’m sure someone will correct me if I’m wrong) but you can do this: f"{float_value:.5f}".rstrip(".0")

rstrip is not removesuffix :

I have a function intif(f) which returns a int if the int round trips with the float . So:

is what I would do. It’s in my cs.numeric module on PyPI (with its vast suite of … 3 functions, of which I basicly only use intif() ).

Or you could just have the code:

Got a lot of numbers?

I see so the canonical type is integer, but there’s some “special” cases that produce floats.

This makes me worried. Consider

What is the correct behavior for this input? Python makes somewhat arbitrary choices about how it round/displays floats when the user doesn’t pass in precision. This is one of the reasons I branched out and made my own more simply specified formatting package. It’s a little tricky to fulfil your requirements as is because I’d say they’re technically unspecified. I assume all of the inputs to your formatting function are floats but you’re wanting some means to detect if they should actually be ints. If you specified that then it would be straightforward to give a function that does what you want.

But anyways, it sounds like you already have some code that converts floats to strings. If you’re happy with that code but just want to strip trailing zeros and decimal points then yes, you can just do

as @MRAB already suggested.

It does in my testing, though. On the other hand, large integers do eventually suffer floating-point imprecision.

So, you will prefer

? (Both representations are exact.)

Then, I guess, repr() is something you are looking for. (BTW float.hex() also prints precise float value.) Existing format() types use a precision of 6 digits per default. For IEEE floats you could pass precision=17 for “g” format to have enough precision to read back same float value. But that’s exactly what repr() is doing.

int/float’s have is_integer() method, so you can print integer-valued float’s separately:

You passed an integer, if you use a float this also happens:

You can use this for example:

The precision matches that of normal floats.

I recently wrote a function to support huge integers for the g format specifier:

I’m not the one making the choice, and it’s not a matter of preference : I’m writing values to a SVG, and there’s only specific formats that are recognized by that format, and scientific notation is not recognized by browsers.

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