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A convenient solution in your case would be to include the configs in a yaml file named **your_config_name.yml** which would look like this: path1: "D:\test1\first". path2: "D:\test2\second". path3: "D:\test2\third". In your python code you can then load the config params into a dictionary by doing this: import yaml.
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For example: 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 o...
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.
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 thats unsuitable. For example:
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 thats unsuitable. For example: The main configuration parser. When defaults is given, it is in...
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.
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.
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.
Apr 29, 2019 · config.read('config.ini') tries to read the file from the same directory as the .py file you're running. So you have 3 options: move the config.ini file to be next to the .py file; use a correct relative path when reading the file (see: https://stackoverflow.com/a/78458469/9835872 for how to do this well)
Apr 15, 2021 · In this article, we will cover what config files are before introducing the Python module configparser.py. We’ll then use some examples to help you to understand, create, read, and update config files of your own. A Quick Introduction To Configuration Files
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Nov 2, 2022 · How the Python configparser module allows you to read, write and update config files. With configparser, you now know how to simplify the way you manage the configuration of your applications. Bonus read: learn more about the Python with open statement that we have used a few times in this tutorial. Claudio Sabato.
Nov 9, 2020 · While there are various ways to support configuration files in your software including JSON, YAML, and plain-text files, this article aims to give you an introduction to the configparser module from the standard library. Note: This article is based on Python 3.9.0 (CPython).
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Jun 25, 2021 · Python can parse it using the built-in configparser module. The parser behaves as a dict-like object, so it can be passed directly to configuration_from_dict: import configparser def configuration_from_ini(data): parser = configparser.ConfigParser() parser.read_string(data) return configuration_from_dict(parser) YAML
