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Install Yaml For Python Mac



YAML is a data serialization format that allows interaction with scripts. It is often used to create configuration files as it prioritizes human readability over JSON. PyYAML is a tool for parsing and outputting YAML for Python. It has many useful features, such as an API for low-level event-based parsing and sending, and full Unicode support. It supports YAML tags and some Python-specific tags that can be used to represent any Python object. This article will show you how to install PyYAML on macOS.




Install Yaml For Python Mac



By default, the output of yamllint is colored when run from a terminal, andpure text in other cases. Add the -f standard arguments to forcenon-colored output. Use the -f colored arguments to force colored output.


With conda, you can create, export, list, remove, and updateenvironments that have different versions of Python and/orpackages installed in them. Switching or moving betweenenvironments is called activating the environment. You can alsoshare an environment file.


By default, environments are installed into the envsdirectory in your conda directory. See Specifying a location for an environmentor run conda create --help for information on specifyinga different path.


To automatically install pip or another program every time a newenvironment is created, add the default programs to thecreate_default_packages sectionof your .condarc configuration file. The default packages areinstalled every time you create a new environment. If you do notwant the default packages installed in a particular environment,use the --no-default-packages flag:


Conda does not check architecture or dependencies when installingfrom a spec file. To ensure that the packages work correctly,make sure that the file was created from a working environment,and use it on the same architecture, operating system, andplatform, such as linux-64 or osx-64.


On Windows, PATH is composed of two parts, the system PATH and theuser PATH. The system PATH always comes first. When you installAnaconda for Just Me, we add it to the user PATH. When you installfor All Users, we add it to the system PATH. In the former case,you can end up with system PATH values taking precedence overour entries. In the latter case, you do not. We do not recommendmulti-user installs.


This setting controls whether or not conda activates your baseenvironment when it first starts up. You'll have the condacommand available either way, but without activating the environment,none of the other programs in the environment will be available untilthe environment is activated with conda activate base. Peoplesometimes choose this setting to speed up the time their shell takesto start up or to keep conda-installed software from automaticallyhiding their other software.


By default, conda activate will deactivate the current environmentbefore activating the new environment and reactivate it whendeactivating the new environment. Sometimes you may want to leavethe current environment PATH entries in place so that you can continueto easily access command-line programs from the first environment.This is most commonly encountered when common command-line utilitiesare installed in the base environment. To retain the current environmentin the PATH, you can activate the new environment using:


Issues may arise when using pip and conda together. When combining conda and pip,it is best to use an isolated conda environment. Only after conda has been used toinstall as many packages as possible should pip be used to install any remainingsoftware. If modifications are needed to the environment, it is best to create anew environment rather than running conda after pip. When appropriate, conda andpip requirements should be stored in text files.


Previously in this chapter, you learned about conda environments and the difference between conda and pip. On this page, you will learn how to create and work with conda environments. You will also learn how to install Python packages using the conda-forge channel.


In order to create a conda environment, you first need to install an conda distribution. To do this, you have two main options: Anaconda and Miniconda.


Anaconda ships with a suite of libraries and software pre-installed, which makes it quite large (3Gb). All of the installed packages can also lead to dependency conflicts as you install new packages.


Miniconda is predominately designed for users who know what packages they need and do not want or need the extra installations. For this textbook, we suggest that you use the Miniconda installation.


With that command, you create a basic conda environment that relies on the base Miniconda installation of packages. If you want to ensure that you are running Python 3.7, you could instead do this:


This will install all of the packages that you will need to complete the exercises in both this textbook and the follow-up intermediate textbook that dives more deeply into spatial and remote sensing data.


Note that the environment with the asterisk (*) next to it is the current active environment. Until you activate a specific environment, the default active environment is the base environment installed with Miniconda.


Once you have activated a conda environment, all installations that you run will be installed specifically to this environment. This allow you to have ultimate control when installing and managing dependencies for each project.


The example below updates the earth-analytics-python environment using the environment.yml file. In this example, the command conda env update is run in the same directory that contains the environment.yml file.


In general it is bad practice to mix channels, and conda-forge currently has the most well maintained and broad range of available libraries. Conda-forge is also currently the most consistent way to install GDAL which you will need for all of the spatial Python packages.


There are multiple Python packages that can parse YAML data. However, PyYAML is the most prevalent and also the most complete implementation for parsing YAML. PyYAML is not part of the standard Python library, meaning you need to install it with Pip. Use the following command to install PyYAML, preferable in a virtual environment:


Loading, parsing, and using this configuration file is very similar to loading JSON with the Python JSON library. First, we open the file. Next, we parse it with the yaml.safe_load() function. Please note that I changed the output a little to make it more readable for you:


YAML allows you to define multiple documents in one file, separating them with a triple dash (---). PyYAML will happily parse such files too, and return a list of documents. You can do so by using the yaml.safe_load_all() function. This function returns a generator that in turn will return all documents, one by one.


Mapping key order is preserved. By default, custom YAML tags andstyles in the input are ignored. Use the --yaml-roundtrip/-Yoption to preserve YAML tags and styles by representing them as extra items in their enclosing mappings and sequenceswhile in JSON:


yq also supports XML. The yq package installs an executable, xq, whichtranscodes XML to JSON usingxmltodict and pipes it to jq. Roundtrip transcoding is available withthe xq --xml-output/xq -x option. Multiple XML documents can be passed in separate files/streams asxq a.xml b.xml. Entity expansion and DTD resolution is disabled to avoid XML parsing vulnerabilities.


yq supports TOML as well. The yq package installs an executable, tomlq, which uses thetoml library to transcode TOML to JSON, then pipes it to jq. Roundtrip transcodingis available with the tomlq --toml-output/tomlq -t option.


You don't have to set up anything for Azure Pipelines to build Python projects. Python is preinstalled on Microsoft-hosted build agents for Linux, macOS, or Windows. To see which Python versions are preinstalled, see Use a Microsoft-hosted agent.


The most difficult part of setting up a new ESPHome device is the initialinstallation. Installation requires that your ESP device is connected witha cable to a computer. Later updates can be installed wirelessly.


Now you can go ahead and add some more components. Once you feel likeyou have something you want to upload to your ESP board, simply plug inthe device via USB and type the following command (replacinglivingroom.yaml with your configuration file):


Whatever text editor you use probably has plugins to make dealing with syntax easier. If you're not using a YAML plugin for your editor, find one and install it. The effort you spend on finding a plugin and configuring it as needed will pay off tenfold the very next time you edit YAML.


Ideally, programming languages and markup languages use predictable syntax. Computers tend to do well with predictability, so the concept of a linter was invented in 1978. If you're not using a linter for YAML, then it's time to adopt this 40-year-old tradition and use yamllint.


If you really hate YAML, stop writing in YAML, at least in the literal sense. You might be stuck with YAML because that's the only format an application accepts, but if the only requirement is to end up in YAML, then work in something else and then convert. Python, along with the excellent pyyaml library, makes this easy, and you have two methods to choose from: self-conversion or scripted.