Introduction
If<span>Python</span> has too many modules, it can cause some confusion. In this case, you can manage the modules using a<span>Python package</span>, which is essentially a folder containing a large number of module files.
1. Custom Python Packages
14.1.1 Creating a Custom Package
In <span>Pycharm</span>, create a new <span>package</span> in the <span>project</span>:
This will create a folder and generate a <span>__init__.py</span> file.
Write the module code:
14.1.2 Importing a Custom Package
The import method is <span>package_name.module_name</span> and the call method is:<span>package_name.module_name.function_name</span>, for example:
The syntax for importing specific functionality is:
The <span>__all__</span> variable needs to be written in <span>__init__.py</span>.
2. Third-Party Python Packages
<span>Python</span> environments contain many third-party packages not officially released by <span>Python</span>, which can greatly improve development efficiency. For example:<span>numpy</span>: for scientific computing<span>pandas</span>: for data analysis<span>pyspark</span>, <span>apache-flink</span>: for big data computing<span>matplotlib</span>, <span>pyecharts</span>: for graphical visualization<span>tensorflow</span>: for artificial intelligence These packages are not included when installing <span>Python</span>, so they need to be installed separately.
14.2.1 Installing with pip
<span>pip install package_name</span> Install via mirror acceleration:<span>pip install -i mirror_site package_name</span>
14.2.2 Installing with conda
The tool <span>conda</span> can still be used:<span>conda install package_name</span><span>mamba</span> installation will be faster:<span>mamba install package_name</span>
14.2.3 Installing with Pycharm
Follow the prompts to open the settings page:
Search and install:
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