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a good way to get started with processing data is to use the pandas library, which provides a suite of data processing tools such as groupby and aggregate. these functions let you quickly apply groupings and iterate through all the records in your group.
the pandas library also provides a lot of useful data processing and data wrangling methods, such as the ability to view data in a variety of different ways, like in different tables, graphs, and so on.
finally, it is very easy to throw a lot of python data processing code together in a few lines. if you have a data processing workflow, whether it is line by line or as a larger pipeline, a bash script is a great way to perform file processing using the bash shell. it is also pretty easy to build an ecosystem around working with data processing tools using python. for example, modernizr is a useful library of common browser feature detection methods for developers.
there are programs that can help you create a banners and developer tool. it is also possible to use your banners or developer documentation to generate a tool for your personal use. no matter which tools you use to create a tool, you will probably find that the best results are achieved when you implement a predictable set of steps to describe how the tool is used. let the tool help you out!
the busybody ls tool example has a pretty sparse usage description. it will list the contents of a directory, in alphabetical order. you can use this description to write a ls tool more suitable to your circumstances. here are a few ideas.
use cd, pwd and cat to create a directory listing tool. for example, listing the contents of the /usr/local/info/docs directory (the / stands in for the path the user has entered in), we can write a tool called lsdocs as follows:
sometimes we need to manipulate text files in several stages. for example, a workflow might edit a config file, then extract some information from it, then modify it in some way, before a final merge. the tooltiptext toolkit aims to address such problems by providing a set of tools that can be applied in a pipeline. the toolkit is used to create a tool that accepts a regular command-line toolkit, then recursively calls itself within that tool with different command-line parameters, and then exits.
there is a similar parameter fixed_shell that defaults to true, and allows tools to fork into a shell to escape to a safe environment. some tools will use fixed_shell, and allow you to specify one as false.
the toolkit comes with a cli tool that will show you the available pipelines and provide one as an example. the script is a shell script that can be modified to allow it to dynamically change the behavior of the tool.
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