So Using virtual Environment is the recommended way for working with Python projects, regardless of how many you might be busy with. To overcome this, we need to use an created python environment which contains everything that a Python project (application) needs in order to run in an organized, isolated fashion. So the python application won’t run on the new machine because of the version difference. In new machine pandas version 0.17.1 we installed. For better understanding, suppose in the development face in our python application we have used python pandas (Python data analysis library) version 0.18.0 function which was not there in pandas 0.17.1. This is because of the dependence issue of python libraries on the new machine (in which we try to run our python application). Most of the time we face environment issues where our python application won’t run on new machines(computer systems). In the world of python projects, it’s obvious to use different python libraries (wrappers) to get our work done, but It won’t be happy ending all the time. Let’s understand virtual environment with its need in the project development.
Virtual Environment is to create a python environment which allows using different python modules & versions, without messing up the system.
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