From the course: Build with AI: Data Pipelines with Cursor, Neon, and Streamlit
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Python virtual environment setup: Data pipeline best practices
From the course: Build with AI: Data Pipelines with Cursor, Neon, and Streamlit
Python virtual environment setup: Data pipeline best practices
- [Instructor] Now as we build our data pipeline in Python, we are going to use several Python libraries. And a library allows you to reuse code that someone else has written to solve a specific problem instead of rewriting all the code from scratch. One library that we will use is Streamlit, which will allow us to build a graphical dashboard for our data. So let's see how we can manage Python libraries in our project. One important principle when working with Python libraries is isolation. We want to have a sandbox where we can install and run our Python libraries in isolation from the rest of our system, so we don't have conflicts and interference between libraries. That sandbox is called a virtual environment, and we need to create one for our project. So let's see how to do that. To create a virtual environment in Cursor, we can go to the menu and we can view the command palette. And in here we can type in our…
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