Data Science Workflow in Python Made Easy

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Have you ever tried to tackle a complex data science workflow using Python, only to feel overwhelmed and confused? In this video, I'll show you how to break down the process step-by-step so you can confidently get through your data science workflow in Python.

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  1. ArjanCodes
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  3. complex workflow python
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Комментарии
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Check out my channel links for a free code diagnosis workshop or my free design guide! 🔥

ArjanCodes
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We validate the structures coming in and going out using Pandera and it has been a game changer! Definitely keeps things more manageable, predictable, and maintains our sanity.

JeremyLangdon
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Having small steps helps a tonne with developing tests as well. It's also really important for recovery operations in the middle of the pipeline. E.g. if you suddenly have an operation that imputes na, and so pandas does that mad process of flipping ints to floats, and you need to cope with that.

DaveParr
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How would you handle working with dataframes that require many lines of filtering, sorting, copying, melting, and merging, where there are between 15 and 20 lines of code? Would you split them into several small functions following SOLID principles, or is it normal for all those lines to be in a single function when working with dataframes?

danielgalli
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