Pandas on Jupyter: Improving Your Data Analysis Workflow
Nick Macro
https://pyohio.org/2018/schedule/pres...
The need to clean, manipulate, and visualize data is a skill that is increasingly in demand due to the abundance of data available. There are many methods of performing data analysis, each with its own benefits and drawbacks. Of the many features that can be used to evaluate a data analysis method, we will focus on ease of use and reproducibility.
It is crucially important for data analysis to be reproducible from starting data. A reproducible workflow allows others to review your work and can help catch assumptions, bugs, and steps that are not transparent in the final result. Reproducibility improves the transparency of your analysis and therefore makes your results more resistant to misinterpretation.
Python and, more specifically, Jupyter Notebooks enable workflows to be reproducible with very little overhead. Due to these characteristics, Jupyter Notebooks are the current medium for most data science work.
One of largest problems with switching to a reproducible workflow is amount of time you need to sink into converting to the new workflow. I will demonstrate how a data analysis workflow can be modified using python libraries and Jupyter Notebooks.
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