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Automated data exploration edit


The first step in any data-intensive project is understanding the available data. To this end, data scientists spend a significant part of their time carrying out data quality assessments and data exploration. In spite of this being a crucial step, it usually requires repeating a series of menial tasks before the data scientist gains an understanding ofthe dataset and can progress to the next steps in the project.

In this talk I will detail the inner workings of a Python package that we have built which automates this drudge work, enables efficient data exploration, and kickstarts data science projects. A summary is generated for each dataset, including: General information about the dataset, including data quality of each of the columns; Distribution of each of the columns through statistics and plots (histogram, CDF, KDE), optionally grouped by other categorical variables; 2D distribution between pairs of columns; Correlation coefficient matrix for all numerical columns.

Building this tool has provided a unique view into the full Python data stack, from the parallelised analysis of a dataframe within a Dask custom execution graph, to the interactive visualisation with Jupyter widgets and Plotly. During the talk, I will also introduce how Dask works, and demonstrate how to migrate data pipelines to take advantage of its scalable capabilities.


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