Syntax is a fundamental component of language, yet few metrics have been employed to capture syntactic similarity or coherence at the utterance-and document-level.The existing standard document-level syntactic similarity metric is computationally expensive and performs inconsistently when faced with syntactically dissimilar documents.To address these challenges, we present FastKASSIM, a metric for utterance-and document-level syntactic similarity which pairs and averages the most similar constituency parse trees between a pair of documents based on tree kernels.FastKAS-SIM is more robust to syntactic dissimilarities and runs up to to 5.32 times faster than its predecessor over documents in the r/ChangeMyView corpus.FastKASSIM's improvements allow us to examine hypotheses in two settings with large documents.We find that syntactically similar arguments on r/ChangeMyView tend to be more persuasive, and that syntax is predictive of authorship attribution in the Australian High Court Judgment corpus. * denotes equal contribution.Utterance 1: When we hate, we always move away from the grace of God.When we become resentful and unforgiving, the world around us seems spiteful and meaningless.Utterance 2: How can you be skiing if you are already swimming?FastKASSIM Score: 0.219 CASSIM Score: 0.838 LSM Score: 0.623 Utterance 1: I like swimming because it is cool.Utterance 2: I love running because it is fun.

Exploratory data science largely happens in computational notebooks with dataframe APIs, such as pandas, that support flexible means to transform, clean, and analyze data. Yet, visually exploring data in dataframes remains tedious, requiring substantial programming effort for visualization and mental effort to determine what analysis to perform next. We propose Lux, an always-on framework for accelerating visual insight discovery in dataframe workflows. When users print a dataframe in their notebooks, Lux recommends visualizations to provide a quick overview of the patterns and trends and suggests promising analysis directions. Lux features a high-level language for generating visualizations on demand to encourage rapid visual experimentation with data. We demonstrate that through the use of a careful design and three system optimizations, Lux adds no more than two seconds of overhead on top of pandas for over 98% of datasets in the UCI repository. We evaluate Lux in terms of usability via interviews with early adopters, finding that Lux helps fulfill the needs of data scientists for visualization support within their dataframe workflows. Lux has already been embraced by data science practitioners, with over 3.1k stars on Github.