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Exploratory data science largely happens in computational notebooks with dataframe APIs, such as pandas, that support flexible means to transform, clean, and analyze data.
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C. Stolte, D. Tang, and P. Hanrahan · 2002
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Polaris: a system for query, analysis, and visualization of \ \backslash nmultidimensional relational databases
C. Stolte, D. Tang, and P. Hanrahan · 2002
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Selection of views to materialize in a data warehouse
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Matplotlib: A 2d graphics environment
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Show Me: Automatic presentation for visual analysis
J. D. Mackinlay, P. Hanrahan, and C. Stolte · 2007
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How information visualization novices construct visualizations
L. Grammel, M. Tory, and M. Storey · 2010
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Synopses for massive data: Samples, histograms, wavelets, sketches
G. Cormode, M. N. Garofalakis, P. J. Haas, and C. Jermaine · 2012
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Show Me the Numbers: Designing Tables and Graphs to Enlighten
S. Few · 2012
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Enterprise Data Analysis and Visualization: An Interview Study
S. Kandel, A. Paepcke, J. M. Hellerstein, and J. Heer · 2012
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Scorpion: Explaining Away Outliers in Aggregate Queries
E. Wu and S. Madden · 2013
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Collaborative data science, 2015
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Smart Drill-Down : A New Data Exploration Operator
M. Joglekar, H. Garcia-Molina, and A. Parameswaran · 2015
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Rapid sampling for visualizations with ordering guarantees
A. Kim, E. Blais, A. Parameswaran, P. Indyk, S. Madden, and R. Rubinfeld · 2015
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Seedb: efficient data-driven visualization recommendations to support visual analytics
M. Vartak, S. Rahman, S. Madden, A. Parameswaran, and N. Polyzotis · 2015
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Trendquery: A system for interactive exploration of trends
N. Kamat, E. Wu, and A. Nandi · 2016
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Reactive vega: A streaming dataflow architecture for declarative interactive visualization
A. Satyanarayan, R. Russell, J. Hoffswell, and J. Heer · 2016
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Effortless data exploration with zenvisage: an expressive and interactive visual analytics system
T. Siddiqui, A. Kim, J. Lee, K. Karahalios, and A. Parameswaran · 2016
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ggplot2: Elegant Graphics for Data Analysis
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Towards a general-purpose query language for visualization recommendation
K. Wongsuphasawat, D. Moritz, A. Anand, J. Mackinlay, B. Howe, and J. Heer · 2016
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Foresight: Rapid data exploration through guideposts
Ç. Demiralp, P. J. Haas, S. Parthasarathy, and T. Pedapati · 2017
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Fastmatch: Adaptive algorithms for rapid discovery of relevant histogram visualizations
S. Macke, Y. Zhang, S. Huang, and A. Parameswaran · 2017
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Vega-lite: A grammar of interactive graphics
A. Satyanarayan, D. Moritz, K. Wongsuphasawat, and J. Heer · 2017
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Towards visualization recommendation systems
M. Vartak, S. Huang, T. Siddiqui, S. Madden, and A. Parameswaran · 2017
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Visualizing dataflow graphs of deep learning models in tensorflow
World Health Organization
COVID-19 in Pakistan: WHO fighting tirelessly against the odds · 2020
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Faster data exploration in Jupyter through Lux, 2020
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https://papermill.readthedocs.io/ , 2020
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Why Rwanda Is Doing Better Than Ohio When It Comes To Controlling COVID-19
J. Beaubien · 2020
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Data Exploration on NYC Airbnb
Dgomonov · 2020
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Analyzing UCI Crime and Communities Dataset
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Code duplication and reuse in jupyter notebooks
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K. Wongsuphasawat, D. Smilkov, J. Wexler, J. Wilson, D. Mane, D. Fritz, D. Krishnan, F. B. Viégas, and M. Wattenberg · 2017
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The Interactive Visualization Gap in Initial Exploratory Data Analysis
A. Batch and N. Elmqvist · 2018
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Dive: A mixed-initiative system supporting integrated data exploration workflows
K. Hu, D. Orghian, and C. Hidalgo · 2018
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The Case for a Visual Discovery Assistant: A Holistic Solution for Accelerating Visual Data Exploration
D. J.-L. Lee and A. Parameswaran · 2018
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Expressive Time Series Querying with Hand-Drawn Scale-Free Sketches
M. Mannino and A. Abouzied · 2018
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Aiding Collaborative Reuse of Computational Notebooks with Annotated Cell Folding
A. Rule and U. C. S. Diego · 2018
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Altair: Interactive statistical visualizations for python
J. VanderPlas, B. Granger, J. Heer, D. Moritz, K. Wongsuphasawat, A. Satyanarayan, E. Lees, I. Timofeev, B. Welsh, and S. Sievert · 2018
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A. P. Koenzen, N. A. Ernst, and M. A. D. Storey · 2020
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Dziban : Balancing Agency & Automation in Visualization Design via Anchored Recommendations
H. Lin, D. Moritz, and J. Heer · 2020
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Towards scalable dataframe systems
D. Petersohn, S. Macke, D. Xin, W. Ma, D. Lee, X. Mo, J. E. Gonzalez, J. M. Hellerstein, A. D. Joseph, and A. Parameswaran · 2020
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pandas-dev/pandas: Pandas, Feb. 2020
The pandas development team · 2020
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Auto-suggest: Learning-to-recommend data preparation steps using data science notebooks
C. Yan and Y. He · 2020
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https://datapane.com/ , 2021
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A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)
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Deconstructing Categorization in Visualization Recommendation: A Taxonomy and Comparative Study
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Lux: Always-on visualization recommendations for exploratory data science, 2021
D. J.-L. Lee, D. Tang, K. Agarwal, T. Boonmark, C. Chen, J. Kang, U. Mukhopadhyay, J. Song, M. Yong, M. A. Hearst, and A. G. Parameswaran · 2021
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Intelligent visual data discovery with lux: A python library
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