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[DOCX File]Table of Figures - Virginia Tech
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Next, we mapped the bi-gram dataframe into another dataframe with columns for each semantic value by feeding the bi-gram string into separately defined regex search functions, for each semantic value. The regex search functions searched for the most frequent values that meet the criteria for each semantic value within the article. If there were no matching search results, number values were ...
python dataframe groupby multiple columns

[DOC File]corptocorp.org
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Mapped data elements from multiple legacy systems to EDW tables, including querying, reviewing, & correcting legacy data. Providing a complete BI (OLAP) solution to any organization by Designing and\or enhancing existing data warehouse\Data Models. Migration from various sources like Oracle, SQL Server , Rest API , Microsoft SharePoint etc., to Amazon Redshift using Python and AWS services ...
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[DOCX File]Table of Tables .edu
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For multiple datasets, there would be a trace for each dataset with subtraces for the emotions within each Dataframe. Multiple traces are placed in a Python list, named ‘data’ by standard, to then be described by the layout. The layout of a plot determines the properties for how traces are displayed. These are also implemented as a Python dictionary, where the keys are properties of the ...
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[DOC File]Notes on Apache Spark 2 - The Risberg Family
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2017-04-04 · The 1.x versions were the initial releases, and created the basic Spark concepts of RDDs and operations on them. The interface was focused on Scala and Python. Starting in release 1.3, the DataFrame object was added as a layer above the RDD, which also included support for columns and database query-like operations.
dataframe group by multiple columns

[DOCX File]Abstract - Virginia Tech
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At present, we have deployed ArchiveSpark in a stand-alone machine due to the version conflict of Spark. The version of Spark for running ArchiveSpark is 1.6.0 or 2.1.0. Unfortunately, the Spark version is 1.5.0 in our Hadoop Cluster. Therefore, we need to upgrade the cluster and then deploy our framework to process big collections.
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[DOCX File]Table of Figures .edu
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Next, we create two new attributes within the data breach DataFrame - StartDate and EndDate. These columns will hold the boundary values for our timeframe for each given data breach. We iterate over the rows in dataBreachesActive.csv and use the datetime Python library to calculate the date 120 days before and 30 days after the date found in the ‘Date Made Public’ attribute, storing these ...
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Spark dataframe groupby multiple columns