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[4]: 114 A DataFrame is a 2-dimensional data structure of rows and columns, similar to a spreadsheet, and analogous to a Python dictionary mapping column names (keys) to Series (values), with each Series sharing an index. [4]: 115 DataFrames can be concatenated together or "merged" on columns or indices in a manner similar to joins in SQL.
Dataframe may refer to: A tabular data structure common to many data processing libraries: pandas (software) § DataFrames; The Dataframe API in Apache Spark; Data frames in the R programming language; Frame (networking)
Dask delayed [20] is an interface used to parallelize generic Python code that does not fit into high level collections like Dask Array or Dask DataFrame. Python functions decorated with Dask delayed adopt a lazy evaluation strategy by deferring execution and generating a task graph with the function and its arguments.
The decorator pattern is a design pattern used in statically-typed object-oriented programming languages to allow functionality to be added to objects at run time; Python decorators add functionality to functions and methods at definition time, and thus are a higher-level construct than decorator-pattern classes.
In Perl, the push function is equivalent to the append method, and can be used in the following way. my @list ; push @list , 1 ; push @list , 2 , 3 ; The end result is a list containing [1, 2, 3]
In Python, the pandas library offers the Series.clip [1] and DataFrame.clip [2] methods. The NumPy library offers the clip [3] function. In the Wolfram Language, it is implemented as Clip [x, {minimum, maximum}]. [4] In OpenGL, the glClearColor function takes four GLfloat values which are then 'clamped' to the range [,]. [5]
Cascading can be implemented using method chaining by having the method return the current object itself. Cascading is a key technique in fluent interfaces , and since chaining is widely implemented in object-oriented languages while cascading isn't, this form of "cascading-by-chaining by returning this " is often referred to simply as "chaining".
Data source defines where the data comes from. There are various data sources available in RevoScaleR, such as text data, Xdf data, in-SQL data, and a spark dataframe. People can wrap their data in a data source object and use that as run analytics in different compute context. Different data sources are available in different compute context.