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Pandas is built around data structures called Series and DataFrames. Data for these collections can be imported from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. [8] A Series is a 1-dimensional data structure built on top of NumPy's array.
Given a function that accepts an array, a range query (,) on an array = [,..,] takes two indices and and returns the result of when applied to the subarray [, …,].For example, for a function that returns the sum of all values in an array, the range query (,) returns the sum of all values in the range [,].
For one-dimensional arrays, this facility may be provided as an operation append(A,x) that increases the size of the array A by one and then sets the value of the last element to x. Other array types (such as Pascal strings) provide a concatenation operator, which can be used together with slicing to achieve that effect and more.
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]
The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API. In Spark 1.x, the RDD was the primary application programming interface (API), but as of Spark 2.x use of the Dataset API is encouraged [3] even though the RDD API is not deprecated. [4] [5] The RDD technology still underlies the Dataset API. [6] [7]
A range query is a common database operation that retrieves all records where some value is between an upper and lower boundary. [1] For example, list all employees with 3 to 5 years' experience. Range queries are unusual because it is not generally known in advance how many entries a range query will return, or if it will return any at all.
Programming languages or their standard libraries that support multi-dimensional arrays typically have a native row-major or column-major storage order for these arrays. Row-major order is used in C / C++ / Objective-C (for C-style arrays), PL/I , [ 4 ] Pascal , [ 5 ] Speakeasy , [ citation needed ] and SAS .
Data-driven programming is similar to event-driven programming, in that both are structured as pattern matching and resulting processing, and are usually implemented by a main loop, though they are typically applied to different domains.