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Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series .
Wes McKinney is an American software developer and businessman. He is the creator and "Benevolent Dictator for Life" (BDFL) of the open-source pandas package for data analysis in the Python programming language, and has also authored three versions of the reference book Python for Data Analysis.
The pandas package in Python implements this operation as "melt" function which converts a wide table to a narrow one. The process of converting a narrow table to wide table is generally referred to as "pivoting" in the context of data transformations.
Views also function as relational tables, but their data are calculated at query time. External tables (in Informix [3] or Oracle, [4] [5] for example) can also be thought of as views. In many systems for computational statistics, such as R and Python's pandas, a data frame or data table is a data type supporting the table
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
The nested set model is a technique for representing nested set collections (also known as trees or hierarchies) in relational databases. It is based on Nested Intervals, that "are immune to hierarchy reorganization problem, and allow answering ancestor path hierarchical queries algorithmically — without accessing the stored hierarchy relation".
Whether you’re a pumpkin or an apple pie person for Thanksgiving, there’s only one right answer: pumpkin pie. Pumpkin is the flavor of fall, with PSLs to sip, breads, muffins, and bars to bake ...
algorithm nested_loop_join is for each tuple r in R do for each tuple s in S do if r and s satisfy the join condition then yield tuple <r,s> This algorithm will involve n r *b s + b r block transfers and n r +b r seeks, where b r and b s are number of blocks in relations R and S respectively, and n r is the number of tuples in relation R.