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The DROP statement is distinct from the DELETE and TRUNCATE statements, in that DELETE and TRUNCATE do not remove the table itself. For example, a DELETE statement might delete some (or all) data from a table while leaving the table itself in the database, whereas a DROP statement removes the entire table from the database.
In SQL, the TRUNCATE TABLE statement is a data manipulation language (DML) [1] operation that deletes all rows of a table without causing a triggered action. The result of this operation quickly removes all data from a table, typically bypassing a number of integrity enforcing mechanisms.
DELETE requires a shared table lock; Triggers fire; DELETE can be used in the case of: database link; DELETE returns the number of records deleted; Transaction log - DELETE needs to read records, check constraints, update block, update indexes, and generate redo / undo. All of this takes time, hence it takes time much longer than with TRUNCATE
In SQL, the data manipulation language comprises the SQL-data change statements, [3] which modify stored data but not the schema or database objects. Manipulation of persistent database objects, e.g., tables or stored procedures, via the SQL schema statements, [3] rather than the data stored within them, is considered to be part of a separate data definition language (DDL).
Note that winsorizing is not equivalent to simply excluding data, which is a simpler procedure, called trimming or truncation, but is a method of censoring data.. In a trimmed estimator, the extreme values are discarded; in a winsorized estimator, the extreme values are instead replaced by certain percentiles (the trimmed minimum and maximum).
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})
Truncated regression models are a class of models in which the sample has been truncated for certain ranges of the dependent variable.That means observations with values in the dependent variable below or above certain thresholds are systematically excluded from the sample.
If two different pieces of information generate the same hash value, this is known as a collision. The probability of a collision depends mainly on the hash length (see birthday attack ). Thus, the concern arises that data corruption can occur if a hash collision occurs, and additional means of verification are not used to verify whether there ...