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Mastering flat_map in Python with List Comprehension

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Introduction In Python, when working with nested lists or iterables, one common challenge is flattening them into a single list while applying transformations. Many programming languages provide a built-in flatMap function, but Python does not have an explicit flat_map method. However, Python’s powerful list comprehensions offer an elegant way to achieve the same functionality. This article examines implementation behavior using Python’s list comprehensions and other methods. What is flat_map ? Functional programming  flatMap is a combination of map and flatten . It transforms the collection's element and flattens the resulting nested structure into a single sequence. For example, given a list of lists, flat_map applies a function to each sublist and returns a single flattened list. Example in a Functional Programming Language: List(List(1, 2), List(3, 4)).flatMap(x => x.map(_ * 2)) // Output: List(2, 4, 6, 8) Implementing flat_map in Python Using List Comprehension Python’...

Top features in the design of data modelling (1 of 2)

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[Data modelling jobs career] The analogy with architecture is particularly appropriate because architects are designers and data modeling is also a design activity. In design, we do not expect to find a single correct answer, although we will certainly be able to identify many that are patently incorrect. Two data modelers (or architects) given the same set of requirements may produce quite different solutions. Data modeling is not just a simple process of "documenting requirements" though it is sometimes portrayed as such. Several factors contribute to the possibility of there being more than one workable model for most practical situations. First, we have a choice of what symbols or codes we use to represent real-world facts in the database. A person's age could be represented by Birth Date, Age at Date of Policy Issue, or even by a code corresponding to a range ("H" could mean "born between 1961 and 1970"). Second, there is usually more ...