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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’...
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How to Identify Data Relevant for Data Science Analytics

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Your government, your web server, your business partners, even your body. While we aren’t drowning in a sea of data, we’re finding that almost everything can (or has) been instrumented. We frequently combine publishing industry data from Nielsen Book Scan with our own sales data, publicly available Amazon data, and even job data to see what’s happening in the publishing industry.

Data is everywhere
Sites like Infochimps and Factual provide access to many large datasets, including climate data, MySpace activity streams, and game logs from sporting events. Factual enlists users to update and improve its datasets, which cover topics as diverse as endocrinologists to hiking trails.

How the data is growing

Much of the data we currently work with is the direct consequence of Web 2.0, and of Moore’s Law applied to data. The Web has people spending more time online and leaving a trail of data wherever they go. Mobile applications leave an even richer data trail since many of them are annotated with geolocation, or involve video or audio, all of which can be mined.

Point-of-sale devices and frequent shoppers cards make it possible to capture all of your retail transactions, not just the ones you make online. All of this data would be useless if we couldn’t store it, and that’s where Moore’s Law comes in. Since the early ’80s, processor speed has increased from 10 MHz to 3.6 GHz—an increase of 360 (not counting increases in word length and number of cores).

The need for Storage capacity

But we’ve seen much bigger increases in storage capacity, on every level. RAM has moved from $1,000/MB to roughly $25/GB—a price reduction of about 40000, to say nothing of the reduction in size and increase in speed. Hitachi made the first-gigabyte disk drives in 1982, weighing in at roughly 250 pounds; now terabyte drives are consumer equipment, and a 32 GB microSD card weighs about half a gram. Whether you look at bits per gram, bits per dollar, or raw capacity, storage has more than kept pace with the increase of CPU speed.

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