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Python: Built-in Functions vs. For & If Loops – 5 Programs Explained

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Python’s built-in functions make coding fast and efficient. But understanding how they work under the hood is crucial to mastering Python. This post shows five Python tasks, each implemented in two ways: Using built-in functions Using for loops and if statements ✅ 1. Sum of a List ✅ Using Built-in Function: numbers = [ 10 , 20 , 30 , 40 ] total = sum (numbers) print ( "Sum:" , total) 🔁 Using For Loop: numbers = [ 10 , 20 , 30 , 40 ] total = 0 for num in numbers: total += num print ( "Sum:" , total) ✅ 2. Find Maximum Value ✅ Using Built-in Function: values = [ 3 , 18 , 7 , 24 , 11 ] maximum = max (values) print ( "Max:" , maximum) 🔁 Using For and If: values = [ 3 , 18 , 7 , 24 , 11 ] maximum = values[ 0 ] for val in values: if val > maximum: maximum = val print ( "Max:" , maximum) ✅ 3. Count Vowels in a String ✅ Using Built-ins: text = "hello world" vowel_count = sum ( 1 for ch in text if ch i...
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Analytics on Fly - Read It

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The basis for real-time analytics is to have all resources at disposal in the moment they are called for . So far, special materialized data structures, called cubes, have been created to efficiently serve analytical reports. Such cubes are based on a fixed number of dimensions along which analytical reports can define their result sets. Consequently, only a particular set of reports can be served by one cube. If other dimensions are needed, a new cube has to be created or existing ones have to be extended. In the worst case, a linear increase in the number of dimensions of a cube can result in an exponential growth of its storage requirements. Extending a cube can result in a deteriorating performance of those reports already using it. The decision to extend a cube or build a new one has to be considered carefully. 

In any case, a wide variety of cubes may be built during the lifetime of a system to serve reporting, thus increasing storage requirements and also maintenance efforts.

Instead of working with a predefined set of reports, business users should be able to formulate ad-hoc reports. Their playground should be the entire set of data the company owns, possibly including further data from external sources. Assuming a fast in-memory database, no more pre-computed materialized data structures are needed. As soon as changes to data are committed to the database, they will be visible for reporting. 

The preparation and conversion steps of data if still needed for reports are done during query execution and computations take place on the fly. Computation on the fly during reporting on the basis of cubes that do not store data, but only provide the interface for reporting, solves a problem that has existed up to now and allows for performance optimization of all analytical reports likewise

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