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Using NumPy in a project comes with many benefits and conveniences I have collected the most important of them below. Advantages of NumPy Speed ​​ the main advantage of NumPy is speed because the solutions in this library are based on the lowlevel C language. The calculations are optimized and allow operations to be performed in a vector manner which significantly increases performance. Entry threshold NumPy has a low entry threshold for people who don't deal with programming on a daily basis. It enables easy creation or modification of existing solutions that are used in processing large data sets in this case numerical ones.

Range of Operations As discussed in the examples NumPy has a wide range of numerical operations. It allows you to perform simple arithmetic and logical operations but also sorting tasks and statistical operations. Additionally it works regardless of the platform or operating system. If you can use Python you can probably Email Marketing List use NumPy. Integration with other libraries NumPy works well with other libraries that are used in analysis and work with large data sets. An example would be integration with Pandas Matplotlib or SciPy. Disadvantages of NumPy You should also remember about potential difficulties that may result from using the NumPy library not entirely in accordance with its intended purpose. Memory usage if used incorrectly this library can use a lot of memory which in turn can affect the performance of processing huge data sets.



It is important to properly understand the syntax and mathematical concepts behind the functions provided by the library. Limited use NumPy does not support variablelength arrays so it is not suitable for working with for example text data. There are other libraries that will do a better job in this case. Indexing can be complicated this is a topic for a separate article but it is worth mentioning that NumPy allows advanced array indexing which improves program performance.