A solid selection of libraries is an essential element of a developer’s toolkit for researching and developing complicated applications without having to write a lot of code. In general, library functions are thoroughly tested and optimised before they are released, and space and run time remain prioritised. Moreover, a library is a collection of code designed to make common operations go faster. Whereas in base functions, we know the entire skeleton, of course. The question arises – which is better, a base function or a library?
Python is a popular data analysis language because of its extensive libraries for data processing, visualisation, machine learning, and a variety of other tasks. In machine learning, data preparation is crucial. So, let’s explore the base function and library by reading some data.
CSV vs Pandas:
There are several methods and classes in the CSVmodule that allow you to read and write csv files with ease. Similarly, the “PANDAS” library provides quick, versatile, and expressive data structures that enable dealing with “relational” or labelled data easily and naturally for Python programmers to use. Upon using Python and importing both the packages, these are loading time values.
We can see that the basic library imports faster than the advanced library function.
Both do the same task in this case. User-defined functions, on the other hand, are much faster than advanced library functions. The base functions are defined based on the customisation. As a result, the specified conditions are carried out. In the advanced library function, however, all of the cases were covered. As a result, it takes longer than the base functions.
Let’s check how long a csv file takes to read. If the basic function is specified correctly with all of the required arguments, it will process and provide output in a shorter time span. When compared to the base function, the advanced library function takes longer to read the csv file.
Because Pandas is an advanced library, it is capable of handling nearly any situation. The Pandas library function accepts the default value if the delimiter is missing and executes the operation. But, the base function will give an error if the delimiter is not present. If you wish to examine data from a csv file using Pandas, the csv file is converted to a data frame, which is required for data manipulation with Pandas. Therefore you shouldn’t use the CSV module in these instances.
The benefits of using advanced libraries:
- They’re effective: If you want to use library functions, you should do so for one simple reason: they work. Multiple rigorous tests have been conducted on these functionalities, and they have shown to be straightforward to use.
- The functionalities have been optimised for speed: Because the functions are “standard library” functions, they are continually improved by a committed team of engineers. As a result of this, they are able to provide the most efficient code, which is optimised for maximum performance.
The base function can be defined based on the program’s specific requirements. In the base function, we know the entire structure. If no explicit user preference exists, the library function can be used to do the task. The major benefit of using the base function is that it saves process time. In most cases, libraries are maintained by a group of contributors and made available to anybody who wishes to use them over the internet. One must have a thorough understanding of the criteria. It’s sometimes preferable to use the built-in library function because it’s suited to our requirements. Developers can avoid creating repetitive code by using libraries. Otherwise, if the needs are so precise that the performance is the concern or no specialised library is accessible, it’s better to create our own customised structure using the base function. Of course, there’s nothing wrong with doing so. However, knowing alternative methods, such as this base function allows you to build more efficient code.
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Nivash has a doctorate in Information Technology. He has worked as a Research Associate at a University and as a Development Engineer in the IT Industry. He is passionate about data science and machine learning.