When it comes to data science, Python comes with a host of libraries that facilitate common data processing tasks, as well as the development of machine learning models. Using Python, developers can train and apply popular ML models (e.g., Decision Trees, Random Forests, Neural Networks) based on a handful of simple commands. Likewise, Python provides libraries that ease complex data transformations, as well as packages that visualize large datasets. Such Python packages have been around for decades and provide the basis for enabling complex data operations. In recent years, a wave of new and novel packages has also emerged, to facilitate the implementation of latest ML programming paradigms such as Automated Machine Learning (AutoML). These new packages leverage Python’s legacy capabilities to open new horizons in developers’ productivity.
AutoML is about automating ML pipelines, while optimizing them at the same time. Nevertheless, this increased automation does not mean that developers can get completely rid of conventional data operations such as calculation of statistics and data transformations. The latter are very commonly used inside AutoML functions. Hence, traditional packages for data science remain into the foreground.
Python Pandas is one of the most prominent examples of such legacy packages. Pandas is a key enabler of most data operations such as cleaning, transforming and analysing large datasets. Pandas very basic operations involve placing the data into a DataFrame i.e. Pandas tabular structure for managing data. Accordingly, the package provides functions for calculating statistics, producing the distribution of certain attributes and for performing common cleaning operations. The latter may for example include removing missing values and filtering rows or columns by a combination of criteria. In conjunction with the Matplotlib library, Pandas provides also the means for visualizing datasets in the form of bar plots, line charts, histograms, bubble charts and pie charts. Most importantly, Pandas eases the task of storing the transformed (e.g., cleaned) data into proper output channels, mediums and formats such as CSV files and databases.
Python data processing is largely about working with multi-dimensional arrays. The Python NumPy package (i.e. “Numeric Python”) empowers data programmers to work with arrays. For instance, it enables them to perform mathematical and logical operations on arrays, including Fourier transforms and shape manipulation. Moreover, NumPy provides the means for performing linear algebra functions, as well as random number generation. When used in conjunction with the SciPy packages (i.e. “Scientific Python”), NumPy enables a rich set of operations similar to those found in popular mathematical packages like MATLAB. Hence, developers are commonly using these Python libraries in their data science programs. In most cases, the latter combine NumPy with Pandas as well.
AutoML environments and tools aim at automating and optimizing the data science tasks that comprise an end-to-end Machine Learning pipeline. Hence, they support a wide array of activities ranging from data cleaning to feature engineering and automatic selection of the optimal model for a task at hand. During recent years, several Python packages for AutoML have emerged. Some of the most notable mentions are:
Overall, Python provides a rich set of libraries for automating machine learning tasks, including support for traditional machine learning models and for deep learning models. AutoML packages automate application development and bring machine learning closer to users that are not experts in data science. Specifically, it batches together multiple ML tasks that are usually performed manually, while performing intelligence statistical processing functions (e.g., drift removal) that are hardly known and understandable by non-experts. The release and wider use of these packages reinforces Python’s popularity in the data science community. If you are already working with Python, it is certainly worth spending some time learning how to use and fully leverage these AutoML packages.
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