PROGRAMMING BASICS: PYTHON FOR DATA PROCESSING

The article discusses aspects of efficient data processing. Considerable attention is paid to the problems that arise in data modelling and forecasting. In today's digital world, data processing is becoming an essential skill in many professions, from business analytics to psychology. Today, almost every action leaves a digital footprint - online purchases, page views, answers to questionnaires, health indicators, GPS navigation, etc. This generates huge amounts of data that need to be processed, analysed, and used to make decisions. A special place in the paper is occupied by a description of the possibilities of software data processing using the Python programming language, which is becoming increasingly popular due to its simplicity, flexibility, open source, convenience of working with data in various formats, as well as many developed packages that facilitate fast and efficient information processing. NumPy, Pandas, which provide data structures and functions that make working with structured data simple and fast, the most popular data visualisation tool Matplotlib and Seaborn, they help to create graphs, charts and other visual representations of data, packages for various computational tasks SciPy, Statsmodels, and a machine learning-oriented package Scikit-learn which provides simple and effective tools for data analysis, it contains a variety of algorithms for classification, regression, and clustering. An example of using Python for data processing tasks is given. This example demonstrates how Python can be used to easily read data, perform basic statistical processing, and build visualisations, which is useful for psychological or sociological research. The paper contains an analysis of current scientific publications and identifies possible directions for future research.