The article discusses the development of data normalization and standardization tools using Python libraries. A description of the theoretical foundations and formulas used to normalize and standardize data is considered. For internal calculations of the developed software, the Pandas and NumPy libraries were used. The external interface was built on the basis of the Streamlit library, which allows you to deploy web applications without any additional resources. Code fragments are provided and implementation mechanisms are explained. A description of the developed tool is provided: a detailed explanation of the functionality of the tool, user interface and examples of use. The importance of data preprocessing, selection of an appropriate method, and final remarks on the usefulness of interactive data processing tools are discussed.

Keywords: data processing, statistics, information systems, Python web systems.

The development of business analytics, decision-making and resource planning systems is one of the most important components of almost any enterprise. In these matters, enterprises and production facilities of the penitentiary system are no exception. The paper examines the problem of the relationship between existing databases and statistical reporting forms of the production, economic and labor sectors of the penitentiary system. It has been established that indirectly interrelated parameters are quite difficult to compare due to different data recording systems, as well as approved statistical forms. One of the first steps in solving this problem could be the introduction of a generalized data indexing system. The paper discusses data indexing systems, the construction of their hierarchical structures, as well as the possibility of practical application using SQL. Examples of implementation using ORM technology and the Python language are considered.

Keywords: databases, indexing, ORM, SQL, Python, manufacturing sector, economic indicators, penitentiary system

To date, the penitentiary system of the Russian Federation has collected quite extensive databases for the production sector. The collected data is a time series. However, when studying the mutual distributions of parameters, a number of problems arise, the main one of which is that a different data accounting system is maintained for different parameters: in some cases, data accounting is cumulative throughout the year, in other cases, actual values are taken into account (in other words, some time series are trending, while others are seasonal (cyclical)). Data accounting periods also differ: monthly, quarterly, or per year. Thus, at first glance, the related parameters are almost impossible to compare. The paper proposes a number of algorithms that would solve this problem. The aim of the work was to develop new algorithms that allow comparing trend and seasonal time series using the example of the industrial sector of the penitentiary system. The objectives of the study can be designated as: classification of parameters that are taken into account as seasonal and as trend time series; development of algorithms for their comparison; study of the applicability of the results obtained.

Keywords: algorithm, data processing, python, time series, penitentiary system, manufacturing sector.