When carrying out scientific research, it becomes necessary to save and subsequent computer processing of the results of experiments. For this purpose, the Research Laboratory of Functional Nanostructures has developed the database «NIL-FN». This database is a tool for helping in the daily activities of laboratory staff: storage of information about materials and equipment, development and description of experiments, data marking, storage and organization of results. All experimental data is stored and described scientifically significant in terms of target, method, and interpretation of results. To track the creation, changing or deleting any entries in the database using its management system, control logs are generated with login, date and user time stations. For processing and analyzing experimental data, applications are used to the Python programming language using a widespread library with open source PANDAS. This is due to the fact that the main object of this library - DataFrame may contain inhomogeneous data types: a floating point number, integers, lines, dates, time, etc., which can be structured in the form of hierarchy and index, which is consistent with the adopted hierarchical attitude between Tables in which experimental data are stored. In the developed processing and data analysis applications, their visualization is provided, because It simplifies and speeds up the execution of data analysis. The main purpose of data visualization is the study (for example, the search for obvious patterns, emissions, etc.) and the presentation of the results in a visual form. With visual representation of the data (in the form of graphs, histograms or other forms), regularities are becoming obvious. To visualize the results of the experimental analysis in the NIL-FN database applications, the MATPLOTLIB library, designed to build 2D python graphs, which allows you to receive high-quality images to publish in various print copies and in interactive media on different platforms. Thus, users of NIL-FN database can easily build graphs, histograms, scattering diagrams and much more using Python applications with all multiple lines of the link code to the MATPLOTLIB library.