The use of statistical methods in the diagnosis of production processes dates back to the beginning of the 20th century. Widespread computerization of processes made enterprises face the challenge of processing large sets of measurement data. The growing number of sensors on production lines requires the use of faster and more effective methods of both process diagnostics and finding connections between individual systems. This article is devoted to the use of Python libraries to effectively solve some problems related to the analysis of large data sets. The article is based on the experience related to data analysis in a large company in the automotive industry, whose annual production reaches 10 million units. The methods described in this publication were the basis for the initial analysis of production data in the plant, and the obtained results fed the production database and the created automatic anomaly detection system based on artificial intelligence algorithms.