APPLYING PANDAS FOR THE UNIFICATION OF DATA WITH MODAL DISTRIBUTIONS
Дата публикации
25.04.2026
Журнал
Приоритетные области применения искусственного интеллекта в педагогическом образовании
Выпуск
Приоритетные области применения искусственного интеллекта в педагогическом образовании
Страницы
892-898
Авторы
Аннотация
This work explores the use of the Pandas library for handling and unifying modal distributions in datasets, which are common in real-world data containing multiple peaks or clusters. Modal distributions often represent different subgroups within the data that vary in scale, range, or frequency, making direct analysis or machine learning challenging. Using Pandas, these distributions can be efficiently organized, segmented, normalized, and standardized, allowing each mode to be represented consistently. The library’s functions such as DataFrame, groupby(), and pd.cut() enable easy preprocessing, statistical summarization, and preparation of multimodal data for AI modeling. This approach improves data quality, reduces bias, and ensures reliable input for machine learning and predictive analytics.
Ключевые слова
AI
Pandas
Python
data analysis
data preprocessing
data unification
machine learning
modal distribution
normalization
segmentation
standardization
Список литературы
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