  Behavioral studies using personal digital devices typically produce rich
longitudinal datasets of mixed data types. These data provide information about
the behavior of users of these devices in real-time and in the users' natural
environments. Analyzing the data requires multidisciplinary expertise and
dedicated software. Currently, no generalizable, device-agnostic, freely
available software exists within Python scientific computing ecosystem to
preprocess and analyze such data. This paper introduces a Python package,
Niimpy, for analyzing digital behavioral data. The Niimpy toolbox is a
user-friendly open-source package that can quickly be expanded and adapted to
specific research requirements. The toolbox facilitates the analysis phase by
offering tools for preprocessing, extracting features, and exploring the data.
It also aims to educate the user on behavioral data analysis and promotes open
science practices. Over time, Niimpy will expand with extra data analysis
features developed by the core group, new users, and developers. Niimpy can
help the fast-growing number of researchers with diverse backgrounds who
collect data from personal and consumer digital devices to systematically and
efficiently analyze the data and extract useful information. This novel
information is vital for answering research questions in various fields, from
medicine to psychology, sociology, and others.
