Abstract Physiological response is an automatic reaction that triggers a physical response to a stimulus such as stress, emotion, pain, etc. Examples include changes in heart rate, respiration, perspiration, and eye pupil dilation. Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR), measures changes in perspiration by detecting the changes in electrical conductivity of skin. Previous studies have already shown that EDA is one of the leading indicators for a stimulus. However, the EDA signal itself is not trivial to analyze. To detect different stimuli in human subjects, variety of features are extracted from EDA signals such as the number of peaks, max peak amplitude, to name a few, showing the prevalence of this signal in bio-medical as well as ubiquitous and wearable computing research. In this paper, we present an open-source Python toolkit for EDA signal preprocessing and statistical and automatic feature extraction. To the best of our knowledge, this is the first effort for developing a versatile and generic tool to extract any number of automatic features from EDA signals. We evaluate our toolkit using different machine learning algorithms applied to the Wearable Stress and Affect Detection (WESAD) dataset. Our results show higher validation accuracy for a stress detection task using the the features automatically extracted by pyEDA.