Defect prediction has been a popular research topic where deep learning has found numerous applications. However, these deep learning-based defect prediction models are often limited by the quality and size of their datasets. In this paper, we present Defectors, a large dataset for just-in-time and line-level defect prediction. Defectors consists of $\approx$ 213K source code files ($\approx$ 93K defective and $\approx$ 120K defect-free) that span across 24 popular Python projects. These projects come from 18 different domains, including machine learning, automation, and internet-of-things. Such a scale and diversity make Defectors a suitable dataset for training deep learning models, especially transformer models that require large and diverse datasets. We also foresee several application areas of our dataset including defect prediction, defect explanation, and automated program repair.