Visualizing two-dimensional (2D) embeddings (e.g. UMAP or tSNE) is a key step in interrogating single-cell RNA sequencing (scRNA-Seq) data. Subsequently, users typically iterate between programmatic analyses (e.g. clustering and differential expression) and visual exploration (e.g. coloring cells by interesting features) to uncover biological signals in the data. Interactive tools exist to facilitate visual exploration of embeddings such as performing differential expression on user-selected cells. However, the practical utility of these tools is limited because they don’t support rapid movement of data and results to and from the programming environments where the bulk of data analysis takes place, interrupting the iterative process. Here, we present the Single-cell Interactive Viewer (Sciviewer), a tool that overcomes this limitation by allowing interactive visual interrogation of embeddings from within Python. Beyond differential expression analysis of user-selected cells, Sciviewer implements a novel method to identify genes varying locally along any user-specified direction on the embedding. Sciviewer enables rapid and flexible iteration between interactive and programmatic modes of scRNA-Seq exploration, illustrating a useful approach for analyzing high-dimensional data.