Single-cell RNA sequencing (scRNA-seq) allows researchers to investigate the gene expression profiles of individual cells, determining heterogeneity of cell populations that cannot be captured by bulk RNA sequencing. This ability to analyze thousands or even millions of cells at single-cell resolution has driven major advances in fields such as developmental biology, immunology, cancer research, and neuroscience. By capturing the unique transcriptional signatures of individual cells, scRNA-seq enables the discovery of new cell types, characterization of complex tissues, and understanding of dynamic biological processes like differentiation and disease progression. However, working with scRNA-seq data involves a series of computational steps, from raw data preprocessing to advanced analyses, including clustering, cell type identification, and trajectory inference. Python has become a widely used language in this area due to its libraries for bioinformatics, including scanpy and AnnData, which offer tools for processing, visualizing, and analyzing scRNA-seq data. In this chapter, we will explore a brief workflow for analyzing scRNA-seq data using Python.