In my talk, I will primarily focus on tools for data analysis and data visualization tools that Python offers and also will mention where are current limits of Python in Data Visualization & Analysis and how to overcome them. It will be a short overview of PyViz and the whole Data Visualization Landscape that Python offers (in each stage of analysis the best libraries will be shown for the specific purpose; as for data visualization we will focus particularly on matplotlib, Seaborn, Plotly but also will mention altair, Bokeh, Datashader, GeoViews, HoloViews, Param, etc.). In my talk (if there will be enough time) I will also explain how data visualization works (on a high level and on the back-end), what are the most common problems and pitfalls in Data Visualization, and how to avoid it. In this context, my talk will be also a short "philosophical" introduction to The Grammar of Graphics by Leland Wilkinson and I will also mention which Python libraries currently and already adapted to this vision/philosophy in Computer Science and Data Visualization in general. Also, what are the differences with some "Python native" grammar of graphics like f.e. Vega (altair).