1 Answer 1. Reset to default. Highest score (default) Trending (recent votes count more) Date modified (newest first) If you want polars to have a different representation on jupyter without changing jupyter at all then that means you have to change polars. The thing to change would be the _repr_html_ method. I would like to link issue #397 which proposes switching to JupyterLab from Jupyter notebook. Using JupyterLab instead of Jupyter notebook might help those instructors such as @gvwilson who teach Python from a console first and then introduce Jupyter notebooks later. Process of launching the Jupyter lab server is the same as launching the Last week I released v0.4.x that brings a major improvement - the interactive tables now work in every notebook editor: Jupyter Lab, Jupyter Notebook, Google Colab, VS Code, PyCharm. And no Method 1: Using to_string () While this method is simplest of all, it is not advisable for very huge datasets (in order of millions) because it converts the entire data frame into a string object but works very well for data frames for size in the order of thousands. In this post, we introduce the itables Python package that enhances how these DataFrames are displayed, by turning them into interactive HTML DataTables. Using itables is as simple as. from itables import init_notebook_mode init_notebook_mode ( all_interactive=True) Then every DataFrame will appear as an interactive table: import world_bank If you want to adjust the size of your inline plots in Jupyter Notebook, you can use the %matplotlib inline magic command and the plt.rcParams dictionary. This snippet shows you how to use these tools and also provides some links to related questions on Stack Overflow. I run Jupiter and it won't display data frame. I have changed the directories, used full path for file, created specific environments, tried every browser, no matter what, it will not display, I just get unauthorized. So last night I wiped my Mac, did a factory reset just incase I screwed up the directory permissions when I tried installing via 1. Have you tried using the df.show () for example in your case you can try doing edges.show () or g.vertices.show () it should render a basic table. If you are looking for nicer and more advance visualization of your data then you can install sparkmagic which has a built-in visualization library ( autoviz) Here is a nice example notebook QCFr.

jupyter notebook display full dataframe