There are several ways to display a pandas DataFrame with vertical scrollbars in a Jupyter Notebook in VS Code. One way is to use the Jupyter Notebook extension in VS Code. This extension allows you to view and edit Jupyter Notebooks (.ipynb files) directly in VS Code. To use this extension: Install the Jupyter Notebook extension in VS Code. Jupyter Notebook. Jupyter notebook, formerly known as the IPython notebook, is a flexible tool that helps you create readable analyses, as you can keep code, images, comments, formulae and plots together. In this post, we’ve collected some of the top Jupyter notebook tips to quickly turn you into a Jupyter power user! Jupyter Notebook. The Jupyter Notebook is the original web application for creating and sharing computational documents that contain live code, equations, visualizations, and narrative text. It offers a simple, streamlined, document-centric experience. Jupyter has support for over 40 different programming languages and Python is one of them. This makes stuff inside display sideways rather than downwards. We add a margin on the right of each dataframe table. This allows us to add a space between each dataframe. We use HTML on the output string and display it. import pandas as pd from IPython.display import HTML def side_by_side(*dfs): # this is the giant div If you prefer to render only selected DataFrames as interactive tables, use itables.show to show just one Series or DataFrame as an interactive table: Since itables==1.0.0 , the jquery and datatables.net libraries and CSS are injected in the notebook when you execute init_notebook_mode with its default argument connected=False . df.style.format sets precision only for current output. If you call "df" once again, it'll be the same like you've imported. Based on the answer of Malik Asad, I've added if-else conditions in lambda-function so that you could remove trailing-zeros (.0) and set literally "personal" precision for each cell with numeric-value in dataframe: fRUl. If you want to display the entire DataFrame, you can convert it to HTML and display it with IPython's HTML renderer: import pandas as pd from IPython.display import HTML df = HTML(df.to_html()) But note that if your DataFrame is large, this may cause the notebook to be unstable. 1. Solution: Spark DataFrame – Fetch More Than 20 Rows. By default Spark with Scala, Java, or with Python (PySpark), fetches only 20 rows from DataFrame show() but not all rows and the column value is truncated to 20 characters, In order to fetch/display more than 20 rows and column full value from Spark/PySpark DataFrame, you need to pass arguments to the show() method. I'm working with pandas 0.18 in Jupyter. I'd like to configure Jupyter/pandas to display 2 decimal places throughout, and to use comma separators in thousands. You can pretty print pandas dataframe using pd.set_option (‘display.max_columns’, None) statement. Usecase: Your dataframe may contain many columns and when you print it normally, you’ll only see few columns. You can set this option to display all dataframe columns in a jupyter notebook. 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

jupyter notebook display full dataframe