I do not want to go into detail about plotting since pandas is not a data visualization library. However, the plot funct

The pct_change is used to calculate the percent change through the values in a series. It is useful when calculating the percentage of change in a time series or sequential array of elements.

Lucky for us, Jupyter has already come up with a solution to this problem by coming out with an extension of Jupyter Notebooks that runs as a sustainable web application and has built-in user authentication. To add a cherry on top, it can be managed and sustained through Docker allowing for isolated development environments.

We have covered a great deal of the functions and methods for data analysis. There are, of course, a lot more offered by pandas but it is impossible to cover all in one article.

We have covered a great deal of the functions and methods for data analysis. There are, of course, a lot more offered by pandas but it is impossible to cover all in one article.

JupyterHub brings the power of notebooks to groups of users. The idea behind JupyterHub was to scale out the use of Jupyter Notebooks to enterprises, classrooms, and large groups of users. Jupyter Notebook, however, is supposed to run as a local instance, on a single node, by a single developer. Unfortunately, there was no middle ground to have the usability and scalability of JupyterHub and the simplicity of running a local Jupyter Notebook. That is, until now.

The architecture of our JupyterHub server will consist of 2 services: JupyterHub and JupyterLab. JupyterHub will be the entry point and will spawn JupyterLab instances for any user. Each of these services will exist as a Docker container on the host.

We can achieve this by using the Style property which returns a styler object It provides many options for formatting and displaying dataframes. For instance, we can highlight the minimum or maximum values.

As I reflected about the causes of this disconnect I realized that a large part of the reason for the persistence of such misconceptions is that if they were to spend the time to validate their assumptions with benchmarks, the data would show that their assumptions are, or at least appear to be, true.

By the end of this post we will leverage the power of JupyterHub to access a Jupyter Notebook instance which can be accessed without a terminal, from multiple devices within your network, and a more user friendly authentication method.

JupyterHub has pre-built Docker images that we can utilize to spawn a single notebook on a whim, with little to no overhead in technical complexity. We are going to use the combination of Docker and JupyterHub to access Jupyter Notebooks from anytime, anywhere, at the same URL.

We use the pre-built JupyterHub Docker Image and add our own configuration file to stop idle servers, cull_idle_servers.py. Lastly, we install additional packages to spawn JupyterLab instances via Docker.

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sage is flawed, but that doesn’t make it any less powerful. Because I live in an “instant gratification” culture, I’ve been trained to doubt myself if it takes me any time at all to achieve my goals. For example, if I want to get rich, society sells plenty of “get rich quick” ideas.

I recommend doing this on the most powerful device you have and one that is turned on for most of the day, preferably all day. One of the benefits of this setup is that you will be able to use Jupyter Notebook from any device on your network, but have all the computation happen on the device we configure.

Jupyter Notebook provides a great platform to produce human-readable documents containing code, equations, analysis, and their descriptions. Some even consider it a powerful development when combining it with NBDev. For such an integral tool, the out of the box start up is not the best. Each use requires starting the Jupyter web application from the command line and entering your token or password. The entire web application relies on that terminal window being open. Some might “daemonize” the process and then use nohup to detach it from their terminal, but that’s not the most elegant and maintainable solution.

services: # Configuration for Hub Proxy jupyterhub: build: . # Build the container from this folder. container_name: jupyterhub_hub # The service will use this container name. volumes: # Give access to Docker socket. - /var/run/docker.sock:/var/run/docker.sock - jupyterhub_data:/srv/jupyterlab environment: # Env variables passed to the Hub process. DOCKER_JUPYTER_IMAGE: jupyter/tensorflow-notebook DOCKER_NETWORK_NAME: ${COMPOSE_PROJECT_NAME}_default HUB_IP: jupyterhub_hub ports: - 8000:8000 restart: unless-stopped