lsloan

version: '3'

services:
  jupyter:
    container_name: my_jupyter
    #image: jupyter/datascience-notebook
    image: jupyter/minimal-notebook
    ports:
      - '127.0.0.1:8888:8888'
    environment:

lsloan

## Initial setup you should only have to do this once
# Checkout docker stacks project
git clone https://github.com/jupyter/docker-stacks

cd docker-stacks/base-notebook

# Clone the telemetry in this directory, this is currently a private repo. 
git clone https://github.com/educational-technology-collective/hwf-jupyterlab-telemetry	

# git needs to be added to base image

lsloan

jupyter:
  image: jupyter/datascience-notebook
  environment:
    - PASSWORD=${PASSWORD}


nginx:  
  image: nginx
  links: 
    - jupyter

lsloan

lsloan

A collapsible section containing markdown

Click to expand!

Heading

1. A numbered
2. list
   • With some
   • Sub bullets

lsloan

# -*- coding: utf-8 -*-
import functools

from django.contrib.admin import ModelAdmin
from django.contrib.admin.options import InlineModelAdmin
from django.contrib.admin.utils import flatten_fieldsets
from django.core.exceptions import ObjectDoesNotExist
from django.db.models import OneToOneField, ForeignKey
from django.forms import ModelForm
from django.forms.models import BaseModelFormSet, modelformset_factory

lsloan

def recursiveFormat(args, **kwargs):
    """
    Recursively apply `string.format()` to all strings in a data structure.

    This is intended to be used on a data structure that may contain
    format strings just before it is passed to `json.dump()` or `dumps()`.
    
    Ideally, I'd like to build this into a subclass of `json.JsonEncoder`,
    but it's tricky to separate out string handling in that class.  I'll
    continue to think about it.

lsloan

#!/bin/sh --

# Get the local ngrok public URL, which includes the hostname, extracted using jq
# Inspiration: https://gist.github.com/rjz/af40158c529d7c407420fc0de490758b#gistcomment-2594627

curl --silent http://127.0.0.1:4040/api/tunnels | jq -r '.tunnels[0].public_url'

lsloan

1. Substitute the NaN's in a dataframe with values from another dataframe

If you have two DataFrames of the same shape, then:

df[df.isnull()] = d2



2.Replace values in a dataframe with values from another dataframe by conditions