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{
  "rules": {
    "permissions": {
      "$uid": {
        ".read":"auth != null && auth.uid == $uid",
        ".write": "auth != null && auth.uid == $uid"
      }
    },
    "subscriptions": {
      "$uid": {

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#define the function, jacket_alert, and name the input variable temp_in_F
def jacket_alert(temp_in_F):
    
    # check if the temperature is above the specified threshold
    if temp_in_F > 65:
        
        # if so, return the string "no jacket needed"
        return "no jacket needed"
    
    #otherwise we return "jacket needed"

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#define the function "number_squarer" and specify a single argument called "some_number"
def number_squarer(some_number):

    #return the number raised to the second power
    return some_number ** 2

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#define the function and accept one argument, a string, called ticker_symbol. Give the ticker_symbol a default value of "GOOG" for Google.
def stock_info_collector(ticker_symbol = "GOOG"):

    #using the ticker symbol, create the appropriate url for marketwatch.com
    url = "https://www.marketwatch.com/investing/stock/" + ticker_symbol
    
    #use the url to request the page and save the response
    response = requests.get(url)
    
    #pass the response to our HTML parser function (another custom function defined outside of this example).

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def say_hello( first_name, last_name ):
    # combine the first and last names into a full_name string, seperating the names with a space.
    full_name = first_name + " " + last_name
    
    # use the full name variable to create a complete, formatted string.
    formatted_output = "Hello, " + first_name + "!"
    
    # return our formatted output
    return formatted_output

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def page_parse(name, soup, city, state):
    unit_list = []
    
    for row in soup.find_all('tr'):
        prop = {}
        prop['city'] = city
        prop['state'] = state
        prop['name'] = name
        
        try: 

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import requests, time, pandas as pd

posts = []
after = None
for _ in range(4):
    if after == None:
        params = {}
    else:
        params = {'after': after}
        

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Parse Jupyter

This is a basic class that makes it convenient to parse notebooks. I built a larger version of this that was used for clustering documents to create symantic indeices that linked related content together for a personal project. You can use this to parse notebooks for doing things like NLP or preprocessing.

Usage

parser = ParseJupyter("./Untitled.ipynb")
parser.get_cells(source_only = True, source_as_string = True)

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#library imports
import pandas as pd
from itertools import combinations

#read in sample csv
df = pd.read_csv('/Users/teaching/Downloads/mnf.csv', index_col='Unnamed: 0')

#turn our df into a list of rows
data = df.apply(lambda x: x.tolist(), axis=1)