Given a starting date 2024-02-01 I would like to generate 7 days into the future until February 8th (2024-02-08), ex.g.
| dt |
|---|
| 2024-02-01 |
| 2024-02-02 |
| 2024-02-03 |
| 2024-02-04 |
| <!-- Produces a responsive list of top ten posts from a subreddit /worldnews. Working jsfiddle http://jsfiddle.net/KobaKhit/t42zkbnk/ --> | |
| <div id="posts"> | |
| <h2> Today's top ten news <small>from <a href = '//reddit.com/r/worldnews' target = '_blank'>/r/worldnews</a></small></h2> | |
| <hr> | |
| <ul class="list-unstyled"></ul> | |
| </div> | |
| <!-- JS --> | |
| <script src="https://rawgit.com/sahilm/reddit.js/master/reddit.js"></script> | |
| <script src="https://code.jquery.com/jquery-2.1.3.min.js"></script> |
| df = read.csv("your-df.csv") | |
| # Number of items in each chunk | |
| elements_per_chunk = 100000 | |
| # List of vectors [1] 1:100000, [2] 100001:200000, ... | |
| l = split(1:nrow(df), ceiling(seq_along(1:nrow(df))/elements_per_chunk)) | |
| # Write large data frame to csv in chunks | |
| fname = "inventory-cleaned.csv" |
| # http://srome.github.io/Parsing-HTML-Tables-in-Python-with-BeautifulSoup-and-pandas/ | |
| class HTMLTableParser: | |
| @staticmethod | |
| def get_element(node): | |
| # for XPATH we have to count only for nodes with same type! | |
| length = len(list(node.previous_siblings)) + 1 | |
| if (length) > 1: | |
| return '%s:nth-child(%s)' % (node.name, length) | |
| else: | |
| return node.name |
| import requests | |
| import base64 | |
| import pprint | |
| import pandas as pd | |
| import json | |
| from tqdm import tqdm | |
| # https://stubhubapi.zendesk.com/hc/en-us/articles/220922687-Inventory-Search |
| library(tidyr) | |
| setwd("~/Desktop/unnest") | |
| fname = "file-name.csv" | |
| df = read.csv(paste0(fname,'.csv'), stringsAsFactors = F) | |
| df$seats = | |
| sapply(1:nrow(df), function(x) { | |
| seats = c(df[x,]$first_seat,df[x,]$last_seat) |
| class Reddit(): | |
| def __init__(self,client_id, client_secret,user_agent='My agent'): | |
| self.reddit = praw.Reddit(client_id=client_id, | |
| client_secret=client_secret, | |
| user_agent=user_agent) | |
| def get_comments(self, submission): | |
| # get comments information using the Post as a starting comment | |
| comments = [RedditComment(author=submission.author, | |
| commentid = submission.postid, |
| <apex:page > | |
| <html> | |
| <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.7.2/jquery.min.js"></script> | |
| <!-- User Id in a span --> | |
| <span id = 'user' style = 'display: none;'> | |
| <apex:outputText label="Account Owner" value="{!$User.Id}"></apex:outputText> | |
| </span> | |
| <!-- Embed placeholder --> |
| import tableauserverclient as TSC | |
| import pandas as pd | |
| from io import StringIO | |
| class Tableau_Server(object): | |
| """docstring for ClassName""" | |
| def __init__(self,username, password,site_id,url, https = False): | |
| super().__init__() # http://stackoverflow.com/questions/576169/understanding-python-super-with-init-methods |
| from pyspark.sql.functions import monotonically_increasing_id, row_number | |
| from pyspark.sql import Window | |
| from functools import reduce | |
| def partitionIt(size, num): | |
| ''' | |
| Create a list of partition indices each of size num where number of groups is ceiling(len(seq)/num) | |
| Args: | |
| size (int): number of rows/elemets |