Question

我很难计算csv付款日期之间的平均时间。我尝试了多种在线观察方法（更改为data.table，使用ddply），但未成功

WorkerID  PaymentDate
 1       2015-07-18
 1       2015-08-18
 3       2015-09-18
 4       2015-10-18
 4       2015-11-18

这是我的数据集的一个示例-我想以最简单的方式计算出PaymentDate之间的平均时间（以天数为单位）。我想按workerID分组。谢谢！

Answer 1

这是import pytest import time import json from selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.support import expected_conditions from selenium.webdriver.support.wait import WebDriverWait from selenium.webdriver.common.keys import Keys # Open a browser and log in #browser = webdriver.Firefox(firefox_profile=fp) driver = webdriver.Firefox(executable_path =r"C:\Users\") driver.get("website") time.sleep(3) driver.find_element(By.NAME, "username").click() driver.find_element(By.NAME, "username").send_keys("") driver.find_element(By.NAME, "password").click() driver.find_element(By.NAME, "password").send_keys("") driver.find_element(By.CSS_SELECTOR, "span:nth-child(1) > span:nth-child(1)").click() time.sleep(3) driver.find_element(By.CSS_SELECTOR, ".tb-text-box-input").send_keys("integrated systems & automation") driver.find_element(By.CSS_SELECTOR, ".tb-text-box-input").send_keys(Keys.ENTER) #driver.find_element(By.CSS_SELECTOR, ".tb-react-dg-brow:nth-child(3) .shared-widgets-datagrid_bcell-wrapper_f18a59jp").click() time.sleep(3) driver.find_element(By.LINK_TEXT, "").click() time.sleep(3) driver.find_element(By.LINK_TEXT, "").click() time.sleep(3) driver.find_element(By.LINK_TEXT, "Site Details").click() time.sleep(20) driver.switch_to.frame(1) driver.find_element(By.CSS_SELECTOR, ".tab-clip").click() driver.find_element(By.CSS_SELECTOR, ".tab-tvTLSpacer > img").click() time.sleep(5) driver.find_element(By.CSS_SELECTOR, ".tab-icon-download").click() driver.find_element(By.CSS_SELECTOR, ".tab-downloadDialog > div:nth-child(4)").click() time.sleep(30) driver.find_element(By.LINK_TEXT, "Download").click() # Code to auto fill firefox login here.....的完美工作。它将aggregate()按PaymentDate分组，并将功能WorkerID应用于每个组。

mean(diff(.))

Answer 2

作为AkselA答案的另一种选择，如果人们更喜欢使用R而不是基数R，则可以使用data.table包。

这类似于使用aggregate，但有时可能会提高速度。在下面的示例中，我通过将差设置为0来进行了单次处理，以说明如何实现。

library(lubridate)
library(data.table)
df <- fread("WorkerID  PaymentDate
 1       2015-07-18
 1       2015-08-18
 3       2015-09-18
 4       2015-10-18
 4       2015-11-18")
df[,PaymentDate := as.Date(PaymentDate)]
df[,{
    if(length(PaymentDate) > 1){
        mean(diff(as.numeric(PaymentDate)))
    }else
        0
}, by = WorkerID]

计算日期之间的平均时间

2 个答案: