xuefliang

Sys.setenv(http_proxy = "http://127.0.0.1:8001") Sys.setenv(https_proxy = "https://127.0.0.1:8001")

 library(sampling) sam_group <- strata(dat,stratanames = 'xiangzhen',size = seq(1:20),method = 'srswr') sam_group <- strata(dat,stratanames = 'xiangzhen',size = rep(3,20),method = 'srswr') #分组抽样 sam_group <- dat %>%    filter(weihao !='000') %>%    group_by(xiangzhen) %>%    slice(sample(3))

import requests import pandas as pd import numpy as np import matplotlib.pyplot as plt url= 'https://mops.twse.com.tw/mops/web/ajax_t100sb15' proxy = { "http" : "http://127.0.0.1:8889" ,} headers= { 'User-Agent' : 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:77.0) Gecko/20100101 Firefox/77.0' } payload={ 'encodeURIComponent' : "1" , 'step' : "1" , 'firstin' : "1" , 'TYPEK' : "sii" , 'RYEAR' : "108" } res=requests.post(url,data=payload,proxies=proxy,headers=headers) #res=requests.post(url,data=payload) print(res.text) dfs=pd.read_html(res.text) df=dfs[ 0 ].iloc[:,[ 0 , 1 , 2 , 5 , 6 , 7 ]] df.head() df.info() df.columns=[ '產業類別' , '公司代號' , '公司名稱' , '平均數108' , '平均數107' , '中位數108' ] df.sort_values( '中位數108' ,ascending= False ) #df.plot(kind='bar',title='bar title...

 library(tidyverse) library(openxlsx) dat <- read.xlsx('./Documents/镇原县注射器库存.xlsx') %>%    select(b) %>%    as_vector() %>%    str_c(collapse = "','")

library(lubridate) calc_age <- function(birthDate, refDate = Sys.Date()) {   require(lubridate)   period <- as.period(interval(birthDate, refDate),unit = "days")   period$day } #满8月龄 ymd('2020-05-31') %m-% months(3*12) ymd('2019-12-31') %m+% months(1:12)

vim  /etc/hosts 0.0.0.0 account.jetbrains.com 0.0.0.0 www.jetbrains.com 127.0.0.1 transact.netsarang.com 127.0.0.1 update.netsarang.com 127.0.0.1 www.netsarang.com 127.0.0.1 www.netsarang.co.kr 127.0.0.1 sales.netsarang.com

mtcars %>%   group_by(cyl) %>%   group_modify(     ~broom::tidy(lm(mpg ~ wt, data = .))   ) mtcars %>%   group_by(cyl) %>%   summarise(     broom::tidy(lm(mpg ~ wt))   ) mtcars %>%   group_by(cyl) %>%   summarise(     broom::tidy(lm(mpg ~ wt, data = cur_data()))   ) mtcars %>%   group_by(cyl) %>%   nest() %>%   mutate(model = purrr::map(data, ~ lm(mpg ~ wt, data = .))) %>%   mutate(result = purrr::map(model, ~ broom::tidy(.))) %>%   unnest(result) mtcars %>%   nest_by(cyl) %>%   mutate(model = list(lm(mpg ~ wt, data = data))) %>%   summarise(broom::tidy(model)) mtcars %>%   nest_by(cyl) %>%   summarise(     broom::tidy(lm(mpg ~ wt, data = data))   )