xuefliang

sudo apt install cargo cargo install viu vim ~/.bashrc export PATH="$HOME/.cargo/bin:$PATH" source ~/.bashrc viu myplot.png

dev.off() options(device = "RStudioGD") plot(1:5,1:5) dev.new() plot(mtcars)

 arules::interestMeasure() support 前后项同时出现的占比 confidence 发生前项后出现后项的概率 lift 前项和后项一起发生的机率提升多少 count 前后项同时出现的次数 chiSquared 前项和后项是否独立 jaccard 前项和后项的相似度是多少 leverage 前项和后项是否有关联

import pandas as pd import numpy as np import janitor sc = (     pd . read_excel ( "/mnt/c/Users/xuefeng/Downloads/非重卡删除.xlsx" , dtype = { 'SC_GLDW_BM' : 'object' , 'YM_BM' : 'object' }).clean_names() ) (     sc .query( 'ym_mc.str.startswith("新冠")' )     .astype({ 'sc_gldw_bm' : 'string' })     .assign( shi = lambda x : x .sc_gldw_bm.str[ 0 : 4 ])     .groupby( 'shi' )     .agg( count = ( 'shi' , 'count' ))     .reset_index()     .sort_values( 'shi' )     .to_excel( "/mnt/c/Users/xuefeng/Downloads/非重卡删除1.xlsx" ) ) test = (     sc .assign( xian = sc .sc_gldw_bm.str[: 6 ], shi = sc .sc_gldw_bm.str.slice( 0 , 4 ), csrq = sc .zjhm.str[ 6 : 14 ])     .query( "sc_gldw_bm.str.startswith('6211') & csrq.str.len()==8 & csrq.str.slice(0,2) in ('19','20')" )     .assign( age = lambda x :( pd . to_datetime ( x .jz_sj) - pd . to_da...

  # -*- coding: utf-8 -*- import math import pandas as pd import numpy as np import janitor shi_bm = pd . DataFrame . from_dict ({ '地区名称' : { 0 : '兰州市' ,           1 : '嘉峪关市' ,           2 : '金昌市' ,           3 : '白银市' ,           4 : '天水市' ,           5 : '武威市' ,           6 : '张掖市' ,           7 : '平凉市' ,           8 : '酒泉市' ,           9 : '庆阳市' ,           10 : '定西市' ,           11 : '陇南市' ,           12 : '临夏回族自治州' ,           13 : '甘南藏族自治州' ,           14 : '兰州新区' },           '地区编码' : { 0 : 6201 ,           1 : 6202 ,           2 : 6203 ,   ...

  import openai # openai.api_key = "abc" # openai.api_base = "http://172.30.48.1:8085/openai/v1" openai . proxy = {     "http" : "http://172.30.48.1:7890" ,     "https" : "http://172.30.48.1:7890" } openai . api_key = "sk-" #获取模型名称 model_list = [ item [ 'id' ] for item in openai . Model . list ()[ 'data' ]] # response = openai.ChatCompletion.create( #             model="gpt-3.5-turbo", #             messages=[ #                 {"role": "system", "content": "Describe a short paragraph about tamil."}, #             ] #         ) # for solution in response.choices: #     print(solution.message.content) def get_completion ( prompt , model = 'gpt-3.5-turbo' ):     messages = [{ 'role' : "user" , "content" : prompt }]     if model in model_list :         respo...

import pandas as pd import numpy as np import janitor df = (     pd . read_excel ( r "/mnt/c/users/xuefeng/desktop/rk.xlsx" )     .clean_names()     .astype({ 'xt_rksj' : 'datetime64[ns]' })     .query( "xt_rkjgdm.notnull()" ) ) df = (     df .assign(     gp = np . select (         [             df .sum_x_rksl_ < 10000 ,             ( df .sum_x_rksl_ >= 10000 ) & ( df .sum_x_rksl_ < 20000 ),             df .sum_x_rksl_ >= 20000 ,         ],         [ '低' , '中' , '高' ]         , default = '不详'     ) ) ) (     df     .assign( grp = np . select ([ df .xt_rksj <= '2023-01-17' , df .xt_rksj > '2023-01-17' ],                           ...