我有一个数据帧，其中各组的大小各不相同，但是我想对行进行求和并除以 n （存在的整数数），在新列中（ V1 .mean ）
除非有一种方法可以按名称进行选择，否则我们必须选择列号（在这种情况下为[10:18]）。如果是这样，一定要教我，因为我必须像这样转换8个问题（x9）（请参见下面的示例）。
所以我尝试了这个：
df$v1.mean <- rowSums(cbind(df[10:18]), na.rm = T ) / # sums it up
ncol(is.integer(cbind(df[10:18] ))) # sums integers, but no
我看到了this dplyr的示例，但是我不确定如何使其在tally（）中工作
数据框架如下所示，其中 V1.mean 是我正在寻找的解决方案。
V1.1 V1.2 V1.3 V1.4 V1.5 V1.6 V1.7 V1.8 V1.9 V2.1 | V1.mean V2.mean
1 5 4 5 NA NA NA NA NA NA 5 | 4.67 [== (5+4+5)/3]
2 5 5 5 NA NA NA NA NA NA 3
3 5 5 5 5 NA NA NA NA NA ...
4 5 4 5 NA NA NA NA NA NA ...
5 5 5 NA NA NA NA NA NA NA ...
6 5 5 5 5 NA NA NA NA NA ...
7 5 5 5 4 4 NA NA NA NA ...
8 5 5 5 4 5 5 NA NA NA ... | 4.83 [== (5+5+5+4+5+5)/6]
9 4 5 5 5 4 NA NA NA NA ...
10 5 5 5 NA NA NA NA NA NA ...
预先感谢：）
答案 0 :(得分：1)
一种选择是将数据local task_manager_id=`curl -s "http://localhost:8081/taskmanagers/" | jq -r '.taskmanagers[0].id'`
放入from bs4 import BeautifulSoup as bs
import requests
import json
import pandas as pd
import re
headers = {
'User-Agent' : 'Mozilla/5.0',
'X-RequestDigest' : '0x34AF663EB174C6B490674D6D041668FE4928C0A60754D7CAB6C39B120504145C2DD53F657F7D82D78D6EFFDE3ADEDC210520BB12197AC1966ACD9562B94EB096,07 Jun 2019 20:18:44 -0000',
'X-Requested-With': 'XMLHttpRequest'
}
xml = '''<Request xmlns="http://schemas.microsoft.com/sharepoint/clientquery/2009" SchemaVersion="15.0.0.0" LibraryVersion="15.0.0.0" ApplicationName="Javascript Library"><Actions><ObjectPath Id="1" ObjectPathId="0" /><SetProperty Id="2" ObjectPathId="0" Name="TimeZoneId"><Parameter Type="Number">10</Parameter></SetProperty><ObjectPath Id="4" ObjectPathId="3" /><Method Name="Add" Id="5" ObjectPathId="3"><Parameters><Parameter Type="String">RefinableDate03</Parameter><Parameter Type="Number">0</Parameter></Parameters></Method><SetProperty Id="6" ObjectPathId="0" Name="Culture"><Parameter Type="Number">-1</Parameter></SetProperty><SetProperty Id="7" ObjectPathId="0" Name="RowsPerPage"><Parameter Type="Number">10</Parameter></SetProperty><SetProperty Id="8" ObjectPathId="0" Name="RowLimit"><Parameter Type="Number">10</Parameter></SetProperty><SetProperty Id="9" ObjectPathId="0" Name="TotalRowsExactMinimum"><Parameter Type="Number">11</Parameter></SetProperty><SetProperty Id="10" ObjectPathId="0" Name="SourceId"><Parameter Type="Guid">{ec6b6718-a344-4f42-9942-f6c673ab1089}</Parameter></SetProperty><ObjectPath Id="12" ObjectPathId="11" /><Method Name="SetQueryPropertyValue" Id="13" ObjectPathId="11"><Parameters><Parameter Type="String">SourceName</Parameter><Parameter TypeId="{b25ba502-71d7-4ae4-a701-4ca2fb1223be}"><Property Name="BoolVal" Type="Boolean">false</Property><Property Name="IntVal" Type="Number">0</Property><Property Name="QueryPropertyValueTypeIndex" Type="Number">1</Property><Property Name="StrArray" Type="Null" /><Property Name="StrVal" Type="String">AACR Event List Source</Property></Parameter></Parameters></Method><Method Name="SetQueryPropertyValue" Id="14" ObjectPathId="11"><Parameters><Parameter Type="String">SourceLevel</Parameter><Parameter TypeId="{b25ba502-71d7-4ae4-a701-4ca2fb1223be}"><Property Name="BoolVal" Type="Boolean">false</Property><Property Name="IntVal" Type="Number">0</Property><Property Name="QueryPropertyValueTypeIndex" Type="Number">1</Property><Property Name="StrArray" Type="Null" /><Property Name="StrVal" Type="String">SPWeb</Property></Parameter></Parameters></Method><SetProperty Id="15" ObjectPathId="0" Name="Refiners"><Parameter Type="String">RefinableString59(filter=15/0/*),RefinableString58(filter=15/0/*)</Parameter></SetProperty><ObjectPath Id="17" ObjectPathId="16" /><Method Name="Add" Id="18" ObjectPathId="16"><Parameters><Parameter Type="String">Title</Parameter></Parameters></Method><Method Name="Add" Id="19" ObjectPathId="16"><Parameters><Parameter Type="String">Path</Parameter></Parameters></Method><Method Name="Add" Id="20" ObjectPathId="16"><Parameters><Parameter Type="String">Author</Parameter></Parameters></Method><Method Name="Add" Id="21" ObjectPathId="16"><Parameters><Parameter Type="String">SectionNames</Parameter></Parameters></Method><Method Name="Add" Id="22" ObjectPathId="16"><Parameters><Parameter Type="String">SiteDescription</Parameter></Parameters></Method><SetProperty Id="23" ObjectPathId="0" Name="TrimDuplicates"><Parameter Type="Boolean">false</Parameter></SetProperty><Method Name="SetQueryPropertyValue" Id="24" ObjectPathId="11"><Parameters><Parameter Type="String">ListId</Parameter><Parameter TypeId="{b25ba502-71d7-4ae4-a701-4ca2fb1223be}"><Property Name="BoolVal" Type="Boolean">false</Property><Property Name="IntVal" Type="Number">0</Property><Property Name="QueryPropertyValueTypeIndex" Type="Number">1</Property><Property Name="StrArray" Type="Null" /><Property Name="StrVal" Type="String">4dfaa8e2-a519-4988-b774-f81961091dba</Property></Parameter></Parameters></Method><Method Name="SetQueryPropertyValue" Id="25" ObjectPathId="11"><Parameters><Parameter Type="String">ListItemId</Parameter><Parameter TypeId="{b25ba502-71d7-4ae4-a701-4ca2fb1223be}"><Property Name="BoolVal" Type="Boolean">false</Property><Property Name="IntVal" Type="Number">4</Property><Property Name="QueryPropertyValueTypeIndex" Type="Number">2</Property><Property Name="StrArray" Type="Null" /><Property Name="StrVal" Type="Null" /></Parameter></Parameters></Method><SetProperty Id="26" ObjectPathId="0" Name="ResultsUrl"><Parameter Type="String">https://www.aacr.org/MEETINGS/PAGES/EVENTLISTING.ASPX#k=</Parameter></SetProperty><SetProperty Id="27" ObjectPathId="0" Name="ClientType"><Parameter Type="String"></Parameter></SetProperty><Method Name="SetQueryPropertyValue" Id="28" ObjectPathId="11"><Parameters><Parameter Type="String">QuerySession</Parameter><Parameter TypeId="{b25ba502-71d7-4ae4-a701-4ca2fb1223be}"><Property Name="BoolVal" Type="Boolean">false</Property><Property Name="IntVal" Type="Number">0</Property><Property Name="QueryPropertyValueTypeIndex" Type="Number">1</Property><Property Name="StrArray" Type="Null" /><Property Name="StrVal" Type="String">75323e1b-fbc8-437d-a884-9474eb16e68a</Property></Parameter></Parameters></Method><SetProperty Id="29" ObjectPathId="0" Name="ProcessPersonalFavorites"><Parameter Type="Boolean">false</Parameter></SetProperty><SetProperty Id="30" ObjectPathId="0" Name="SafeQueryPropertiesTemplateUrl"><Parameter Type="String">querygroup://webroot/PAGES/EVENTLISTING.ASPX?groupname=Default</Parameter></SetProperty><SetProperty Id="31" ObjectPathId="0" Name="IgnoreSafeQueryPropertiesTemplateUrl"><Parameter Type="Boolean">false</Parameter></SetProperty><ObjectPath Id="33" ObjectPathId="32" /><ExceptionHandlingScope Id="34"><TryScope Id="36"><Method Name="ExecuteQueries" Id="38" ObjectPathId="32"><Parameters><Parameter Type="Array"><Object Type="String">b759b507-ba34-499a-b350-9478f1deb96cDefault</Object></Parameter><Parameter Type="Array"><Object ObjectPathId="0" /></Parameter><Parameter Type="Boolean">true</Parameter></Parameters></Method></TryScope><CatchScope Id="40" /></ExceptionHandlingScope></Actions><ObjectPaths><Constructor Id="0" TypeId="{80173281-fffd-47b6-9a49-312e06ff8428}" /><Property Id="3" ParentId="0" Name="SortList" /><Property Id="11" ParentId="0" Name="Properties" /><Property Id="16" ParentId="0" Name="HitHighlightedProperties" /><Constructor Id="32" TypeId="{8d2ac302-db2f-46fe-9015-872b35f15098}" /></ObjectPaths></Request>'''
r = requests.post('https://www.aacr.org/Meetings/_vti_bin/client.svc/ProcessQuery', data = xml, headers = headers)
soup = BeautifulSoup(r.content, 'lxml')
data = json.loads(soup.select_one('p').text)
results = []
for item in data[12]['b759b507-ba34-499a-b350-9478f1deb96cDefault']['ResultTables'][0]['ResultRows']:
title = item['Title']
start = re.search(r'(\d+)', item['MeetingStartDate']).groups(0)[0]
end = re.search(r'(\d+)', item['MeetingEndDate']).groups(0)[0]
location = item['EventCityStOWSTEXT']
start = pandas.to_datetime(start,unit='ms')
end = pandas.to_datetime(end,unit='ms')
row = [title, start, end, location]
results.append(row)
pd.options.display.max_columns = 4
pd.set_option('display.width', 1000)
df = pd.DataFrame(results, columns = ['Title', 'Start', 'End', 'Location'])
print(df)
的{{1}}并生成split
list或者在tidyverse链中使用相同的逻辑
data.framerowMeans答案 1 :(得分：1)
Akrun给出了正确的答案，但是对于大多数分析而言，您的数据并不是最简单的格式。
您可能要考虑融合数据。
x = melt(as.matrix(df),varnames = c('row','col'))
x$id = substr(x$col,1,2)
ddply(x,c('row','id'),summarise,mean=mean(value,na.rm = T)) # or aggregate, etc.
答案 2 :(得分：1)
我认为row_mean中的hablar是更简单的解决方案。我重用了@akrun的df。
library(hablar)
df1 %>%
mutate(v1.mean = row_mean_(contains("v1")))
为您提供：
V1.1 V1.2 V1.3 V1.4 V1.5 V1.6 V1.7 V1.8 V1.9 V2.1 v1.mean
1 5 4 5 NA NA NA NA NA NA 5 4.666667
2 5 5 5 NA NA NA NA NA NA 3 5.000000
3 5 5 5 5 NA NA NA NA NA 4 5.000000
4 5 4 5 NA NA NA NA NA NA 3 4.666667
5 5 5 NA NA NA NA NA NA NA 2 5.000000
6 5 5 5 5 NA NA NA NA NA 1 5.000000
7 5 5 5 4 4 NA NA NA NA 5 4.600000
8 5 5 5 4 5 5 NA NA NA 4 4.833333
9 4 5 5 5 4 NA NA NA NA 1 4.600000
10 5 5 5 NA NA NA NA NA NA 5 5.000000