使用gsub在两个引号之间插入逗号

Question

我正在尝试在两个由引号分隔的短语之间插入逗号。我通常可以使用"the teaching approach that you used during the class" "the way you answered to student's questions" 来完成它，但出于某种原因，在这种情况下，它不起作用。

打印我的对象导致

the teaching approach that you used during the class, the way you answered to student's questions 

我需要在一系列可能包含两个或更多短语的表达式中插入逗号。在这种情况下，所需的输出将是

select: hide;

编辑：

具体来说：评论中建议的对象是长度为2的向量。我将其转换为单个向量并使用指示的gsub。

感谢任何帮助

Answer 1

字符串是length的向量2.我们用paste连接它们（toString是paste(., collapse=", ")

的包装器

toString(str1)

如果是单个字符串

gsub('" +"', ", ", mystring)
#[1] "the teaching approach that you used during the class, the way you answered to student's questions"

数据

str1 <- c("the teaching approach that you used during the class", 
    "the way you answered to student's questions" )
mystring <- 'the teaching approach that you used during the class" "the way you answered to student\'s questions'

Answer 2

我已经意识到这个问题的答案很简单，但是创建OP提到的样本R很困难。当然，它将为# How to create that sample mystring? # Start with single quotes (') and end with single quotes ('). # Make a note: There is single quotes as student's. Hence you need # escape character before ' in student's. mystring <- 'the teaching approach that you used during the class" "the way you answered to student\'s questions' #Check if mystring matches with that of OP cat(mystring) # the teaching approach that you used during the class" "the way you answered to student's questions 的新用户提供学习机会。

gsub('(\\"\\s+\\")',", ",mystring)
[1] "the teaching approach that you used during the class, the way you answered to student's questions"

答案：

"

正则表达式：仅隔空格查找x=[ 1151.41 1155.26 1159.11 1162.96 1166.8 1170.65 1174.5 1178.35 1182.19 1186.03 1189.88 1193.72 1197.57 1201.41 1205.25 1209.08 1212.92 1216.77 1220.6 1224.44 1228.27 1232.11 1235.94 1239.78 1243.61 1247.44 1251.27 1255.1 1258.94 1262.77 1266.6 1270.42 1274.25 1278.08 1281.91 1285.73 1289.55 1293.37 1297.2 1301.02 1304.84 1308.66 1312.48 1316.3 1320.12 1323.94 1327.75 1331.57 1335.39 1339.2 1343.01 1346.83 1350.64 1354.45 1358.26 1362.07 1365.88 1369.69 1373.49 1377.31 1381.11 1384.92 1388.72 1392.52 1396.33 1400.13 1403.93 1407.73 1411.53 1415.34 1419.13 1422.93 1426.73 1430.52 1434.32 1438.12 1441.91 1445.7 1449.49 1453.29 1457.08 1460.87 1464.66 1468.45 1472.24 1476.03 1479.81 1483.6 1487.38 1491.17 1494.96 1498.74] y=[ 3.34835702 3.37753701 3.46421441 3.47561446 3.52906995 3.7430628 3.9445303 4.08069468 4.26772632 4.57141717 4.62653264 4.95543734 5.00090221 5.03820472 4.96421951 4.94593035 4.71712245 4.73136333 4.5449431 4.54198878 4.68326867 4.72099151 4.79832143 4.93313301 5.23041882 5.67948661 6.14714543 6.650372 7.289351 7.85672892 8.52692445 9.19116051 10.00674694 10.54570038 10.87682289 11.35534123 11.42260359 11.19421866 10.80316156 10.50632887 9.62797798 9.1298809 8.28793321 7.73275794 7.0211812 6.81838292 6.39480371 6.08652915 5.72148564 5.51527137 5.21654715 5.25030395 5.28647568 5.2812212 5.36064974 5.41940211 5.10942821 5.10866681 5.03248779 4.87465847 4.62568491 4.60337401 4.57096411 4.56132562 4.55625841 4.81601962 4.91783646 5.40526849 5.7393016 6.44435143 7.27609291 8.17028904 9.53128053 10.71589492 11.74175004 12.82955492 13.1016751 13.1616784 12.64563727 11.91554063 10.84031597 9.74302278 8.93611092 8.01037116 7.37684127 7.08713295 6.77556246 6.8143279 6.85773728 6.87895641 7.02718543 7.10869694] weights=[ 0.01244515 0.0056474 0.00926648 0.00674491 0.00553186 0.00589553 0.00576142 0.00527653 0.00599983 0.00787368 0.00708644 0.00357355 0.00303342 0.00916585 0.00275235 0.00328612 0.00256573 0.00370105 0.00428572 0.00332175 0.00352448 0.00506917 0.00357058 0.00351362 0.0040452 0.00512055 0.00217692 0.00228909 0.00237594 0.00141826 0.00278388 0.00107677 0.00139026 0.00171414 0.0015294 0.00124306 0.00139236 0.00160101 0.00113102 0.00125891 0.00131655 0.00172122 0.00261752 0.00199811 0.00259736 0.00315851 0.00434901 0.00293013 0.00338841 0.00230605 0.00272563 0.00596109 0.00420324 0.00510059 0.00326837 0.00373911 0.00418471 0.00413105 0.00581997 0.0047461 0.00297461 0.00713362 0.00438222 0.00304455 0.00672188 0.00444613 0.00375129 0.00312274 0.00429026 0.00340182 0.00453605 0.00175878 0.0021822 0.0018159 0.0014724 0.00155655 0.00093935 0.00067046 0.00074241 0.00097113 0.0008329 0.00107734 0.00133742 0.00119752 0.00142113 0.00174815 0.00254059 0.0033123 0.0030637 0.00280913 0.00314901 0.00259063] import os import matplotlib.pyplot as plt import pandas as pd from lmfit.models import LinearModel from lmfit.models import GaussianModel import numpy as np plotme = True # import calibrated file filename = 'file.csv' data = pd.read_csv('file.csv', sep=",", index_col=[0], header=0) samplename = os.path.splitext(os.path.basename(filename))[0] # section out the data to use data = data.set_index('x_ref', drop=False) fingerprint = data[data.index > 1150] fingerprint = fingerprint[fingerprint.index < 1500] # define the x, y, and weights x = x y = y wv = weights lin_mod1 = LinearModel(prefix='lin1_') pars = lin_mod1.guess(y, x=x) gauss1 = GaussianModel(prefix='one_') pars.update(gauss1.make_params()) pars['one_center'].set(1200) pars['one_sigma'].set(2) pars['one_amplitude'].set(10, min=0) gauss2 = GaussianModel(prefix='two_') pars.update(gauss2.make_params()) pars['two_center'].set(1275) pars['two_sigma'].set(2) pars['two_amplitude'].set(10, min=0) gauss3 = GaussianModel(prefix='three_') pars.update(gauss3.make_params()) pars['three_center'].set(1450) pars['three_sigma'].set(2) pars['three_amplitude'].set(10, min=0) gauss4 = GaussianModel(prefix='unknown_') pars.update(gauss4.make_params()) pars['unknown_center'].set(1355) pars['unknown_sigma'].set(2) pars['unknown_amplitude'].set(10, min=0) gauss5 = GaussianModel(prefix='unknown2_') pars.update(gauss5.make_params()) pars['unknown2_center'].set(1510) pars['unknown2_sigma'].set(2) pars['unknown2_amplitude'].set(10, min=0) gauss6 = GaussianModel(prefix='six_') pars.update(gauss6.make_params()) pars['six_center'].set(1550) pars['six_sigma'].set(2) pars['six_amplitude'].set(10, min=0) gauss7 = GaussianModel(prefix='seven_') pars.update(gauss7.make_params()) pars['seven_center'].set(1600) pars['seven_sigma'].set(2) pars['seven_amplitude'].set(10, min=0) mod = gauss1 + gauss2 + gauss3 + gauss4 + gauss5 + gauss6 + gauss7 + lin_mod1 init = mod.eval(pars, x=x) out = mod.fit(y, pars, x=x, weights=wv) print(out.fit_report()) if plotme: plt.subplot(2, 1, 1) plt.plot(x, y, 'bo') # plt.plot(x, init, 'k--') plt.plot(x, out.best_fit, 'r-') comps = out.eval_components(x=x) plt.plot(x, comps['one_'], '--') plt.plot(x, comps['two_'], '--') plt.plot(x, comps['three_'], '--') plt.plot(x, comps['unknown_'], '--') plt.plot(x, comps['six_'], '--') plt.plot(x, comps['unknown2_'], '--') plt.plot(x, comps['seven_'], '--') plt.plot(x, comps['lin1_'], '--') plt.subplot(2, 1, 2) plt.title('residuals') plt.plot(x, out.residual, '-') plt.subplots_adjust(top=0.95, bottom=0.10, left=0.10, right=0.95, hspace=0.50, wspace=0.35) plt.show() 。如果找到，则替换为“，”。