在满足条件之前查找值

Question

有什么方法可以简化以下代码，使其看起来更清晰，更优雅？

以下代码使用Linq和regex返回在文本集合中找到的值的集合：

IEnumerable<double> _results = pages.Select(result => {
    Regex _regex = new Regex("<my regex here>", RegexOptions.None);
    MatchCollection _matches = _regex.Matches(result);
    double _number = 0.0;

    foreach (Match _match in _matches) {
        if (_match.Groups["value"].Value.Contains("("))
            break;
        else
           double.TryParse(_match.Groups["value"].Value, out _number);
    }

    return _number;
});

如您所见，正则表达式很棘手，它基本上是在满足条件之前返回每个文本中找到的最后一个值，这就是期望的结果。

您如何简化先前的代码以求美观？内存和CPU利用率不是问题。

Answer 1

我会这样做，如果我能正确理解您的代码，则此语法在C＃7.0中具有内联import numpy as np from numpy.lib.recfunctions import append_fields import matplotlib.pyplot as plt data_y = np.genfromtxt('data/housing-and-development-board-resale-price-index-1q2009-100-quarterly.csv', names=True, dtype=None, delimiter=",", missing_values='na,-', filling_values=-1, encoding=None) # check if data load correctly print(data_y) years = [] for quarter in data_y['quarter']: year, q = quarter.split('-') # the new column name is year years.append(int(year)) years = np.array(years) data_y = append_fields(data_y, 'year', years) print(data_y) # is there a way to make the following of 29 extractions more elegant? data_1990 = data_y[data_y['year']==1990] data_1991 = data_y[data_y['year']==1991] data_1992 = data_y[data_y['year']==1992] data_1993 = data_y[data_y['year']==1993] data_1994 = data_y[data_y['year']==1994] data_1995 = data_y[data_y['year']==1995] data_1996 = data_y[data_y['year']==1996] data_1997 = data_y[data_y['year']==1997] data_1998 = data_y[data_y['year']==1998] data_1999 = data_y[data_y['year']==1999] data_2000 = data_y[data_y['year']==2000] data_2001 = data_y[data_y['year']==2001] data_2002 = data_y[data_y['year']==2002] data_2003 = data_y[data_y['year']==2003] data_2004 = data_y[data_y['year']==2004] data_2005 = data_y[data_y['year']==2005] data_2006 = data_y[data_y['year']==2006] data_2007 = data_y[data_y['year']==2007] data_2008 = data_y[data_y['year']==2008] data_2009 = data_y[data_y['year']==2009] data_2010 = data_y[data_y['year']==2010] data_2011 = data_y[data_y['year']==2011] data_2012 = data_y[data_y['year']==2012] data_2013 = data_y[data_y['year']==2013] data_2014 = data_y[data_y['year']==2014] data_2015 = data_y[data_y['year']==2015] data_2016 = data_y[data_y['year']==2016] data_2017 = data_y[data_y['year']==2017] data_2018 = data_y[data_y['year']==2018] # is there a way to make the following of 29 extractions more elegant? data_90 = data_1990['index'] data_91 = data_1991['index'] data_92 = data_1992['index'] data_93 = data_1993['index'] data_94 = data_1994['index'] data_95 = data_1995['index'] data_96 = data_1996['index'] data_97 = data_1997['index'] data_98 = data_1998['index'] data_99 = data_1999['index'] data_00 = data_2000['index'] data_01 = data_2001['index'] data_02 = data_2002['index'] data_03 = data_2003['index'] data_04 = data_2004['index'] data_05 = data_2005['index'] data_06 = data_2006['index'] data_07 = data_2007['index'] data_08 = data_2008['index'] data_09 = data_2009['index'] data_10 = data_2010['index'] data_11 = data_2011['index'] data_12 = data_2012['index'] data_13 = data_2013['index'] data_14 = data_2014['index'] data_15 = data_2015['index'] data_16 = data_2016['index'] data_17 = data_2017['index'] data_18 = data_2018['index'] data_combined = np.empty([len(year), 29]) for i in range(len(data_90)): data_combined[i] = np.array([data_90[i], data_91[i], data_92[i], data_93[i], data_94[i], data_95[i], data_96[i], data_97[i], data_98[i], data_99[i], data_00[i], data_01[i], data_02[i], data_03[i], data_04[i], data_05[i], data_06[i], data_07[i], data_08[i], data_09[i], data_10[i], data_11[i], data_12[i], data_13[i], data_14[i], data_15[i], data_16[i], data_17[i], data_18[i]]) # is there a way to make the following of 29 extractions of labels more elegant? labels = np.array(['1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', ' 2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']) boxprops = dict(linestyle='-', linewidth=2, color='blue') flierprops = dict(marker='o', markerfacecolor='green', markersize=8) medianprops = dict(linewidth=2, color='red') plt.figure(figsize=(60, 60)) plt.title('Movement of Resale Price Index (RPI)', fontsize=15, weight='bold') plt.boxplot(data_combined, labels=labels, flierprops=flierprops, medianprops=medianprops, boxprops=boxprops) plt.ylabel('Resale Price Index (RPI)', labelpad=20, fontsize=12) plt.xlabel('Years', labelpad=20, fontsize=12) plt.show() 变量声明是有效的：

out

Answer 2

尽管bobince's advice关于正则表达式和HTML :)，这是基于正则表达式的解决方案：

.NET的正则表达式引擎可以向后移动，因此我们可以利用它并让rex在>中找到最后一个括号中的值最近的数字（使用悲观匹配器import UIKit class cell1: UICollectionViewCell { @IBOutlet var BB: UIButton! override func awakeFromNib() { super.awakeFromNib() BB.layer.cornerRadius = 10 } func congigureCell(EditLater: Ques){ BB.setImage(UIImage(named: EditLater.LIMG), for: .normal) } } ）

.*?

这是“匹配并命名>之间的数字，然后是所有字符中最短的数字，然后是（）之间的数字”-根据需要进行调整

>(?<v>[,.0-9]+)<.*?\([.0-9]+\)

例如：

See it here

Answer 3

除了@ dan-d的答案外，这也许是最简单易读且更优雅的代码：

double[] _results = _pages
    .Select(page => _regex.Matches(page).Cast<Match>().Select(value => value.Groups["value"].Value))
    .Select(value => value.TakeWhile(condition => !condition.Contains("(")).Last())
    .Select(number => double.TryParse(number, out double _result) ? _result : _result)
    .ToArray();

第一个 select 遍历所有数据页，并使用正则表达式返回具有所有找到的值的数组。第二个 select 为每个页面找到条件之前的最后一个值（该值是否带有括号）；而最终的 select 会评估结果，并返回一个双精度数组。

最后，在遵循@ caius-jard的建议之后，改进正则表达式现在返回一个值，因此将linq语句进一步简化为以下内容：

double[] _results = _pages
    .Select(page => _regex.Matches(page).Cast<Match>().Select(value => value.Groups["value"].Value).First())
    .Select(number => double.TryParse(number, out double _result) ? _result : _result)
    .ToArray();