Question

我的代码中有如下内容：

mean_realp = [[df[(df['fvpwm'] == v) & (df['gppwm'] == w)]['P'].mean()                                          for v in uvpwms] for w in upwms]
mean_setp = [[df[(df['fvpwm'] == v) & (df['gppwm'] == w)]['pressure setpoint [mbar]'].mean()                    for v in uvpwms] for w in upwms]
stddev_realp = [[df[(df['fvpwm'] == v) & (df['gppwm'] == w)]['P'].std()                                         for v in uvpwms] for w in upwms]
stddev_pbefore = [[df[(df['fvpwm'] == v) & (df['gppwm'] == w)]['Pbefore [mbar]'].std()                          for v in uvpwms] for w in upwms]
maxes_realp = [[df[(df['fvpwm'] == v) & (df['gppwm'] == w)]['P'].max()                                          for v in uvpwms] for w in upwms]
mins_realp = [[df[(df['fvpwm'] == v) & (df['gppwm'] == w)]['P'].min()                                           for v in uvpwms] for w in upwms]

有没有一种方法可以简化此过程，而无需使用外部循环？我没有什么反对他们的，但必须有一个更优雅的解决方案...

对于后一部分

for v in uvpwms] for w in upwms]

我想不出比外循环更简单的东西。

但是内在部分呢？是否有一些更简单的解决方案（例如lambda表达式）？

Answer 1

提供帮助功能以强调和参数化差异。这是一个开始，您可能应该找到一个更合适的名称，以更好地与数据集的真实含义联系起来：

def transformMatrix(matrix, label, transformer):
    return [[df[transformer((df['fvpwm'] == v) & (df['gppwm'] == w)][label]) for v in matrix] for w in matrix]

mean_realp = transformMatrix(upwms, 'P', lambda elements: elements.mean())
mean_setp = transformMatrix(upwms, 'pressure setpoint [mbar]', lambda elements: elements.mean())
stddev_realp = transformMatrix(upwms, 'P', lambda elements: elements.std())
stddev_pbefore = transformMatrix(upwms, 'Pbefore [mbar]', lambda elements: elements.std())
maxes_realp = transformMatrix(upwms, 'P', lambda elements: elements.max())
mins_realp = transformMatrix(upwms, 'P', lambda elements: elements.min())

有没有一种方法可以简化多个列表的理解？

1 个答案: