如何根据每个记录及其上一个记录过滤NumPy数组

2024-05-19 • 问答

我正在考虑将代码转换为可与NumPy一起使用，并且具有以下功能：

def Clean_One_State(ListsToConvert):
    # Removes duplicate states
    prev_bool = 2
    returnlist = []
    for value in enumerate(ListsToConvert[1:],0):
        if value[1] != prev_bool:
            try:
                returnlist.append(value)
                prev_bool = value[1]
            except IndexError:
                returnlist.append(value)
                prev_bool = value[1]
    return returnlist

该函数在句子中删除输入中与先前记录具有相同状态的记录。根据您自己的需要，该函数的输入和输出是：

In:[['Event Time','State at A'],[0.0,1],[0.03253,[0.04757,0],[0.08479,[0.98534,[0.98748,[1.03602,[1.03717,[1.95898,[1.96456,[2.00913,[2.01547,0]...
Out: [[0.0,0]...

理想情况下，我希望能够查看输入列表的视图（在NumPy中），以便可以删除将影响输入列表的输出列表中的记录。我在网上看了一些示例，但仍然对如何执行此操作持怀疑态度。

data = [['Event Time','State at A'],[0.0,1],[0.03253,[0.04757,0],[0.08479,[0.98534,[0.98748,[1.03602,[1.03717,[1.95898,[1.96456,[2.00913,[2.01547,0]] # convert to array ar = np.array([*map(tuple,data[1:])],dtype=[*zip(data[0],(float,int))]) ar # array([(0.,1),(0.03253,(0.04757,0),(0.08479,# (0.98534,(0.98748,(1.03602,(1.03717,# (1.95898,(1.96456,(2.00913,(2.01547,0)],# dtype=[('Event Time','<f8'),('State at A','<i8')]) # find places where State at A changes and select them from ar # prepend something that is not equal to the first State at A,so the # very first item is also selected ar[np.diff(ar['State at A'],prepend=ar['State at A'][0]-1).nonzero()] # array([(0.,(0.98534,# (1.96456,'<i8')])

如何根据每个记录及其上一个记录过滤NumPy数组

yxm407949656 回答：如何根据每个记录及其上一个记录过滤NumPy数组

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