python - numpy 是否为此积累了正确的东西？

Question

我有一个 pandas.Series 整数，如下所示:

1959-09-22    191.0
1959-09-23    196.0
1959-09-24    222.0
1959-09-25    232.0
1959-09-28    232.0
1959-09-29    242.0
1959-09-30    241.0
1959-10-01    247.0
1959-10-02    251.0
1959-10-05    275.0
1959-10-06    294.0
1959-10-07    313.0
1959-10-08    332.0
1959-10-09    343.0
1959-10-12    346.0
1959-10-13    344.0
1959-10-14    351.0
1959-10-15    336.0
1959-10-16    330.0
1959-10-19    319.0
1959-10-20    329.0
1959-10-21    356.0
1959-10-22    374.0

我想做的是处理这个列表，使连续整数之间的差异永远不会小于 10%。如果是，则保留最后一个值，直到与当前值的差异超过 10%。

最符合 Python 风格的方法是什么？

作为一些背景，这是一个财务头寸列表，其想法是通过不交易整体头寸的微小差异来降低交易成本。

Answer 1

这是一种也支持前向填充的方法-

def process_close_diffs(arr,percent_close,FORWARD_FILL=False):
    # Get thresholds for each element starting from the second element until last
    thresh = (percent_close/100.0)*(arr[:-1])

    # Get the differentiations betwen consecutive elements
    diffs = np.abs(np.diff(arr))    

    # See which elements are more than or equal to the thresh and 
    # select those with with boolean indexing. 
    # Additionally for the optional FORWARD_FILL criteria, use cumulative 
    # summation on the mask to create a replicated kind of array, which when 
    # indexed into the mask selected elements would give us forward-filled array
    mask = np.append(True,diffs >= thresh)
    if FORWARD_FILL:
        return (arr[np.where(mask)[0]])[mask.cumsum()-1]
    else:
        return arr[mask]

sample 运行-

In [215]: data
Out[215]: array([25, 29, 27, 27, 17, 14,  7, 20, 21,  5])

In [216]: process_close_diffs(data,40) # Using 40% to see noticeable changes
Out[216]: array([25,  7, 20,  5])

In [217]: process_close_diffs(data,40,FORWARD_FILL=True)
Out[217]: array([25, 25, 25, 25, 25, 25,  7, 20, 20,  5])