当且仅当最后一个和下一个非缺失值为1时，如何用1填充Pandas系列中缺失的值

Question

我有一个只带有0、1和np.nan值的熊猫系列：

pd.Series([0, np.nan, np.nan, 0, np.nan, np.nan, np.nan, 1, np.nan, 1, np.nan, 
np.nan, np.nan, 1, np.nan, 0, np.nan, np.nan, 1, np.nan, np.nan, np.nan, 1])

我想填充缺少的值，但我的逻辑是用1替换np.nan值，当且仅当上一个和下一个非缺少的值也为1，否则为0。因此，预期产出为：

pd.Series([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1])

我该怎么做

Answer 1

没有内置的方法可以做到这一点，您必须为此编写自己的代码

import pandas as pd
import numpy as np

s = pd.Series([0, np.nan, np.nan, 0, np.nan, np.nan, np.nan, 1, np.nan, 1, np.nan,
               np.nan, np.nan, 1, np.nan, 0, np.nan, np.nan, 1, np.nan, np.nan, np.nan, 1])

vals = s.values.tolist()
look_left = [-1] * len(vals)
look_right = [-1] * len(vals)

for i in range(2):
    check = vals if i == 0 else vals[::-1]
    arr = look_left if i == 0 else look_right
    prev_seen = None
    for i, elem in enumerate(check):
        if elem in [0, 1]:
            arr[i] = elem
            prev_seen = elem
        else:
            arr[i] = prev_seen

look_right = look_right[::-1]

for i in range(len(vals)):

    if np.isnan(vals[i]):
        vals[i] = 1 if look_right[i] == 1 and look_left[i] == 1 else  0

s = pd.Series(vals)
print(s)