Python 如何在列表的间隔之间填充元素

Python 如何在列表的间隔之间填充元素,python,pandas,numpy,dataframe,Python,Pandas,Numpy,Dataframe,我有这样一份清单: list_1 = [np.NaN, np.NaN, 1, np.NaN, np.NaN, np.NaN, 0, np.NaN, 1, np.NaN, 0, 1, np.NaN, 0, np.NaN, 1, np.NaN] list_2 = [np.NaN, np.NaN, 1, 1, 1, 1, 0, np.NaN, 1, 1, 0, 1, 1, 0, np.NaN, 1, np.NaN] 因此,存在以1开始,以0结束的间隔。 如何将这些间隔中的值替换为1?结果如下:

我有这样一份清单:

list_1 = [np.NaN, np.NaN, 1, np.NaN, np.NaN, np.NaN, 0, np.NaN, 1, np.NaN, 0, 1, np.NaN, 0, np.NaN,  1, np.NaN]
list_2 = [np.NaN, np.NaN, 1, 1, 1, 1, 0, np.NaN, 1, 1, 0, 1, 1, 0, np.NaN, 1, np.NaN]
因此,存在以
1
开始,以
0
结束的间隔。 如何将这些间隔中的值替换为1?结果如下:

list_1 = [np.NaN, np.NaN, 1, np.NaN, np.NaN, np.NaN, 0, np.NaN, 1, np.NaN, 0, 1, np.NaN, 0, np.NaN,  1, np.NaN]
list_2 = [np.NaN, np.NaN, 1, 1, 1, 1, 0, np.NaN, 1, 1, 0, 1, 1, 0, np.NaN, 1, np.NaN]

在本例中,我使用了
NaN
,但是可以应用于任何值的通用解决方案也很好

解决方案:

s = pd.Series(list_1)
s1 = s.eq(1)
s0 = s.eq(0)
m = (s1 | s0).where(s1.cumsum().ge(1),False).cumsum().mod(2).eq(1)
s.loc[m & s.isna()] = 1
print(s.tolist())
#[nan, nan, 1.0, 1.0, 1.0, 1.0, 0.0, nan, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, nan, 1.0, 1.0]

但是如果只有
1
0
NaN
可以执行以下操作:

s = pd.Series(list_1)
s.fillna(s.ffill().where(lambda x: x.eq(1))).tolist()
输出

[nan,
 nan,
 1.0,
 1.0,
 1.0,
 1.0,
 0.0,
 nan,
 1.0,
 1.0,
 0.0,
 1.0,
 1.0,
 0.0,
 nan,
 1.0,
 1.0]

您可以使用检索索引1和0,然后填充每个切片中的值:

将numpy导入为np
a=np.数组([np.NaN,np.NaN,1,np.NaN,np.NaN,0,np.NaN,1,np.NaN,0,1,np.NaN,0,np.NaN,1,np.NaN])
一=np.挤压(np.arg,其中(a==1))
零=np.压缩(np.arg,其中(a==0))
如果一[0]>零[0]:
零=零[1:]
值=-999
对于zip中的i,j(1,0):
a[i+1:j]=值
A.
数组([nan,nan,1.,-999.,-999.,-999.,0.,nan,1。,
-999,0,1.,-999,0,nan,1,nan])

以下是一种基于numpy的方法,使用:



这是一个代码,其中变量
replace
将确定是否应该替换元素,并且
for
将从区间的
0
迭代到
len
,如果它找到
1
则replace将为真,则元素将被替换,并且当它找到下一个
0
replace时,将被替换在再次出现
1

  replace = False
    for i in (len(interval)-1):
        if interval[i]==1:
            replace = True
        elif interval[i]==0:
            replace = False
        if replace:
            list[i]=inerval[i]

假设每个1后跟0(减去最后1):

输出:

[nan nan  1.  1.  1.  1.  0. nan  1.  1.  0.  1.  1.  0. nan  1. nan]

这是一个基于NumPy的-

def fill_inbetween(a):
    m1 = a==1
    m2 = a==0
    id_ar = m1.astype(int)-m2
    idc = id_ar.cumsum()
    idc[len(m1)-m1[::-1].argmax():] =  0
    return np.where(idc.astype(bool), 1, a)
样本运行-

In [44]: a # input as array
Out[44]: 
array([nan, nan,  1., nan, nan, nan,  0., nan,  1., nan,  0.,  1., nan,
        0., nan,  1., nan])

In [45]: fill_inbetween(a)
Out[45]: 
array([nan, nan,  1.,  1.,  1.,  1.,  0., nan,  1.,  1.,  0.,  1.,  1.,
        0., nan,  1., nan])
使用阵列输入对NumPy解决方案进行基准测试 为了简单起见,我们将通过平铺和测试基于NumPy的示例,将给定的示例放大到
10000x

其他NumPy解决方案-

#@yatu's soln
def func_yatu(a):
    ix0 = (a == 0).cumsum()
    ix1 = (a == 1).cumsum()
    dec = (ix1 - ix0).astype(float)
    ix = len(a)-(a[::-1]==1).argmax()
    last = ix1[-1]-ix0[-1]
    if last > 0:
        dec[ix:] = a[ix:]
    out = np.where(dec==1, dec, a)
    return out

# @FBruzzesi's soln (with the output returned in a separate array)
def func_FBruzzesi(a, value=1):
    ones = np.squeeze(np.argwhere(a==1))
    zeros = np.squeeze(np.argwhere(a==0))   
    if ones[0]>zeros[0]:
        zeros = zeros[1:]   
    out = a.copy()
    for i,j in zip(ones,zeros):
        out[i+1:j] = value
    return out

# @Ehsan's soln (with the output returned in a separate array)
def func_Ehsan(list_1):
    zeros_ind = np.where(list_1 == 0)[0]
    ones_ind = np.where(list_1 == 1)[0]
    ones_ind = ones_ind[:zeros_ind.size]        
    indexer = np.r_[tuple([np.s_[i:j] for (i,j) in zip(ones_ind,zeros_ind)])]
    out = list_1.copy()
    out[indexer] = 1
    return out
时间安排-

In [48]: list_1 = [np.NaN, np.NaN, 1, np.NaN, np.NaN, np.NaN, 0, np.NaN, 1, np.NaN, 0, 1, np.NaN, 0, np.NaN,  1, np.NaN]
    ...: a = np.array(list_1)

In [49]: a = np.tile(a,10000)

In [50]: %timeit func_Ehsan(a)
    ...: %timeit func_FBruzzesi(a)
    ...: %timeit func_yatu(a)
    ...: %timeit fill_inbetween(a)
4.86 s ± 325 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
253 ms ± 29.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
3.39 ms ± 205 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
2.01 ms ± 168 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
复制过程不会占用很多运行时间,因此可以忽略这一点-

In [51]: %timeit a.copy()
78.3 µs ± 571 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

1后面必须跟0吗?或者在连续的1-0对之间是否还有其他1?换句话说,每1后面是否有0?请不要只发布代码作为答案,还要解释代码的作用以及它如何解决问题。带有解释的答案通常质量更高,更容易吸引选票。
In [51]: %timeit a.copy()
78.3 µs ± 571 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)