Pandas Series.ne operator returning unexpected result against two slices of same Series

[hunterjackson]

So I have this series of integers shown below

    from pandas import Series
    s = Series([1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

And I want to see how many times the numbers changes over the series, so I compare two slices of the same string to one another.

    s[1:].ne(s[:-1])
    Out[4]: 
    0      True
    1     False
    2     False
    3     False
    4     False
    5     False
    6     False
    7     False
    8     False
    9     False
    10    False
    11    False
    12    False
    13    False
    14    False
    15    False
    16    False
    17    False
    18    False
    19    False
    20    False
    21    False
    22    False
    23    False
    24    False
    25    False
    26    False
    27    False
    28    False
    29    False
    30    False
    31    False
    32    False
    33    False
    34    False
    35    False
    36    False
    37    False
    38    False
    39     True
    dtype: bool

Not only does the output using the Series.ne method not make any logical sense to me but the output is also longer than either of the inputs which is especially confusing.

I think this might be related to this #1134

Apologies if this isn't an issue but I haven't been able to find any satisfactory explanation for this behavior

tl;dr:

Where s is a pandas.Series of int's

[i != s[:-1][idx] for idx, i in enumerate(s[1:])] != s[:-1].ne(s[1:]).tolist()

[jreback]

This was changed here: #13894

use a shorter sequence just to make this fit in a reasonable amount of space

In [38]: s = s[0:5]

In [39]: s
Out[39]: 
0    1
1    2
2    3
3    1
4    2
dtype: int64

In [40]: a = s[1:]

In [41]: b = s[:-1]

In [42]: a
Out[42]: 
1    2
2    3
3    1
4    2
dtype: int64

In [43]: b
Out[43]: 
0    1
1    2
2    3
3    1
dtype: int64

these align, meaning that they are reindexed to the union of the series

In [44]: a, b = a.align(b)

In [45]: a
Out[45]: 
0    NaN
1    2.0
2    3.0
3    1.0
4    2.0
dtype: float64

In [46]: b
Out[46]: 
0    1.0
1    2.0
2    3.0
3    1.0
4    NaN
dtype: float64

The comparison is then clear (== for clarity here)

In [47]: a == b
Out[47]: 
0    False
1     True
2     True
3     True
4    False
dtype: bool