Closed
Description
these cases should give the same results. Somehow the ops order is being reversed or something with numexpr.
numpy 1.8.1, numexpr 2.4, pandas 0.14rc1
Can be worked around like:
In [39]: df = DataFrame(dict(A=np.random.randn(25000)))
In [40]: df.iloc[0:5] = np.nan
In [41]: (1-np.isnan(df)).iloc[0:25]
Out[41]:
A
0 0
1 0
2 0
3 0
4 0
5 -1
6 -1
7 -1
8 -1
9 -1
10 -1
11 -1
12 -1
13 -1
14 -1
15 -1
16 -1
17 -1
18 -1
19 -1
20 -1
21 -1
22 -1
23 -1
24 -1
25 -1
In [43]: df = DataFrame(dict(A=np.random.randn(25)))
In [44]: df.iloc[0:5] = np.nan
In [45]: (1-np.isnan(df)).iloc[0:25]
Out[45]:
A
0 0
1 0
2 0
3 0
4 0
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1