Summary statistics could be incorrect when using

I created a simple dataframe with age, salary, and num_obs:

import pandas as pd
from optbinning import ContinuousOptimalBinning

df = pd.DataFrame({'age': {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 7, 7: 8, 8: 9, 9: 10},
 'salary': {0: 0.7739560485559633,
  1: 0.4388784397520523,
  2: 0.8585979199113825,
  3: 0.6973680290593639,
  4: 0.09417734788764953,
  5: 0.9756223516367559,
  6: 0.761139701990353,
  7: 0.7860643052769538,
  8: 0.12811363267554587,
  9: 0.45038593789556713},
 'num_obs': {0: 5, 1: 4, 2: 3, 3: 7, 4: 6, 5: 6, 6: 5, 7: 7, 8: 5, 9: 5}})

Better displayed as:

age	salary	num_obs
1	0.773956	5
2	0.438878	4
3	0.858598	3
4	0.697368	7
5	0.0941773	6
6	0.975622	6
7	0.76114	5
8	0.786064	7
9	0.128114	5
10	0.450386	5

I then generated optimal bins using num_obs as sample weights:

optb = ContinuousOptimalBinning(dtype="numerical")
optb.fit(df['age'], df['salary'], sample_weight=df['num_obs'])

binning_table = optb.binning_table
binning_table.build()

Which results in:

	Bin	Count	Count (%)	Sum	Std	Mean	Min	Max	WoE	IV
0	(-inf, 1.50)	5	0.0943396	3.86978	1.5479120971119267	0.773956	3.86978	3.86978	0.175803	0.0165852
1	[1.50, 4.50)	14	0.264151	9.21288	1.401113568517211	0.658063	1.75551	4.88158	0.0599101	0.0158253
2	[4.50, 8.50)	24	0.45283	15.7269	1.6960750859575562	0.655289	0.565064	5.85373	0.0571365	0.0258731
3	[8.50, 9.50)	5	0.0943396	0.640568	0.25622726535109175	0.128114	0.640568	0.640568	-0.470039	0.0443433
4	[9.50, inf)	5	0.0943396	2.25193	0.9007718757911344	0.450386	2.25193	2.25193	-0.147767	0.0139403
5	Special	0	0	0	nan	0	nan	nan	-0.598153	0
6	Missing	0	0	0	nan	0	nan	nan	-0.598153	0
Totals		53	1	31.7021		0.598153	0.565064	5.85373	2.10696	0.116567

Notice how row 3 (with bin [8.50, 9.50)) has Std different than 0. Since the only age that falls on that bin is 8, I don't understand how the std could be different than 0. The other statistics are also quite odd/don't make sense.

Please let me know if there is an issue when using weights or if I'm understanding the results wrong.

Thanks!

Ps: this might be related to this issue: https://github.com/guillermo-navas-palencia/optbinning/issues/323

guillermo-navas-palencia / optbinning

Summary statistics could be incorrect when using #324