Python - 累积总和达到某个阈值
Python - cumulative sum up to a certain threshold
我有一个名为 df_sorted 的数据框（如下图所示）。
我需要的是df_sorted['turnover']按df_sorted['year_week']分组的累加和满足以下条件：
df_sorted['kg_cum'] < df_sorted['kg_cum_year_week_20%']
根据图片答案应该是成交额104012左右
由于我对 python 比较陌生，我很想听听如何解决这个问题。
如果以上内容不够清楚，请随时询问更多信息。
这里的数据为字典：
{'orderline': {0: 1418863,
1: 1418860,
2: 1418718,
3: 1418864,
4: 1418745,
5: 1418862,
6: 1418707,
7: 1418738,
8: 1418699,
9: 1418853,
10: 1418722,
11: 1418739,
12: 1418724,
13: 1418763,
14: 1418876,
15: 1418851,
16: 1418761,
17: 1418710,
18: 1418777,
19: 1418903,
20: 1418902,
21: 1418901,
22: 1418852,
23: 1418703,
24: 1418858,
25: 1418702,
26: 1418857,
27: 1418896,
28: 1418781,
29: 1418741,
30: 1418883,
31: 1418740,
32: 1418734,
33: 1418753,
34: 1418890,
35: 1418726,
36: 1418882,
37: 1418744,
38: 1418752,
39: 1418885,
40: 1418894,
41: 1418889,
42: 1418749,
43: 1418879,
44: 1418886,
45: 1418729,
46: 1418732,
47: 1418751,
48: 1418895,
49: 1418730,
50: 1418878,
51: 1418694,
52: 1418849,
53: 1418717,
54: 1419013,
55: 1418941,
56: 1419015,
57: 1418926,
58: 1419194,
59: 1418979,
60: 1419014,
61: 1419060,
62: 1419063,
63: 1419172,
64: 1419217,
65: 1419368,
66: 1418965,
67: 1419085,
68: 1419216,
69: 1419329,
70: 1418917,
71: 1419109,
72: 1418930,
73: 1419075},
'year_week': {0: '2019-01',
1: '2019-01',
2: '2019-01',
3: '2019-01',
4: '2019-01',
5: '2019-01',
6: '2019-01',
7: '2019-01',
8: '2019-01',
9: '2019-01',
10: '2019-01',
11: '2019-01',
12: '2019-01',
13: '2019-01',
14: '2019-01',
15: '2019-01',
16: '2019-01',
17: '2019-01',
18: '2019-01',
19: '2019-01',
20: '2019-01',
21: '2019-01',
22: '2019-01',
23: '2019-01',
24: '2019-01',
25: '2019-01',
26: '2019-01',
27: '2019-01',
28: '2019-01',
29: '2019-01',
30: '2019-01',
31: '2019-01',
32: '2019-01',
33: '2019-01',
34: '2019-01',
35: '2019-01',
36: '2019-01',
37: '2019-01',
38: '2019-01',
39: '2019-01',
40: '2019-01',
41: '2019-01',
42: '2019-01',
43: '2019-01',
44: '2019-01',
45: '2019-01',
46: '2019-01',
47: '2019-01',
48: '2019-01',
49: '2019-01',
50: '2019-01',
51: '2019-01',
52: '2019-01',
53: '2019-01',
54: '2019-02',
55: '2019-02',
56: '2019-02',
57: '2019-02',
58: '2019-02',
59: '2019-02',
60: '2019-02',
61: '2019-02',
62: '2019-02',
63: '2019-02',
64: '2019-02',
65: '2019-02',
66: '2019-02',
67: '2019-02',
68: '2019-02',
69: '2019-02',
70: '2019-02',
71: '2019-02',
72: '2019-02',
73: '2019-02'},
'kg_orderline': {0: 39600.0,
1: 1200.0,
2: 6240.0,
3: 10200.0,
4: 19800.0,
5: 19800.0,
6: 7200.0,
7: 6600.0,
8: 9000.0,
9: 9000.0,
10: 12600.0,
11: 4200.0,
12: 19800.0,
13: 1200.0,
14: 19800.0,
15: 3600.0,
16: 3000.0,
17: 19800.0,
18: 6600.0,
19: 1200.0,
20: 19800.0,
21: 3000.0,
22: 10200.0,
23: 5846.4,
24: 7308.0,
25: 2923.2,
26: 1044.0,
27: 3000.0,
28: 4320.0,
29: 8991.36,
30: 261.0,
31: 870.0,
32: 19800.0,
33: 16484.16,
34: 4495.68,
35: 7992.32,
36: 2396.8,
37: 19800.0,
38: 19800.0,
39: 19800.0,
40: 19800.0,
41: 11504.64,
42: 8960.0,
43: 3920.0,
44: 5600.0,
45: 8400.0,
46: 10080.0,
47: 18480.0,
48: 6720.0,
49: 14520.0,
50: 16720.0,
51: 15840.0,
52: 15840.0,
53: 5200.0,
54: 19800.0,
55: 19800.0,
56: 19800.0,
57: 5200.0,
58: 7800.0,
59: 12000.0,
60: 12000.0,
61: 19800.0,
62: 7800.0,
63: 19800.0,
64: 19800.0,
65: 31200.0,
66: 19800.0,
67: 19800.0,
68: 19800.0,
69: 19800.0,
70: 6000.0,
71: 6000.0,
72: 19800.0,
73: 19800.0},
'Price': {0: 0.743472,
1: 0.877065,
2: 0.896822,
3: 0.899015,
4: 0.900508,
5: 0.900508,
6: 1.011472,
7: 1.011472,
8: 1.015272,
9: 1.015272,
10: 1.110932,
11: 1.110932,
12: 1.111472,
13: 1.111472,
14: 1.111472,
15: 1.115272,
16: 1.160932,
17: 1.161472,
18: 1.161472,
19: 1.161472,
20: 1.190932,
21: 1.191472,
22: 1.215272,
23: 1.21580229885057,
24: 1.21580229885057,
25: 1.27547126436782,
26: 1.27588122605364,
27: 1.311472,
28: 1.34901875,
29: 1.34937892376682,
30: 1.37190114942529,
31: 1.37320459770115,
32: 1.377472,
33: 1.39461434977578,
34: 1.39461434977578,
35: 1.39870627802691,
36: 1.41605841121495,
37: 1.456678,
38: 1.456678,
39: 1.456678,
40: 1.456678,
41: 1.46319626168224,
42: 1.480638,
43: 1.480638,
44: 1.480638,
45: 1.500638,
46: 1.500638,
47: 1.500638,
48: 1.500638,
49: 1.515914,
50: 1.515914,
51: 1.516132,
52: 1.516132,
53: 1.516353,
54: 1.059765,
55: 1.076573,
56: 1.077065,
57: 1.096822,
58: 1.096822,
59: 1.098558,
60: 1.099015,
61: 1.099015,
62: 1.099015,
63: 1.099015,
64: 1.099015,
65: 1.099015,
66: 1.100508,
67: 1.100508,
68: 1.100508,
69: 1.100508,
70: 1.130932,
71: 1.135272,
72: 1.147472,
73: 1.147472},
'kg_cum': {0: 39600.0,
1: 40800.0,
2: 47040.0,
3: 57240.0,
4: 77040.0,
5: 96840.0,
6: 104040.0,
7: 110640.0,
8: 119640.0,
9: 128640.0,
10: 141240.0,
11: 145440.0,
12: 165240.0,
13: 166440.0,
14: 186240.0,
15: 189840.0,
16: 192840.0,
17: 212640.0,
18: 219240.0,
19: 220440.0,
20: 240240.0,
21: 243240.0,
22: 253440.0,
23: 259286.4,
24: 266594.4,
25: 269517.60000000003,
26: 270561.60000000003,
27: 273561.60000000003,
28: 277881.60000000003,
29: 286872.96,
30: 287133.96,
31: 288003.96,
32: 307803.96,
33: 324288.12,
34: 328783.8,
35: 336776.12,
36: 339172.92,
37: 358972.92,
38: 378772.92,
39: 398572.92,
40: 418372.92,
41: 429877.56,
42: 438837.56,
43: 442757.56,
44: 448357.56,
45: 456757.56,
46: 466837.56,
47: 485317.56,
48: 492037.56,
49: 506557.56,
50: 523277.56,
51: 539117.56,
52: 554957.56,
53: 560157.56,
54: 19800.0,
55: 39600.0,
56: 59400.0,
57: 64600.0,
58: 72400.0,
59: 84400.0,
60: 96400.0,
61: 116200.0,
62: 124000.0,
63: 143800.0,
64: 163600.0,
65: 194800.0,
66: 214600.0,
67: 234400.0,
68: 254200.0,
69: 274000.0,
70: 280000.0,
71: 286000.0,
72: 305800.0,
73: 325600.0},
'kg_cum_year_week': {0: 560157.56,
1: 560157.56,
2: 560157.56,
3: 560157.56,
4: 560157.56,
5: 560157.56,
6: 560157.56,
7: 560157.56,
8: 560157.56,
9: 560157.56,
10: 560157.56,
11: 560157.56,
12: 560157.56,
13: 560157.56,
14: 560157.56,
15: 560157.56,
16: 560157.56,
17: 560157.56,
18: 560157.56,
19: 560157.56,
20: 560157.56,
21: 560157.56,
22: 560157.56,
23: 560157.56,
24: 560157.56,
25: 560157.56,
26: 560157.56,
27: 560157.56,
28: 560157.56,
29: 560157.56,
30: 560157.56,
31: 560157.56,
32: 560157.56,
33: 560157.56,
34: 560157.56,
35: 560157.56,
36: 560157.56,
37: 560157.56,
38: 560157.56,
39: 560157.56,
40: 560157.56,
41: 560157.56,
42: 560157.56,
43: 560157.56,
44: 560157.56,
45: 560157.56,
46: 560157.56,
47: 560157.56,
48: 560157.56,
49: 560157.56,
50: 560157.56,
51: 560157.56,
52: 560157.56,
53: 560157.56,
54: 1567901.1299999997,
55: 1567901.1299999997,
56: 1567901.1299999997,
57: 1567901.1299999997,
58: 1567901.1299999997,
59: 1567901.1299999997,
60: 1567901.1299999997,
61: 1567901.1299999997,
62: 1567901.1299999997,
63: 1567901.1299999997,
64: 1567901.1299999997,
65: 1567901.1299999997,
66: 1567901.1299999997,
67: 1567901.1299999997,
68: 1567901.1299999997,
69: 1567901.1299999997,
70: 1567901.1299999997,
71: 1567901.1299999997,
72: 1567901.1299999997,
73: 1567901.1299999997},
'kg_cum_year_week_20%': {0: 112031.51200000002,
1: 112031.51200000002,
2: 112031.51200000002,
3: 112031.51200000002,
4: 112031.51200000002,
5: 112031.51200000002,
6: 112031.51200000002,
7: 112031.51200000002,
8: 112031.51200000002,
9: 112031.51200000002,
10: 112031.51200000002,
11: 112031.51200000002,
12: 112031.51200000002,
13: 112031.51200000002,
14: 112031.51200000002,
15: 112031.51200000002,
16: 112031.51200000002,
17: 112031.51200000002,
18: 112031.51200000002,
19: 112031.51200000002,
20: 112031.51200000002,
21: 112031.51200000002,
22: 112031.51200000002,
23: 112031.51200000002,
24: 112031.51200000002,
25: 112031.51200000002,
26: 112031.51200000002,
27: 112031.51200000002,
28: 112031.51200000002,
29: 112031.51200000002,
30: 112031.51200000002,
31: 112031.51200000002,
32: 112031.51200000002,
33: 112031.51200000002,
34: 112031.51200000002,
35: 112031.51200000002,
36: 112031.51200000002,
37: 112031.51200000002,
38: 112031.51200000002,
39: 112031.51200000002,
40: 112031.51200000002,
41: 112031.51200000002,
42: 112031.51200000002,
43: 112031.51200000002,
44: 112031.51200000002,
45: 112031.51200000002,
46: 112031.51200000002,
47: 112031.51200000002,
48: 112031.51200000002,
49: 112031.51200000002,
50: 112031.51200000002,
51: 112031.51200000002,
52: 112031.51200000002,
53: 112031.51200000002,
54: 313580.22599999997,
55: 313580.22599999997,
56: 313580.22599999997,
57: 313580.22599999997,
58: 313580.22599999997,
59: 313580.22599999997,
60: 313580.22599999997,
61: 313580.22599999997,
62: 313580.22599999997,
63: 313580.22599999997,
64: 313580.22599999997,
65: 313580.22599999997,
66: 313580.22599999997,
67: 313580.22599999997,
68: 313580.22599999997,
69: 313580.22599999997,
70: 313580.22599999997,
71: 313580.22599999997,
72: 313580.22599999997,
73: 313580.22599999997},
'turnover': {0: 29441.4912,
1: 1052.478,
2: 5596.16928,
3: 9169.953,
4: 17830.058399999998,
5: 17830.058399999998,
6: 7282.5984,
7: 6675.7152,
8: 9137.448,
9: 9137.448,
10: 13997.7432,
11: 4665.914400000001,
12: 22007.1456,
13: 1333.7664,
14: 22007.1456,
15: 4014.9792,
16: 3482.7960000000003,
17: 22997.1456,
18: 7665.715200000001,
19: 1393.7664,
20: 23580.4536,
21: 3574.416,
22: 12395.774399999998,
23: 7108.066559999971,
24: 8885.083199999965,
25: 3728.4576000000116,
26: 1332.0200000000002,
27: 3934.4159999999997,
28: 5827.7609999999995,
29: 12132.751680000034,
30: 358.06620000000066,
31: 1194.6880000000006,
32: 27273.9456,
33: 22989.04607999992,
34: 6269.739839999978,
35: 11178.908160000032,
36: 3394.0087999999923,
37: 28842.2244,
38: 28842.2244,
39: 28842.2244,
40: 28842.2244,
41: 16833.546239999967,
42: 13266.516479999998,
43: 5804.10096,
44: 8291.5728,
45: 12605.359199999999,
46: 15126.43104,
47: 27731.79024,
48: 10084.28736,
49: 22011.07128,
50: 25346.08208,
51: 24015.530880000002,
52: 24015.530880000002,
53: 7885.0356,
54: 20983.347,
55: 21316.1454,
56: 21325.887,
57: 5703.4744,
58: 8555.2116,
59: 13182.696,
60: 13188.18,
61: 21760.497000000003,
62: 8572.317000000001,
63: 21760.497000000003,
64: 21760.497000000003,
65: 34289.268000000004,
66: 21790.0584,
67: 21790.0584,
68: 21790.0584,
69: 21790.0584,
70: 6785.592000000001,
71: 6811.6320000000005,
72: 22719.945600000003,
73: 22719.945600000003},
'new_turnover': {0: 29441.4912,
1: 30493.9692,
2: 36090.13848,
3: 45260.09148,
4: 63090.14988,
5: 80920.20827999999,
6: 88202.80668,
7: 94878.52188,
8: 104015.96988,
9: nan,
10: nan,
11: nan,
12: nan,
13: nan,
14: nan,
15: nan,
16: nan,
17: nan,
18: nan,
19: nan,
20: nan,
21: nan,
22: nan,
23: nan,
24: nan,
25: nan,
26: nan,
27: nan,
28: nan,
29: nan,
30: nan,
31: nan,
32: nan,
33: nan,
34: nan,
35: nan,
36: nan,
37: nan,
38: nan,
39: nan,
40: nan,
41: nan,
42: nan,
43: nan,
44: nan,
45: nan,
46: nan,
47: nan,
48: nan,
49: nan,
50: nan,
51: nan,
52: nan,
53: nan,
54: 124999.31688,
55: 146315.46228,
56: 167641.34928,
57: 173344.82368,
58: 181900.03528,
59: 195082.73128,
60: 208270.91128,
61: 230031.40828,
62: 238603.72528,
63: 260364.22228000002,
64: 282124.71928,
65: 316413.98728,
66: 338204.04568,
67: 359994.10407999996,
68: 381784.16247999994,
69: 403574.2208799999,
70: 410359.8128799999,
71: 417171.4448799999,
72: nan,
73: nan}}
IIUC，这是你要找的输出，lmk:
new_df = df_sorted.groupby('year_week')['turnover'].cumsum()
df_sorted['new_turnover'] = new_df[new_df < df_sorted['kg_cum_year_week_20%']]
输出：
orderline year_week kg_orderline Price kg_cum kg_cum_year_week kg_cum_year_week_20% turnover turnover_20% new_turnover
0 1418863 2019-01 39600.00 0.743472 39600.00 560157.56 112031.512 29441.49120 29441.49120 29441.49120
1 1418860 2019-01 1200.00 0.877065 40800.00 560157.56 112031.512 1052.47800 30493.96920 30493.96920
2 1418718 2019-01 6240.00 0.896822 47040.00 560157.56 112031.512 5596.16928 36090.13848 36090.13848
3 1418864 2019-01 10200.00 0.899015 57240.00 560157.56 112031.512 9169.95300 45260.09148 45260.09148
4 1418745 2019-01 19800.00 0.900508 77040.00 560157.56 112031.512 17830.05840 63090.14988 63090.14988
5 1418862 2019-01 19800.00 0.900508 96840.00 560157.56 112031.512 17830.05840 80920.20828 80920.20828
6 1418707 2019-01 7200.00 1.011472 104040.00 560157.56 112031.512 7282.59840 88202.80668 88202.80668
7 1418738 2019-01 6600.00 1.011472 110640.00 560157.56 112031.512 6675.71520 94878.52188 94878.52188
8 1418699 2019-01 9000.00 1.015272 119640.00 560157.56 112031.512 9137.44800 NaN 104015.96988
9 1418853 2019-01 9000.00 1.015272 128640.00 560157.56 112031.512 9137.44800 NaN NaN
10 1418722 2019-01 12600.00 1.110932 141240.00 560157.56 112031.512 13997.74320 NaN NaN
11 1418739 2019-01 4200.00 1.110932 145440.00 560157.56 112031.512 4665.91440 NaN NaN
12 1418724 2019-01 19800.00 1.111472 165240.00 560157.56 112031.512 22007.14560 NaN NaN
13 1418763 2019-01 1200.00 1.111472 166440.00 560157.56 112031.512 1333.76640 NaN NaN
14 1418876 2019-01 19800.00 1.111472 186240.00 560157.56 112031.512 22007.14560 NaN NaN
15 1418851 2019-01 3600.00 1.115272 189840.00 560157.56 112031.512 4014.97920 NaN NaN
16 1418761 2019-01 3000.00 1.160932 192840.00 560157.56 112031.512 3482.79600 NaN NaN
17 1418710 2019-01 19800.00 1.161472 212640.00 560157.56 112031.512 22997.14560 NaN NaN
18 1418777 2019-01 6600.00 1.161472 219240.00 560157.56 112031.512 7665.71520 NaN NaN
19 1418903 2019-01 1200.00 1.161472 220440.00 560157.56 112031.512 1393.76640 NaN NaN
20 1418902 2019-01 19800.00 1.190932 240240.00 560157.56 112031.512 23580.45360 NaN NaN
21 1418901 2019-01 3000.00 1.191472 243240.00 560157.56 112031.512 3574.41600 NaN NaN
22 1418852 2019-01 10200.00 1.215272 253440.00 560157.56 112031.512 12395.77440 NaN NaN
23 1418703 2019-01 5846.40 1.215802 259286.40 560157.56 112031.512 7108.06656 NaN NaN
24 1418858 2019-01 7308.00 1.215802 266594.40 560157.56 112031.512 8885.08320 NaN NaN
25 1418702 2019-01 2923.20 1.275471 269517.60 560157.56 112031.512 3728.45760 NaN NaN
26 1418857 2019-01 1044.00 1.275881 270561.60 560157.56 112031.512 1332.02000 NaN NaN
27 1418896 2019-01 3000.00 1.311472 273561.60 560157.56 112031.512 3934.41600 NaN NaN
28 1418781 2019-01 4320.00 1.349019 277881.60 560157.56 112031.512 5827.76100 NaN NaN
29 1418741 2019-01 8991.36 1.349379 286872.96 560157.56 112031.512 12132.75168 NaN NaN
30 1418883 2019-01 261.00 1.371901 287133.96 560157.56 112031.512 358.06620 NaN NaN
31 1418740 2019-01 870.00 1.373205 288003.96 560157.56 112031.512 1194.68800 NaN NaN
32 1418734 2019-01 19800.00 1.377472 307803.96 560157.56 112031.512 27273.94560 NaN NaN
33 1418753 2019-01 16484.16 1.394614 324288.12 560157.56 112031.512 22989.04608 NaN NaN
34 1418890 2019-01 4495.68 1.394614 328783.80 560157.56 112031.512 6269.73984 NaN NaN
35 1418726 2019-01 7992.32 1.398706 336776.12 560157.56 112031.512 11178.90816 NaN NaN
36 1418882 2019-01 2396.80 1.416058 339172.92 560157.56 112031.512 3394.00880 NaN NaN
37 1418744 2019-01 19800.00 1.456678 358972.92 560157.56 112031.512 28842.22440 NaN NaN
38 1418752 2019-01 19800.00 1.456678 378772.92 560157.56 112031.512 28842.22440 NaN NaN
39 1418885 2019-01 19800.00 1.456678 398572.92 560157.56 112031.512 28842.22440 NaN NaN
40 1418894 2019-01 19800.00 1.456678 418372.92 560157.56 112031.512 28842.22440 NaN NaN
41 1418889 2019-01 11504.64 1.463196 429877.56 560157.56 112031.512 16833.54624 NaN NaN
42 1418749 2019-01 8960.00 1.480638 438837.56 560157.56 112031.512 13266.51648 NaN NaN
43 1418879 2019-01 3920.00 1.480638 442757.56 560157.56 112031.512 5804.10096 NaN NaN
44 1418886 2019-01 5600.00 1.480638 448357.56 560157.56 112031.512 8291.57280 NaN NaN
45 1418729 2019-01 8400.00 1.500638 456757.56 560157.56 112031.512 12605.35920 NaN NaN
46 1418732 2019-01 10080.00 1.500638 466837.56 560157.56 112031.512 15126.43104 NaN NaN
47 1418751 2019-01 18480.00 1.500638 485317.56 560157.56 112031.512 27731.79024 NaN NaN
48 1418895 2019-01 6720.00 1.500638 492037.56 560157.56 112031.512 10084.28736 NaN NaN
49 1418730 2019-01 14520.00 1.515914 506557.56 560157.56 112031.512 22011.07128 NaN NaN
50 1418878 2019-01 16720.00 1.515914 523277.56 560157.56 112031.512 25346.08208 NaN NaN
51 1418694 2019-01 15840.00 1.516132 539117.56 560157.56 112031.512 24015.53088 NaN NaN
52 1418849 2019-01 15840.00 1.516132 554957.56 560157.56 112031.512 24015.53088 NaN NaN
53 1418717 2019-01 5200.00 1.516353 560157.56 560157.56 112031.512 7885.03560 NaN NaN
54 1419013 2019-02 19800.00 1.059765 19800.00 1567901.13 313580.226 20983.34700 115861.86888 20983.34700
55 1418941 2019-02 19800.00 1.076573 39600.00 1567901.13 313580.226 21316.14540 137178.01428 42299.49240
56 1419015 2019-02 19800.00 1.077065 59400.00 1567901.13 313580.226 21325.88700 158503.90128 63625.37940
57 1418926 2019-02 5200.00 1.096822 64600.00 1567901.13 313580.226 5703.47440 164207.37568 69328.85380
58 1419194 2019-02 7800.00 1.096822 72400.00 1567901.13 313580.226 8555.21160 172762.58728 77884.06540
59 1418979 2019-02 12000.00 1.098558 84400.00 1567901.13 313580.226 13182.69600 185945.28328 91066.76140
60 1419014 2019-02 12000.00 1.099015 96400.00 1567901.13 313580.226 13188.18000 199133.46328 104254.94140
61 1419060 2019-02 19800.00 1.099015 116200.00 1567901.13 313580.226 21760.49700 220893.96028 126015.43840
62 1419063 2019-02 7800.00 1.099015 124000.00 1567901.13 313580.226 8572.31700 229466.27728 134587.75540
63 1419172 2019-02 19800.00 1.099015 143800.00 1567901.13 313580.226 21760.49700 251226.77428 156348.25240
64 1419217 2019-02 19800.00 1.099015 163600.00 1567901.13 313580.226 21760.49700 272987.27128 178108.74940
65 1419368 2019-02 31200.00 1.099015 194800.00 1567901.13 313580.226 34289.26800 307276.53928 212398.01740
66 1418965 2019-02 19800.00 1.100508 214600.00 1567901.13 313580.226 21790.05840 329066.59768 234188.07580
67 1419085 2019-02 19800.00 1.100508 234400.00 1567901.13 313580.226 21790.05840 350856.65608 255978.13420
68 1419216 2019-02 19800.00 1.100508 254200.00 1567901.13 313580.226 21790.05840 372646.71448 277768.19260
69 1419329 2019-02 19800.00 1.100508 274000.00 1567901.13 313580.226 21790.05840 394436.77288 299558.25100
70 1418917 2019-02 6000.00 1.130932 280000.00 1567901.13 313580.226 6785.59200 401222.36488 306343.84300
71 1419109 2019-02 6000.00 1.135272 286000.00 1567901.13 313580.226 6811.63200 408033.99688 313155.47500
72 1418930 2019-02 19800.00 1.147472 305800.00 1567901.13 313580.226 22719.94560 430753.94248 NaN
73 1419075 2019-02 19800.00 1.147472 325600.00 1567901.13 313580.226 22719.94560 NaN NaN
74 1419349 2019-02 19800.00 1.147472 345400.00 1567901.13 313580.226 22719.94560 NaN NaN
我有一个名为 df_sorted 的数据框（如下图所示）。
我需要的是df_sorted['turnover']按df_sorted['year_week']分组的累加和满足以下条件：
df_sorted['kg_cum'] < df_sorted['kg_cum_year_week_20%']
根据图片答案应该是成交额104012左右
由于我对 python 比较陌生，我很想听听如何解决这个问题。
如果以上内容不够清楚，请随时询问更多信息。
这里的数据为字典：
{'orderline': {0: 1418863,
1: 1418860,
2: 1418718,
3: 1418864,
4: 1418745,
5: 1418862,
6: 1418707,
7: 1418738,
8: 1418699,
9: 1418853,
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11: 1418739,
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14: 1418876,
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22: 1418852,
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24: 1418858,
25: 1418702,
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39: 1418885,
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43: 1418879,
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47: 1418751,
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49: 1418730,
50: 1418878,
51: 1418694,
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53: 1418717,
54: 1419013,
55: 1418941,
56: 1419015,
57: 1418926,
58: 1419194,
59: 1418979,
60: 1419014,
61: 1419060,
62: 1419063,
63: 1419172,
64: 1419217,
65: 1419368,
66: 1418965,
67: 1419085,
68: 1419216,
69: 1419329,
70: 1418917,
71: 1419109,
72: 1418930,
73: 1419075},
'year_week': {0: '2019-01',
1: '2019-01',
2: '2019-01',
3: '2019-01',
4: '2019-01',
5: '2019-01',
6: '2019-01',
7: '2019-01',
8: '2019-01',
9: '2019-01',
10: '2019-01',
11: '2019-01',
12: '2019-01',
13: '2019-01',
14: '2019-01',
15: '2019-01',
16: '2019-01',
17: '2019-01',
18: '2019-01',
19: '2019-01',
20: '2019-01',
21: '2019-01',
22: '2019-01',
23: '2019-01',
24: '2019-01',
25: '2019-01',
26: '2019-01',
27: '2019-01',
28: '2019-01',
29: '2019-01',
30: '2019-01',
31: '2019-01',
32: '2019-01',
33: '2019-01',
34: '2019-01',
35: '2019-01',
36: '2019-01',
37: '2019-01',
38: '2019-01',
39: '2019-01',
40: '2019-01',
41: '2019-01',
42: '2019-01',
43: '2019-01',
44: '2019-01',
45: '2019-01',
46: '2019-01',
47: '2019-01',
48: '2019-01',
49: '2019-01',
50: '2019-01',
51: '2019-01',
52: '2019-01',
53: '2019-01',
54: '2019-02',
55: '2019-02',
56: '2019-02',
57: '2019-02',
58: '2019-02',
59: '2019-02',
60: '2019-02',
61: '2019-02',
62: '2019-02',
63: '2019-02',
64: '2019-02',
65: '2019-02',
66: '2019-02',
67: '2019-02',
68: '2019-02',
69: '2019-02',
70: '2019-02',
71: '2019-02',
72: '2019-02',
73: '2019-02'},
'kg_orderline': {0: 39600.0,
1: 1200.0,
2: 6240.0,
3: 10200.0,
4: 19800.0,
5: 19800.0,
6: 7200.0,
7: 6600.0,
8: 9000.0,
9: 9000.0,
10: 12600.0,
11: 4200.0,
12: 19800.0,
13: 1200.0,
14: 19800.0,
15: 3600.0,
16: 3000.0,
17: 19800.0,
18: 6600.0,
19: 1200.0,
20: 19800.0,
21: 3000.0,
22: 10200.0,
23: 5846.4,
24: 7308.0,
25: 2923.2,
26: 1044.0,
27: 3000.0,
28: 4320.0,
29: 8991.36,
30: 261.0,
31: 870.0,
32: 19800.0,
33: 16484.16,
34: 4495.68,
35: 7992.32,
36: 2396.8,
37: 19800.0,
38: 19800.0,
39: 19800.0,
40: 19800.0,
41: 11504.64,
42: 8960.0,
43: 3920.0,
44: 5600.0,
45: 8400.0,
46: 10080.0,
47: 18480.0,
48: 6720.0,
49: 14520.0,
50: 16720.0,
51: 15840.0,
52: 15840.0,
53: 5200.0,
54: 19800.0,
55: 19800.0,
56: 19800.0,
57: 5200.0,
58: 7800.0,
59: 12000.0,
60: 12000.0,
61: 19800.0,
62: 7800.0,
63: 19800.0,
64: 19800.0,
65: 31200.0,
66: 19800.0,
67: 19800.0,
68: 19800.0,
69: 19800.0,
70: 6000.0,
71: 6000.0,
72: 19800.0,
73: 19800.0},
'Price': {0: 0.743472,
1: 0.877065,
2: 0.896822,
3: 0.899015,
4: 0.900508,
5: 0.900508,
6: 1.011472,
7: 1.011472,
8: 1.015272,
9: 1.015272,
10: 1.110932,
11: 1.110932,
12: 1.111472,
13: 1.111472,
14: 1.111472,
15: 1.115272,
16: 1.160932,
17: 1.161472,
18: 1.161472,
19: 1.161472,
20: 1.190932,
21: 1.191472,
22: 1.215272,
23: 1.21580229885057,
24: 1.21580229885057,
25: 1.27547126436782,
26: 1.27588122605364,
27: 1.311472,
28: 1.34901875,
29: 1.34937892376682,
30: 1.37190114942529,
31: 1.37320459770115,
32: 1.377472,
33: 1.39461434977578,
34: 1.39461434977578,
35: 1.39870627802691,
36: 1.41605841121495,
37: 1.456678,
38: 1.456678,
39: 1.456678,
40: 1.456678,
41: 1.46319626168224,
42: 1.480638,
43: 1.480638,
44: 1.480638,
45: 1.500638,
46: 1.500638,
47: 1.500638,
48: 1.500638,
49: 1.515914,
50: 1.515914,
51: 1.516132,
52: 1.516132,
53: 1.516353,
54: 1.059765,
55: 1.076573,
56: 1.077065,
57: 1.096822,
58: 1.096822,
59: 1.098558,
60: 1.099015,
61: 1.099015,
62: 1.099015,
63: 1.099015,
64: 1.099015,
65: 1.099015,
66: 1.100508,
67: 1.100508,
68: 1.100508,
69: 1.100508,
70: 1.130932,
71: 1.135272,
72: 1.147472,
73: 1.147472},
'kg_cum': {0: 39600.0,
1: 40800.0,
2: 47040.0,
3: 57240.0,
4: 77040.0,
5: 96840.0,
6: 104040.0,
7: 110640.0,
8: 119640.0,
9: 128640.0,
10: 141240.0,
11: 145440.0,
12: 165240.0,
13: 166440.0,
14: 186240.0,
15: 189840.0,
16: 192840.0,
17: 212640.0,
18: 219240.0,
19: 220440.0,
20: 240240.0,
21: 243240.0,
22: 253440.0,
23: 259286.4,
24: 266594.4,
25: 269517.60000000003,
26: 270561.60000000003,
27: 273561.60000000003,
28: 277881.60000000003,
29: 286872.96,
30: 287133.96,
31: 288003.96,
32: 307803.96,
33: 324288.12,
34: 328783.8,
35: 336776.12,
36: 339172.92,
37: 358972.92,
38: 378772.92,
39: 398572.92,
40: 418372.92,
41: 429877.56,
42: 438837.56,
43: 442757.56,
44: 448357.56,
45: 456757.56,
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48: 492037.56,
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52: 554957.56,
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54: 19800.0,
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56: 59400.0,
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60: 96400.0,
61: 116200.0,
62: 124000.0,
63: 143800.0,
64: 163600.0,
65: 194800.0,
66: 214600.0,
67: 234400.0,
68: 254200.0,
69: 274000.0,
70: 280000.0,
71: 286000.0,
72: 305800.0,
73: 325600.0},
'kg_cum_year_week': {0: 560157.56,
1: 560157.56,
2: 560157.56,
3: 560157.56,
4: 560157.56,
5: 560157.56,
6: 560157.56,
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48: 560157.56,
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50: 560157.56,
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52: 560157.56,
53: 560157.56,
54: 1567901.1299999997,
55: 1567901.1299999997,
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57: 1567901.1299999997,
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63: 1567901.1299999997,
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66: 1567901.1299999997,
67: 1567901.1299999997,
68: 1567901.1299999997,
69: 1567901.1299999997,
70: 1567901.1299999997,
71: 1567901.1299999997,
72: 1567901.1299999997,
73: 1567901.1299999997},
'kg_cum_year_week_20%': {0: 112031.51200000002,
1: 112031.51200000002,
2: 112031.51200000002,
3: 112031.51200000002,
4: 112031.51200000002,
5: 112031.51200000002,
6: 112031.51200000002,
7: 112031.51200000002,
8: 112031.51200000002,
9: 112031.51200000002,
10: 112031.51200000002,
11: 112031.51200000002,
12: 112031.51200000002,
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14: 112031.51200000002,
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70: 313580.22599999997,
71: 313580.22599999997,
72: 313580.22599999997,
73: 313580.22599999997},
'turnover': {0: 29441.4912,
1: 1052.478,
2: 5596.16928,
3: 9169.953,
4: 17830.058399999998,
5: 17830.058399999998,
6: 7282.5984,
7: 6675.7152,
8: 9137.448,
9: 9137.448,
10: 13997.7432,
11: 4665.914400000001,
12: 22007.1456,
13: 1333.7664,
14: 22007.1456,
15: 4014.9792,
16: 3482.7960000000003,
17: 22997.1456,
18: 7665.715200000001,
19: 1393.7664,
20: 23580.4536,
21: 3574.416,
22: 12395.774399999998,
23: 7108.066559999971,
24: 8885.083199999965,
25: 3728.4576000000116,
26: 1332.0200000000002,
27: 3934.4159999999997,
28: 5827.7609999999995,
29: 12132.751680000034,
30: 358.06620000000066,
31: 1194.6880000000006,
32: 27273.9456,
33: 22989.04607999992,
34: 6269.739839999978,
35: 11178.908160000032,
36: 3394.0087999999923,
37: 28842.2244,
38: 28842.2244,
39: 28842.2244,
40: 28842.2244,
41: 16833.546239999967,
42: 13266.516479999998,
43: 5804.10096,
44: 8291.5728,
45: 12605.359199999999,
46: 15126.43104,
47: 27731.79024,
48: 10084.28736,
49: 22011.07128,
50: 25346.08208,
51: 24015.530880000002,
52: 24015.530880000002,
53: 7885.0356,
54: 20983.347,
55: 21316.1454,
56: 21325.887,
57: 5703.4744,
58: 8555.2116,
59: 13182.696,
60: 13188.18,
61: 21760.497000000003,
62: 8572.317000000001,
63: 21760.497000000003,
64: 21760.497000000003,
65: 34289.268000000004,
66: 21790.0584,
67: 21790.0584,
68: 21790.0584,
69: 21790.0584,
70: 6785.592000000001,
71: 6811.6320000000005,
72: 22719.945600000003,
73: 22719.945600000003},
'new_turnover': {0: 29441.4912,
1: 30493.9692,
2: 36090.13848,
3: 45260.09148,
4: 63090.14988,
5: 80920.20827999999,
6: 88202.80668,
7: 94878.52188,
8: 104015.96988,
9: nan,
10: nan,
11: nan,
12: nan,
13: nan,
14: nan,
15: nan,
16: nan,
17: nan,
18: nan,
19: nan,
20: nan,
21: nan,
22: nan,
23: nan,
24: nan,
25: nan,
26: nan,
27: nan,
28: nan,
29: nan,
30: nan,
31: nan,
32: nan,
33: nan,
34: nan,
35: nan,
36: nan,
37: nan,
38: nan,
39: nan,
40: nan,
41: nan,
42: nan,
43: nan,
44: nan,
45: nan,
46: nan,
47: nan,
48: nan,
49: nan,
50: nan,
51: nan,
52: nan,
53: nan,
54: 124999.31688,
55: 146315.46228,
56: 167641.34928,
57: 173344.82368,
58: 181900.03528,
59: 195082.73128,
60: 208270.91128,
61: 230031.40828,
62: 238603.72528,
63: 260364.22228000002,
64: 282124.71928,
65: 316413.98728,
66: 338204.04568,
67: 359994.10407999996,
68: 381784.16247999994,
69: 403574.2208799999,
70: 410359.8128799999,
71: 417171.4448799999,
72: nan,
73: nan}}
IIUC，这是你要找的输出，lmk:
new_df = df_sorted.groupby('year_week')['turnover'].cumsum()
df_sorted['new_turnover'] = new_df[new_df < df_sorted['kg_cum_year_week_20%']]
输出：
orderline year_week kg_orderline Price kg_cum kg_cum_year_week kg_cum_year_week_20% turnover turnover_20% new_turnover
0 1418863 2019-01 39600.00 0.743472 39600.00 560157.56 112031.512 29441.49120 29441.49120 29441.49120
1 1418860 2019-01 1200.00 0.877065 40800.00 560157.56 112031.512 1052.47800 30493.96920 30493.96920
2 1418718 2019-01 6240.00 0.896822 47040.00 560157.56 112031.512 5596.16928 36090.13848 36090.13848
3 1418864 2019-01 10200.00 0.899015 57240.00 560157.56 112031.512 9169.95300 45260.09148 45260.09148
4 1418745 2019-01 19800.00 0.900508 77040.00 560157.56 112031.512 17830.05840 63090.14988 63090.14988
5 1418862 2019-01 19800.00 0.900508 96840.00 560157.56 112031.512 17830.05840 80920.20828 80920.20828
6 1418707 2019-01 7200.00 1.011472 104040.00 560157.56 112031.512 7282.59840 88202.80668 88202.80668
7 1418738 2019-01 6600.00 1.011472 110640.00 560157.56 112031.512 6675.71520 94878.52188 94878.52188
8 1418699 2019-01 9000.00 1.015272 119640.00 560157.56 112031.512 9137.44800 NaN 104015.96988
9 1418853 2019-01 9000.00 1.015272 128640.00 560157.56 112031.512 9137.44800 NaN NaN
10 1418722 2019-01 12600.00 1.110932 141240.00 560157.56 112031.512 13997.74320 NaN NaN
11 1418739 2019-01 4200.00 1.110932 145440.00 560157.56 112031.512 4665.91440 NaN NaN
12 1418724 2019-01 19800.00 1.111472 165240.00 560157.56 112031.512 22007.14560 NaN NaN
13 1418763 2019-01 1200.00 1.111472 166440.00 560157.56 112031.512 1333.76640 NaN NaN
14 1418876 2019-01 19800.00 1.111472 186240.00 560157.56 112031.512 22007.14560 NaN NaN
15 1418851 2019-01 3600.00 1.115272 189840.00 560157.56 112031.512 4014.97920 NaN NaN
16 1418761 2019-01 3000.00 1.160932 192840.00 560157.56 112031.512 3482.79600 NaN NaN
17 1418710 2019-01 19800.00 1.161472 212640.00 560157.56 112031.512 22997.14560 NaN NaN
18 1418777 2019-01 6600.00 1.161472 219240.00 560157.56 112031.512 7665.71520 NaN NaN
19 1418903 2019-01 1200.00 1.161472 220440.00 560157.56 112031.512 1393.76640 NaN NaN
20 1418902 2019-01 19800.00 1.190932 240240.00 560157.56 112031.512 23580.45360 NaN NaN
21 1418901 2019-01 3000.00 1.191472 243240.00 560157.56 112031.512 3574.41600 NaN NaN
22 1418852 2019-01 10200.00 1.215272 253440.00 560157.56 112031.512 12395.77440 NaN NaN
23 1418703 2019-01 5846.40 1.215802 259286.40 560157.56 112031.512 7108.06656 NaN NaN
24 1418858 2019-01 7308.00 1.215802 266594.40 560157.56 112031.512 8885.08320 NaN NaN
25 1418702 2019-01 2923.20 1.275471 269517.60 560157.56 112031.512 3728.45760 NaN NaN
26 1418857 2019-01 1044.00 1.275881 270561.60 560157.56 112031.512 1332.02000 NaN NaN
27 1418896 2019-01 3000.00 1.311472 273561.60 560157.56 112031.512 3934.41600 NaN NaN
28 1418781 2019-01 4320.00 1.349019 277881.60 560157.56 112031.512 5827.76100 NaN NaN
29 1418741 2019-01 8991.36 1.349379 286872.96 560157.56 112031.512 12132.75168 NaN NaN
30 1418883 2019-01 261.00 1.371901 287133.96 560157.56 112031.512 358.06620 NaN NaN
31 1418740 2019-01 870.00 1.373205 288003.96 560157.56 112031.512 1194.68800 NaN NaN
32 1418734 2019-01 19800.00 1.377472 307803.96 560157.56 112031.512 27273.94560 NaN NaN
33 1418753 2019-01 16484.16 1.394614 324288.12 560157.56 112031.512 22989.04608 NaN NaN
34 1418890 2019-01 4495.68 1.394614 328783.80 560157.56 112031.512 6269.73984 NaN NaN
35 1418726 2019-01 7992.32 1.398706 336776.12 560157.56 112031.512 11178.90816 NaN NaN
36 1418882 2019-01 2396.80 1.416058 339172.92 560157.56 112031.512 3394.00880 NaN NaN
37 1418744 2019-01 19800.00 1.456678 358972.92 560157.56 112031.512 28842.22440 NaN NaN
38 1418752 2019-01 19800.00 1.456678 378772.92 560157.56 112031.512 28842.22440 NaN NaN
39 1418885 2019-01 19800.00 1.456678 398572.92 560157.56 112031.512 28842.22440 NaN NaN
40 1418894 2019-01 19800.00 1.456678 418372.92 560157.56 112031.512 28842.22440 NaN NaN
41 1418889 2019-01 11504.64 1.463196 429877.56 560157.56 112031.512 16833.54624 NaN NaN
42 1418749 2019-01 8960.00 1.480638 438837.56 560157.56 112031.512 13266.51648 NaN NaN
43 1418879 2019-01 3920.00 1.480638 442757.56 560157.56 112031.512 5804.10096 NaN NaN
44 1418886 2019-01 5600.00 1.480638 448357.56 560157.56 112031.512 8291.57280 NaN NaN
45 1418729 2019-01 8400.00 1.500638 456757.56 560157.56 112031.512 12605.35920 NaN NaN
46 1418732 2019-01 10080.00 1.500638 466837.56 560157.56 112031.512 15126.43104 NaN NaN
47 1418751 2019-01 18480.00 1.500638 485317.56 560157.56 112031.512 27731.79024 NaN NaN
48 1418895 2019-01 6720.00 1.500638 492037.56 560157.56 112031.512 10084.28736 NaN NaN
49 1418730 2019-01 14520.00 1.515914 506557.56 560157.56 112031.512 22011.07128 NaN NaN
50 1418878 2019-01 16720.00 1.515914 523277.56 560157.56 112031.512 25346.08208 NaN NaN
51 1418694 2019-01 15840.00 1.516132 539117.56 560157.56 112031.512 24015.53088 NaN NaN
52 1418849 2019-01 15840.00 1.516132 554957.56 560157.56 112031.512 24015.53088 NaN NaN
53 1418717 2019-01 5200.00 1.516353 560157.56 560157.56 112031.512 7885.03560 NaN NaN
54 1419013 2019-02 19800.00 1.059765 19800.00 1567901.13 313580.226 20983.34700 115861.86888 20983.34700
55 1418941 2019-02 19800.00 1.076573 39600.00 1567901.13 313580.226 21316.14540 137178.01428 42299.49240
56 1419015 2019-02 19800.00 1.077065 59400.00 1567901.13 313580.226 21325.88700 158503.90128 63625.37940
57 1418926 2019-02 5200.00 1.096822 64600.00 1567901.13 313580.226 5703.47440 164207.37568 69328.85380
58 1419194 2019-02 7800.00 1.096822 72400.00 1567901.13 313580.226 8555.21160 172762.58728 77884.06540
59 1418979 2019-02 12000.00 1.098558 84400.00 1567901.13 313580.226 13182.69600 185945.28328 91066.76140
60 1419014 2019-02 12000.00 1.099015 96400.00 1567901.13 313580.226 13188.18000 199133.46328 104254.94140
61 1419060 2019-02 19800.00 1.099015 116200.00 1567901.13 313580.226 21760.49700 220893.96028 126015.43840
62 1419063 2019-02 7800.00 1.099015 124000.00 1567901.13 313580.226 8572.31700 229466.27728 134587.75540
63 1419172 2019-02 19800.00 1.099015 143800.00 1567901.13 313580.226 21760.49700 251226.77428 156348.25240
64 1419217 2019-02 19800.00 1.099015 163600.00 1567901.13 313580.226 21760.49700 272987.27128 178108.74940
65 1419368 2019-02 31200.00 1.099015 194800.00 1567901.13 313580.226 34289.26800 307276.53928 212398.01740
66 1418965 2019-02 19800.00 1.100508 214600.00 1567901.13 313580.226 21790.05840 329066.59768 234188.07580
67 1419085 2019-02 19800.00 1.100508 234400.00 1567901.13 313580.226 21790.05840 350856.65608 255978.13420
68 1419216 2019-02 19800.00 1.100508 254200.00 1567901.13 313580.226 21790.05840 372646.71448 277768.19260
69 1419329 2019-02 19800.00 1.100508 274000.00 1567901.13 313580.226 21790.05840 394436.77288 299558.25100
70 1418917 2019-02 6000.00 1.130932 280000.00 1567901.13 313580.226 6785.59200 401222.36488 306343.84300
71 1419109 2019-02 6000.00 1.135272 286000.00 1567901.13 313580.226 6811.63200 408033.99688 313155.47500
72 1418930 2019-02 19800.00 1.147472 305800.00 1567901.13 313580.226 22719.94560 430753.94248 NaN
73 1419075 2019-02 19800.00 1.147472 325600.00 1567901.13 313580.226 22719.94560 NaN NaN
74 1419349 2019-02 19800.00 1.147472 345400.00 1567901.13 313580.226 22719.94560 NaN NaN