NextJS error on start and build Assertion `(insertion_info.second) == (true)' failed - npm

All of the sudden my NextJS app won't start or build, I get the following message:
npm[213732]: c:\ws\src\env-inl.h:1041: Assertion `(insertion_info.second) == (true)' failed.
1: 00007FF7A493052F napi_wrap+109311
2: 00007FF7A48D5256 v8::internal::OrderedHashTable<v8::internal::OrderedHashSet,1>::NumberOfElementsOffset+33302
3: 00007FF7A48D55D1 v8::internal::OrderedHashTable<v8::internal::OrderedHashSet,1>::NumberOfElementsOffset+34193
4: 00007FF7A494E69F node::AddEnvironmentCleanupHook+127
5: 00007FF7A48FD5A1 napi_add_env_cleanup_hook+49
6: 00007FFF90FE5057 napi_register_module_v1+8071
7: 00007FF7A48FD25B node_module_register+7275
8: 00007FF7A48F9AE0 node::Buffer::New+6352
9: 00007FF7A48FA873 node::Buffer::New+9827
10: 00007FF7A514E85F v8::internal::Builtins::builtin_handle+321471
11: 00007FF7A514DDF4 v8::internal::Builtins::builtin_handle+318804
12: 00007FF7A514E0E7 v8::internal::Builtins::builtin_handle+319559
13: 00007FF7A514DF33 v8::internal::Builtins::builtin_handle+319123
14: 00007FF7A522A0CD v8::internal::SetupIsolateDelegate::SetupHeap+464173
15: 00007FF7A51C29D2 v8::internal::SetupIsolateDelegate::SetupHeap+40498
16: 00007FF7A51C29D2 v8::internal::SetupIsolateDelegate::SetupHeap+40498
17: 00007FF7A51C29D2 v8::internal::SetupIsolateDelegate::SetupHeap+40498
18: 00007FF7A51C29D2 v8::internal::SetupIsolateDelegate::SetupHeap+40498
19: 00007FF7A51C29D2 v8::internal::SetupIsolateDelegate::SetupHeap+40498
20: 00007FF7A51C29D2 v8::internal::SetupIsolateDelegate::SetupHeap+40498
21: 00007FF7A51C29D2 v8::internal::SetupIsolateDelegate::SetupHeap+40498
22: 00007FF7A526A190 v8::internal::SetupIsolateDelegate::SetupHeap+726512
23: 00007FF7A51BE8DA v8::internal::SetupIsolateDelegate::SetupHeap+23866
24: 00007FF7A52A8473 v8::internal::SetupIsolateDelegate::SetupHeap+981203
25: 00007FF7A51C29D2 v8::internal::SetupIsolateDelegate::SetupHeap+40498
26: 00007FF7A51EF7C0 v8::internal::SetupIsolateDelegate::SetupHeap+224288
27: 00007FF7A526BBBE v8::internal::SetupIsolateDelegate::SetupHeap+733214
28: 00007FF7A51E293D v8::internal::SetupIsolateDelegate::SetupHeap+171421
29: 00007FF7A51C057C v8::internal::SetupIsolateDelegate::SetupHeap+31196
30: 00007FF7A509081F v8::internal::Execution::CallWasm+1839
31: 00007FF7A509092B v8::internal::Execution::CallWasm+2107
32: 00007FF7A509136A v8::internal::Execution::TryCall+378
33: 00007FF7A5071B85 v8::internal::MicrotaskQueue::RunMicrotasks+501
34: 00007FF7A50718E0 v8::internal::MicrotaskQueue::PerformCheckpoint+32
35: 00007FF7A49541C0 node::CallbackScope::~CallbackScope+672
36: 00007FF7A49545BB node::CallbackScope::~CallbackScope+1691
37: 00007FF7A4954A01 node::MakeCallback+209
38: 00007FF7A491F04E napi_wrap+38430
39: 00007FF7A49796C8 uv_check_init+120
40: 00007FF7A49842E8 uv_run+664
41: 00007FF7A4890255 v8::internal::OrderedHashTable<v8::internal::OrderedHashSet,1>::NumberOfBucketsOffset+9365
42: 00007FF7A49039B7 node::Start+311
43: 00007FF7A476686C RC4_options+339820
44: 00007FF7A570619C v8::internal::compiler::RepresentationChanger::Uint32OverflowOperatorFor+153532
45: 00007FF8741626BD BaseThreadInitThunk+29
46: 00007FF8759EDFB8 RtlUserThreadStart+40
npm ERR! code ELIFECYCLE
npm ERR! errno 1
npm ERR! my-app#0.1.0 dev: `next dev -p 5000`
npm ERR! Exit status 1
I even checked out main branch and removed all local changes (few content pages and one form that should hit API to send data) to get back to previous state that worked, but it still doesn't work. I am on Win11 machine and have Node server running normally.


I had the same problem. Just delete the ".next" directory and run "npm run dev" that it will work again.

Related

Number of instances in a list variable pandas

in my database I have an id (docdb_family_id) and a list of ids (cited_docdb_list) as follows:
{'docdb_family_id': {0: 3498148,
1: 3512921,
2: 3525647,
3: 3636418,
4: 3673165,
5: 3680127,
6: 3688953,
7: 3689983,
8: 3700898,
9: 3768731,
10: 3770463,
11: 3771404,
12: 3771425,
13: 3771495,
14: 3771604,
15: 3772274,
16: 3772510,
17: 3772940,
18: 3775109,
19: 3779413,
20: 3783583,
21: 3784332,
22: 3784469,
23: 3787179,
24: 3787982,
25: 3790639,
26: 3790670,
27: 3792458,
28: 3795015,
29: 3799670,
30: 3800683,
31: 3802132,
32: 3802281,
33: 3803326,
34: 3803728,
35: 3808684,
36: 3809416,
37: 3810114,
38: 3811389,
39: 3812435,
40: 3813073,
41: 3813312,
42: 3815934,
43: 3816821,
44: 3816927,
45: 3817424,
46: 3818542,
47: 3818766,
48: 3819057,
49: 3819335,
50: 3820633,
51: 3820694,
52: 3821540,
53: 3821838,
54: 3822049,
55: 3822089,
56: 3823057,
57: 3823114,
58: 3824187,
59: 3824375,
60: 3825785,
61: 3826171,
62: 3826211,
63: 3827560,
64: 3828464,
65: 3829519,
66: 3829990,
67: 3831455,
68: 3831510,
69: 3831784,
70: 3831999,
71: 3832248,
72: 3832987,
73: 3834046,
74: 3834444,
75: 3835251,
76: 3886195,
77: 3887480,
78: 3890389,
79: 3892024,
80: 3944218},
'cited_docdb_list': {0: '[3454392.0, 3489764.0, 3492286.0, 3802281.0, 3944218.0, 4161113.0, 6055754.0, 4167218.0, 6245259.0, 6310327.0, 6339325.0, 7865817.0, 10818295.0, 21820994.0, 25257112.0, 25333370.0, 25421470.0]',
1: '[22785397.0, 3800683.0]',
2: '[3508710.0, 3832248.0, 6015961.0, 9173676.0, 22615010.0]',
3: '[3482303.0, 3518675.0, 3688207.0, 3688953.0, 7856041.0, 9893906.0, 9911676.0, 21740142.0, 22095959.0, 22224845.0, 22455261.0, 22522023.0, 23039462.0, 23149018.0, 23248627.0, 25608484.0, 26145960.0, 26246393.0, 27122358.0, 27215945.0, 27267946.0, 27368911.0, 27535943.0, 27569239.0, 27759996.0, 34107815.0, 35219296.0, 46248356.0]',
4: '[7917626.0, 13587294.0, 15860525.0, 16099836.0, 18349663.0, 18831836.0, 24223941.0, 26558149.0]',
5: '[3680147.0, 3680169.0, 6442447.0, 8168860.0, 8170479.0, 8178540.0, 8178541.0, 10655404.0, 10764890.0, 10765687.0, 11600956.0, 14593411.0, 22296890.0, 22471622.0, 24169239.0, 24966171.0, 25033444.0, 25166841.0, 25372199.0, 25459000.0, 25533862.0, 25918313.0, 26371384.0, 26439834.0, 27274967.0, 27294655.0, 27523014.0]',
6: '[5459370.0, 16645542.0, 17462457.0, 21959571.0, 22010115.0, 22296144.0, 26927437.0, 33041169.0, 33101777.0, 34066530.0]',
7: '[7650618.0, 7806400.0, 7835575.0, 7857812.0, 8210353.0, 8232323.0, 8239494.0, 10024300.0, 11566936.0, 11637978.0, 11942149.0, 12192469.0, 12437164.0, 12474858.0, 12862377.0, 13357403.0, 13391145.0, 13884195.0, 14268316.0, 14780600.0, 14837681.0, 14959673.0, 15493334.0, 15660109.0, 15690908.0, 15706187.0, 15740492.0, 16185014.0, 16286275.0, 16301821.0, 16400795.0, 16599264.0, 16867936.0, 17017842.0, 17303135.0, 18156945.0, 18168645.0, 18351330.0, 18357701.0, 18361853.0, 18553020.0, 18665747.0, 22042028.0, 22509938.0, 22752953.0, 22752985.0, 22955054.0, 23605846.0, 23635250.0, 24042617.0, 24281660.0, 24426092.0, 24470177.0, 25217414.0, 25342266.0, 25399276.0, 25481652.0, 26026958.0, 26034429.0, 26150729.0, 26427482.0, 26488815.0, 26500234.0, 26537700.0, 26644976.0, 26692209.0, 26785282.0, 27339916.0, 27370666.0, 27372394.0, 27524906.0, 27563165.0, 29229947.0, 49274340.0]',
8: '[3764296.0, 3770459.0, 3773222.0, 3811210.0, 3825785.0, 6119308.0, 6262275.0, 6409776.0, 6450504.0, 6484157.0, 7640046.0, 7646955.0, 7762359.0, 7813503.0, 7823236.0, 7886063.0, 8103745.0, 10347742.0, 10563528.0, 11894269.0, 12556976.0, 12589238.0, 12666170.0, 12673679.0, 12702964.0, 13630878.0, 14026520.0, 14271281.0, 14325872.0, 14416179.0, 15383496.0, 15479503.0, 15920227.0, 16127226.0, 16222285.0, 16339588.0, 16871054.0, 16912938.0, 16912954.0, 16913656.0, 17401011.0, 17461197.0, 17474177.0, 17663812.0, 17724327.0, 18063449.0, 18227455.0, 18250669.0, 18386252.0, 18426307.0, 18587018.0, 18654484.0, 19300409.0, 19312456.0, 19372912.0, 19550439.0, 19638358.0, 19704233.0, 21801532.0, 21877403.0, 21974791.0, 22002267.0, 22067617.0, 22089128.0, 22098429.0, 22223747.0, 22276463.0, 22298327.0, 22341037.0, 22385483.0, 22395684.0, 22676560.0, 22731313.0, 22904054.0, 22918676.0, 23080548.0, 23084056.0, 23402016.0, 23516757.0, 23601888.0, 23628604.0, 23848237.0, 24030077.0, 24083853.0, 24132340.0, 24248118.0, 24251602.0, 24295241.0, 24316904.0, 24422851.0, 24429865.0, 24443752.0, 24547890.0, 24589548.0, 24632640.0, 24770649.0, 24785182.0, 24839047.0, 24962082.0, 25028009.0, 25378809.0, 25397848.0, 25410040.0, 25434196.0, 25449992.0, 25470970.0, 25494098.0, 25514405.0, 25525923.0, 25540364.0, 26040210.0, 26438189.0, 26450647.0, 26486031.0, 26707770.0, 26723069.0, 26723453.0, 26748272.0, 26870598.0, 26889379.0, 26889380.0, 26901249.0, 26985941.0, 26990011.0, 27000869.0, 27018916.0, 27025822.0, 27060755.0, 27060756.0, 27311622.0, 27315336.0, 27340467.0, 27569697.0, 37944191.0, 46149961.0, 46255262.0]',
9: '[8583594.0, 9119276.0, 21793982.0, 22133036.0, 24149220.0, 25776190.0, 26736757.0]',
10: '[10568655.0, 13302684.0, 19844775.0, 22493955.0, 26714695.0, 26997884.0]',
11: '[4344006.0, 24838031.0, 25098959.0, 25395637.0, 27025593.0]',
12: '[25642630.0, 25642846.0, 25642930.0, 26279148.0, 26287348.0]',
13: '[10451245.0, 10564358.0, 22491246.0, 24064440.0, 24279325.0, 24519613.0, 24651262.0, 25072503.0, 26461666.0, 26692304.0]',
14: '[4351264.0, 4384434.0, 6117960.0, 9116940.0, 10999954.0, 22148709.0, 22562211.0, 23862977.0, 24037344.0, 24361917.0, 24432647.0, 25076138.0, 26840072.0, 27429215.0]',
15: '[3692248.0, 6053171.0, 6226485.0, 12362875.0, 27371744.0]',
16: '[5933264.0, 6125219.0, 6247996.0, 10521070.0, 13063586.0, 15774983.0, 16803481.0, 16904934.0, 22065174.0, 27127184.0, 27496706.0, 27624793.0]',
17: '[3526456.0, 6170998.0, 6335295.0, 10505184.0, 11549684.0, 14422646.0, 15088415.0, 17645959.0, 22169836.0, 22901756.0, 22994874.0, 22994878.0, 23172874.0, 23925148.0, 25244507.0, 27389063.0]',
18: '[6350760.0, 20369026.0, 24216636.0, 26762272.0, 26927655.0, 27126594.0, 27371255.0]',
19: '[3775878.0, 6008063.0, 12812693.0, 13575794.0, 14790639.0, 22013262.0, 24622370.0, 26901485.0, 26985941.0, 27076644.0, 27112632.0]',
20: '[3775488.0, 10948289.0, 10952971.0, 10952974.0, 11367322.0, 12710129.0, 15469131.0, 22577881.0, 25644554.0, 26467182.0, 26933783.0, 27401801.0]',
21: '[6134715.0, 6350620.0, 15983939.0, 16269143.0, 17680987.0, 23994234.0, 24992672.0, 26268730.0, 26367621.0, 26629308.0, 26787837.0, 26988835.0, 27365620.0, 27455735.0, 27476152.0, 41508342.0]',
22: '[3690998.0, 3779413.0, 8103745.0, 10528617.0, 10533016.0, 14026520.0, 17474177.0, 21959397.0, 22069056.0, 23038428.0, 23077293.0, 24078130.0, 24160889.0, 25618055.0, 26462451.0, 27407332.0, 27569697.0]',
23: '[6512805.0, 8105738.0, 10680104.0, 10719170.0, 18290174.0, 22237701.0, 22290947.0, 23695912.0, 23765282.0, 24565635.0, 26289399.0, 27358491.0, 27420192.0]',
24: '[6462400.0, 16101703.0, 24045826.0, 25612324.0, 26283893.0, 26434155.0]',
25: '[8208100.0, 23566456.0, 23702554.0, 25266985.0, 26142859.0]',
26: '[3771632.0, 14240231.0, 15623240.0, 22486268.0, 23605938.0, 27170740.0]',
27: '[3798105.0, 46299235.0, 46299236.0, 46299237.0, 46299238.0, 46299740.0, 46299800.0]',
28: '[2631556.0, 2944019.0, 10790311.0, 13793711.0, 18470587.0, 21851951.0, 21924559.0, 23889759.0, 23927439.0, 23963011.0, 24766696.0, 26713651.0, 26990589.0, 27287227.0]',
29: '[3796218.0, 24589826.0, 25624390.0, 25765848.0]',
30: '[3772972.0, 6025591.0, 7764892.0, 12805981.0, 15547363.0, 16262273.0, 21905352.0, 22082762.0, 23922610.0, 23984212.0, 24257317.0, 25315731.0, 25402356.0, 25518280.0, 26719186.0, 26734227.0, 26940453.0, 26979759.0, 27025821.0, 27025822.0]',
31: '[3779080.0, 3794389.0, 9425562.0, 10768435.0, 22860582.0, 25471727.0, 25617513.0, 25620315.0, 25644721.0, 26092132.0, 27153345.0]',
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64: '[3775561.0, 3778209.0, 3780242.0, 3783251.0, 3784665.0, 3788774.0, 3798212.0, 3811858.0, 3812283.0, 21830878.0, 21921748.0, 21993829.0, 22457245.0, 22460889.0, 23262728.0, 23400964.0, 23566456.0, 24092138.0, 24403780.0, 25289929.0, 25369658.0, 25618677.0, 25619320.0, 25629177.0, 25634619.0, 25645458.0, 26901477.0, 27038338.0, 27156461.0, 27158001.0, 27372667.0, 27391046.0, 27503418.0, 27537075.0]',
65: '[3769083.0, 3838826.0, 6518919.0, 7655380.0, 7671393.0, 9161974.0, 11933062.0, 12421582.0, 14111284.0, 15041555.0, 17038380.0, 17934524.0, 17951479.0, 17951704.0, 18736765.0, 21855631.0, 22254687.0, 22522730.0, 22525819.0, 22654614.0, 23072375.0, 23161341.0, 23682934.0, 23928270.0, 24002481.0, 25012845.0, 25464571.0, 25530090.0, 25936857.0, 26407346.0, 26861077.0, 41210539.0]',
66: '[3771617.0, 3807056.0, 8167498.0, 9489516.0, 13059819.0, 15236705.0, 17288890.0, 18106562.0, 18243976.0, 19449212.0, 19549705.0, 20360746.0, 21950670.0, 22523056.0, 22590937.0, 22822082.0, 22985088.0, 23085669.0, 23264894.0, 23454885.0, 23791789.0, 24158232.0, 24239892.0, 24257894.0, 24280874.0, 24434788.0, 24953310.0, 24990933.0, 25037706.0, 26312302.0, 26461656.0, 26569604.0, 26755930.0, 26802300.0, 26860472.0, 26891244.0, 26998345.0, 27036330.0, 27157297.0, 27377463.0]',
67: '[8223754.0, 21700957.0, 22248239.0, 24188773.0, 25199790.0, 25489601.0, 27370550.0]',
68: '[3824061.0, 10778962.0, 27157905.0]',
69: '[3885448.0, 4265687.0, 6453737.0, 15055174.0, 21588115.0, 22803210.0, 22810531.0, 22830406.0, 23778134.0, 23779509.0, 26598222.0, 27395145.0, 27536489.0]',
70: '[3817251.0, 3824297.0, 11604215.0, 13348182.0, 15295862.0, 17007082.0, 19729972.0, 19731450.0, 22867664.0, 23356034.0, 24169834.0, 25375270.0, 26970267.0, 27553681.0, 31500731.0, 31500732.0, 35705261.0]',
71: '[5931149.0, 19811894.0, 19812444.0, 22378265.0, 22409405.0, 23400964.0, 24164668.0, 25377816.0, 25484442.0, 26737825.0, 27395052.0, 27403058.0, 27517636.0]',
72: '[3772180.0, 4094759.0, 4099701.0, 4109923.0, 21758734.0, 22489510.0, 22802791.0, 23109074.0, 23332890.0, 23945495.0, 25404671.0, 26988331.0]',
73: '[22556333.0, 23537378.0, 23653584.0, 26050881.0, 26840895.0, 26877180.0, 27462050.0, 27463470.0]',
74: '[3775845.0, 24206625.0]',
75: '[4064369.0, 4172630.0, 8512849.0, 8513675.0, 10827902.0, 22681078.0, 24186095.0, 24990003.0, 26677157.0]',
76: '[4215108.0, 5754390.0, 6381956.0, 9309964.0, 13707851.0, 22117877.0]',
77: '[10969359.0, 11059344.0, 17714515.0, 19284446.0, 22690303.0, 26320567.0, 26415947.0]',
78: '[3888446.0, 3888996.0, 14727195.0, 22113364.0, 22782837.0, 25044309.0, 25167905.0, 26670443.0]',
79: '[3887054.0, 3889614.0, 3890522.0, 9303701.0, 9484895.0, 11363415.0, 14241244.0, 15291648.0, 16966026.0, 23250732.0, 24016081.0, 24393431.0, 24563127.0, 24788233.0, 25941613.0, 26366102.0, 27392409.0]',
80: '[27415886.0]'}}
within the list cited_docdb_list, however, there are ids that do not appear id docdb_family_id. What I would like to do is to detect the number of ids within cited_docdb_list which also appear in docdb_family_id. Is there a way to do so? My df is very large actually (almost 700000 observations). Please notice that the type of docdb_family_id and cited_docdb_list differs in the data.
The expected outcome, for instance for the first couple of docdb_family_ids should be:
docdb_family_id nb_included
3498148, 2
3512921, 1
...
where 3498148, 2 comes from the fact that the cited_docdb_list related to 3498148 cites 2 indices that appear in docdb_family_id, namely 3802281 and 3944218. In the same fashion, 3512921 cites 3800683 within cited_docdb_list.
Thank you

First idea is test intersection of sets with converted lists of strings to list of integers and get length of sets for nb_included:
import ast
df['cited_docdb_list'] = df['cited_docdb_list'].apply(ast.literal_eval)
sets = set(df['docdb_family_id'])
df['nb_included']=[len(set(map(int,x)).intersection(sets)) for x in df['cited_docdb_list']]
print (df)
docdb_family_id cited_docdb_list \
0 3498148 [3454392.0, 3489764.0, 3492286.0, 3802281.0, 3...
1 3512921 [22785397.0, 3800683.0]
2 3525647 [3508710.0, 3832248.0, 6015961.0, 9173676.0, 2...
3 3636418 [3482303.0, 3518675.0, 3688207.0, 3688953.0, 7...
4 3673165 [7917626.0, 13587294.0, 15860525.0, 16099836.0...
.. ... ...
76 3886195 [4215108.0, 5754390.0, 6381956.0, 9309964.0, 1...
77 3887480 [10969359.0, 11059344.0, 17714515.0, 19284446....
78 3890389 [3888446.0, 3888996.0, 14727195.0, 22113364.0,...
79 3892024 [3887054.0, 3889614.0, 3890522.0, 9303701.0, 9...
80 3944218 [27415886.0]
nb_included
0 2
1 1
2 1
3 1
4 0
.. ...
76 0
77 0
78 0
79 0
80 0
[81 rows x 3 columns]
Pandas solution with DataFrame.explode and Series.isin for test membership, last for count Trues aggregate sum:
df = (df.assign(cited_docdb_list = df['cited_docdb_list'].apply(ast.literal_eval))
.explode('cited_docdb_list')
.astype({'cited_docdb_list':int})
.assign(nb_included=lambda x: x['cited_docdb_list'].isin(x['docdb_family_id']))
.groupby('docdb_family_id', as_index=False)['nb_included']
.sum())
print (df)
docdb_family_id nb_included
0 3498148 2
1 3512921 1
2 3525647 1
3 3636418 1
4 3673165 0
.. ... ...
76 3886195 0
77 3887480 0
78 3890389 0
79 3892024 0
80 3944218 0
[81 rows x 2 columns]

What exactly set(CMAKE_CXX_VISIBILITY_PRESET hidden) does with cmake?

I mean, I know that it's supposed to default everything that isn't explicitly exported as "default" as hidden, but when I try to use it (on a debug build) and compare builds of the shared library with it and without it, the output of
readelf -Ws --dyn-syms libLibrary.so
is exactly the same, with everything having default visibility. What can I be missing?
This option comes with GenerateExternalHeader module from its example but I don't want to just mindlessly set flags because the (often incorrect) documentation tells me to. And in this case I cannot understand what this option even does if anything.

Adds -fvisibility=hidden to command line arguments of GCC and clang.
Adds here: https://gitlab.kitware.com/cmake/cmake/-/blob/master/Source/cmLocalGenerator.cxx#L2200 .
Option comes from CMAKE_${LANG}_COMPILE_OPTIONS_VISIBILITY property:
/usr/share/cmake $ ag _COMPILE_OPTIONS_VISIBILITY
Modules/Compiler/AppleClang-CXX.cmake
15: set(CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY_INLINES_HIDDEN "-fvisibility-inlines-hidden")
Modules/Compiler/AppleClang-OBJCXX.cmake
13:set(CMAKE_OBJCXX_COMPILE_OPTIONS_VISIBILITY_INLINES_HIDDEN "-fvisibility-inlines-hidden")
Modules/Compiler/Clang-CXX.cmake
15: set(CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY_INLINES_HIDDEN "-fvisibility-inlines-hidden")
Modules/Compiler/Clang-HIP.cmake
9: set(CMAKE_HIP_COMPILE_OPTIONS_VISIBILITY_INLINES_HIDDEN "-fvisibility-inlines-hidden")
Modules/Compiler/Clang.cmake
36: set(CMAKE_${lang}_COMPILE_OPTIONS_VISIBILITY "-fvisibility=")
Modules/Compiler/GNU-CXX.cmake
17: set(CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY_INLINES_HIDDEN "-fno-keep-inline-dllexport")
21: set(CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY_INLINES_HIDDEN "-fvisibility-inlines-hidden")
Modules/Compiler/GNU-OBJCXX.cmake
14: set(CMAKE_OBJCXX_COMPILE_OPTIONS_VISIBILITY_INLINES_HIDDEN "-fvisibility-inlines-hidden")
Modules/Compiler/GNU.cmake
32: set(CMAKE_${lang}_COMPILE_OPTIONS_VISIBILITY "-fvisibility=")
Modules/Compiler/IntelLLVM-CXX.cmake
23: set(CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY_INLINES_HIDDEN "-fvisibility-inlines-hidden")
Modules/Compiler/IntelLLVM.cmake
54: set(CMAKE_${lang}_COMPILE_OPTIONS_VISIBILITY "-fvisibility=")
Modules/Compiler/NVIDIA-CUDA.cmake
48: set(CMAKE_CUDA_COMPILE_OPTIONS_VISIBILITY -Xcompiler=-fvisibility=)
Modules/Compiler/QCC-CXX.cmake
15:set(CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY_INLINES_HIDDEN "-fvisibility-inlines-hidden")
Modules/Platform/AIX-GNU-CXX.cmake
3:unset(CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY_INLINES_HIDDEN)
Modules/Platform/AIX-GNU.cmake
20: unset(CMAKE_${lang}_COMPILE_OPTIONS_VISIBILITY)
Modules/Platform/Apple-Intel.cmake
17: set(CMAKE_${lang}_COMPILE_OPTIONS_VISIBILITY "-fvisibility=")
Modules/Platform/Apple-IntelLLVM.cmake
16: set(CMAKE_${lang}_COMPILE_OPTIONS_VISIBILITY "-fvisibility=")
Modules/Platform/HP-UX-GNU-CXX.cmake
3:unset(CMAKE_CXX_COMPILE_OPTIONS_VISIBILITY_INLINES_HIDDEN)
Modules/Platform/HP-UX-GNU.cmake
19: unset(CMAKE_${lang}_COMPILE_OPTIONS_VISIBILITY)
Modules/Platform/Linux-Fujitsu.cmake
14: set(CMAKE_${lang}_COMPILE_OPTIONS_VISIBILITY "-fvisibility=")
Modules/Platform/Linux-Intel.cmake
57: set(CMAKE_${lang}_COMPILE_OPTIONS_VISIBILITY "-fvisibility=")
Modules/Platform/Linux-IntelLLVM.cmake
54: set(CMAKE_${lang}_COMPILE_OPTIONS_VISIBILITY "-fvisibility=")

Using shift function along with max function Pandas

I am attempting to create a technical indicator ('Supertrend') using Pandas. The formula for this column is recursive.
(For people familiar with Pinescript, this column will replicate the result of this Pinescript function):
df['st_trendup'] = np.select(df['Close'].shift() > df['st_trendup'].shift(),df[['st_up','st_trendup'.shift()]].max(axis=1),df['st_up'])
The problem occurs in the true part of the np.select()because I cannot call .shift() on a string.
Normally, I would make a new column that uses .shift() beforehand but since this is recursive, I have to do it all in one line.
If possible I'd like to avoid using loops for speed; prefer solutions using native pandas or numpy functions.
What I am looking for
A way to find max function that can accomodate a .shift() call
Columns that are used:
def tr(high,low,close1):
return max(high - low, abs(high - close1), abs(low - close1))
df['st_closeprev'] = df['Close'].shift()
df['st_hl2'] = (df['High']+df['Low'])/2
df['st_tr'] = df.apply(lambda row: tr(row['High'],row['Low'],row['st_closeprev']),axis=1)
df['st_atr'] = df['st_tr'].ewm(alpha = 1/pd,adjust=False,min_periods=pd).mean()
df['st_up'] = df['st_hl2'] - factor * df['st_atr']
df['st_dn'] = df['st_hl2'] + factor * df['st_atr']
df['st_trendup'] = np.select(df['Close'].shift() > df['st_trendup'].shift(),df[['st_up','st_trendup'.shift()]].max(axis=1),df['st_up'])
Sample data obtained by the df.to_dict
{'Date': {0: Timestamp('2021-01-01 09:15:00'),
1: Timestamp('2021-01-01 09:30:00'),
2: Timestamp('2021-01-01 09:45:00'),
3: Timestamp('2021-01-01 10:00:00'),
4: Timestamp('2021-01-01 10:15:00'),
5: Timestamp('2021-01-01 10:30:00'),
6: Timestamp('2021-01-01 10:45:00'),
7: Timestamp('2021-01-01 11:00:00'),
8: Timestamp('2021-01-01 11:15:00'),
9: Timestamp('2021-01-01 11:30:00'),
10: Timestamp('2021-01-01 11:45:00'),
11: Timestamp('2021-01-01 12:00:00'),
12: Timestamp('2021-01-01 12:15:00'),
13: Timestamp('2021-01-01 12:30:00'),
14: Timestamp('2021-01-01 12:45:00'),
15: Timestamp('2021-01-01 13:00:00'),
16: Timestamp('2021-01-01 13:15:00'),
17: Timestamp('2021-01-01 13:30:00'),
18: Timestamp('2021-01-01 13:45:00'),
19: Timestamp('2021-01-01 14:00:00'),
20: Timestamp('2021-01-01 14:15:00'),
21: Timestamp('2021-01-01 14:30:00'),
22: Timestamp('2021-01-01 14:45:00'),
23: Timestamp('2021-01-01 15:00:00'),
24: Timestamp('2021-01-01 15:15:00'),
25: Timestamp('2021-01-04 09:15:00')},
'Open': {0: 31250.0,
1: 31376.0,
2: 31405.0,
3: 31389.4,
4: 31377.5,
5: 31347.8,
6: 31310.8,
7: 31343.4,
8: 31349.5,
9: 31349.9,
10: 31325.1,
11: 31310.9,
12: 31329.0,
13: 31376.0,
14: 31375.5,
15: 31357.4,
16: 31325.0,
17: 31341.1,
18: 31300.0,
19: 31324.5,
20: 31353.3,
21: 31350.0,
22: 31346.9,
23: 31330.0,
24: 31314.3,
25: 31450.2},
'High': {0: 31407.0,
1: 31425.0,
2: 31411.95,
3: 31389.45,
4: 31382.0,
5: 31350.0,
6: 31354.6,
7: 31359.0,
8: 31370.0,
9: 31364.7,
10: 31350.0,
11: 31337.9,
12: 31378.9,
13: 31419.5,
14: 31377.75,
15: 31360.0,
16: 31367.15,
17: 31345.2,
18: 31340.0,
19: 31367.0,
20: 31375.0,
21: 31370.0,
22: 31350.0,
23: 31334.6,
24: 31329.6,
25: 31599.0},
'Low': {0: 31250.0,
1: 31367.95,
2: 31352.5,
3: 31331.65,
4: 31301.4,
5: 31303.05,
6: 31310.0,
7: 31325.05,
8: 31335.35,
9: 31315.35,
10: 31281.9,
11: 31292.0,
12: 31316.25,
13: 31352.05,
14: 31335.0,
15: 31322.0,
16: 31318.25,
17: 31261.55,
18: 31283.3,
19: 31324.5,
20: 31322.0,
21: 31332.15,
22: 31324.1,
23: 31300.15,
24: 31280.0,
25: 31430.0},
'Close': {0: 31375.0,
1: 31398.3,
2: 31386.0,
3: 31377.0,
4: 31342.3,
5: 31311.7,
6: 31345.0,
7: 31349.0,
8: 31344.2,
9: 31327.6,
10: 31311.3,
11: 31325.6,
12: 31373.0,
13: 31375.0,
14: 31357.4,
15: 31326.0,
16: 31345.9,
17: 31300.6,
18: 31324.4,
19: 31353.8,
20: 31345.6,
21: 31341.6,
22: 31332.5,
23: 31311.0,
24: 31285.0,
25: 31558.4},
'Volume': {0: 259952,
1: 163775,
2: 105900,
3: 99725,
4: 115175,
5: 78625,
6: 67675,
7: 46575,
8: 53350,
9: 54175,
10: 96975,
11: 80925,
12: 79475,
13: 147775,
14: 38900,
15: 64925,
16: 52425,
17: 142175,
18: 81800,
19: 74950,
20: 68550,
21: 40350,
22: 47150,
23: 119200,
24: 222875,
25: 524625}}

Change:
df[['st_up','st_trendup'.shift()]].max(axis=1)
to:
df[['st_up','st_trendup']].assign(st_trendup = df['st_trendup'].shift()).max(axis=1)

joining/merging both index and non-index columns in a pandas multi-index

Context:
I have two very large pandas dataframes to join which barely fit in memory (8GB each, millions of rows) and have the challenge of performing a performant join using combinations of both indexed and non-indexed columns. Fuzzy joining is out of scope.
Variables in order of cardinality:
dataset_1 has these variables:
postcode, street_name, secondary_number, primary_number, unique_id
dataset_2 has these variables:
postcode, street_name, house_number, house_name, sub_building_name, different_unique_id
postcode and street_name are shared keys, and multiindexing seems the correct choice to improve joining performance in pandas:
dataset_1 = dataset_1.set_index(['postcode', 'street', "unique_id"]).sort_index()
dataset_2 = dataset_2.set_index(['postcode', 'street', "different_unique_id"]).sort_index()
Processing:
At this stage I can compute in pandas if memory allows. If not, I would use Dask, however it can't handle multi-indexes. In the event this were possible (or unnecessary) the sorting would still need to be handled in pandas as Dask cannot manage this. If Dask were an option this is how I would convert:
dd1 = dd.from_pandas(dataset_1, npartitions=1) #large left dataframe
del dataset_1 #to release the memory
dd2 = dd.from_pandas(dataset_2, npartitions=3) #partitioned right dataframe for performance
del dataset_2 #to release the memory
Problem:
The challenge is performing an inner join on non-null variables using the indexes ("postcode" and "street"), alongside non-indexed columns. Combinations of the non-indexed variables will be iterated in a for loop.
Solution Sketch:
This gives an idea what I would like to do to maintain the performance gains from the indexing, but is of course not syntactically possible:
output = pd.merge(df1, df2, how='inner', left_on=["postcode", "street_name", "secondary_number", "primary_number"], right_on=["postcode", "street_name", "house_name", "house_number"], left_index=[True,True,False,False], right_index=[True,True,False,False])
Summary:
My understanding is that pd.join can handle non-indexed and indexed columns, whereas pd.merge cannot. As a result I'm unsure how to achieve this join in pd.join where there is a combination of both indexed and non-indexed columns.
Example of intersects:
{'different_unique_id': {27: '{582D0636-8DEF-8F22-E053-6C04A8C01BAC}',
41: '{D9E869FE-7B55-4C36-AC43-695B9033A13B}',
33: '{93E6821E-554E-40FD-E053-6B04A8C0C1DF}',
1: '{288DCE29-0589-E510-E050-A8C06205480E}',
48: '{3A23DDD5-A0E8-41D2-A514-5B09385C301F}',
52: '{CEB16957-F7FA-4D1B-B45F-A390214735BC}',
13: '{404A5AF3-9B20-CD2B-E050-A8C063055C7B}',
16: '{64342BFD-FD07-422C-E053-6C04A8C0FB8A}',
57: '{29A8E769-8A10-4477-9494-FF55EF5FAE4B}',
10: '{404A5AF3-0B58-CD2B-E050-A8C063055C7B}',
21: '{55BDCAE6-0C10-521D-E053-6B04A8C0DD7A}',
31: '{5C676A02-1781-4152-950C-6E5CA2CBC487}',
7: '{68FEB20B-142E-38DA-E053-6C04A8C051AE}',
45: '{8F1B26BD-673F-53DB-E053-6C04A8C03649}',
12: '{2F115F7A-8F81-4124-9FD4-FB76E742B2C1}',
36: '{344AB2D7-4B59-4AB4-8F52-75B29BE8C509}',
20: '{965B6D91-D4B6-95E4-E053-6C04A8C07729}',
56: '{59872FD9-F39D-4BB9-95F6-91E002D948B1}',
22: '{6141DFF0-973F-4FEC-A582-7F310B566031}'},
'unique_id': {27: 10002277489,
41: 64023255,
33: 10007367447,
1: 22229221,
48: 10033235735,
52: 100062162615,
13: 50103744,
16: 10022903998,
57: 12015624,
10: 12154940,
21: 10024247587,
31: 100041193990,
7: 10008230730,
45: 10091640210,
12: 202107394,
36: 5062293,
20: 48114659,
56: 10001311242,
22: 10000443154},
'street': {27: 'thewharf',
41: 'parkroad',
33: 'oldmillclose',
1: 'thirdavenue',
48: 'woolnersway',
52: 'sumnerroad',
13: 'cliftongardens',
16: 'windhamroad',
57: 'westparkroad',
10: 'grangeroad',
21: 'staplersroad',
31: 'strand',
7: 'amhurstroad',
45: 'eatonroad',
12: 'northendroad',
36: 'belsizegrove',
20: 'watermillway',
56: 'orchardplace',
22: 'thurlowparkroad'},
'postcode': {27: 'lu72la',
41: 'cf626nt',
33: 'hr40aq',
1: 'bn32pd',
48: 'sg13ae',
52: 'gu97jx',
13: 'ct202ef',
16: 'bh14rn',
57: 'ub24af',
10: 'w55bu',
21: 'po302dp',
31: 'tq148aq',
7: 'e82ag',
45: 'ch47ew',
12: 'ha90ae',
36: 'nw34tt',
20: 'sw192rw',
56: 'so143hw',
22: 'se218hp'},
'secondary_number': {27: '76',
41: 'flat6',
33: '49',
1: 'flat10',
48: '145',
52: '31',
13: 'flat19',
16: 'flat7',
57: '76',
10: 'flat1',
21: 'flat1',
31: 'flat43',
7: 'flata',
45: '8',
12: '42',
36: 'flat9',
20: 'flat43',
56: 'flat156',
22: 'flat2'},
'primary_number': {27: 'eastdock',
41: 'courtlands',
33: 'watkinscourt',
1: 'ascothouse',
48: 'monumentcourt',
52: 'sumnercourt',
13: '22-24',
16: '77',
57: 'osterleyviews',
10: '55-59',
21: '138',
31: 'leandercourt',
7: '130',
45: 'greenbankhall',
12: 'danescourt',
36: 'holmefieldcourt',
20: 'bennetscourtyard',
56: 'oceanaboulevard',
22: '124f'},
'building_name': {27: 'eastdock',
41: 'courtlands',
33: 'watkinscourt',
1: 'ascothouse',
48: 'monumentcourt',
52: 'sumnercourt',
13: None,
16: None,
57: 'osterleyviews',
10: None,
21: None,
31: 'leandercourt',
7: None,
45: 'greenbankhall',
12: 'danescourt',
36: 'holmefieldcourt',
20: 'bennetscourtyard',
56: 'oceanaboulevard',
22: None},
'building_number': {27: None,
41: None,
33: None,
1: '18-20',
48: None,
52: None,
13: '22-24',
16: '77',
57: None,
10: '55-59',
21: '138',
31: None,
7: '130',
45: None,
12: None,
36: None,
20: None,
56: None,
22: '124f'},
'sub_building': {27: '76',
41: 'flat6',
33: '49',
1: 'flat10',
48: '145',
52: '31',
13: 'flat19',
16: 'flat7',
57: '76',
10: 'flat1',
21: 'flat1',
31: 'flat43',
7: 'flata',
45: '8',
12: '42',
36: 'flat9',
20: 'flat43',
56: 'flat156',
22: 'flat2'}}

Pandas Multivariate Linear Regression by Group and Saving Results as csv

I am trying to calculate linear regression of Y=C-A column, x = ['Plate X', 'Plate Y', 'Field X'] and group those values by Drum and Plate. Additional question - how to save results as a file, csv preferable.
Is pandas package is sufficient for this task or other package needed.
Thank you
There is my data set:
DF = {'A': {0: 305.03277000000003,
1: 304.42513500000001,
2: 305.119575,
3: 304.42513500000001,
4: 304.07791500000002,
5: 304.85916000000003,
6: 305.72721000000001,
7: 305.81401499999998,
8: 304.07791500000002,
9: 305.03277000000003,
10: 304.68554999999998,
11: 304.945965,
12: 303.38347499999998,
13: 304.945965,
14: 304.51193999999998,
15: 304.25152500000002,
16: 304.51193999999998,
17: 304.25152500000002,
18: 304.42513500000001,
19: 304.85916000000003,
20: 303.8175,
21: 305.119575,
22: 304.59874500000001,
23: 304.68554999999998,
24: 304.33832999999998,
25: 303.90430499999997,
26: 304.68554999999998,
27: 304.772355,
28: 304.59874500000001,
29: 304.772355,
30: 304.59874500000001,
31: 305.119575,
32: 305.37998999999996,
33: 304.59874500000001,
34: 304.42513500000001,
35: 304.33832999999998,
36: 304.51193999999998,
37: 305.46679499999999,
38: 304.59874500000001,
39: 305.29318499999999,
40: 304.85916000000003,
41: 305.29318499999999,
42: 305.119575,
43: 304.945965,
44: 305.29318499999999,
45: 304.85916000000003,
46: 305.72721000000001,
47: 306.16123500000003,
48: 305.37998999999996,
49: 305.03277000000003,
50: 305.20637999999997,
51: 304.51193999999998,
52: 308.33136000000002,
53: 305.81401499999998,
54: 305.55360000000002,
55: 306.42165,
56: 305.64040499999999,
57: 305.29318499999999,
58: 305.37998999999996,
59: 304.772355,
60: 305.37998999999996,
61: 305.72721000000001,
62: 305.90082000000001,
63: 305.64040499999999,
64: 305.81401499999998,
65: 304.85916000000003,
66: 305.20637999999997,
67: 306.42165,
68: 305.64040499999999,
69: 305.55360000000002,
70: 304.59874500000001,
71: 305.55360000000002,
72: 306.07443000000001,
73: 306.42165,
74: 305.98762499999998,
75: 306.68206499999997,
76: 305.03277000000003,
77: 305.46679499999999,
78: 306.42165,
79: 304.85916000000003,
80: 304.51193999999998,
81: 303.8175,
82: 304.51193999999998,
83: 304.16472000000005,
84: 304.51193999999998,
85: 303.73069500000003,
86: 303.29667000000001,
87: 304.68554999999998,
88: 303.73069500000003,
89: 304.42513500000001,
90: 304.51193999999998,
91: 304.16472000000005,
92: 304.945965,
93: 304.772355,
94: 304.42513500000001,
95: 304.16472000000005,
96: 305.119575,
97: 304.16472000000005,
98: 304.25152500000002,
99: 305.20637999999997},
'B': {0: 311.10912000000002,
1: 310.93551000000002,
2: 313.279245,
3: 313.19243999999998,
4: 309.11260499999997,
5: 309.0258,
6: 309.72023999999999,
7: 313.279245,
8: 311.89036499999997,
9: 311.19592499999999,
10: 308.76538500000004,
11: 309.72023999999999,
12: 312.15078,
13: 309.19941,
14: 308.50497000000001,
15: 308.33136000000002,
16: 309.89384999999999,
17: 310.848705,
18: 312.23758500000002,
19: 313.53966000000003,
20: 309.72023999999999,
21: 309.11260499999997,
22: 311.89036499999997,
23: 309.98065499999996,
24: 309.19941,
25: 310.41467999999998,
26: 311.62995000000001,
27: 311.02231499999999,
28: 310.32787500000001,
29: 310.06745999999998,
30: 311.89036499999997,
31: 311.89036499999997,
32: 309.98065499999996,
33: 312.06397500000003,
34: 306.85567500000002,
35: 309.98065499999996,
36: 311.80356,
37: 309.19941,
38: 312.41119500000002,
39: 310.848705,
40: 311.10912000000002,
41: 310.501485,
42: 313.80007499999999,
43: 308.24455499999999,
44: 312.49799999999999,
45: 313.10563500000001,
46: 313.19243999999998,
47: 309.63343500000002,
48: 311.10912000000002,
49: 310.501485,
50: 310.58828999999997,
51: 314.23410000000001,
52: 312.41119500000002,
53: 313.01882999999998,
54: 311.19592499999999,
55: 311.54314500000004,
56: 313.279245,
57: 311.54314500000004,
58: 311.45634000000001,
59: 313.19243999999998,
60: 312.15078,
61: 312.15078,
62: 313.452855,
63: 311.02231499999999,
64: 311.02231499999999,
65: 311.28272999999996,
66: 311.02231499999999,
67: 307.897335,
68: 313.19243999999998,
69: 311.97717,
70: 311.10912000000002,
71: 312.58480499999996,
72: 312.58480499999996,
73: 315.01534500000002,
74: 311.97717,
75: 313.452855,
76: 311.80356,
77: 308.67857999999995,
78: 311.71675499999998,
79: 311.36953499999998,
80: 310.501485,
81: 308.85219000000001,
82: 311.10912000000002,
83: 309.37302,
84: 307.98413999999997,
85: 311.10912000000002,
86: 311.28272999999996,
87: 310.93551000000002,
88: 310.24107000000004,
89: 307.11608999999999,
90: 307.55011500000001,
91: 308.76538500000004,
92: 310.848705,
93: 307.02928500000002,
94: 309.89384999999999,
95: 311.28272999999996,
96: 307.81052999999997,
97: 309.72023999999999,
98: 311.54314500000004,
99: 310.32787500000001},
'C': {0: 305.72721000000001,
1: 306.00498599999997,
2: 306.49109399999998,
3: 306.59526,
4: 305.48415599999998,
5: 305.24110200000001,
6: 306.28276199999999,
7: 306.97720199999998,
8: 306.80359199999998,
9: 307.081368,
10: 306.10915199999999,
11: 304.47721799999999,
12: 305.24110200000001,
13: 304.68554999999998,
14: 306.35220600000002,
15: 305.17165799999998,
16: 306.45637200000004,
17: 305.86609800000002,
18: 306.734148,
19: 306.28276199999999,
20: 305.51887799999997,
21: 308.053584,
22: 306.52581600000002,
23: 305.935542,
24: 306.56053800000001,
25: 306.10915199999999,
26: 306.56053800000001,
27: 305.79665399999999,
28: 305.761932,
29: 304.75499400000001,
30: 306.07443000000001,
31: 306.35220600000002,
32: 305.86609800000002,
33: 307.01192400000002,
34: 306.28276199999999,
35: 305.55360000000002,
36: 306.35220600000002,
37: 306.80359199999998,
38: 305.90082000000001,
39: 306.03970800000002,
40: 307.18553399999996,
41: 304.82443799999999,
42: 305.83137599999998,
43: 306.97720199999998,
44: 306.38692799999995,
45: 306.49109399999998,
46: 306.38692799999995,
47: 306.52581600000002,
48: 305.06749200000002,
49: 306.07443000000001,
50: 306.56053800000001,
51: 305.48415599999998,
52: 305.69248799999997,
53: 307.63692000000003,
54: 307.28969999999998,
55: 305.62304399999999,
56: 306.38692799999995,
57: 305.86609800000002,
58: 306.56053800000001,
59: 305.55360000000002,
60: 306.07443000000001,
61: 306.52581600000002,
62: 306.56053800000001,
63: 305.34526800000003,
64: 305.24110200000001,
65: 304.58138399999996,
66: 307.04664600000001,
67: 306.00498599999997,
68: 305.79665399999999,
69: 306.49109399999998,
70: 305.51887799999997,
71: 305.72721000000001,
72: 306.31748399999998,
73: 306.03970800000002,
74: 307.15081200000003,
75: 307.60219799999999,
76: 304.92860400000001,
77: 304.68554999999998,
78: 305.58832200000001,
79: 305.449434,
80: 306.83831400000003,
81: 306.49109399999998,
82: 306.94247999999999,
83: 304.963326,
84: 307.25497799999999,
85: 305.97026399999999,
86: 306.07443000000001,
87: 305.761932,
88: 305.90082000000001,
89: 306.31748399999998,
90: 306.69942599999996,
91: 306.07443000000001,
92: 305.449434,
93: 304.789716,
94: 304.72027200000002,
95: 306.10915199999999,
96: 305.449434,
97: 305.31054599999999,
98: 305.31054599999999,
99: 306.45637200000004},
'C-A': {0: 0.69443999999999995,
1: 1.5798510000000001,
2: 1.3715190000000002,
3: 2.1701250000000001,
4: 1.4062410000000001,
5: 0.381942,
6: 0.55555200000000005,
7: 1.163187,
8: 2.7256770000000001,
9: 2.0485980000000001,
10: 1.423602,
11: -0.46874700000000002,
12: 1.8576270000000001,
13: -0.26041500000000001,
14: 1.840266,
15: 0.92013299999999998,
16: 1.9444319999999999,
17: 1.614573,
18: 2.3090130000000002,
19: 1.423602,
20: 1.7013779999999998,
21: 2.9340090000000001,
22: 1.927071,
23: 1.249992,
24: 2.2222080000000002,
25: 2.204847,
26: 1.8749880000000001,
27: 1.0242990000000001,
28: 1.163187,
29: -0.017361000000000001,
30: 1.4756850000000001,
31: 1.232631,
32: 0.48610799999999998,
33: 2.413179,
34: 1.8576270000000001,
35: 1.2152700000000001,
36: 1.840266,
37: 1.336797,
38: 1.3020750000000001,
39: 0.74652299999999994,
40: 2.3263739999999999,
41: -0.46874700000000002,
42: 0.71180100000000002,
43: 2.031237,
44: 1.0937430000000001,
45: 1.631934,
46: 0.65971800000000003,
47: 0.36458099999999999,
48: -0.312498,
49: 1.04166,
50: 1.354158,
51: 0.97221599999999997,
52: -2.6388720000000001,
53: 1.822905,
54: 1.7361,
55: -0.79860600000000004,
56: 0.74652299999999994,
57: 0.57291300000000001,
58: 1.1805479999999999,
59: 0.78124499999999997,
60: 0.69443999999999995,
61: 0.79860600000000004,
62: 0.65971800000000003,
63: -0.29513699999999998,
64: -0.57291300000000001,
65: -0.27777600000000002,
66: 1.840266,
67: -0.41666400000000003,
68: 0.156249,
69: 0.93749400000000005,
70: 0.92013299999999998,
71: 0.17360999999999999,
72: 0.24305399999999999,
73: -0.381942,
74: 1.163187,
75: 0.92013299999999998,
76: -0.10416600000000001,
77: -0.78124499999999997,
78: -0.83332800000000007,
79: 0.59027399999999997,
80: 2.3263739999999999,
81: 2.673594,
82: 2.4305400000000001,
83: 0.79860600000000004,
84: 2.7430380000000003,
85: 2.2395689999999999,
86: 2.7777599999999998,
87: 1.0763819999999999,
88: 2.1701250000000001,
89: 1.8923490000000001,
90: 2.1874860000000003,
91: 1.9097099999999998,
92: 0.50346899999999994,
93: 0.017361000000000001,
94: 0.29513699999999998,
95: 1.9444319999999999,
96: 0.32985900000000001,
97: 1.145826,
98: 1.059021,
99: 1.249992},
'Drum': {0: 'LAAA',
1: 'LAAA',
2: 'LAAA',
3: 'LAAA',
4: 'LAAA',
5: 'LAAA',
6: 'LAAA',
7: 'LAAA',
8: 'LAAA',
9: 'LAAA',
10: 'LAAA',
11: 'LAAA',
12: 'LAAA',
13: 'LAAA',
14: 'LAAA',
15: 'LAAA',
16: 'LAAA',
17: 'LAAA',
18: 'LAAA',
19: 'LAAA',
20: 'LAAA',
21: 'LAAA',
22: 'LAAA',
23: 'LAAA',
24: 'LAAA',
25: 'LAAA',
26: 'LAAA',
27: 'LAAA',
28: 'LAAA',
29: 'LAAA',
30: 'LAAA',
31: 'LAAA',
32: 'LAAA',
33: 'LAAA',
34: 'LAAA',
35: 'LAAA',
36: 'LAAA',
37: 'LAAA',
38: 'LAAA',
39: 'LAAA',
40: 'LAAA',
41: 'LAAA',
42: 'LAAA',
43: 'LAAA',
44: 'LAAA',
45: 'LAAA',
46: 'LAAA',
47: 'LAAA',
48: 'LAAA',
49: 'LAAA',
50: 'LAAA',
51: 'LAAA',
52: 'LAAA',
53: 'LAAA',
54: 'LAAA',
55: 'LAAA',
56: 'LAAA',
57: 'LAAA',
58: 'LAAA',
59: 'LAAA',
60: 'LAAA',
61: 'LAAA',
62: 'LAAA',
63: 'LAAA',
64: 'LAAA',
65: 'LAAA',
66: 'LAAA',
67: 'LAAA',
68: 'LAAA',
69: 'LAAA',
70: 'LAAA',
71: 'LAAA',
72: 'LAAA',
73: 'LAAA',
74: 'LAAA',
75: 'LAAA',
76: 'LAAA',
77: 'LAAA',
78: 'LAAA',
79: 'LAAA',
80: 'LAAA',
81: 'LAAA',
82: 'LAAA',
83: 'LAAA',
84: 'LAAA',
85: 'LAAA',
86: 'LAAA',
87: 'LAAA',
88: 'LAAA',
89: 'LAAA',
90: 'LAAA',
91: 'LAAA',
92: 'LAAA',
93: 'LAAA',
94: 'LAAA',
95: 'LAAA',
96: 'LAAA',
97: 'LAAA',
98: 'LAAA',
99: 'LAAA'},
'FIELD X': {0: 4.7949800000000007,
1: -5.5198839999999993,
2: 4.7949800000000007,
3: 4.7949800000000007,
4: -5.5198839999999993,
5: 4.7949800000000007,
6: -5.5198839999999993,
7: 4.7949800000000007,
8: 4.7949800000000007,
9: -5.5198839999999993,
10: -5.5198839999999993,
11: 4.7949800000000007,
12: 4.7949800000000007,
13: -5.5198839999999993,
14: 4.7949800000000007,
15: -5.5198839999999993,
16: 4.7949800000000007,
17: -5.5198839999999993,
18: 4.7949800000000007,
19: 4.7949800000000007,
20: -5.5198839999999993,
21: 4.7949800000000007,
22: -5.5198839999999993,
23: 4.7949800000000007,
24: 4.7949800000000007,
25: -5.5198839999999993,
26: 4.7949800000000007,
27: -5.5198839999999993,
28: -5.5198839999999993,
29: 4.7949800000000007,
30: -5.5198839999999993,
31: 4.7949800000000007,
32: 4.7949800000000007,
33: -5.5198839999999993,
34: 4.7949800000000007,
35: -5.5198839999999993,
36: 4.7949800000000007,
37: -5.5198839999999993,
38: 4.7949800000000007,
39: -5.5198839999999993,
40: 4.7949800000000007,
41: -5.5198839999999993,
42: 4.7949800000000007,
43: -5.5198839999999993,
44: 4.7949800000000007,
45: -5.5198839999999993,
46: 4.7949800000000007,
47: -5.5198839999999993,
48: 4.7949800000000007,
49: -5.5198839999999993,
50: -5.5198839999999993,
51: 4.7949800000000007,
52: -5.5198839999999993,
53: 4.7949800000000007,
54: 4.7949800000000007,
55: -5.5198839999999993,
56: 4.7949800000000007,
57: -5.5198839999999993,
58: 4.7949800000000007,
59: -5.5198839999999993,
60: 4.7949800000000007,
61: 4.7949800000000007,
62: -5.5198839999999993,
63: 4.7949800000000007,
64: -5.5198839999999993,
65: 4.7949800000000007,
66: 4.7949800000000007,
67: -5.5198839999999993,
68: 4.7949800000000007,
69: -5.5198839999999993,
70: -5.5198839999999993,
71: 4.7949800000000007,
72: -5.5198839999999993,
73: 4.7949800000000007,
74: -5.5198839999999993,
75: 4.7949800000000007,
76: -5.5198839999999993,
77: -5.5198839999999993,
78: 4.7949800000000007,
79: -5.5198839999999993,
80: 4.7949800000000007,
81: -5.5198839999999993,
82: 4.7949800000000007,
83: 4.7949800000000007,
84: -5.5198839999999993,
85: 4.7949800000000007,
86: -5.5198839999999993,
87: 4.7949800000000007,
88: 4.7949800000000007,
89: -5.5198839999999993,
90: -5.5198839999999993,
91: 4.7949800000000007,
92: 4.7949800000000007,
93: -5.5198839999999993,
94: 4.7949800000000007,
95: -5.5198839999999993,
96: 4.7949800000000007,
97: -5.5198839999999993,
98: 4.7949800000000007,
99: 4.7949800000000007},
'FIELD Y': {0: 1.8893500000000001,
1: 1.8893500000000001,
2: 1.8893500000000001,
3: 1.8893500000000001,
4: 1.8893500000000001,
5: 1.8893500000000001,
6: 1.8893500000000001,
7: 1.8893500000000001,
8: 1.8893500000000001,
9: 1.8893500000000001,
10: 1.8893500000000001,
11: 1.8893500000000001,
12: 1.8893500000000001,
13: 1.8893500000000001,
14: 1.8893500000000001,
15: 1.8893500000000001,
16: 1.8893500000000001,
17: 1.8893500000000001,
18: 1.8893500000000001,
19: 1.8893500000000001,
20: 1.8893500000000001,
21: 1.8893500000000001,
22: 1.8893500000000001,
23: 1.8893500000000001,
24: 1.8893500000000001,
25: 1.8893500000000001,
26: 1.8893500000000001,
27: 1.8893500000000001,
28: 1.8893500000000001,
29: 1.8893500000000001,
30: 1.8893500000000001,
31: 1.8893500000000001,
32: 1.8893500000000001,
33: 1.8893500000000001,
34: 1.8893500000000001,
35: 1.8893500000000001,
36: 1.8893500000000001,
37: 1.8893500000000001,
38: 1.8893500000000001,
39: 1.8893500000000001,
40: 1.8893500000000001,
41: 1.8893500000000001,
42: 1.8893500000000001,
43: 1.8893500000000001,
44: 1.8893500000000001,
45: 1.8893500000000001,
46: 1.8893500000000001,
47: 1.8893500000000001,
48: 1.8893500000000001,
49: 1.8893500000000001,
50: 1.8893500000000001,
51: 1.8893500000000001,
52: 1.8893500000000001,
53: 1.8893500000000001,
54: 1.8893500000000001,
55: 1.8893500000000001,
56: 1.8893500000000001,
57: 1.8893500000000001,
58: 1.8893500000000001,
59: 1.8893500000000001,
60: 1.8893500000000001,
61: 1.8893500000000001,
62: 1.8893500000000001,
63: 1.8893500000000001,
64: 1.8893500000000001,
65: 1.8893500000000001,
66: 1.8893500000000001,
67: 1.8893500000000001,
68: 1.8893500000000001,
69: 1.8893500000000001,
70: 1.8893500000000001,
71: 1.8893500000000001,
72: 1.8893500000000001,
73: 1.8893500000000001,
74: 1.8893500000000001,
75: 1.8893500000000001,
76: 1.8893500000000001,
77: 1.8893500000000001,
78: 1.8893500000000001,
79: 1.8893500000000001,
80: 1.8893500000000001,
81: 1.8893500000000001,
82: 1.8893500000000001,
83: 1.8893500000000001,
84: 1.8893500000000001,
85: 1.8893500000000001,
86: 1.8893500000000001,
87: 1.8893500000000001,
88: 1.8893500000000001,
89: 1.8893500000000001,
90: 1.8893500000000001,
91: 1.8893500000000001,
92: 1.8893500000000001,
93: 1.8893500000000001,
94: 1.8893500000000001,
95: 1.8893500000000001,
96: 1.8893500000000001,
97: 1.8893500000000001,
98: 1.8893500000000001,
99: 1.8893500000000001},
'Plate': {0: 72,
1: 72,
2: 72,
3: 72,
4: 72,
5: 72,
6: 72,
7: 72,
8: 72,
9: 72,
10: 72,
11: 72,
12: 72,
13: 72,
14: 72,
15: 72,
16: 72,
17: 72,
18: 72,
19: 72,
20: 72,
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27: -30.985420000000001,
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31: -30.985420000000001,
32: -10.355691999999999,
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28: 27.092099999999999,
29: 54.144300000000001,
30: 108.2487,
31: 135.30090000000001,
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98: 98,
99: 99}}

From your question it doesnt sound like you want a multivariate regression (i.e. multiple Y's). If you're just predicting a single Y from multiple X's, you can do it like this with pandas, then save the results to a txt file:
import pandas as pd
df = pd.DataFrame(DF)
res = pd.stats.api.ols(y=df['C-A'], x=df[['Plate X','Plate Y','FIELD X']])
file = open("C:/Users/Simon/Desktop/results.txt", "w")
file.write(str(res))
file.close()
You mentioned in the question that you want to group the analyses by Drum and Plate. However, every value is the same for the Drum rows. If you want to group by Plate, however, and then run OLS on each subgroup, you can do something like this:
import pandas as pd
df = pd.DataFrame(DF)
results = []
def ols_res(df):
results.append( pd.stats.api.ols(y=df['C-A'], x=df[['Plate X','Plate Y','FIELD X']]))
df.groupby('Plate').apply(lambda newdf: ols_res(newdf))
file = open("C:/Users/Simon/Desktop/results.txt", "w")
for el in results:
file.write(str(el))
file.close()
If you want to also group by Drum, and note which drum/plate combination each analysis is for, you can do something like this and just add some extra txt to the results file:
import pandas as pd
df = pd.DataFrame(DF)
results = []
def ols_res(df):
curCombo = "plate:" + str(df["Plate"].mean()) + ", drum:" + str(df["Drum"].unique())
regression_results = pd.stats.api.ols(y=df['C-A'], x=df[['Plate X','Plate Y','FIELD X']])
results.append([curCombo, regression_results])
df.groupby(['Plate', 'Drum']).apply(lambda newdf: ols_res(newdf))
file = open("C:/Users/Simon/Desktop/results.txt", "w")
for el in results:
file.write(str(el))
file.write("\n\n")
file.close()