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7c99777b2f4c5a9c88cc1f04d0345ac7b1e9dea2c7ac74b3fbf683e59bbf38f4 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: Areas,freq_1,freq_2,freq_3,freq_4,freq_5,freq_6\\n11.46297225301157,0.750090555540225,1.0,0.0602354836548662,0.1838822583531753,0.0853333802592762,0.046024792724136\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n11.239817102920368,1.0,0.3186042752037932,0.1344797605815425,0.0786915134946252,0.0291092349742216,0.0462109552890391\\n14.225572256061094,0.3560941668350856,0.286557320911586,0.371644358207699,0.4729787680332255,0.3101131011117374,0.7074703432609266\\n9.865012036104266,1.0,0.2397341537732411,0.0729735395233181,0.0223524205245781,0.0287815331852048,0.0101898116116331\\n2.0757099662356238,0.9347092851067056,0.9400697206071236,1.0,0.9287615956012136,0.7355906053486795,0.5181680119786722\\n2.9067636626783804,1.0,0.1447597464229583,0.0480965667856174,0.0205783381644516,0.0171364415449829,0.0115787651851685\\n14.339409909977467,1.0,0.4250899142632741,0.1643871449873558,0.1020228497986892,0.041877682820639,0.0281545945678505\\n5.896129616650832,1.0,0.5067710275772761,0.1627128555154097,0.121165802190262,0.0619750338712106,0.0394802988626596\\n5.015217739188724,1.0,0.2137852227488661,0.0986187661484963,0.0384073657935623,0.022448891250256,0.0185346492464125\\n5.093743471481292,0.1329717423185582,0.1273505058545859,0.0590673294823516,0.0315282671087803,0.1411126511020878,0.2762081522183985\\n9.575908391909108,0.0937816299058494,0.0677546139020085,0.040494588488153,0.1130365447476912,0.0458418554377786,0.3351258627571026\\n12.43899843516728,1.0,0.2174001466603657,0.1215194187495121,0.0473273252051433,0.0278033476514428,0.021856868652518\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n \\n CSV Table B: 7raemdfhCtY,+xshpVlCqD4,QjH4XnyfWuI,vuFoAPLYFL8,Yz4/hhaFlUQ,NYLj0y6YLFA\\nNo,0.2710952149558612,6040452,0.1241531998855021,27.356016993528257,0\\nNo,0.0,6038888,0.0,0.0,0\\nNo,0.0,5941356,0.0,0.0,0\\nNo,0.0,6040452,0.0,0.0,0\\nNo,0.2134908745410948,5941356,0.057705281989179,21.995223196929345,0\\nSi,0.3283789206311447,5510456,0.100397995844769,14.12757778606885,0\\nSi,0.1982944056887898,6040452,0.0349326900415004,3.8333505006554778,0\\nSi,0.0,5510456,0.0,0.0,0\\nNo,0.0,6038888,0.0,0.0,0\\nNo,0.0,5026787,0.0,0.0,0\\nSi,0.2504480400031245,6040452,0.0446140544381391,6.936822133643822,0\\nNo,0.0,5510456,0.0,0.0,0\\nSi,0.2556343349867265,6038888,0.0652165586167969,29.10991285009921,0\\nSi,0.265151197362279,5941356,0.0603377249806183,15.422577029258743,0\\nNo,0.0,5510456,0.0,0.0,0\\n \\n Output: \\n"
] | {"freq_2": "+xshpVlCqD4", "Areas": "Yz4/hhaFlUQ", "freq_4": "vuFoAPLYFL8"} | tablejoin |
7d3b232a7df622492efaa9230b09fe5a5e45c12d35ed346a99b6ec201497a1e3 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: date,bundesland,gemeindeschluessel,anzahl_standorte,anzahl_meldebereiche,faelle_covid_aktuell,faelle_covid_aktuell_invasiv_beatmet,betten_frei,betten_belegt,betten_belegt_nur_erwachsen\\n2020-11-25,9,9762,1,1,7,3,4,14,14\\n2020-08-23,6,6440,5,5,1,0,20,76,76\\n2021-11-01,1,1056,2,2,1,1,3,34,34\\n2020-07-05,6,6633,3,3,0,0,7,28,28\\n2020-05-28,9,9678,2,2,1,0,2,6,6\\n2021-08-20,5,5124,5,7,9,4,18,131,122\\n2021-10-28,9,9576,1,1,0,0,0,5,5\\n2021-01-30,9,9672,4,4,3,2,3,37,37\\n2021-03-02,3,3101,5,7,8,4,19,113,99\\n2021-08-31,5,5762,5,6,2,1,9,26,24\\n2020-11-20,5,5911,6,8,18,12,33,166,153\\n2020-09-07,1,1003,2,2,1,0,110,107,107\\n2020-12-05,3,3354,1,1,0,0,0,6,6\\n2020-08-12,6,6435,4,7,0,0,25,65,55\\n2020-05-17,5,5962,8,8,6,3,55,71,71\\n2020-11-24,3,3455,2,2,2,1,14,23,23\\n \\n CSV Table B: T7gS0B9wuO8,5ArEgCtuDyM,IBOO7n66j2I,/8WN7SwQxtM,+TcFRhetc3o,XmI4BR0CDwY,xEEeWKcl26k,0bFLf6WxD8A,zSt62OHmjJ8\\n9777,24591000,Weak,gas,6040452,20,0,15.6466,5.0 out of 5 stars\\n12054,8334800,Weak,gas,6038888,55,0,15.6466,5.0 out of 5 stars\\n9462,9875400,Weak,gas,5941356,50,0,15.6466,5.0 out of 5 stars\\n15001,8338300,New,gas,6040452,25,0,15.6466,5.0 out of 5 stars\\n9362,8995500,Weak,gas,5941356,184,0,15.6466,5.0 out of 5 stars\\n3257,8564500,New,gas,5510456,22,0,15.6466,4.0 out of 5 stars\\n9572,8948500,New,gas,6040452,4,0,15.6466,5.0 out of 5 stars\\n13072,11859900,New,gas,5510456,33,0,15.6466,5.0 out of 5 stars\\n3153,16537400,Weak,gas,6038888,40,0,15.6466,5.0 out of 5 stars\\n15088,11010400,New,gas,5026787,16,0,15.6466,5.0 out of 5 stars\\n9371,7534000,New,gas,6040452,9,0,15.6466,5.0 out of 5 stars\\n8417,9818100,Weak,gas,5510456,19,0,15.6466,5.0 out of 5 stars\\n5711,9965000,Weak,gas,6038888,138,0,15.6466,5.0 out of 5 stars\\n7232,20254600,Good,gas,5941356,12,0,15.6466,5.0 out of 5 stars\\n9173,9989300,New,gas,5510456,22,0,15.6466,5.0 out of 5 stars\\n9676,12805200,Weak,gas,5026787,10,0,15.6466,5.0 out of 5 stars\\n6532,12652800,New,gas,5510456,47,0,15.6466,5.0 out of 5 stars\\n \\n Output: \\n"
] | {"betten_belegt": "XmI4BR0CDwY", "gemeindeschluessel": "T7gS0B9wuO8"} | tablejoin |
d89584191190995d5cb7307c938dbfb201e3af17ed7f666c2afae0fe2ad55985 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: hospital_pk,collection_week,state,ccn,hospital_name,address,city,zip,hospital_subtype,fips_code\\n131302,2020-04-05T00:00:00.,ID,131302.0,NORTH CANYON MEDICAL,267 NORTH CANYON DR,GOODING,83330,Critical Access Hosp,16047.0\\n420023,2020-05-10T00:00:00.,SC,420023.0,ST FRANCIS-DOWNTOWN,ONE ST FRANCIS DR,GREENVILLE,29601,Short Term,45045.0\\n030016,2020-05-10T00:00:00.,AZ,30016.0,BANNER CASA GRANDE M,1800 EAST FLORENCE B,CASA GRANDE,85122,Short Term,4021.0\\n452019,2020-05-17T00:00:00.,TX,452019.0,KINDRED HOSPITAL FOR,1802 HIGHWAY 157 NOR,MANSFIELD,76063,Long Term,48439.0\\n400005,2020-05-31T00:00:00.,PR,400005.0,HIMA SAN PABLO HUMAC,CALLE FONT MARTELO #,HUMACAO,791,Short Term,72069.0\\n650003,2020-06-21T00:00:00.,GU,650003.0,GUAM REGIONAL MEDICA,133 ROUTE 3,DEDEDO,96929,Short Term,66010.0\\n440183,2020-05-17T00:00:00.,TN,440183.0,ST FRANCIS HOSPITAL,5959 PARK AVE,MEMPHIS,38119,Short Term,47157.0\\n490060,2020-06-07T00:00:00.,VA,490060.0,CLINCH VALLEY MEDICA,6801 GOVERNOR GC PER,RICHLANDS,24641,Short Term,51185.0\\n110226,2020-06-28T00:00:00.,GA,110226.0,EMORY HILLANDALE HOS,2801 DEKALB MEDICAL ,LITHONIA,30058,Short Term,13089.0\\n410012,2020-06-21T00:00:00.,RI,410012.0,THE MIRIAM HOSPITAL,164 SUMMIT AVENUE,PROVIDENCE,2906,Short Term,44007.0\\n010095,2020-05-17T00:00:00.,AL,10095.0,HALE COUNTY HOSPITAL,508 GREEN STREET,GREENSBORO,36744,Short Term,1065.0\\n231305,2020-05-31T00:00:00.,MI,231305.0,ASCENSION STANDISH H,805 W CEDAR ST,STANDISH,48658,Critical Access Hosp,26011.0\\n360029,2020-05-31T00:00:00.,OH,360029.0,WOOD COUNTY HOSPITAL,950 WEST WOOSTER STR,BOWLING GREEN,43402,Short Term,39173.0\\n310040,2020-08-02T00:00:00.,NJ,310040.0,CAREPOINT HEALTH-HOB,308 WILLOW AVE,HOBOKEN,7030,Short Term,34017.0\\n140289,2020-05-24T00:00:00.,IL,140289.0,ANDERSON HOSPITAL,6800 STATE ROUTE 162,MARYVILLE,62062,Short Term,17119.0\\n140122,2020-03-29T00:00:00.,IL,140122.0,UCHICAGO MEDICINE AD,120 NORTH OAK ST,HINSDALE,60521,Short Term,17043.0\\n192037,2020-05-10T00:00:00.,LA,192037.0,HOUMA - AMG SPECIALT,629 DUNN STREET,HOUMA,70360,Long Term,22109.0\\n140100,2020-04-12T00:00:00.,IL,140100.0,MIDWESTERN REGION ME,2520 ELISHA AVENUE,ZION,60099,Short Term,17097.0\\n010150,2020-04-19T00:00:00.,AL,10150.0,REGIONAL MEDICAL CEN,29 L V STABLER DRIVE,GREENVILLE,36037,Short Term,1013.0\\n \\n CSV Table B: LB1c5bVtloU,NWoi+UEeAUY,cOXVTPLBCRY,eaRWRFfT5Wg,am9yrWhMHrw,RKRCNpVVdoc\\n6040452,0,15.6466,55422,3300 OAKDALE NORTH,Short Term\\n6038888,1,15.6466,68632,372 SOUTH 9TH STREET,Critical Access Hosp\\n5941356,2,15.6466,30286,801 W GORDON STREET,Short Term\\n6040452,3,15.6466,51401,311 SOUTH CLARK STRE,Short Term\\n5941356,4,15.6466,60451,1900 SILVER CROSS BL,Short Term\\n5510456,5,15.6466,46011,1515 N MADISON AVE,Short Term\\n6040452,6,15.6466,82443,150 EAST ARAPAHOE,Critical Access Hosp\\n5510456,7,15.6466,63368,2 PROGRESS POINT PKW,Short Term\\n6038888,8,15.6466,97845,170 FORD ROAD,Critical Access Hosp\\n5026787,9,15.6466,70633,110 WEST 4TH STREET,Critical Access Hosp\\n6040452,10,15.6466,70128,14500 HAYNE BLVD,Long Term\\n5510456,11,15.6466,79410,3815 20TH STREET,Long Term\\n6038888,12,15.6466,97225,9205 SW BARNES ROAD,Short Term\\n5941356,13,15.6466,47882,2200 N SECTION ST,Critical Access Hosp\\n5510456,14,15.6466,48202,2799 W GRAND BLVD,Short Term\\n5026787,15,15.6466,79347,708 S 1ST ST,Critical Access Hosp\\n5510456,16,15.6466,15801,100 HOSPITAL AVENUE,Short Term\\n5026787,17,15.6466,19301,255 WEST LANCASTER A,Short Term\\n5510456,18,15.6466,47804,1606 N SEVENTH ST,Short Term\\n \\n Output: \\n"
] | {"zip": "eaRWRFfT5Wg", "address": "am9yrWhMHrw", "hospital_subtype": "RKRCNpVVdoc"} | tablejoin |
1620e3381c6b9ba1ff0bcde15d816ec23ce445e1de6ed45de56ca41b0d1ae855 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: Areas,freq_1,freq_2,freq_3,freq_4,freq_5,freq_6\\n5.933795753838489,1.0,0.7714353152956073,0.3375919869424647,0.0704448788641532,0.0107929607876282,0.0267687337606832\\n1.5210910200051493,1.0,0.3352216459590461,0.3142629045582596,0.018591929252257,0.0044317931629377,0.0180898247588335\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n1.6806327718556786,1.0,0.2886022195535446,0.1519876382827813,0.0955270177197378,0.0582274733294353,0.0120363467931941\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n3.394541372160921,0.9340198828403428,0.5170177427626574,0.8907295186595751,0.6248519995457857,0.4801956382727493,0.0963058220609996\\n1.940443897590438,1.0,0.0168048360419492,0.0684236444875642,0.0197865184978094,0.0085870714109561,0.0218420918462181\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n22.69973176183243,1.0,0.2635890581296524,0.1015738531735589,0.0557092844099098,0.0389717755071762,0.0268118043445155\\n15.72102675863944,1.0,0.2534177765079918,0.1213851367645493,0.0758989580007738,0.0497306692526718,0.0423569503878933\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n16.790685004304716,1.0,0.4596285598249906,0.2470266743171786,0.159609995246162,0.0683835858311823,0.0611051507365258\\n3.775196155630213,1.0,0.1484267571813163,0.0838537815456624,0.0467573958130329,0.0290824998529619,0.0202236843754584\\n \\n CSV Table B: 9DjQ3tK+uag,ei1O4ueH08o,a6oKqAbhiYE,oZa6HchyMZU,KaFTwefModI\\n0.0889692177421741,4.451112936702725,gas,1.0,0.0518831658900293\\n0.0,0.0,gas,0.0,0.0\\n0.0,0.0,gas,0.0,0.0\\n0.3500152338519772,2.6029018246824216,gas,0.5115910674487147,0.4856065717300028\\n0.0312477623708865,6.100652645212125,gas,1.0,0.0280783737865971\\n0.0,0.0,gas,0.0,0.0\\n0.0,0.0,gas,0.0,0.0\\n0.1195854319548732,5.928007798057385,gas,1.0,0.0520140122427527\\n0.4863107106367197,3.990970350783068,gas,1.0,0.3519195684437978\\n0.0,0.0,gas,0.0,0.0\\n0.1889284571653062,8.889283224092921,gas,1.0,0.0781596355026045\\n0.0,0.0,gas,0.0,0.0\\n0.0,0.0,gas,0.0,0.0\\n0.0879670614404105,4.20557923909491,gas,1.0,0.0952474046083429\\n0.0,0.0,gas,0.0,0.0\\n \\n Output: \\n"
] | {"freq_1": "oZa6HchyMZU", "Areas": "ei1O4ueH08o", "freq_3": "9DjQ3tK+uag", "freq_4": "KaFTwefModI"} | tablejoin |
01fc14e123214c67cbf235824d1ec952a825d5f78464ecc18fb9609c2781f50c | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: email,label\\nAct now! Limited-tim,spam\\nUpgrade to our premi,ham\\nThank you for subscr,ham\\nYour order has been ,ham\\nWe're excited to sha,ham\\nURGENT: Your account,spam\\nWe've extended our s,ham\\nYou've been selected,spam\\nYour account has bee,spam\\nUnlock exclusive dis,spam\\n \\n CSV Table B: lG1K/C5s5Ww,t8DtGa8xUVw\\nham,0\\nham,0\\nham,0\\nham,0\\nham,0\\nham,0\\nspam,0\\nham,0\\nham,0\\nham,0\\nham,0\\n \\n Output: \\n"
] | {"label": "lG1K/C5s5Ww"} | tablejoin |
490dfdc0383f199c870aa7710499c4081c35ff3545415dab3904f64e7526a809 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: name,id,nametype,recclass,mass,fall,year,reclat,reclong,geolocation\\nRepeev Khutor,22590,Valid,\"Iron, IIF\",7000.0,Fell,1933-01-01T00:00:00.,48.6,45.66667,\"{\\'latitude\\': \\'48.6\\',\"\\nKhmelevka,12297,Valid,L5,6109.0,Fell,1929-01-01T00:00:00.,56.75,75.33333,{\\'latitude\\': \\'56.75\\'\\nRichland Springs,22602,Valid,OC,1900.0,Fell,1980-01-01T00:00:00.,31.25,-99.03333,{\\'latitude\\': \\'31.25\\'\\nLichtenberg,14646,Valid,H6,4000.0,Fell,1973-01-01T00:00:00.,-26.15,26.18333,{\\'latitude\\': \\'-26.15\\nDjati-Pengilon,7652,Valid,H6,166000.0,Fell,1884-01-01T00:00:00.,-7.5,111.5,\"{\\'latitude\\': \\'-7.5\\',\"\\nJohnstown,12198,Valid,Diogenite,40300.0,Fell,1924-01-01T00:00:00.,40.35,-104.9,{\\'latitude\\': \\'40.35\\'\\nDanville,5514,Valid,L6,2000.0,Fell,1868-01-01T00:00:00.,34.4,-87.06667,\"{\\'latitude\\': \\'34.4\\',\"\\nDesuri,6693,Valid,H6,25400.0,Fell,1962-01-01T00:00:00.,25.73333,73.61667,{\\'latitude\\': \\'25.733\\nMyhee Caunta,16887,Valid,OC,,Fell,1842-01-01T00:00:00.,23.05,72.63333,{\\'latitude\\': \\'23.05\\'\\nGlanerbrug,10923,Valid,L/LL5,670.0,Fell,1990-01-01T00:00:00.,52.2,6.86667,\"{\\'latitude\\': \\'52.2\\',\"\\nElenovka,7824,Valid,L5,54640.0,Fell,1951-01-01T00:00:00.,47.83333,37.66667,{\\'latitude\\': \\'47.833\\n \\n CSV Table B: +wt5tR9hUmk,qYGU6k7IF84,SfVC0olx/OE,dpKqmiM3LcE,NljmnVvMvfc,q4yxeqSsc3o,SeflMNbyB9c\\n2405.0,gas,24591000,1955-01-01T00:00:00.,Fell,5.0 out of 5 stars,Weak\\n650.0,gas,8334800,1868-01-01T00:00:00.,Fell,5.0 out of 5 stars,Weak\\n737.6,gas,9875400,1962-01-01T00:00:00.,Fell,5.0 out of 5 stars,Weak\\n61.4,gas,8338300,1981-01-01T00:00:00.,Fell,5.0 out of 5 stars,New\\n85000.0,gas,8995500,1961-01-01T00:00:00.,Fell,5.0 out of 5 stars,Weak\\n9.6,gas,8564500,2003-01-01T00:00:00.,Found,4.0 out of 5 stars,New\\n350.0,gas,8948500,1908-01-01T00:00:00.,Fell,5.0 out of 5 stars,New\\n1393.0,gas,11859900,1883-01-01T00:00:00.,Fell,5.0 out of 5 stars,New\\n680.5,gas,16537400,1998-01-01T00:00:00.,Fell,5.0 out of 5 stars,Weak\\n22.0,gas,11010400,1866-01-01T00:00:00.,Fell,5.0 out of 5 stars,New\\n0.5,gas,7534000,1814-01-01T00:00:00.,Fell,5.0 out of 5 stars,New\\n \\n Output: \\n"
] | {"mass": "+wt5tR9hUmk", "fall": "NljmnVvMvfc", "year": "dpKqmiM3LcE"} | tablejoin |
0764131eaf30bb8af36ad749f144da01c0113b1cee00092dde2919287df2ba78 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: Period\\\\Unit:,[Australian dollar ],[Bulgarian lev ],[Brazilian real ],[Canadian dollar ],[Swiss franc ],[Chinese yuan renminbi ],[Cypriot pound ],[Czech koruna ],[Danish krone ]\\n2012-10-11,1.2573,1.9558,2.6339,1.2645,1.2087,8.1086,,24.940,7.4588\\n2001-05-25,1.6485,1.9461,2.0210,1.3240,1.5272,7.1108,0.57697,34.288,7.4592\\n2009-11-30,1.6452,1.9558,2.6251,1.5882,1.5071,10.2564,,26.135,7.4424\\n2007-08-17,1.7213,1.9558,2.7736,1.4416,1.6245,10.2184,0.58420,27.663,7.4409\\n2005-06-16,1.5738,1.9560,2.9448,1.4984,1.5395,10.0270,0.57420,29.960,7.4429\\n2023-08-14,1.6853,1.9558,5.3764,1.47,0.9608,7.9356,,24.038,7.4515\\n2021-05-24,1.5804,1.9558,6.5299,1.4731,1.0957,7.8487,,25.424,7.4364\\n2011-04-12,1.3783,1.9558,2.2859,1.3864,1.3017,9.4638,,24.448,7.4584\\n2015-09-18,1.5709,1.9558,4.4370,1.4876,1.0913,7.2674,,27.071,7.4612\\n2022-05-16,1.5057,1.9558,5.2819,1.3473,1.0479,7.0786,,24.710,7.4418\\n \\n CSV Table B: crjCpvL6IHM,PzdYfZWVuZ8,NxnXOP1axWA,qQ/ysRVsisg,bG37FIQSUl4,ZTaHTGeeVq0,GChDi7tNjcY,sCAriUO7mec\\n2014-01-07,1.2367,6040452,5.0 out of 5 stars,gas,24591000,27.454,3.2241\\n2021-04-14,1.1033,6038888,5.0 out of 5 stars,gas,8334800,25.929,6.8189\\n2024-02-09,0.9432,5941356,5.0 out of 5 stars,gas,9875400,25.172,5.3637\\n1999-07-05,1.6055,6040452,5.0 out of 5 stars,gas,8338300,36.188,\\n1999-02-25,1.5905,5941356,5.0 out of 5 stars,gas,8995500,37.994,\\n1999-05-14,1.6020,5510456,4.0 out of 5 stars,gas,8564500,37.627,\\n2012-09-19,1.2095,6040452,5.0 out of 5 stars,gas,8948500,24.870,2.6317\\n2018-10-25,1.1407,5510456,5.0 out of 5 stars,gas,11859900,25.831,4.2357\\n2024-02-20,0.9526,6038888,5.0 out of 5 stars,gas,16537400,25.429,5.3521\\n2001-03-14,1.5361,5026787,5.0 out of 5 stars,gas,11010400,34.608,1.9048\\n \\n Output: \\n"
] | {"[Czech koruna ]": "GChDi7tNjcY", "[Swiss franc ]": "PzdYfZWVuZ8", "Period\\Unit:": "crjCpvL6IHM", "[Brazilian real ]": "sCAriUO7mec"} | tablejoin |
55d610b0b74c049e9664df825f1bffcb7999fffc0576ff3317960a2124c3feaf | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: Unnamed: 0,military_base_name,coordinates,longtitudes,latitudes,description\\n231,Warehouses,\"36.192135119525,51.7\",36.192135119525,51.76504015277498,military unit 55443-\\n2549,\"FGKU plant \"\"Zaliv\"\", \",\"91.2538259396279,53.\",91.2538259396279,53.84058923722024,\\n2268,Training Center for ,\"37.45257182147071,55\",37.45257182147071,55.65068030560189,A special object of \\n2463,Foreign Intelligence,\"37.51818966901558,55\",37.51818966901558,55.58494050230941,\\n2904,Testing Facility of ,\"30.17821336359249,60\",30.17821336359249,60.29493749739285,Testing of missiles \\n2566,\"FGKU plant \"\"Argun\"\", \",\"114.3215040279572,51\",114.3215040279572,51.61993889490242,\\n974,122nd Missile Regime,\"45.38931092844241,52\",45.38931092844241,52.23762486615308,\"military unit 77980,\"\\n1221,874th Radio-Technica,\"40.42184468866319,56\",40.42184468866319,56.13374562694942,military unit 30790\\n443,Warehouse,\"83.06531660551912,54\",83.06531660551912,54.95831270373129,military unit 58661-\\n2769,Training Ground,\"33.17734347037145,68\",33.17734347037145,68.88951166395577,\\n2621,/A Combined Arms Aca,\"37.6956668243265,55.\",37.6956668243265,55.76136846272302,\\n1746,280th Guards Motor R,\"22.2162231483651,54.\",22.2162231483651,54.59815334275081,\\n2696,Transmitting Radio C,\"40.13394840314977,62\",40.13394840314977,62.65320112079713,\\n1650,332nd Radio-Technica,\"40.68273814029152,64\",40.68273814029152,64.5187161106319,military unit 21514\\n2666,Z/4,\"143.0899635435795,59\",143.0899635435795,59.41749468741156,\\n2412,94th Internal Troops,\"43.31647007301511,54\",43.31647007301511,54.9363508702557,military unit 3274\\n2732,Training Grounds,\"36.92967872777752,55\",36.92967872777752,55.54215358750233,\\n \\n CSV Table B: dldBxBN4tl4,SmRhS/d2xpk,gVRuuM0qimI,7SxcDOM+98w,VP8coLynuXw\\n44.51916101735122,6040452,33.48334624839457,0,\\n51.82107969463786,6038888,107.6915756165818,0,\\n61.83338956320217,5941356,34.25154208925353,0,military unit 18558\\n55.8398933314324,6040452,37.56263109395489,0,Estabilished in Janu\\n56.19537331447595,5941356,37.04376605026997,0,military unit 92154\\n43.75156070078539,5510456,44.01921733219185,0,\"military unit 31681,\"\\n49.9425896490698,6040452,40.4966289477541,0,military unit 83833\\n48.68547115904807,5510456,45.72473406052717,0,\\n67.66637512688602,6038888,49.037423858874,0,Designed to detect a\\n51.5646535131477,5026787,113.0394034094085,0,military unit 48271 \\n55.47150518695323,6040452,28.78653481318823,0,military unit 32404\\n47.21956872393976,5510456,39.70363102317334,0,\\n46.3954054309925,6038888,47.90753819956586,0,\"MiG-29UBM, MiG-29SMT\"\\n52.5842238897004,5941356,39.56394893283026,0,military unit 5961\\n50.70253121855274,5510456,136.7369473000318,0,military unit 47127\\n56.46296735538946,5026787,48.14977296610531,0,military unit 58661-\\n51.59114083272477,5510456,39.09266975663168,0,\"military unit 51025,\"\\n43.9348278717269,5026787,131.8872930091488,0,\\n \\n Output: \\n"
] | {"latitudes": "dldBxBN4tl4", "description": "VP8coLynuXw", "longtitudes": "gVRuuM0qimI"} | tablejoin |
9d53b3ca366bedc7b149a5d41a4dc5c52cd76f1989a0cb6020d304fef6eb8d8d | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: valor,unidad,vigenciadesde,vigenciahasta\\n3843.59,COP,2020-10-15T00:00:00.,2020-10-15T00:00:00.\\n3997.09,COP,2021-12-24T00:00:00.,2021-12-24T00:00:00.\\n3450.74,COP,2021-01-06T00:00:00.,2021-01-06T00:00:00.\\n4003.95,COP,2022-01-20T00:00:00.,2022-01-20T00:00:00.\\n3993.53,COP,2023-09-13T00:00:00.,2023-09-13T00:00:00.\\n3639.12,COP,2021-04-22T00:00:00.,2021-04-22T00:00:00.\\n3784.44,COP,2021-10-30T00:00:00.,2021-11-02T00:00:00.\\n3927.25,COP,2022-02-19T00:00:00.,2022-02-22T00:00:00.\\n4039.31,COP,2022-01-07T00:00:00.,2022-01-07T00:00:00.\\n3905.95,COP,2023-09-19T00:00:00.,2023-09-19T00:00:00.\\n4506.49,COP,2023-05-16T00:00:00.,2023-05-16T00:00:00.\\n3827.27,COP,2020-08-22T00:00:00.,2020-08-24T00:00:00.\\n3743.79,COP,2020-05-28T00:00:00.,2020-05-28T00:00:00.\\n \\n CSV Table B: e8EOCOtc2tE,92E9ya41vLI,Qiz4gNNSkjU\\nCOP,2023-01-20T00:00:00.,0\\nCOP,2022-12-23T00:00:00.,0\\nCOP,2023-07-06T00:00:00.,0\\nCOP,2023-05-15T00:00:00.,0\\nCOP,2021-11-18T00:00:00.,0\\nCOP,2021-08-25T00:00:00.,0\\nCOP,2022-10-03T00:00:00.,0\\nCOP,2022-01-27T00:00:00.,0\\nCOP,2022-08-18T00:00:00.,0\\nCOP,2022-03-24T00:00:00.,0\\nCOP,2021-04-14T00:00:00.,0\\nCOP,2023-06-05T00:00:00.,0\\nCOP,2021-03-26T00:00:00.,0\\nCOP,2023-08-14T00:00:00.,0\\n \\n Output: \\n"
] | {"vigenciahasta": "92E9ya41vLI", "unidad": "e8EOCOtc2tE"} | tablejoin |
d4b2efd567053821eedf1ea3f759d4948f50264b94bd6ff37b18bc92e79d4fc1 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: DeviceTimeStamp,WL1,WL2,WL3,VAL1,VAL2,VAL3,RVAL1,RVAL2,RVAL3\\n2019-10-04T15:30,34.3,24.5,32.1,34.9,24.8,32.2,5.9,3.8,0.0032\\n2019-09-13T19:15,32.1,29.3,36.5,32.6,29.3,36.7,5.5,0.7,0.0037\\n2019-07-14T15:30,15.8,9.9,16.3,15.9,10.2,17.4,1.8,2.7,0.0059\\n2020-02-15T15:00,22.6,12.2,22.8,22.7,12.5,23.9,1.6,2.7,0.0072\\n2019-07-16T21:30,30.5,17.9,23.0,30.6,18.2,23.8,1.6,3.0,0.0058\\n2020-01-21T04:45,7.5,3.2,8.0,7.5,3.5,8.2,0.0,1.4,0.0016\\n2019-10-12T02:15,16.3,16.0,22.4,16.3,16.2,22.7,1.3,2.3,0.0041\\n2019-07-17T21:45,27.1,21.7,35.6,27.1,21.8,35.9,0.5,1.8,0.0052\\n2020-02-14T18:32,25.6,23.3,33.1,25.7,23.4,33.2,2.0,1.1,0.0031\\n2019-10-13T09:30,11.5,8.4,13.0,11.6,8.6,13.5,1.4,1.9,0.0036\\n2019-07-21T03:00,21.1,14.4,15.5,21.1,14.9,16.0,0.5,3.6,0.0042\\n2019-07-17T11:30,28.1,33.4,21.8,28.2,33.8,22.4,2.5,5.3,0.0051\\n2019-09-29T02:30,13.9,10.6,17.5,14.1,10.8,17.5,2.8,1.8,0.0003\\n2019-10-25T03:15,9.1,8.9,12.6,9.1,9.0,12.8,0.0,1.4,0.0019\\n2019-11-16T14:45,24.8,17.4,24.9,24.9,17.6,25.7,1.8,2.6,0.0061\\n2019-08-12T23:15,18.3,23.5,29.8,18.3,23.8,30.0,1.0,3.8,0.0038\\n2019-11-12T00:15,9.9,7.3,13.0,9.9,7.5,13.1,0.0,1.7,0.0018\\n2020-02-22T12:00,20.5,15.0,21.6,20.6,15.1,22.6,1.9,1.7,0.0066\\n2019-08-13T08:30,12.8,11.5,16.7,12.9,11.9,17.2,1.4,3.1,0.0042\\n \\n CSV Table B: cHPoo7lgKBA,TeH5/klJBIw,MaSbo+Z2DHA,36f4XRtKk+w,I6bLqKSl6OM,09ii68KGAcU,mlTxGdesaBg,ApUalwZOj0I,qVjPndX/zGk\\n0.0,0.0,0.0,2019-06-28T16:08,5.0 out of 5 stars,6040452,No,0.0,2024-04-23T05:00:01.\\n1.7,11.3,17.9,2019-12-04T13:00,5.0 out of 5 stars,6038888,No,11.9,2024-04-23T05:00:01.\\n2.6,6.8,11.9,2020-03-02T07:45,5.0 out of 5 stars,5941356,No,7.1,2024-04-23T05:00:01.\\n-1.0,4.7,8.2,2020-02-16T01:30,5.0 out of 5 stars,6040452,No,5.0,2024-04-23T05:00:01.\\n-0.6,3.2,7.3,2020-01-29T04:00,5.0 out of 5 stars,5941356,No,3.3,2024-04-23T05:00:01.\\n1.7,13.4,16.0,2019-10-27T21:15,4.0 out of 5 stars,5510456,Si,13.7,2024-04-23T05:00:01.\\n-0.2,4.5,8.1,2020-02-21T06:45,5.0 out of 5 stars,6040452,Si,4.5,2024-04-23T05:00:01.\\n2.6,21.5,33.7,2019-11-04T14:45,5.0 out of 5 stars,5510456,Si,21.9,2024-04-23T05:00:01.\\n1.0,4.3,8.9,2019-11-26T06:00,5.0 out of 5 stars,6038888,No,4.6,2024-04-23T05:00:01.\\n1.8,11.3,18.7,2020-02-01T15:30,5.0 out of 5 stars,5026787,No,11.5,2024-04-23T05:00:01.\\n1.4,12.8,15.6,2019-07-23T07:30,5.0 out of 5 stars,6040452,Si,13.1,2024-04-23T05:00:01.\\n2.2,19.6,24.3,2020-03-23T19:45,5.0 out of 5 stars,5510456,No,19.7,2024-04-23T05:00:01.\\n1.3,11.2,19.0,2019-10-29T21:45,5.0 out of 5 stars,6038888,Si,11.5,2024-04-23T05:00:01.\\n1.3,12.2,16.7,2019-12-01T20:45,5.0 out of 5 stars,5941356,Si,12.6,2024-04-23T05:00:01.\\n-0.3,3.2,7.1,2020-01-21T04:15,5.0 out of 5 stars,5510456,No,3.5,2024-04-23T05:00:01.\\n5.9,30.2,38.2,2019-09-26T18:45,5.0 out of 5 stars,5026787,No,30.2,2024-04-23T05:00:01.\\n4.5,11.3,12.4,2020-03-03T09:30,5.0 out of 5 stars,5510456,No,11.8,2024-04-23T05:00:01.\\n0.4,13.2,13.1,2019-08-01T01:30,5.0 out of 5 stars,5026787,No,13.6,2024-04-23T05:00:01.\\n-0.4,7.7,8.3,2020-01-30T07:30,5.0 out of 5 stars,5510456,No,8.1,2024-04-23T05:00:01.\\n0.9,9.7,14.6,2019-10-28T05:00,5.0 out of 5 stars,6038888,No,9.8,2024-04-23T05:00:01.\\n \\n Output: \\n"
] | {"WL2": "TeH5/klJBIw", "VAL2": "ApUalwZOj0I", "VAL1": "MaSbo+Z2DHA", "RVAL1": "cHPoo7lgKBA", "DeviceTimeStamp": "36f4XRtKk+w"} | tablejoin |
d60522bc74ae4e6d7ba1a5e0401e53e4a3d7a7182fed328e72825445ceafba9d | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: URI,Age,2024 Net Worth,Industry,Source of Wealth,Title,Organization,Self-Made,Self-Made Score,Philanthropy Score\\nMarijke Mars,59.0,$9.6B,Food & Beverage,\"Candy, pet food\",,,False,2.0,\\nRay Lee Hunt,81.0,$7.2B,Energy,\"Oil, real estate\",,,False,5.0,2.0\\nArvind Poddar,66.0,$3.2B,Automotive,Tires,,,False,,\\nRoman Abramovich & f,57.0,$9.7B,Diversified,\"Steel, investments\",,,True,,\\nSudhir Mehta,69.0,$5.8B,Healthcare,\"Pharmaceuticals, pow\",,,False,,\\nWang Xing,45.0,$8.8B,Technology,Food delivery,,,True,,\\nTran Ba Duong & fami,64.0,$1.2B,Automotive,Automotive,,,True,,\\nYuri Shefler,56.0,$1.6B,Food & Beverage,Alcohol,,,True,,\\nSeo Jung-jin,66.0,$7.3B,Healthcare,Biotech,,Celltrion Inc.,True,,\\nBenu Gopal Bangur,92.0,$6.8B,Manufacturing,Cement,,,False,,\\nStuart Hoegner,,$2.5B,Finance & Investment,Cryptocurrency,,,True,,\\nGyorgy Gattyan,,$1.1B,Media & Entertainmen,Adult Entertainment,,,True,,\\nKevin David Lehmann,21.0,$3.3B,Fashion & Retail,Drugstores,,,False,,\\nDaniel Kretinsky,48.0,$9.4B,Energy,\"Energy, investments\",,,True,,\\nAndreas Pohl,59.0,$2.4B,Finance & Investment,Mutual funds,,,False,,\\nJared Isaacman,41.0,$1.9B,Technology,Payment processing,,,True,8.0,\\nElisabeth DeLuca & f,76.0,$8.2B,Food & Beverage,Subway,,,False,2.0,2.0\\n \\n CSV Table B: 3dYEUhFn25k,GYfbnsuJx3c,qec7t3TedKU,SmRhS/d2xpk,g4xCeD41TZs,7MoRrR9ITEw,7SxcDOM+98w,j4MgzSCqO6Q\\nNo,0,Weak,6040452,5.0 out of 5 stars,,0,24591000\\nNo,1,Weak,6038888,5.0 out of 5 stars,,0,8334800\\nNo,2,Weak,5941356,5.0 out of 5 stars,,0,9875400\\nNo,3,New,6040452,5.0 out of 5 stars,,0,8338300\\nNo,4,Weak,5941356,5.0 out of 5 stars,Ford Financial Fund,0,8995500\\nSi,5,New,5510456,4.0 out of 5 stars,,0,8564500\\nSi,6,New,6040452,5.0 out of 5 stars,Antofagasta PLC,0,8948500\\nSi,7,New,5510456,5.0 out of 5 stars,,0,11859900\\nNo,8,Weak,6038888,5.0 out of 5 stars,,0,16537400\\nNo,9,New,5026787,5.0 out of 5 stars,,0,11010400\\nSi,10,New,6040452,5.0 out of 5 stars,,0,7534000\\nNo,11,Weak,5510456,5.0 out of 5 stars,,0,9818100\\nSi,12,Weak,6038888,5.0 out of 5 stars,,0,9965000\\nSi,13,Good,5941356,5.0 out of 5 stars,Adani Group,0,20254600\\nNo,14,New,5510456,5.0 out of 5 stars,,0,9989300\\nNo,15,Weak,5026787,5.0 out of 5 stars,,0,12805200\\nNo,16,New,5510456,5.0 out of 5 stars,,0,12652800\\nNo,17,New,5026787,5.0 out of 5 stars,,0,9834300\\n \\n Output: \\n"
] | {"Organization": "7MoRrR9ITEw"} | tablejoin |
e824359153d4fea96a9257ecceb44a3bb95dd0c84f95e2e3964ebdcdf8e8b32b | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: ticker,month,trend,REVS10,REVS20,REVS5,RSTR12,RSTR24,EARNMOM,FiftyTwoWeekHigh\\n600522,2022/6/30,0,1.2333,1.2616,1.1159,0.8618,0.7484,2,1.0\\n423,2018/1/31,0,1.0274,1.0521,0.967,0.1947,0.4284,6,0.6423\\n601877,2021/1/31,0,0.9706,0.9446,0.931,0.3211,0.3986,2,0.798\\n600048,2022/10/31,1,0.8075,0.7801,0.8498,0.0997,-0.0357,2,0.2813\\n300033,2021/10/31,1,0.9708,0.8623,0.9624,-0.2148,0.0836,8,0.3073\\n600029,2019/5/31,1,1.007,0.8479,1.0056,-0.31,-0.1422,2,0.2882\\n601018,2018/9/30,0,1.0049,1.0123,1.0049,-0.3574,-0.1692,4,0.0436\\n600009,2019/12/31,0,0.9994,1.0436,1.0122,0.4317,0.5976,8,0.784\\n60,2018/3/31,1,0.9465,0.9333,1.0319,-0.1841,-0.151,4,0.0677\\n600023,2019/2/28,1,1.0414,1.0717,1.0437,-0.1304,-0.1258,-4,0.3134\\n601211,2019/11/30,1,0.9988,0.9681,1.0109,0.0672,-0.1566,0,0.2955\\n600309,2020/8/31,0,1.0908,1.0842,1.0294,0.5123,0.4557,-6,0.9659\\n2624,2019/11/30,1,1.1367,1.2008,1.0073,0.337,0.0987,2,0.905\\n \\n CSV Table B: NGeDFcnzn7Q,tbWH4NW21KE,urGRA/BeJ1g,ASvdFX/j0/E,80Qm2D0L2Xw,6V+5/UuEIB0,UzDJiMPnvzM,5s14gRQnpFg\\n0.9453,15.6466,0,24591000,6040452,Weak,0.9304,gas\\n1.0154,15.6466,1,8334800,6038888,Weak,0.994,gas\\n1.0249,15.6466,2,9875400,5941356,Weak,0.9896,gas\\n1.0761,15.6466,3,8338300,6040452,New,1.3318,gas\\n0.9926,15.6466,4,8995500,5941356,Weak,1.063,gas\\n1.0123,15.6466,5,8564500,5510456,New,0.9844,gas\\n0.9394,15.6466,6,8948500,6040452,New,0.8686,gas\\n0.9607,15.6466,7,11859900,5510456,New,0.9144,gas\\n1.0,15.6466,8,16537400,6038888,Weak,1.0197,gas\\n0.9579,15.6466,9,11010400,5026787,New,0.9259,gas\\n1.1432,15.6466,10,7534000,6040452,New,1.18,gas\\n0.9908,15.6466,11,9818100,5510456,Weak,0.9134,gas\\n0.9474,15.6466,12,9965000,6038888,Weak,0.9057,gas\\n \\n Output: \\n"
] | {"REVS10": "UzDJiMPnvzM", "REVS5": "NGeDFcnzn7Q"} | tablejoin |
519653e1054c2c48e303e4f8fb1fa2e5fe01d1fd1fb4d26fa45a33b5eb781a3c | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: DeviceTimeStamp,WL1,WL2,WL3,VAL1,VAL2,VAL3,RVAL1,RVAL2,RVAL3\\n2019-07-25T08:01,15.5,10.9,16.3,15.9,11.3,17.3,3.7,2.7,0.0057\\n2020-03-04T15:00,30.3,13.1,25.7,30.7,14.0,28.5,4.6,4.8,0.0122\\n2020-03-24T21:00,15.2,9.7,21.3,15.3,10.1,21.7,2.1,2.7,0.004\\n2019-10-30T04:10,13.8,8.0,15.7,13.8,8.2,16.1,1.0,1.6,0.0034\\n2019-10-30T09:15,16.7,15.8,15.9,17.0,16.1,17.0,3.1,3.1,0.006\\n2020-02-08T06:45,8.3,4.0,9.8,8.3,4.4,10.1,0.5,1.7,0.0025\\n2019-12-08T17:20,14.4,11.9,23.1,14.4,12.4,23.5,0.2,3.3,0.0046\\n2019-08-14T18:00,27.4,33.8,34.8,27.5,33.9,35.4,0.2,3.6,0.0065\\n2019-09-10T19:45,34.0,40.3,39.5,34.2,40.3,39.7,3.9,1.6,0.0033\\n2019-09-13T21:45,20.1,24.4,21.3,20.3,24.5,21.4,3.2,1.8,0.0023\\n2019-11-24T16:45,13.2,11.0,15.5,13.2,11.4,15.9,0.4,3.1,0.0037\\n2020-02-27T16:30,19.3,12.3,22.4,20.0,12.7,22.5,5.3,2.9,0.0021\\n2019-08-28T10:00,14.6,14.3,22.6,14.6,15.1,23.2,0.3,4.8,0.005\\n2019-08-18T02:45,11.0,8.4,14.8,11.0,8.6,15.1,0.0,1.7,0.0027\\n2020-04-10T20:00,20.8,13.2,22.4,20.9,13.3,22.7,2.1,1.4,0.0036\\n2019-08-18T03:55,8.4,8.2,13.5,8.4,8.5,13.6,1.0,1.9,0.002\\n2019-08-18T10:30,15.9,11.1,14.4,16.0,11.3,15.0,1.0,1.8,0.0039\\n2019-08-29T06:45,13.6,9.1,17.3,13.7,9.5,17.7,1.0,2.8,0.0036\\n2019-10-08T04:30,15.4,11.3,25.3,15.7,11.7,25.4,2.8,3.1,0.0008\\n \\n CSV Table B: mlTxGdesaBg,6kQGdj2iXsU,hQKNy+86p+0,2xE2qVXr7UM,J92S/IDpPZA,eshSFvEUsMY,v3NEVV2Owbs\\nNo,1.8,31.1,33.6,33.6,4.4,0\\nNo,1.8,33.2,19.6,19.5,2.7,1\\nNo,2.6,24.5,21.0,20.9,2.7,2\\nNo,1.4,18.0,10.2,10.1,1.4,3\\nNo,0.0,0.0,0.0,0.0,0.0,4\\nSi,1.8,17.9,16.6,16.5,1.6,5\\nSi,1.2,14.6,7.7,7.6,1.2,6\\nSi,0.0,0.0,0.0,0.0,0.0,7\\nNo,2.0,12.5,7.8,7.5,0.9,8\\nNo,1.6,35.5,31.6,31.6,2.0,9\\nSi,2.0,27.2,20.7,20.6,1.4,10\\nNo,3.8,36.4,35.1,34.9,2.0,11\\nSi,1.4,17.5,11.1,11.0,2.0,12\\nSi,3.2,35.0,38.9,38.8,1.4,13\\nNo,4.0,17.6,12.9,12.3,1.5,14\\nNo,3.1,15.7,13.6,13.2,0.0,15\\nNo,4.8,32.1,23.6,23.1,5.6,16\\nNo,1.2,7.5,5.8,5.6,0.7,17\\nNo,2.1,11.2,9.3,9.1,0.0,18\\nNo,2.3,13.0,7.8,7.5,1.8,19\\n \\n Output: \\n"
] | {"RVAL1": "eshSFvEUsMY", "RVAL2": "6kQGdj2iXsU", "WL2": "J92S/IDpPZA", "VAL2": "2xE2qVXr7UM", "VAL1": "hQKNy+86p+0"} | tablejoin |
a783dc9652728632d05f85ac5f944f71ffdfb2cc9dc6ea27e21ad80a96f44e48 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: interaction_id,query_time,domain,question_type,static_or_dynamic,query,answer,alternative_answers,split,page_name\\n144bd3d2-be2b-4fcb-a,\"02/28/2024, 10:04:20\",open,simple_w_condition,static,who is the last empe,toghon temür,[],0,Yuan dynasty - Wikip\\na91df871-089c-4b91-9,\"03/19/2024, 23:17:23\",movie,simple,static,who directed bridget,beeban kidron,[],1,Bridget Jones: The E\\nc4388294-a648-414b-8,\"03/13/2024, 10:07:09\",music,multi-hop,static,who is the american ,lady gaga is the ame,[],1,Grammy Award for Son\\n0b18bc03-a372-4860-a,\"02/28/2024, 07:29:24\",finance,false_premise,fast-changing,on the day that cgi ,invalid question,[],1,Stock info GIB | CGI\\ne04341c6-c7f6-415f-b,\"03/10/2024, 21:43:12\",sports,comparison,static,which team\\'s home ar,chicago bulls,[],1,The Madhouse on Madi\\n07c155bc-34c4-4e8e-a,\"02/28/2024, 07:53:27\",finance,simple,real-time,what\\'s today\\'s curre,i don\\'t know,[],1,DCFC | Tritium DCFC \\n42fa780d-1b01-4dac-a,\"03/15/2024, 15:56:22\",sports,simple_w_condition,slow-changing,who was the leader f,brendan chardonnet,[],0,French Ligue 1 Stats\\n8a687b2a-38db-4132-8,\"03/13/2024, 09:43:37\",music,comparison,slow-changing,who has had more num,drake has had more n,[],0,Hot 100 Songs\\n1c96bf4f-a404-4982-9,\"03/17/2024, 16:46:21\",finance,simple_w_condition,static,what was the low pri,meta low stock price,[],1,\"Meta Platforms, Inc.\"\\n71af3fb4-bb37-4720-b,\"03/13/2024, 09:04:34\",finance,multi-hop,fast-changing,which company in the,the company with the,[],1,D | S&P 500 Stock | \\n655d2141-1090-4aab-8,\"03/05/2024, 23:22:11\",music,aggregation,slow-changing,how many successful ,3,[],1,\"Chris Cornell Songs,\"\\ne6b1f088-a55e-41bd-9,\"03/05/2024, 23:37:26\",movie,post-processing,slow-changing,what was the average,\"$191,671,856\",[],0,\\'Black Panther: Waka\\nb62fdd74-69ec-48e1-9,\"03/15/2024, 16:02:55\",sports,simple_w_condition,static,\"on 2022-10-12, what \",94,[],1,Charlotte Hornets ac\\n \\n CSV Table B: aONjSdwYYDk,PjOW3vib37M,N63uV44/QbQ,31Z18wvwUiM,eJJm7lex974,V9rPaOdeODk,8b3ewM26+SI,AUUii56u8tg\\n[],multi-hop,The 17 Football Club,2024-04-23T05:00:01.,1cba1106-7e25-4777-8,6040452,No,7\\n[],false_premise,Wadishewadi Dam - Wi,2024-04-23T05:00:01.,5c727dee-a307-4c15-a,6038888,No,invalid question\\n[],multi-hop,Drake Albums and Dis,2024-04-23T05:00:01.,21da19e6-56a8-439a-9,5941356,No,drake released his f\\n[],simple_w_condition,Ranking Every NBA De,2024-04-23T05:00:01.,521b6740-ce8d-4cd6-a,6040452,No,tina charles has the\\n[],simple,Trading Volume: Anal,2024-04-23T05:00:01.,76129ef6-369c-481e-a,5941356,No,119\\n[],aggregation,Marilyn Monroe\\'s Hus,2024-04-23T05:00:01.,ff7d4fd0-dccb-4d5c-8,5510456,Si,1\\n[],simple_w_condition,Miami Heat News and ,2024-04-23T05:00:01.,5c5234a3-d684-42ba-8,6040452,Si,denver nuggets\\n[],aggregation,National Football Le,2024-04-23T05:00:01.,639d2cc0-99d6-4346-a,5510456,Si,32\\n[],simple,Pitch Perfect Movie ,2024-04-23T05:00:01.,e2941d28-c26e-4d88-9,6038888,No,9/28/12\\n[],comparison,Bigger career: Adele,2024-04-23T05:00:01.,999a7f32-8a87-4026-b,5026787,No,shakira had more par\\n[],comparison,Sporting Speed Recor,2024-04-23T05:00:01.,d7bcbd24-a0fb-4139-8,6040452,Si,bolt\\n[],aggregation,Super Bowls - Dallas,2024-04-23T05:00:01.,3b9e7284-41a2-43aa-a,5510456,No,the dallas cowboys h\\n[],simple_w_condition,Kelly Gallant | Rott,2024-04-23T05:00:01.,45037240-6762-488e-a,6038888,Si,talons of the eagle\\n[],simple_w_condition,Nike Inc Stock Price,2024-04-23T05:00:01.,8135a393-aedc-4073-a,5941356,Si,$118.55\\n \\n Output: \\n"
] | {"question_type": "PjOW3vib37M", "interaction_id": "eJJm7lex974", "page_name": "N63uV44/QbQ", "answer": "AUUii56u8tg", "alternative_answers": "aONjSdwYYDk"} | tablejoin |
4d351c29bdddf5c41d59cd7bd1b70bb4d2ae2a071ada382d7690066b1cd7764c | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: :@computed_region_dqjc_k29y,:@computed_region_jdnu_jmst,:@computed_region_5d9v_6bui,permitnum,worktype,applicationtype,location,:@computed_region_mfuy_bee2,:@computed_region_2fpw_swv9,:@computed_region_9p4x_9cjt\\n,,,BLD2023-04121,Residential,Building,{'human_address': '{,,,\\n1.0,80.0,26.0,BLD2023-06991,Commercial,Building,{'latitude': '40.771,19.0,18.0,12.0\\n24.0,97.0,26.0,BLD2023-08421,Residential,Building,{'latitude': '40.713,19.0,27.0,573.0\\n12.0,67.0,26.0,BLD2023-05798,Commercial,Building,{'latitude': '40.739,19.0,26.0,358.0\\n1.0,72.0,26.0,BLD2023-07147,Commercial,Building,{'latitude': '40.762,19.0,21.0,495.0\\n23.0,68.0,26.0,BLD2023-03932,Commercial,Building,{'latitude': '40.729,19.0,24.0,243.0\\n12.0,68.0,26.0,BLD2023-06214,Residential,Building,{'latitude': '40.737,19.0,24.0,583.0\\n1.0,72.0,26.0,BLD2023-08511,Commercial,Building,{'latitude': '40.727,19.0,21.0,364.0\\n24.0,68.0,26.0,BLD2023-08557,Residential,Building,{'latitude': '40.744,19.0,24.0,244.0\\n12.0,67.0,26.0,BLD2023-06743,Commercial,Building,{'latitude': '40.734,19.0,26.0,358.0\\n \\n CSV Table B: CMSip4kAsFA,v02+v1698aE,sXpNMhZkCLA,t8DtGa8xUVw,WPAmEDDzzew,SfVC0olx/OE,MOmbowjYQ+I,hOL2mHzD+cg\\nBLD2023-06614,No,26.0,0,358.0,24591000,21.0,Commercial\\nBLD2023-06869,No,26.0,0,361.0,8334800,20.0,Residential\\nBLD2023-05395,No,26.0,0,364.0,9875400,21.0,Residential\\nBLD2023-07713,No,26.0,0,242.0,8338300,21.0,Residential\\nBLD2023-05391,No,26.0,0,364.0,8995500,21.0,Residential\\nBLD2023-02758,Si,26.0,0,474.0,8564500,20.0,Residential\\nBLD2023-06021,Si,26.0,0,357.0,8948500,21.0,Commercial\\nBLD2023-06051,Si,26.0,0,161.0,11859900,20.0,Residential\\nBLD2023-08747,No,26.0,0,14.0,16537400,24.0,Commercial\\nBLD2023-07969,No,26.0,0,573.0,11010400,27.0,Residential\\nBLD2023-05155,Si,26.0,0,567.0,7534000,21.0,Commercial\\n \\n Output: \\n"
] | {":@computed_region_2fpw_swv9": "MOmbowjYQ+I", "worktype": "hOL2mHzD+cg", ":@computed_region_9p4x_9cjt": "WPAmEDDzzew", "permitnum": "CMSip4kAsFA", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA"} | tablejoin |
44953ce33916e7caae16bbce54fbd5a4e00d438924e5e53c0b5c5765ce5a583f | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: tweet_id,airline_sentiment,airline_sentiment_confidence,negativereason,negativereason_confidence,airline,airline_sentiment_gold,name,negativereason_gold,retweet_count\\n567849102731526144,negative,1.0,Customer Service Iss,1.0,US Airways,,TerriHaisten,,0\\n568210087212388353,neutral,1.0,,,Southwest,,livvyports16,,1\\n569824906638073856,negative,1.0,Bad Flight,0.3451,United,,bmalones44,,1\\n569558589628502016,negative,0.6927,Can't Tell,0.6927,United,,4geiger,,0\\n569627744021184513,negative,1.0,Cancelled Flight,0.6673,American,,MatthewJMedlin,,0\\n568809369678315521,negative,1.0,Cancelled Flight,1.0,US Airways,,JeffreyWhitmore,,0\\n569456828511326208,negative,1.0,Late Flight,0.6478,US Airways,,CJLarcheveque,,0\\n569615736387325952,negative,1.0,Bad Flight,0.3487,Southwest,,Ekanewilliams,,0\\n568519360953716736,neutral,1.0,,,Southwest,,MikeWJZ,,1\\n569638848214507520,positive,1.0,,,Delta,,oggito17,,0\\n569275566077165568,neutral,1.0,,,United,,SallyM0nster,,0\\n569826992251473921,neutral,0.6471,,0.0,United,,ohlesliebarker,,0\\n569598614235942912,negative,1.0,Late Flight,1.0,Southwest,,BattleB_studios,,0\\n568460037737324545,neutral,1.0,,,United,,JerseyRic,,0\\n568491905903939584,negative,1.0,Customer Service Iss,0.6579,US Airways,,jekyllandheid12,,0\\n \\n CSV Table B: 3sk7jMfQzck,NYLj0y6YLFA,AG1gKyPX4RQ,QgYMUapyJlU,7dYptJU3eKE,c2A+LJlP174,6lLeTaOQ74g,DAzjs8gwVB0\\nUS Airways,0,5.0 out of 5 stars,0,24591000,,Weak,2024-04-23T05:00:01.\\nAmerican,0,5.0 out of 5 stars,0,8334800,,Weak,2024-04-23T05:00:01.\\nDelta,0,5.0 out of 5 stars,0,9875400,,Weak,2024-04-23T05:00:01.\\nAmerican,0,5.0 out of 5 stars,0,8338300,,New,2024-04-23T05:00:01.\\nUnited,0,5.0 out of 5 stars,0,8995500,,Weak,2024-04-23T05:00:01.\\nAmerican,0,4.0 out of 5 stars,0,8564500,,New,2024-04-23T05:00:01.\\nDelta,0,5.0 out of 5 stars,0,8948500,,New,2024-04-23T05:00:01.\\nUnited,0,5.0 out of 5 stars,0,11859900,,New,2024-04-23T05:00:01.\\nAmerican,0,5.0 out of 5 stars,0,16537400,,Weak,2024-04-23T05:00:01.\\nDelta,0,5.0 out of 5 stars,0,11010400,,New,2024-04-23T05:00:01.\\nUS Airways,0,5.0 out of 5 stars,0,7534000,,New,2024-04-23T05:00:01.\\nSouthwest,0,5.0 out of 5 stars,0,9818100,,Weak,2024-04-23T05:00:01.\\nAmerican,0,5.0 out of 5 stars,0,9965000,,Weak,2024-04-23T05:00:01.\\nUnited,0,5.0 out of 5 stars,0,20254600,,Good,2024-04-23T05:00:01.\\nUnited,0,5.0 out of 5 stars,1,9989300,,New,2024-04-23T05:00:01.\\n \\n Output: \\n"
] | {"airline": "3sk7jMfQzck", "negativereason_gold": "c2A+LJlP174", "retweet_count": "QgYMUapyJlU"} | tablejoin |
a9622ef291b2ff5dac8ee5335d50d52a7bc8bd9fa001130fabaf3ae3d1505100 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: drugName,url,description\\nDexamethasone,https://www.drugs.co,dexamethasone is a c\\nGaramycin,https://www.drugs.co,garamycin is an anti\\nDicyclomine,https://www.drugs.co,dicyclomine relieves\\nOrphenadrine,https://www.drugs.co,orphenadrine is a mu\\nStrattera,https://www.drugs.co,strattera (atomoxeti\\nValsartan,https://www.drugs.co,valsartan is used to\\nSingulair,https://www.drugs.co,singulair (monteluka\\nYupelri,https://www.drugs.co,yupelri (revefenacin\\nKetoconazole,https://www.drugs.co,ketoconazole is an a\\nZolpidem,https://www.drugs.co,zolpidem is a sedati\\nVivitrol,https://www.drugs.co,vivitrol (naltrexone\\nGlimepiride,https://www.drugs.co,glimepiride is an or\\nGlucosamine,https://www.drugs.co,glucosamine is sugar\\nBasaglar,https://www.drugs.co,basaglar (insulin gl\\nAleve,https://www.drugs.co,aleve (naproxen) is \\nStelara,https://www.drugs.co,stelara (ustekinumab\\nYervoy,https://www.drugs.co,yervoy (ipilimumab) \\n \\n CSV Table B: wmYO8hwe094,7SxcDOM+98w\\neffexor xr is a sele,0\\nqdolo is: a strong p,0\\nketotifen is an anti,0\\ntoprol-xl (metoprolo,0\\namlodipine is a calc,0\\nvitamin e is an anti,0\\nprevacid (lansoprazo,0\\nferrous sulfate is a,0\\nbacitracin is an ant,0\\noxybutynin reduces m,0\\njanuvia (sitagliptin,0\\nskelaxin (metaxalone,0\\nwitch hazel is a pla,0\\ntestosterone is a na,0\\nflagyl (metronidazol,0\\nascorbic acid (vitam,0\\n\"niacin, also called \",0\\nprednisolone is a st,0\\n \\n Output: \\n"
] | {"description": "wmYO8hwe094"} | tablejoin |
0bf086ff674cfda54c0293a3ae03a3720d2d1cb755748cc4800d43b375d20a3c | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: Age ,Gender,BMI,Fever,Nausea/Vomting,Headache ,Diarrhea ,Fatigue & generalized bone ache ,Jaundice ,Epigastric pain \\n59,2,25,1,1,2,2,2,1,2\\n42,1,28,2,1,2,2,2,1,1\\n61,1,27,2,2,2,2,2,2,1\\n33,2,24,2,1,1,1,2,2,2\\n38,1,29,1,1,2,2,2,1,2\\n49,2,30,2,1,1,1,1,1,2\\n42,1,35,2,1,2,1,2,2,2\\n61,2,23,2,2,1,2,1,2,1\\n34,1,26,1,2,1,2,2,1,2\\n38,1,33,2,2,2,2,2,1,2\\n54,2,30,1,2,2,1,2,2,2\\n \\n CSV Table B: oOd+cX72roM,I4BVsbooFyQ,cslDY8TWfKw,cIESFwIKxuA,F2WS20DtzCs,huCAhXWo21c,YH4pJE8EqH0\\n36,gas,1,Weak,5.0 out of 5 stars,1,6040452\\n53,gas,1,Weak,5.0 out of 5 stars,2,6038888\\n36,gas,2,Weak,5.0 out of 5 stars,2,5941356\\n47,gas,1,New,5.0 out of 5 stars,1,6040452\\n44,gas,2,Weak,5.0 out of 5 stars,1,5941356\\n53,gas,1,New,4.0 out of 5 stars,2,5510456\\n44,gas,1,New,5.0 out of 5 stars,1,6040452\\n37,gas,1,New,5.0 out of 5 stars,2,5510456\\n46,gas,1,Weak,5.0 out of 5 stars,2,6038888\\n61,gas,2,New,5.0 out of 5 stars,2,5026787\\n49,gas,2,New,5.0 out of 5 stars,1,6040452\\n37,gas,2,Weak,5.0 out of 5 stars,2,5510456\\n \\n Output: \\n"
] | {"Fever": "huCAhXWo21c", "Age ": "oOd+cX72roM", "Epigastric pain ": "cslDY8TWfKw"} | tablejoin |
dd7ff515b9cd4c4a6e1d3fe3cb5e14c77123225c73193ce89c104b4f3f80cf22 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: app_no,type,app_date,status,fru_interview_scheduled,drug_test,wav_course,defensive_driving,driver_exam,medical_clearance_form\\n6068038,HDR,2024-02-14T00:00:00.,Approved - License I,Not Applicable,Complete,Complete,Complete,Complete,Complete\\n6070024,HDR,2024-03-11T00:00:00.,Approved - License I,Not Applicable,Complete,Complete,Complete,Complete,Complete\\n6071255,HDR,2024-03-27T00:00:00.,Approved - License I,Not Applicable,Complete,Complete,Complete,Complete,Complete\\n6071006,HDR,2024-03-24T00:00:00.,Incomplete,Not Applicable,Needed,Needed,Needed,Needed,Needed\\n6065967,HDR,2024-01-18T00:00:00.,Incomplete,Not Applicable,Needed,Complete,Complete,Needed,Needed\\n6072382,HDR,2024-04-13T00:00:00.,Incomplete,Not Applicable,Needed,Complete,Complete,Needed,Needed\\n6069398,HDR,2024-03-02T00:00:00.,Incomplete,Not Applicable,Needed,Needed,Needed,Needed,Needed\\n6070427,HDR,2024-03-16T00:00:00.,Incomplete,Not Applicable,Needed,Complete,Needed,Needed,Needed\\n6071162,HDR,2024-03-26T00:00:00.,Approved - License I,Not Applicable,Complete,Complete,Complete,Complete,Complete\\n6067621,HDR,2024-02-08T00:00:00.,Approved - License I,Not Applicable,Complete,Complete,Complete,Complete,Complete\\n6071150,HDR,2024-03-26T00:00:00.,Approved - License I,Not Applicable,Complete,Complete,Complete,Complete,Complete\\n6072162,HDR,2024-04-10T00:00:00.,Incomplete,Not Applicable,Needed,Needed,Needed,Needed,Needed\\n6071242,HDR,2024-03-27T00:00:00.,Incomplete,Not Applicable,Needed,Complete,Needed,Needed,Needed\\n6068081,HDR,2024-02-14T00:00:00.,Approved - License I,Not Applicable,Complete,Complete,Complete,Complete,Complete\\n \\n CSV Table B: kT8cHJ58B7E,LAjKEsrx0pI,qU8fN4BcOE4,4MSYlVBQT9Y,qrA0NE/ugMQ,8QouQFH8JWo,Qiz4gNNSkjU,BkPad8F1Zfw\\nComplete,15.6466,Not Applicable,Complete,5.0 out of 5 stars,0,0,Weak\\nNeeded,15.6466,Not Applicable,Complete,5.0 out of 5 stars,1,0,Weak\\nComplete,15.6466,Not Applicable,Complete,5.0 out of 5 stars,2,0,Weak\\nNeeded,15.6466,Not Applicable,Needed,5.0 out of 5 stars,3,0,New\\nComplete,15.6466,Not Applicable,Complete,5.0 out of 5 stars,4,0,Weak\\nNeeded,15.6466,Not Applicable,Complete,4.0 out of 5 stars,5,0,New\\nNeeded,15.6466,Not Applicable,Complete,5.0 out of 5 stars,6,0,New\\nComplete,15.6466,Not Applicable,Complete,5.0 out of 5 stars,7,0,New\\nComplete,15.6466,Not Applicable,Complete,5.0 out of 5 stars,8,0,Weak\\nNeeded,15.6466,Not Applicable,Needed,5.0 out of 5 stars,9,0,New\\nComplete,15.6466,Not Applicable,Complete,5.0 out of 5 stars,10,0,New\\nComplete,15.6466,Not Applicable,Complete,5.0 out of 5 stars,11,0,Weak\\nNeeded,15.6466,Not Applicable,Complete,5.0 out of 5 stars,12,0,Weak\\nComplete,15.6466,Not Applicable,Complete,5.0 out of 5 stars,13,0,Good\\n \\n Output: \\n"
] | {"defensive_driving": "kT8cHJ58B7E", "fru_interview_scheduled": "qU8fN4BcOE4", "wav_course": "4MSYlVBQT9Y"} | tablejoin |
52b2630e360ae523378662c58b554046d5086033761e830cee61d24e46850889 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: job__,doc__,borough,house__,street_name,block,lot,bin__,job_type,job_status\\n102353819,1,MANHATTAN,200,VESEY STREET,16,140,1000059,A2,R\\n301890522,1,BROOKLYN,3057,BRIGHTON 6 STREET,8676,18,3397165,A2,P\\n421743297,1,QUEENS,35-06,UNION STREET,4961,19,4112190,A3,X\\n301890611,1,BROOKLYN,799,LINCOLN AVENUE,4271,75,3095894,A2,P\\n301812821,1,BROOKLYN,252,HEYWARD STREET,2234,10,3061217,A1,R\\n420181494,1,QUEENS,84-01,37 AVENUE,1458,40,4035835,DM,X\\n301907300,1,BROOKLYN,1224,MYRTLE AVENUE,3216,1,3073099,A2,Q\\n301876469,1,BROOKLYN,1858,61 STREET,5526,29,3132483,A2,X\\n123923861,2,MANHATTAN,122 CANOPY,WEST 145 STREET,2013,44,1060173,DM,E\\n440673718,1,QUEENS,13815,111TH AVENUE,11923,42,4257665,A2,X\\n301927565,1,BROOKLYN,767,MARCY AVENUE,1804,1,3050668,A1,X\\n310061410,1,BROOKLYN,2848,BRIGHTON 7 STREET,7263,44,3392249,A3,X\\n401178569,1,QUEENS,105-50,87 STREET,9149,31,4190407,A2,R\\n301896580,1,BROOKLYN,343,89 STREET,6062,57,3154082,A1,R\\n \\n CSV Table B: Bezp8Kegeiw,pCAjik4u8jI,Qiz4gNNSkjU,qrA0NE/ugMQ,aMV7Uv4npe4,o6kyvs5L8qM,SDXgS2fule4,V9rPaOdeODk\\n24591000,16,0,5.0 out of 5 stars,A2,1000059,MANHATTAN,6040452\\n8334800,6242,0,5.0 out of 5 stars,DM,3161109,BROOKLYN,6038888\\n9875400,1352,0,5.0 out of 5 stars,A2,3324609,BROOKLYN,5941356\\n8338300,15652,0,5.0 out of 5 stars,A2,4299432,QUEENS,6040452\\n8995500,12050,0,5.0 out of 5 stars,A2,4261657,QUEENS,5941356\\n8564500,6802,0,4.0 out of 5 stars,NB,3392757,BROOKLYN,5510456\\n8948500,409,0,5.0 out of 5 stars,A2,1005301,MANHATTAN,6040452\\n11859900,892,0,5.0 out of 5 stars,A2,1078770,MANHATTAN,5510456\\n16537400,1084,0,5.0 out of 5 stars,A3,3414197,BROOKLYN,6038888\\n11010400,6086,0,5.0 out of 5 stars,A2,3154739,BROOKLYN,5026787\\n7534000,2309,0,5.0 out of 5 stars,A1,3061729,BROOKLYN,6040452\\n9818100,13436,0,5.0 out of 5 stars,NB,4286222,QUEENS,5510456\\n9965000,792,0,5.0 out of 5 stars,A2,3013325,BROOKLYN,6038888\\n20254600,4971,0,5.0 out of 5 stars,A3,4112252,QUEENS,5941356\\n \\n Output: \\n"
] | {"block": "pCAjik4u8jI", "bin__": "o6kyvs5L8qM", "job_type": "aMV7Uv4npe4", "borough": "SDXgS2fule4"} | tablejoin |
a215b90180b104679133c979614fe0feeb770b6a3d1df4d41065e15be2ed7051 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: center,center_search_status,facility,occupied,record_date,last_update,country,contact,phone,location\\nKennedy Space Center,Public,Support Areas/1726/H,1957-01-01T00:00:00.,1996-03-01T00:00:00.,2015-06-22T00:00:00.,US,Sheryl Chaffee,321-867-8047,{'latitude': '28.538\\nMichoud Assembly Fac,Public,Port Michoud Facilit,1963-01-01T00:00:00.,2009-01-29T00:00:00.,2013-02-19T00:00:00.,US,Ernest Graham,504.257-2619,{'latitude': '29.950\\nMarshall Space Fligh,Public,ET Acoustic Test Fac,1959-01-01T00:00:00.,1996-03-01T00:00:00.,2014-03-31T00:00:00.,US,Pam Caruso,256-544-7795,{'latitude': '34.729\\nGlenn Research Cente,Public,Hypersonic Tunnel Fa,1966-01-01T00:00:00.,1996-03-01T00:00:00.,2015-03-04T00:00:00.,US,Linda C. Elonen-Wrig,216-433-9370,{'latitude': '41.430\\nArmstrong Flight Res,Public,Bldg. 4982 - Aeronau,,2010-04-13T00:00:00.,2014-12-19T00:00:00.,US,Facilities Utilizati,661-276-2585,{'latitude': '35.000\\nLangley Research Cen,Public,Structural Acoustic ,,2012-08-01T00:00:00.,2012-08-02T00:00:00.,US,Sherry Johnson,757.864-3848,{'latitude': '37.086\\nLangley Research Cen,Public,Research Laboratory,1967-01-01T00:00:00.,1996-03-01T00:00:00.,2013-02-25T00:00:00.,US,Sherry Johnson,757.864-3848,{'latitude': '37.086\\nKennedy Space Center,Public,High Bay/M7-360/SSPF,1995-01-01T00:00:00.,1996-03-01T00:00:00.,2015-06-22T00:00:00.,US,Sheryl Chaffee,321-867-8047,{'latitude': '28.538\\nStennis Space Center,Public,Test Facility E-1 #4,1992-01-01T00:00:00.,1996-03-01T00:00:00.,2015-04-06T00:00:00.,US,Robert Bruce,228-688-1646,{'latitude': '30.385\\nMarshall Space Fligh,Public,EP Propulsion Techno,1965-01-01T00:00:00.,1996-03-01T00:00:00.,2014-03-31T00:00:00.,US,Pam Caruso,256-544-7795,{'latitude': '34.729\\nAmes Research Center,Public,N237 - HYPERVELOCITY,1964-01-01T00:00:00.,1996-03-01T00:00:00.,2014-06-13T00:00:00.,US,Rocci Caringello,650 603-9506,{'latitude': '37.414\\nAmes Research Center,Public,N204A - SPACE TECHNO,1966-01-01T00:00:00.,1996-03-01T00:00:00.,2014-06-12T00:00:00.,US,Rocci Caringello,650 603-9506,{'latitude': '37.414\\nLangley Research Cen,Public,Materials Processing,1960-01-01T00:00:00.,1996-03-01T00:00:00.,2013-02-19T00:00:00.,US,Sherry Johnson,757.864-3848,{'latitude': '37.086\\nMarshall Space Fligh,Public,EM-20 Automated Ultr,,2006-08-11T00:00:00.,2014-06-02T00:00:00.,US,Pam Caruso,256-544-7795,{'latitude': '34.729\\n \\n CSV Table B: NYLj0y6YLFA,YuvUZcQJObM,7dYptJU3eKE,ObftKnUmRWM,DAzjs8gwVB0,mo27EyZRoiE\\n0,Public,24591000,{'latitude': '41.430,2024-04-23T05:00:01.,2015-03-04T00:00:00.\\n0,Public,8334800,{'latitude': '34.178,2024-04-23T05:00:01.,2013-08-07T00:00:00.\\n0,Public,9875400,{'latitude': '34.178,2024-04-23T05:00:01.,2013-08-07T00:00:00.\\n0,Public,8338300,{'latitude': '34.729,2024-04-23T05:00:01.,2014-06-02T00:00:00.\\n0,Public,8995500,{'latitude': '28.538,2024-04-23T05:00:01.,2015-06-22T00:00:00.\\n0,Public,8564500,{'latitude': '37.086,2024-04-23T05:00:01.,2013-02-25T00:00:00.\\n0,Public,8948500,{'latitude': '37.086,2024-04-23T05:00:01.,2013-02-25T00:00:00.\\n0,Public,11859900,{'latitude': '37.086,2024-04-23T05:00:01.,2013-01-28T00:00:00.\\n0,Public,16537400,{'latitude': '29.950,2024-04-23T05:00:01.,2013-02-19T00:00:00.\\n0,Public,11010400,{'latitude': '34.729,2024-04-23T05:00:01.,2014-06-02T00:00:00.\\n0,Public,7534000,{'latitude': '34.178,2024-04-23T05:00:01.,2013-08-07T00:00:00.\\n0,Public,9818100,{'latitude': '38.995,2024-04-23T05:00:01.,2013-08-16T00:00:00.\\n0,Public,9965000,{'latitude': '34.729,2024-04-23T05:00:01.,2014-06-02T00:00:00.\\n0,Public,20254600,{'latitude': '41.430,2024-04-23T05:00:01.,2015-03-04T00:00:00.\\n0,Public,9989300,{'latitude': '34.729,2024-04-23T05:00:01.,2014-06-02T00:00:00.\\n \\n Output: \\n"
] | {"location": "ObftKnUmRWM", "center_search_status": "YuvUZcQJObM", "last_update": "mo27EyZRoiE"} | tablejoin |
d03bcee55bda5e582cc13547ab9bf898fbd1324fd5690481cc0d8a4ae9fd24f9 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: tweet_id,airline_sentiment,airline_sentiment_confidence,negativereason,negativereason_confidence,airline,airline_sentiment_gold,name,negativereason_gold,retweet_count\\n569518979103924224,neutral,0.64,,0.0,United,,throthra,,0\\n569407352299847680,negative,0.7029,Late Flight,0.3619,United,,MarkGilden,,0\\n570177012360462336,negative,1.0,longlines,0.3611,American,,JayFranceschi,,0\\n568808318560550912,positive,0.6838,,,Delta,,matthewhirsch,,0\\n569490427625086976,negative,1.0,Late Flight,1.0,Delta,,TIURach2014,,0\\n569925291331735552,negative,1.0,Customer Service Iss,1.0,American,,JustineTomkins,,0\\n568148213418455041,positive,1.0,,,United,,IrisSanchezCDE,,0\\n568172386903851008,positive,1.0,,,Delta,,MarissaBreton,,0\\n569342508553121795,negative,1.0,Customer Service Iss,1.0,US Airways,,realmattberry,,0\\n569667638651170816,neutral,1.0,,,Southwest,,OneToughShark,,0\\n568272244792631296,negative,1.0,Late Flight,1.0,United,,Atrain_8,,1\\n569661113593425920,negative,1.0,Bad Flight,0.3481,US Airways,,ElmiraBudMan,,0\\n569941957490774016,positive,1.0,,,Virgin America,,TaylorLumsden,,0\\n570296616688750592,negative,0.6725,Flight Booking Probl,0.6725,American,,AesaGaming,,0\\n569826992251473921,neutral,0.6471,,0.0,United,,ohlesliebarker,,0\\n \\n CSV Table B: a6oKqAbhiYE,C8eRZt40qKM,c2A+LJlP174,jUs0oGda1Ms,3nNNqrYxl08,q76k2bUnOlk,NYLj0y6YLFA\\ngas,American,,Can't Tell,0.6753,569895817403768833,0\\ngas,American,,Cancelled Flight,1.0,569870252508635136,0\\ngas,US Airways,,,0.6682,569638479157723136,0\\ngas,United,,Customer Service Iss,1.0,569722020776116224,0\\ngas,Delta,,Late Flight,0.682,569535236884664320,0\\ngas,US Airways,,Cancelled Flight,1.0,569698944084680704,0\\ngas,Southwest,,,1.0,568981498046623744,0\\ngas,United,,Flight Booking Probl,1.0,568840701850419200,0\\ngas,United,,Customer Service Iss,1.0,567789435795861504,0\\ngas,United,,Customer Service Iss,1.0,568574014505029632,0\\ngas,Southwest,,Customer Service Iss,1.0,569334621252526080,0\\ngas,Southwest,,,1.0,570041591714455552,0\\ngas,American,,,0.6677,570033000777457664,0\\ngas,Virgin America,,,1.0,570010571707256832,0\\ngas,Delta,,,1.0,568910753652199424,0\\n \\n Output: \\n"
] | {"negativereason_gold": "c2A+LJlP174", "airline": "C8eRZt40qKM", "airline_sentiment_confidence": "3nNNqrYxl08", "tweet_id": "q76k2bUnOlk", "negativereason": "jUs0oGda1Ms", "retweet_count": "NYLj0y6YLFA"} | tablejoin |
b8a3e0f6c177bbef546e0dd490a0193b02124e193d5ffe093d86963449cba596 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: Age ,Gender,BMI,Fever,Nausea/Vomting,Headache ,Diarrhea ,Fatigue & generalized bone ache ,Jaundice ,Epigastric pain \\n39,2,33,2,1,2,1,1,1,2\\n48,1,24,1,1,1,2,2,2,2\\n52,1,28,2,2,1,2,1,2,2\\n58,1,31,2,2,2,1,1,1,1\\n49,1,33,2,2,1,1,2,1,1\\n58,2,23,1,1,2,2,1,2,2\\n53,2,31,1,1,1,1,2,2,2\\n35,2,25,2,2,1,2,2,2,1\\n54,2,34,1,2,1,1,2,2,2\\n38,1,27,1,2,2,1,1,2,2\\n56,1,26,1,2,1,1,1,2,1\\n \\n CSV Table B: F2WS20DtzCs,ODDCZ5voqXs,YH4pJE8EqH0,kbyPjM4nFp0,cIESFwIKxuA,o1aE2g76cKc,w8B7SY5DO6Y\\n5.0 out of 5 stars,15.6466,6040452,2024-04-23T05:00:01.,Weak,1,No\\n5.0 out of 5 stars,15.6466,6038888,2024-04-23T05:00:01.,Weak,2,No\\n5.0 out of 5 stars,15.6466,5941356,2024-04-23T05:00:01.,Weak,1,No\\n5.0 out of 5 stars,15.6466,6040452,2024-04-23T05:00:01.,New,1,No\\n5.0 out of 5 stars,15.6466,5941356,2024-04-23T05:00:01.,Weak,2,No\\n4.0 out of 5 stars,15.6466,5510456,2024-04-23T05:00:01.,New,2,Si\\n5.0 out of 5 stars,15.6466,6040452,2024-04-23T05:00:01.,New,2,Si\\n5.0 out of 5 stars,15.6466,5510456,2024-04-23T05:00:01.,New,1,Si\\n5.0 out of 5 stars,15.6466,6038888,2024-04-23T05:00:01.,Weak,1,No\\n5.0 out of 5 stars,15.6466,5026787,2024-04-23T05:00:01.,New,2,No\\n5.0 out of 5 stars,15.6466,6040452,2024-04-23T05:00:01.,New,1,Si\\n5.0 out of 5 stars,15.6466,5510456,2024-04-23T05:00:01.,Weak,2,No\\n \\n Output: \\n"
] | {"Headache ": "o1aE2g76cKc"} | tablejoin |
2f1500d37ffd0e42cd2c89c04011cbbf5dd6b1f71f495156b016a967270cdded | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: REC_ID,Species,Continent.of.Origin,Country.of.Origin,Harvest.Year,Expiration,Variety,Color,Processing.Method,Aroma\\n1285,Arabica,North America,Mexico,2013.0,03/29/14,Typica,Green,Washed / Wet,7.08\\n454,Arabica,Africa,Tanzania,2014.0,12/12/15,Other,Bluish-Green,Washed / Wet,7.58\\n913,Arabica,North America,Guatemala,2017.0,06/01/18,Bourbon,Green,,7.5\\n864,Arabica,North America,Mexico,2012.0,09/10/13,Mundo Novo,Green,Washed / Wet,7.42\\n596,Arabica,North America,United States,2013.0,02/05/15,Hawaiian Kona,Blue-Green,Natural / Dry,7.67\\n1138,Arabica,North America,United States,,09/21/12,,,,7.5\\n985,Arabica,North America,United States,,09/21/12,,,,7.25\\n1260,Arabica,Asia,India,2016.0,01/16/18,,Green,Natural / Dry,7.67\\n820,Arabica,North America,Guatemala,2015.0,04/19/16,Catuai,Green,Washed / Wet,7.58\\n1294,Arabica,North America,Mexico,2014.0,05/08/15,Typica,,Washed / Wet,7.08\\n246,Arabica,North America,Guatemala,2014.0,06/27/15,Bourbon,Green,Other,7.75\\n1193,Arabica,North America,United States,2013.0,06/09/15,Other,Green,Washed / Wet,7.42\\n916,Arabica,North America,Costa Rica,2014.0,01/07/16,Caturra,Green,Washed / Wet,7.83\\n1076,Arabica,North America,United States,2013.0,02/04/15,Hawaiian Kona,Green,Natural / Dry,7.42\\n735,Arabica,Asia,Taiwan,2016.0,02/13/18,,Blue-Green,,7.0\\n328,Arabica,South America,Colombia,2012.0,11/22/13,Caturra,Green,Washed / Wet,7.75\\n312,Arabica,South America,Colombia,2010.0,02/09/12,,,,7.75\\n625,Arabica,Asia,Thailand,2012.0,06/13/13,Other,Bluish-Green,Washed / Wet,7.83\\n1333,Robusta,North America,United States,2012.0,02/28/13,Arusha,Green,Natural / Dry,7.92\\n \\n CSV Table B: x0YTt9hPYFI,vU50Gku+N1g,fg/VVHUVHIQ,zfzQ4Z9Dt5o,9lfBveG7CWM,6oyt+mdSeHI,iJKOBRCgJI0,LOldZF4dJII\\n2012.0,Bluish-Green,806,Typica,Weak,7.42,Washed / Wet,Asia\\n2014.0,,641,Other,Weak,7.75,Washed / Wet,Africa\\n2013.0,Green,406,Catuai,Weak,7.5,Washed / Wet,North America\\n2010.0,,1167,,New,7.25,,South America\\n2009.0,,531,Caturra,Weak,7.58,,North America\\n2013.0,Bluish-Green,1267,,New,7.5,Natural / Dry,North America\\n2012.0,Bluish-Green,430,Hawaiian Kona,New,7.58,Natural / Dry,North America\\n2012.0,Green,155,Caturra,New,7.42,Washed / Wet,South America\\n2012.0,Green,1126,,Weak,7.33,Washed / Wet,Asia\\n2014.0,,989,Pache Comun,New,7.42,Natural / Dry,North America\\n2012.0,Green,1203,Typica,New,7.17,Washed / Wet,North America\\n2012.0,,1153,Bourbon,Weak,7.25,Washed / Wet,North America\\n2014.0,,455,Caturra,Weak,7.58,Washed / Wet,South America\\n2012.0,Green,1058,Bourbon,Good,7.0,Washed / Wet,North America\\n2011.0,Green,32,Bourbon,New,8.5,Natural / Dry,South America\\n2016.0,Bluish-Green,1158,Bourbon,Weak,7.25,Washed / Wet,North America\\n2014.0,,10,,New,8.17,Natural / Dry,Africa\\n2012.0,Green,1258,Other,New,7.08,Washed / Wet,North America\\n2012.0,,1268,Typica,New,7.42,Washed / Wet,North America\\n \\n Output: \\n"
] | {"Continent.of.Origin": "LOldZF4dJII", "Variety": "zfzQ4Z9Dt5o", "REC_ID": "fg/VVHUVHIQ", "Color": "vU50Gku+N1g", "Processing.Method": "iJKOBRCgJI0", "Harvest.Year": "x0YTt9hPYFI", "Aroma": "6oyt+mdSeHI"} | tablejoin |
b2c9accaab7ee5cac67f482c19dcda8942fb409b25b604ef1136367f56d07fd0 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: drugName,url,description\\nSimvastatin,https://www.drugs.co,simvastatin belongs \\nOxandrolone,https://www.drugs.co,oxandrolone is a man\\nEnbrel,https://www.drugs.co,enbrel (etanercept) \\nGeodon,https://www.drugs.co,geodon (ziprasidone)\\nBotox,https://www.drugs.co,botox (onabotulinumt\\nDigoxin,https://www.drugs.co,digoxin is derived f\\nFlexeril,https://www.drugs.co,flexeril (cyclobenza\\nMethadone,https://www.drugs.co,methadone is an opio\\nLosartan,https://www.drugs.co,losartan (cozaar) be\\nHyoscyamine,https://www.drugs.co,hyoscyamine is used \\nQbrelis,https://www.drugs.co,qbrelis is an ace in\\nKeflex,https://www.drugs.co,keflex (cephalexin) \\nTemazepam,https://www.drugs.co,temazepam is a benzo\\nVicodin,https://www.drugs.co,vicodin contains a c\\nMorphine,https://www.drugs.co,morphine is an opioi\\nNystatin and triamci,https://www.drugs.co,nystatin is an antif\\nMethotrexate,https://www.drugs.co,methotrexate interfe\\n \\n CSV Table B: 7SxcDOM+98w,d6QN21UPOVs,ChUIBl78HP8\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n0,https://www.drugs.co,gas\\n \\n Output: \\n"
] | {"url": "d6QN21UPOVs"} | tablejoin |
9318064da8b360eff10f17cdbde9ee624a2112203d8239516e536a0e5bec44e9 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: Country,Inequality HDI\\nNauru,2\\nKuwait,1\\nCongo (Democratic Re,3\\nLiechtenstein,0\\nCzechia,0\\nEl Salvador,3\\nParaguay,2\\nNicaragua,3\\nBelize,2\\nBelgium,0\\nSouth Sudan,3\\nBotswana,3\\nAngola,3\\nUnited Arab Emirates,0\\n \\n CSV Table B: L3foh6+TuqY,NYLj0y6YLFA\\nCyprus,0\\nUkraine,0\\nEcuador,0\\nBrazil,0\\nLibya,0\\nLiberia,0\\nBolivia (Plurination,0\\nKiribati,0\\nGuatemala,0\\nBahamas,0\\nLebanon,0\\nIndia,0\\nYemen,0\\nBarbados,0\\nBurundi,0\\n \\n Output: \\n"
] | {"Country": "L3foh6+TuqY"} | tablejoin |
04ba0a2b8fe86cdd255723961356723f6de221cbe6bbc7af4b9ac93d45cd40ec | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: longitude,latitude,start_date,end_date,source,horizon_lower,horizon_upper,aluminium_extractable,boron_extractable,calcium_extractable\\n35.50963,-13.41183,01/01/2008,31/12/2018,afsis_spectral,20,0,920.734,,1042.361\\n34.22425,-11.65423,01/01/2008,31/12/2018,afsis_spectral,20,0,1339.417,,2882.606\\n31.81264,-8.63489,01/01/2008,31/12/2018,afsis_spectral,20,0,668.024,,360.559\\n36.487,-6.07697,01/01/2008,31/12/2018,afsis_spectral,20,0,677.402,,811.649\\n35.46519,-7.72076,01/01/2008,31/12/2018,afsis_spectral,50,20,506.082,,395.229\\n34.26721,-4.26873,01/01/2008,31/12/2018,afsis_spectral,50,20,849.618,,1295.836\\n32.34213,-3.17727,01/01/2008,31/12/2018,afsis_spectral,50,20,844.028,,999.168\\n31.06515,-6.21487,01/01/2008,31/12/2018,afsis_spectral,50,20,500.886,,292.74\\n36.00592,-7.66049,01/01/2008,31/12/2018,afsis_spectral,50,20,795.988,,452.385\\n-2.38906,7.39374,01/01/2008,31/12/2018,afsis_spectral,50,20,523.359,,2391.241\\n \\n CSV Table B: MkLAdzp+esw,+I7cBfMYFoQ,SeflMNbyB9c,6oYoa6ynUjM,+ppuhrWxZm0,UHgQMYIJ9TU,GlQankwBpC4,lGwUkVW6H7g\\nafsis_spectral,15.6466,Weak,708.277,0,,0,20\\nafsis_spectral,15.6466,Weak,682.892,1,,0,20\\nafsis_spectral,15.6466,Weak,1036.355,2,,20,50\\nafsis_spectral,15.6466,New,1264.034,3,,20,50\\nafsis_spectral,15.6466,Weak,597.63,4,,20,50\\nafsis_spectral,15.6466,New,772.719,5,,20,50\\nafsis_spectral,15.6466,New,588.3375,6,,0,20\\nafsis_spectral,15.6466,New,913.833,7,,20,50\\nafsis_spectral,15.6466,Weak,778.952,8,,20,50\\nafsis_spectral,15.6466,New,581.775,9,,20,50\\nafsis_spectral,15.6466,New,518.874,10,,0,20\\n \\n Output: \\n"
] | {"horizon_upper": "GlQankwBpC4", "horizon_lower": "lGwUkVW6H7g", "aluminium_extractable": "6oYoa6ynUjM", "boron_extractable": "UHgQMYIJ9TU", "source": "MkLAdzp+esw"} | tablejoin |
145cfcc10c148be13cc52c96a77611ff6fa5a2b2f756b7f8f9bc0220404a83d7 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: id,dept_name,program_name,org_number,measure_name,measure_id,active,priority_measure,budget_book,fiscal_year\\n35,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2017-18\\n1,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2011-12\\n41,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2019-20\\n21,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2015-16\\n3,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2013-14\\n4,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2014-15\\n4,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2014-15\\n4,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2014-15\\n3,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2013-14\\n40,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2018-19\\n \\n CSV Table B: SHtiPaG4vSU,bG37FIQSUl4,qQ/ysRVsisg,53NiJOr4DrA,NxnXOP1axWA,0dfsuiTLoSQ,sLO/8JuHP+A,Gu1a6Jx2RSE\\n15.6466,gas,5.0 out of 5 stars,YES,6040452,4510B,Weak,0\\n15.6466,gas,5.0 out of 5 stars,YES,6038888,4510B,Weak,1\\n15.6466,gas,5.0 out of 5 stars,YES,5941356,4510B,Weak,2\\n15.6466,gas,5.0 out of 5 stars,YES,6040452,4510B,New,3\\n15.6466,gas,5.0 out of 5 stars,YES,5941356,4510B,Weak,4\\n15.6466,gas,4.0 out of 5 stars,YES,5510456,4510B,New,5\\n15.6466,gas,5.0 out of 5 stars,YES,6040452,4510B,New,6\\n15.6466,gas,5.0 out of 5 stars,YES,5510456,4510B,New,7\\n15.6466,gas,5.0 out of 5 stars,YES,6038888,4510B,Weak,8\\n15.6466,gas,5.0 out of 5 stars,YES,5026787,4510B,New,9\\n \\n Output: \\n"
] | {"org_number": "0dfsuiTLoSQ", "priority_measure": "53NiJOr4DrA"} | tablejoin |
1555bac3606cf98dc257767598c8a85738893f74b07a0a7f2d150751d0ab4939 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: zipcode,year,life_expectancy\\n94965,2000,78.37\\n94103,2000,72.79\\n94560,2013,82.51\\n94519,2000,77.55\\n94514,2013,84.76\\n95694,2013,80.28\\n94550,2013,81.33\\n94014,2013,81.85\\n95419,2000,79.57\\n94920,2000,83.01\\n94972,2000,79.81\\n94602,2000,78.07\\n95465,2013,82.92\\n94803,2000,77.16\\n94542,2000,77.27\\n94924,2000,79.37\\n94598,2013,84.46\\n94596,2000,81.06\\n94526,2013,84.11\\n \\n CSV Table B: j0ihiCMCXaU,5P5CL2d6lvo\\n0,2013\\n0,2000\\n0,2000\\n0,2000\\n0,2000\\n0,2013\\n0,2000\\n0,2013\\n0,2013\\n0,2013\\n0,2000\\n0,2000\\n0,2013\\n0,2000\\n0,2000\\n0,2000\\n0,2000\\n0,2000\\n0,2000\\n0,2000\\n \\n Output: \\n"
] | {"year": "5P5CL2d6lvo"} | tablejoin |
fd0046f3c752ad7a6ce735aff42247b449563c3c664852793c698369c0046c93 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: zipcode,year,life_expectancy\\n94531,2013,79.02\\n94539,2013,85.45\\n94533,2013,79.4\\n94518,2000,79.18\\n95132,2013,82.45\\n95430,2000,79.81\\n94924,2000,79.37\\n94549,2000,80.92\\n95461,2000,81.04\\n94577,2013,81.02\\n94305,2000,81.45\\n94535,2013,79.4\\n94930,2013,85.98\\n94619,2000,78.3\\n94063,2000,78.4\\n95070,2000,81.04\\n95401,2013,79.95\\n94074,2000,80.36\\n94609,2013,78.0\\n \\n CSV Table B: j0ihiCMCXaU,gG+PnzOD1mw,DOgXTTuHGbo\\n0,94583,2000\\n0,94506,2013\\n0,95446,2000\\n0,94567,2013\\n0,95120,2000\\n0,94306,2000\\n0,95687,2000\\n0,94040,2013\\n0,94567,2000\\n0,95688,2013\\n0,94938,2013\\n0,95037,2000\\n0,94702,2013\\n0,95121,2000\\n0,95037,2013\\n0,94607,2013\\n0,94929,2000\\n0,94705,2013\\n0,94608,2000\\n0,94109,2013\\n \\n Output: \\n"
] | {"year": "DOgXTTuHGbo", "zipcode": "gG+PnzOD1mw"} | tablejoin |
31b308131501939d06a5af26b6e26500ab71fc1585a16324abda514a2276ed14 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: Unnamed: 0,carat,cut,color,clarity,depth,table,price,x,y\\n32692,0.31,Premium,G,VS1,62.8,58.0,802,4.3,4.27\\n23608,1.56,Ideal,H,VS2,61.5,56.0,11636,7.5,7.46\\n590,0.82,Very Good,H,SI1,60.7,56.0,2836,6.04,6.06\\n35579,0.35,Ideal,F,VS2,62.4,55.0,906,4.53,4.51\\n4129,1.52,Premium,I,I1,61.2,58.0,3541,7.43,7.35\\n19543,1.59,Ideal,J,SI1,62.4,55.0,8176,7.45,7.48\\n1140,0.65,Ideal,F,VVS2,61.3,56.0,2921,5.58,5.61\\n50452,0.7,Ideal,F,SI1,59.9,57.0,2264,5.74,5.82\\n18989,1.34,Premium,H,VS2,62.3,60.0,7816,7.05,7.02\\n38141,0.3,Ideal,G,VVS1,62.6,54.0,1013,4.28,4.25\\n17329,1.01,Ideal,G,VS1,62.7,56.0,6951,6.4,6.35\\n28904,0.3,Good,H,VVS1,63.3,55.0,684,4.29,4.34\\n44114,0.46,Ideal,G,IF,61.6,54.0,1558,4.97,5.0\\n40890,0.56,Fair,F,SI1,61.6,61.0,1176,5.38,5.21\\n51423,0.57,Ideal,E,VVS2,62.5,54.0,2372,5.35,5.28\\n53649,0.71,Ideal,E,SI1,61.3,57.0,2704,5.81,5.78\\n44809,0.5,Ideal,E,VS2,60.0,57.0,1624,5.12,5.15\\n28132,0.29,Very Good,D,VVS2,62.9,58.0,664,4.2,4.29\\n \\n CSV Table B: ChUIBl78HP8,SmRhS/d2xpk,v8hZSaJ4hmU,flTrJL0jwco,AHrHgGEpT+w,g4xCeD41TZs,DyGrEveH2Yg,Rjl6n9rquo8,aJYFJF6+PfY,j4MgzSCqO6Q\\ngas,6040452,D,Premium,2387,5.0 out of 5 stars,5.14,51555,2024-04-23T05:00:01.,24591000\\ngas,6038888,D,Ideal,1763,5.0 out of 5 stars,5.27,46383,2024-04-23T05:00:01.,8334800\\ngas,5941356,E,Fair,3508,5.0 out of 5 stars,6.03,3971,2024-04-23T05:00:01.,9875400\\ngas,6040452,F,Premium,7632,5.0 out of 5 stars,6.56,18669,2024-04-23T05:00:01.,8338300\\ngas,5941356,H,Ideal,17141,5.0 out of 5 stars,8.03,27014,2024-04-23T05:00:01.,8995500\\ngas,5510456,I,Ideal,4511,4.0 out of 5 stars,6.36,8998,2024-04-23T05:00:01.,8564500\\ngas,6040452,G,Good,4678,5.0 out of 5 stars,6.51,9860,2024-04-23T05:00:01.,8948500\\ngas,5510456,J,Good,3149,5.0 out of 5 stars,6.33,2249,2024-04-23T05:00:01.,11859900\\ngas,6038888,F,Very Good,5078,5.0 out of 5 stars,6.4,11755,2024-04-23T05:00:01.,16537400\\ngas,5026787,F,Ideal,673,5.0 out of 5 stars,4.32,28497,2024-04-23T05:00:01.,11010400\\ngas,6040452,G,Ideal,9465,5.0 out of 5 stars,6.54,21310,2024-04-23T05:00:01.,7534000\\ngas,5510456,E,Very Good,5113,5.0 out of 5 stars,6.32,11887,2024-04-23T05:00:01.,9818100\\ngas,6038888,G,Very Good,15241,5.0 out of 5 stars,7.86,26042,2024-04-23T05:00:01.,9965000\\ngas,5941356,G,Ideal,1868,5.0 out of 5 stars,5.34,47524,2024-04-23T05:00:01.,20254600\\ngas,5510456,D,Premium,11760,5.0 out of 5 stars,7.23,23696,2024-04-23T05:00:01.,9989300\\ngas,5026787,F,Premium,17746,5.0 out of 5 stars,7.96,27281,2024-04-23T05:00:01.,12805200\\ngas,5510456,G,Very Good,4922,5.0 out of 5 stars,6.2,11075,2024-04-23T05:00:01.,12652800\\ngas,5026787,D,Very Good,4466,5.0 out of 5 stars,6.17,8758,2024-04-23T05:00:01.,9834300\\n \\n Output: \\n"
] | {"price": "AHrHgGEpT+w", "color": "v8hZSaJ4hmU", "Unnamed: 0": "Rjl6n9rquo8", "cut": "flTrJL0jwco", "y": "DyGrEveH2Yg"} | tablejoin |
27da7f0ed5df368fa2d311fe3be17bbece8769109b41fc6e7768706d5d26f662 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: basisid,data_category,data_subcategory,data_set,description,data_steward,primary_uses,format,unit_of_analysis,principal_use\\n7dc60380-2dea-449a-a,Policy,Land Use,Farmland Mapping and,\"Established in 1982,\",Michael Smith,UrbanSim Modeling; P,geo,,TBD\\n849c4c98-4731-45bd-b,Environment,Natural Hazards,Fire Severity Risk: ,Features represent M,Michael Germeraad,Resiliance Programs;,geo,,TBD\\nd2f53550-37ec-4d98-9,Environment,Physical,Ultramafic Rock (200,Ultramafic rock depo,Michael Smith,Resiliance Programs;,geo,,Plan Bay Area 2040 E\\ndb70b910-7741-11e9-8,Environment,Natural Hazards,Alquist-Priolo Earth,This feature set con,Michael Germeraad,Resiliance Programs;,geo,parcel,TBD\\ndb70c7ca-7741-11e9-8,Environment,Natural Hazards,Liquefaction Suscept,This data set repres,Michael Germeraad,Resiliance Programs;,geo,parcel,TBD\\ndb70b17c-7741-11e9-8,Environment,Natural Hazards,Landslide Study Zone,Earthquake induced l,Michael Germeraad,Resiliance Programs;,geo,parcel,TBD\\ndb70c1d0-7741-11e9-8,Environment,Natural Hazards,Federal Emergency Ma,Federal Emergency Ma,Michael Germeraad,Resiliance Programs;,geo,parcel,TBD\\ndb70cdce-7741-11e9-8,Environment,Natural Hazards,Sea Level Rise (0 to,Locations along shor,Michael Germeraad,Resiliance Programs;,geo,parcel,TBD\\ndb70a3da-7741-11e9-8,Policy,Land Use,General Plan Land Us,Land Use Policies de,Michael Reilly,\"UrbanSim Modeling, R\",geo,parcel,TBD\\ndb70af1a-7741-11e9-8,Policy,Regional Policies,Transit Priority Are,Areas that are withi,Dave Vautin,UrbanSim Modeling; R,geo,sub city areas,TBD\\ndb70bca8-7741-11e9-8,Policy,Land Use,Non-Developable Site,Sites designated by ,Michael Reilly,UrbanSim Modeling,\"table, geo\",parcel,TBD\\n \\n CSV Table B: YH4pJE8EqH0,6D6C5OoLPL0,3h5pywnGh5w,7rZUjQZBAfU,g2kuxlmrx7M,EDrdgfL7sCc,UtepfhoKJl0\\n6040452,UrbanSim Modeling,db70b7da-7741-11e9-8,table,parcel,Development Policies,Michael Reilly\\n6038888,Housing Program; Res,db709656-7741-11e9-8,table,parcel,Housing Preservation,Gillian Adams\\n5941356,Resiliance Programs;,6b68ee2c-53d4-4b00-8,geo,,Fire Severity Risk: ,Michael Germeraad\\n6040452,Resiliance Programs;,c6ba8375-8a35-4ded-9,geo,,NOAA 2ft Sea Level R,Michael Germeraad\\n5941356,\"UrbanSim Modeling, R\",db70b67c-7741-11e9-8,geo,jurisdiction,Urban Growth Boundar,Michael Reilly\\n5510456,Housing Program; Res,db70a8a8-7741-11e9-8,geo,parcel,Bay Area Housing Opp,Gillian Adams\\n6040452,Resiliance Programs;,df8deccc-87cf-4796-8,geo,,NOAA 2ft Sea Level R,Michael Germeraad\\n5510456,Resiliance Programs;,db70ba46-7741-11e9-8,geo,parcel,Historic Wildfire Pe,Michael Germeraad\\n6038888,Resiliance Programs;,db70cb44-7741-11e9-8,geo,parcel,Wildfire Threat,Michael Germeraad\\n5026787,Resiliance Programs;,db70926e-7741-11e9-8,table,parcel,Local Hazard Resilie,Michael Germeraad\\n6040452,Resiliance Programs;,db70c43c-7741-11e9-8,geo,parcel,Probabilistic Seismi,Michael Germeraad\\n5510456,Resiliance Programs;,27920239-c9fd-4a31-a,geo,,Adapting to Rising T,Michael Smith\\n \\n Output: \\n"
] | {"data_set": "EDrdgfL7sCc", "data_steward": "UtepfhoKJl0", "unit_of_analysis": "g2kuxlmrx7M", "primary_uses": "6D6C5OoLPL0", "format": "7rZUjQZBAfU", "basisid": "3h5pywnGh5w"} | tablejoin |
eeec6c1afcb16c44895a770343d4c21c6eb88d2902ac8dc1568a6940d9502610 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: time,power,temp,humidity,light,CO2,dust\\n2015-08-06 13:35:30,0.572,34,34,23,1329,6.49\\n2015-08-05 08:34:28,0.0,31,40,8,1184,14.42\\n2015-08-30 12:00:30,-1.0,34,29,20,2000,9.52\\n2015-08-14 05:36:37,0.0,34,33,0,2000,12.63\\n2015-08-17 14:26:16,0.0,35,29,11,2000,9.94\\n2015-08-11 01:17:52,0.0,33,34,0,2000,25.68\\n2015-08-01 01:48:22,0.0,32,41,0,973,25.11\\n2015-08-29 18:59:33,-1.0,35,28,23,2000,5.32\\n2015-08-09 11:57:26,0.528,32,35,7,1806,10.68\\n2015-08-06 06:26:53,0.0,31,38,0,1300,12.87\\n2015-08-17 21:01:45,0.0,35,30,26,2000,5.08\\n2015-08-06 11:37:33,0.0,34,36,22,1374,14.07\\n2015-08-01 23:56:50,0.0,33,40,0,956,20.39\\n2015-08-04 10:11:26,0.0,32,39,19,1102,10.26\\n2015-08-10 08:12:01,-1.0,33,34,18,2000,15.09\\n2015-08-10 12:07:54,0.088,33,33,14,2000,8.53\\n \\n CSV Table B: +TcFRhetc3o,0bFLf6WxD8A,Y70Tlv14K3Y,5ArEgCtuDyM,9etcI5xa42c\\n6040452,15.6466,-1.0,24591000,2024-04-23T05:00:01.\\n6038888,15.6466,0.0,8334800,2024-04-23T05:00:01.\\n5941356,15.6466,0.0,9875400,2024-04-23T05:00:01.\\n6040452,15.6466,-1.0,8338300,2024-04-23T05:00:01.\\n5941356,15.6466,-1.0,8995500,2024-04-23T05:00:01.\\n5510456,15.6466,-1.0,8564500,2024-04-23T05:00:01.\\n6040452,15.6466,0.0,8948500,2024-04-23T05:00:01.\\n5510456,15.6466,0.0,11859900,2024-04-23T05:00:01.\\n6038888,15.6466,0.11,16537400,2024-04-23T05:00:01.\\n5026787,15.6466,0.0,11010400,2024-04-23T05:00:01.\\n6040452,15.6466,0.418,7534000,2024-04-23T05:00:01.\\n5510456,15.6466,-1.0,9818100,2024-04-23T05:00:01.\\n6038888,15.6466,-1.0,9965000,2024-04-23T05:00:01.\\n5941356,15.6466,0.0,20254600,2024-04-23T05:00:01.\\n5510456,15.6466,0.682,9989300,2024-04-23T05:00:01.\\n5026787,15.6466,0.0,12805200,2024-04-23T05:00:01.\\n5510456,15.6466,0.0,12652800,2024-04-23T05:00:01.\\n \\n Output: \\n"
] | {"power": "Y70Tlv14K3Y"} | tablejoin |
cb29bb1e6915d8366ff58783e47c9939d3d30712f2643cd23d6cbecc4210a2b2 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: training_title,training_type,training_description,training_provider,target_audience\\nAdvanced Data Analys,Online Class,Topics Include: Piv,Smartforce,\\nCulture and Its Effe,Online Class,Effective communicat,SkillSoft,\\nCisco SECURE 1.0: Ad,Online Class,In an Open Systems I,SkillSoft,\\nCustom Controls and ,Online Class,Developers often nee,SkillSoft,\\nCisco TVOICE 8.0: Tr,Online Class,The conference bridg,SkillSoft,\\nConfigure Terminal S,Online Class,\"Windows Server 2008,\",SkillSoft,\\n11 - Intel Property ,Online Class,,Bureau of Economic G,\\nCISM 2012: Informati,Online Class,Preparing incident r,SkillSoft,\\nAccounting for Sales,Online Class,Returns are an expec,SkillSoft,\\nCustomer Interaction,Online Class,Failing to realize t,SkillSoft,\\nCompressed Gas Safet,Online Class,Many industrial and ,SkillSoft,\\nCisco CWLF 1.0 Instr,Online Class,This course is part ,SkillSoft,\\nCommunicating Succes,Online Class,When you start worki,SkillSoft,\\nCISM 2012: Informati,Online Class,Information security,SkillSoft,\\nAdobe® Premiere® Ele,Online Class,Understanding the di,SkillSoft,\\n \\n CSV Table B: sNKw3v+J9DY,I2/J6hhVbCs,DMg+ND8pojM,o9rYtCP+WBg\\nOver the last 50 yea,,SkillSoft,15.6466\\nSection 508 requires,-,Smartforce,15.6466\\nWindows Forms and Wi,,SkillSoft,15.6466\\nCompTIA Security+ 20,,SkillSoft,15.6466\\nWhether you are a ho,,SkillSoft,15.6466\\nSolutions to busines,,SkillSoft,15.6466\\nTo recognize the fea,,Smartforce,15.6466\\nBuilding profitable ,,SkillSoft,15.6466\\nUsing Access macros ,,SkillSoft,15.6466\\nTo finalize and dist,,Smartforce,15.6466\\nThe Cisco ASA adapti,,SkillSoft,15.6466\\nTo describe how to u,,Smartforce,15.6466\\nWindows Vista replac,,SkillSoft,15.6466\\nThis course is part ,,SkillSoft,15.6466\\n,,QED/GLS,15.6466\\nTo recognize how thr,,Smartforce,15.6466\\n \\n Output: \\n"
] | {"training_description": "sNKw3v+J9DY", "target_audience": "I2/J6hhVbCs", "training_provider": "DMg+ND8pojM"} | tablejoin |
2e645a9a481f16ce14b5d069b62520852babd3b55383e00a75f675707088fddc | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: :@computed_region_dqjc_k29y,:@computed_region_jdnu_jmst,:@computed_region_5d9v_6bui,permitnum,worktype,applicationtype,location,:@computed_region_mfuy_bee2,:@computed_region_2fpw_swv9,:@computed_region_9p4x_9cjt\\n16.0,78.0,26.0,BLD2023-08018,Residential,Building,{'latitude': '40.785,19.0,19.0,350.0\\n12.0,78.0,26.0,BLD2023-08311,Residential,Building,{'latitude': '40.777,19.0,19.0,582.0\\n12.0,70.0,26.0,BLD2023-07867,Residential,Building,{'latitude': '40.759,19.0,24.0,567.0\\n12.0,71.0,26.0,BLD2023-02507,Residential,Building,{'latitude': '40.762,19.0,21.0,567.0\\n1.0,77.0,26.0,BLD2023-07072,Commercial,Building,{'latitude': '40.782,19.0,18.0,367.0\\n1.0,72.0,26.0,BLD2023-08689,Commercial,Building,{'latitude': '40.735,19.0,21.0,364.0\\n24.0,97.0,26.0,BLD2023-06295,Residential,Building,{'latitude': '40.708,19.0,27.0,245.0\\n12.0,72.0,26.0,BLD2023-05359,Residential,Building,{'latitude': '40.738,19.0,21.0,472.0\\n16.0,80.0,26.0,BLD2023-06139,Commercial,Building,{'latitude': '40.808,19.0,18.0,278.0\\n12.0,78.0,26.0,BLD2023-07750,Commercial,Building,{'latitude': '40.770,19.0,19.0,240.0\\n \\n CSV Table B: v02+v1698aE,ZswU2nie504,q6rFvdGN4F0,sXpNMhZkCLA,R1VkE8XKb0E,+nTxjQhBWmY,a8tgQid0Dvs,AJ7cmCm31yg\\nNo,Building,{'latitude': '40.739,26.0,472.0,19.0,BLD2023-08495,21.0\\nNo,Building,{'latitude': '40.738,26.0,358.0,19.0,BLD2023-04923,26.0\\nNo,Building,{'latitude': '40.715,26.0,384.0,19.0,BLD2023-07730,27.0\\nNo,Building,{'latitude': '40.733,26.0,360.0,19.0,BLD2023-07089,24.0\\nNo,Building,{'latitude': '40.786,26.0,352.0,19.0,BLD2023-04229,18.0\\nSi,Building,{'latitude': '40.749,26.0,361.0,19.0,BLD2023-08476,20.0\\nSi,Building,{'latitude': '40.739,26.0,474.0,19.0,BLD2023-05808,20.0\\nSi,Building,{'latitude': '40.785,26.0,350.0,19.0,BLD2023-08019,19.0\\nNo,Building,{'latitude': '40.725,26.0,277.0,19.0,BLD2023-03316,27.0\\nNo,Building,{'latitude': '40.784,26.0,495.0,19.0,BLD2023-04556,18.0\\nSi,Building,{'latitude': '40.714,26.0,573.0,19.0,BLD2023-07673,27.0\\n \\n Output: \\n"
] | {"location": "q6rFvdGN4F0", "applicationtype": "ZswU2nie504", ":@computed_region_mfuy_bee2": "+nTxjQhBWmY", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", ":@computed_region_2fpw_swv9": "AJ7cmCm31yg", "permitnum": "a8tgQid0Dvs", ":@computed_region_9p4x_9cjt": "R1VkE8XKb0E"} | tablejoin |
539fd06729e1f852302dd51aab15ffa115225362425ef04808cdef88d000d300 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: cleanup_site_name,location,zipcode,city,responsible_section,:@computed_region_fny7_vc3j,:@computed_region_x4ys_rtnd,region,latitude,cleanup_site_id\\nRAINBOW MINI MART,{'latitude': '47.528,98815,CASHMERE,Central,8,2956.0,Central,47.528331,11012\\nLake Chelan SD Athle,{'latitude': '47.842,98816,CHELAN,Central,8,2956.0,Central,47.842097,1448\\nGRAMOR DEVELOPMENT,{'latitude': '45.641,98661-6548,VANCOUVER,Southwest,3,2977.0,Southwest,45.64106,4871\\nASTRO MINIT MART 726,{'latitude': '45.614,98661,VANCOUVER,Southwest,3,2977.0,Southwest,45.614722,905\\nSequim RV Park,{'latitude': '48.023,98382,SEQUIM,Southwest,6,2976.0,Southwest,48.023378,7714\\nRichland Uptown Shop,{'latitude': '46.288,99354,RICHLAND,Central,4,2955.0,Central,46.28863,11640\\nMidland Trucking,{'latitude': '47.480,98801,WENATCHEE,Central,8,2956.0,Central,47.480129,11504\\nEXHAUST SHOP,{'latitude': '48.116,98362-3111,PORT ANGELES,Southwest,6,2976.0,Southwest,48.11676,7775\\nUS DOE 100-DR-2,{'latitude': '46.688,99352,RICHLAND,Nuclear Waste Prgm,4,2955.0,Central,46.688728,4610\\nEastmont Junior High,{'latitude': '47.416,98802,EAST WENATCHEE,Central,8,2979.0,Central,47.41673,1904\\nBNRR PROSSER MICROWA,{'latitude': '46.208,99350,PROSSER,Central,4,2955.0,Central,46.208744,10066\\nUSFS CHELATCHIE PRAI,{'latitude': '45.926,98601-9715,AMBOY,Headquarters,3,2977.0,Southwest,45.92699,8623\\nPacific Rim Land,{'latitude': '47.620,98801,OLDS STATION,Central,8,2956.0,Central,47.6203,593\\nWillard Aldridge & A,{'latitude': '47.418,98801,WENATCHEE,Central,8,2956.0,Central,47.418403,3282\\nGRACES CLEANERS,{'latitude': '45.780,98604,Battle Ground,Southwest,3,2977.0,Southwest,45.780563,578\\nUS DOE 100-HR-2,{'latitude': '46.699,99352,RICHLAND,Nuclear Waste Prgm,4,2955.0,Central,46.699242,2989\\nTIME OIL HANDY ANDY ,{'latitude': '45.653,98663-2187,VANCOUVER,Southwest,3,2977.0,Southwest,45.65333,4981\\n \\n CSV Table B: /8WN7SwQxtM,IBOO7n66j2I,sK4/vfuebl0,+TcFRhetc3o,xEEeWKcl26k,aFVTAGS5OJI,MVALsqWWTVY,cVvd7+Y4m6s,0bFLf6WxD8A,yxJQbHxz2Ew\\ngas,Weak,No,6040452,0,{'latitude': '45.587,3,11792,15.6466,726 NE 5TH AVE CAMAS\\ngas,Weak,No,6038888,0,{'latitude': '46.975,6,5218,15.6466,SUNSHINE CAR WASH\\ngas,Weak,No,5941356,0,{'latitude': '46.285,4,7512,15.6466,MCCUES TEXACO\\ngas,New,No,6040452,0,{'latitude': '48.119,6,9873,15.6466,LOG CABIN RESORT\\ngas,Weak,No,5941356,0,{'latitude': '46.234,4,1497,15.6466,Lithia Ford of Tri C\\ngas,New,Si,5510456,0,{'latitude': '48.123,6,1301,15.6466,PORT ANGELES PORT OF\\ngas,New,Si,6040452,0,{'latitude': '45.578,3,2482,15.6466,HAMBLETON BROS LOG Y\\ngas,New,Si,5510456,0,{'latitude': '47.050,6,330,15.6466,North Beach PAWS She\\ngas,Weak,No,6038888,0,{'latitude': '45.571,3,4118,15.6466,Cascade Paint\\ngas,New,No,5026787,0,{'latitude': '45.636,3,9558,15.6466,ABANDON TANK SITE\\ngas,New,Si,6040452,0,{'latitude': '46.274,4,6112,15.6466,Columbia Oil Company\\ngas,Weak,No,5510456,0,{'latitude': '48.107,6,1649,15.6466,TRUCK TOWN 1921 HWY \\ngas,Weak,Si,6038888,0,{'latitude': '46.118,3,1539,15.6466,TRANSMISSION TRADING\\ngas,Good,Si,5941356,0,{'latitude': '45.671,3,273,15.6466,Boomsnub Inc\\ngas,New,No,5510456,0,{'latitude': '46.815,4,6952,15.6466,UNOCAL BULK PLANT 05\\ngas,Weak,No,5026787,0,{'latitude': '46.213,4,14385,15.6466,Oil Re Refining Comp\\ngas,New,No,5510456,0,{'latitude': '48.104,6,4517,15.6466,MANKE LOG YARD\\n \\n Output: \\n"
] | {"location": "aFVTAGS5OJI", "cleanup_site_id": "cVvd7+Y4m6s", "cleanup_site_name": "yxJQbHxz2Ew", ":@computed_region_fny7_vc3j": "MVALsqWWTVY"} | tablejoin |
a50e16a7dec04c766f864754305d6b28a99fe54602c7c913c525c067c405d279 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: Vehicle_Model,Mileage,Maintenance_History,Reported_Issues,Vehicle_Age,Fuel_Type,Transmission_Type,Engine_Size,Odometer_Reading,Last_Service_Date\\nVan,61745,Poor,1,1,Petrol,Manual,2000,145019,2023-10-19\\nBus,58742,Average,2,7,Diesel,Manual,2000,130003,2023-12-18\\nMotorcycle,57412,Good,3,10,Diesel,Manual,800,139794,2023-11-15\\nCar,43158,Good,1,2,Electric,Automatic,800,51215,2023-10-04\\nVan,73695,Average,3,2,Electric,Automatic,1000,15453,2023-04-09\\nTruck,43662,Good,1,8,Petrol,Automatic,2500,70976,2023-05-16\\nVan,42638,Average,0,10,Electric,Manual,800,46541,2023-08-02\\nSUV,50613,Average,2,2,Electric,Automatic,1500,101947,2023-07-23\\nCar,31839,Good,4,10,Diesel,Automatic,2500,137976,2023-10-05\\nBus,72112,Average,2,5,Diesel,Automatic,800,110035,2024-02-23\\nSUV,73526,Average,1,8,Diesel,Automatic,2000,61287,2023-04-16\\n \\n CSV Table B: ZxQEcZfVyiA,4lnA15H3a94,O5PnzZQwWvU,YbimjSBeMkI,t8DtGa8xUVw,iZrkpx1ubOo\\nManual,39324,5,Bus,0,2024-01-07\\nManual,65451,3,Van,0,2023-09-08\\nManual,131118,2,SUV,0,2024-01-24\\nAutomatic,148084,3,Van,0,2023-07-13\\nAutomatic,66820,2,SUV,0,2023-07-05\\nAutomatic,66707,2,Motorcycle,0,2023-11-27\\nAutomatic,117639,5,Van,0,2023-07-05\\nAutomatic,97214,5,Truck,0,2024-02-11\\nAutomatic,11947,0,Motorcycle,0,2023-07-28\\nAutomatic,124606,4,SUV,0,2023-05-31\\nAutomatic,30057,0,SUV,0,2024-02-07\\n \\n Output: \\n"
] | {"Odometer_Reading": "4lnA15H3a94", "Vehicle_Model": "YbimjSBeMkI", "Last_Service_Date": "iZrkpx1ubOo", "Reported_Issues": "O5PnzZQwWvU", "Transmission_Type": "ZxQEcZfVyiA"} | tablejoin |
75fca1a433c6e663241c1941e6034cd7625cd4b5981159c7f4ad74703df98b53 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: Outlook,Temperature,Humidity,Wind,Play_Badminton\\nRain,Cool,Normal,Weak,No\\nOvercast,Cool,Normal,Weak,Yes\\nSunny,Mild,Normal,Strong,No\\nRain,Mild,High,Strong,No\\nOvercast,Mild,High,Weak,Yes\\nRain,Cool,Normal,Strong,No\\nRain,Cool,High,Weak,No\\nOvercast,Hot,High,Strong,No\\nOvercast,Hot,High,Weak,Yes\\nRain,Hot,High,Strong,No\\nRain,Cool,High,Strong,No\\nSunny,Hot,High,Strong,No\\nRain,Mild,Normal,Weak,No\\nRain,Hot,Normal,Weak,No\\nOvercast,Hot,Normal,Weak,Yes\\nRain,Mild,Normal,Strong,No\\nOvercast,Hot,Normal,Strong,No\\n \\n CSV Table B: ijAq03/9VNE,9etcI5xa42c,/8WN7SwQxtM,YvXYPZhNyxA\\nWeak,2024-04-23T05:00:01.,gas,Sunny\\nStrong,2024-04-23T05:00:01.,gas,Sunny\\nWeak,2024-04-23T05:00:01.,gas,Sunny\\nWeak,2024-04-23T05:00:01.,gas,Sunny\\nStrong,2024-04-23T05:00:01.,gas,Sunny\\nStrong,2024-04-23T05:00:01.,gas,Sunny\\nWeak,2024-04-23T05:00:01.,gas,Overcast\\nStrong,2024-04-23T05:00:01.,gas,Rain\\nWeak,2024-04-23T05:00:01.,gas,Rain\\nStrong,2024-04-23T05:00:01.,gas,Sunny\\nWeak,2024-04-23T05:00:01.,gas,Sunny\\nStrong,2024-04-23T05:00:01.,gas,Overcast\\nStrong,2024-04-23T05:00:01.,gas,Overcast\\nWeak,2024-04-23T05:00:01.,gas,Overcast\\nWeak,2024-04-23T05:00:01.,gas,Rain\\nWeak,2024-04-23T05:00:01.,gas,Sunny\\nWeak,2024-04-23T05:00:01.,gas,Sunny\\n \\n Output: \\n"
] | {"Outlook": "YvXYPZhNyxA", "Wind": "ijAq03/9VNE"} | tablejoin |
140b7ab87b7be33e80fff3cfc052077d34cc51b5038c1c390cfb9780ad948c04 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: :@computed_region_dqjc_k29y,:@computed_region_jdnu_jmst,:@computed_region_5d9v_6bui,permitnum,worktype,applicationtype,location,:@computed_region_mfuy_bee2,:@computed_region_2fpw_swv9,:@computed_region_9p4x_9cjt\\n12.0,68.0,26.0,BLD2023-07925,Residential,Building,{'latitude': '40.738,19.0,24.0,73.0\\n12.0,72.0,26.0,BLD2023-05473,Commercial,Building,{'latitude': '40.738,19.0,21.0,472.0\\n24.0,68.0,26.0,BLD2023-07876,Residential,Building,{'latitude': '40.735,19.0,24.0,360.0\\n16.0,80.0,26.0,BLD2023-02640,Commercial,Building,{'latitude': '40.801,19.0,18.0,278.0\\n1.0,72.0,26.0,BLD2023-08689,Commercial,Building,{'latitude': '40.735,19.0,21.0,364.0\\n1.0,80.0,26.0,BLD2023-03353,Residential,Building,{'latitude': '40.780,19.0,18.0,12.0\\n16.0,80.0,26.0,BLD2023-07162,Residential,Building,{'latitude': '40.785,19.0,18.0,352.0\\n12.0,113.0,26.0,BLD2023-06120,Residential,Building,{'latitude': '40.748,19.0,20.0,361.0\\n12.0,78.0,26.0,BLD2023-08556,Residential,Building,{'latitude': '40.788,19.0,19.0,366.0\\n23.0,68.0,26.0,BLD2023-08383,Commercial,Building,{'latitude': '40.731,19.0,24.0,243.0\\n \\n CSV Table B: sXpNMhZkCLA,Jez514k++0Q,AVoxAgMZHug,SfVC0olx/OE,t8DtGa8xUVw,tKc+06TrJ9c,PMUacJBoTFo,+I7cBfMYFoQ\\n26.0,6040452,355.0,24591000,0,12.0,{'latitude': '40.764,15.6466\\n26.0,6038888,469.0,8334800,0,12.0,{'latitude': '40.781,15.6466\\n26.0,5941356,122.0,9875400,0,12.0,{'latitude': '40.772,15.6466\\n26.0,6040452,361.0,8338300,0,12.0,{'latitude': '40.747,15.6466\\n26.0,5941356,239.0,8995500,0,1.0,{'latitude': '40.799,15.6466\\n26.0,5510456,567.0,8564500,0,12.0,{'latitude': '40.755,15.6466\\n26.0,6040452,474.0,8948500,0,24.0,{'latitude': '40.738,15.6466\\n26.0,5510456,70.0,11859900,0,12.0,{'latitude': '40.774,15.6466\\n26.0,6038888,367.0,16537400,0,1.0,{'latitude': '40.792,15.6466\\n26.0,5026787,71.0,11010400,0,12.0,{'latitude': '40.752,15.6466\\n26.0,6040452,582.0,7534000,0,16.0,{'latitude': '40.782,15.6466\\n \\n Output: \\n"
] | {":@computed_region_dqjc_k29y": "tKc+06TrJ9c", ":@computed_region_5d9v_6bui": "sXpNMhZkCLA", "location": "PMUacJBoTFo", ":@computed_region_9p4x_9cjt": "AVoxAgMZHug"} | tablejoin |
5063b77b06647a10818a76a2feda884741860ca4ef5816ae4580babafea11fb0 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: Symptom,Remedy,RemedyStrength,Part_of_remedy,Final_remedy\\nAbdominal respiratio,Thuj.,1,True,False\\nRattling,Sep.,2,True,False\\nSnoring,Nit-ac.,1,False,False\\nSobbing,Nit-ac.,1,False,False\\nLoud respiration,Squil.,1,True,False\\nGasping,Merc.,1,False,False\\nIrregular respiratio,Calad.,1,False,False\\nImperceptible respir,Ars.,2,True,True\\nRough respiration,Plb.,1,True,False\\nSighing,Tax.,1,False,False\\n\"Impeded,obstructed r\",Abrot.,2,False,False\\nSlow respiration,Asaf.,2,False,False\\nSlow respiration,Colch.,2,False,False\\nHot breath,Cann-s.,1,False,False\\nDifficult respiratio,Carb-v.,1,False,False\\nLoud respiration,Ars.,1,True,False\\n\"Impeded,obstructed r\",Puls.,1,False,False\\n \\n CSV Table B: tsBRUXdOa3Q,JT9OTPbY4r4,0bFLf6WxD8A,Xl360xlCCTk\\nPlan.,True,15.6466,False\\nCalc.,False,15.6466,False\\nStram.,True,15.6466,True\\nCanth.,False,15.6466,False\\nColch.,False,15.6466,False\\nKali-i.,False,15.6466,False\\nNit-ac.,True,15.6466,False\\nSulf.,True,15.6466,False\\nColoc.,False,15.6466,False\\nBry.,True,15.6466,True\\nOp.,False,15.6466,False\\nNux-m.,True,15.6466,True\\nSquil.,True,15.6466,False\\nHep.,True,15.6466,False\\nBell.,True,15.6466,True\\nSpong.,True,15.6466,False\\nCarb-v.,True,15.6466,False\\n \\n Output: \\n"
] | {"Part_of_remedy": "JT9OTPbY4r4", "Final_remedy": "Xl360xlCCTk", "Remedy": "tsBRUXdOa3Q"} | tablejoin |
ac146c48d703160bded02521568583372fc6b10bdbd98f36f57fcff7d0790d10 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: id,original_text,rewritten_text,rewrite_prompt\\n295,Report: Smoke was de,\"Bewilderingly, smoke\",Use more complex and\\n243,\"Hey Julia, just want\",\"Hi Julia, please sen\",La différence est de\\n249,Marcia blamed hersel,\"Marcia, the petition\",Use a more formal an\\n81,Subject: Urgent Fold,Subject: Timeless Ca,Revise the text to h\\n186,Ladies and gentlemen,Ladies and gentlemen,Include a somber not\\n198,\"Once upon a time, in\",\"Once in Oakville, Mi\",Summarize the story \\n298,\"Nathan, a renowned h\",\"Nathan, a ruthless h\",Add an unexpected tw\\n155,\"Marilyn, a strugglin\",\"Marilyn, a talented \",Make the text more c\\n59,\"Hi Christopher, coul\",Hey Christopher! Can,Revise the text to a\\n9,\"Today, Angela and I \",\"Today, Angela and I \",Revise the text with\\n192,\"Hi Eva, \\\\n\\\\nJust wan\",\"Hi Eva, \\\\n\\\\nI hope t\",Revise the text with\\n352,\"December 24, 2021: S\",\"December 24, 2021: A\",Elevate the tone and\\n330,Rebecca eagerly awai,Rebecca cautiously a,Reflect a more cauti\\n175,Hey Robert! I just h,\"Hey Robert, remember\",Reframe the invitati\\n123,Ladies and gentlemen,Ladies and gentlemen,Include a health adv\\n166,\"Today, while on safa\",\"Today, during my enc\",Revise the text with\\n214,\"Dear Anibal,\\\\n\\\\nI ho\",\"Dear Anibal,\\\\n\\\\nI fo\",La diferencia es red\\n \\n CSV Table B: xEEeWKcl26k,/8WN7SwQxtM,3i4QkTML4G0,9etcI5xa42c\\n0,gas,Hey Esther! Did you ,2024-04-23T05:00:01.\\n0,gas,\"Anna, cradling her r\",2024-04-23T05:00:01.\\n0,gas,\"Dear Mr. Johnson,\\\\n\\\\\",2024-04-23T05:00:01.\\n0,gas,Ladies and gentlemen,2024-04-23T05:00:01.\\n0,gas,\"Today, James and I i\",2024-04-23T05:00:01.\\n0,gas,Title: Buffalo Bonan,2024-04-23T05:00:01.\\n0,gas,75% of people believ,2024-04-23T05:00:01.\\n0,gas,Remove the squatter ,2024-04-23T05:00:01.\\n0,gas,\"Hi Sara, \\\\n\\\\nI hope \",2024-04-23T05:00:01.\\n0,gas,Hey Charles! Remembe,2024-04-23T05:00:01.\\n0,gas,In a world where tru,2024-04-23T05:00:01.\\n0,gas,\"Walter, a farmer, fo\",2024-04-23T05:00:01.\\n0,gas,\"Today, I bought fres\",2024-04-23T05:00:01.\\n0,gas,Through every strugg,2024-04-23T05:00:01.\\n0,gas,\"In Eldoria, Kevin as\",2024-04-23T05:00:01.\\n0,gas,\"Jerry, a gifted musi\",2024-04-23T05:00:01.\\n0,gas,Journal Entry - Acco,2024-04-23T05:00:01.\\n \\n Output: \\n"
] | {"rewritten_text": "3i4QkTML4G0"} | tablejoin |
10047d040ef1e563f1db3278979d56d1182617b3484c63ed53a388a0d006a7e4 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: id,dept_name,program_name,org_number,measure_name,measure_id,active,priority_measure,budget_book,fiscal_year\\n2,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2012-13\\n41,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2019-20\\n4,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2014-15\\n21,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2015-16\\n2,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2012-13\\n3,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2013-14\\n2,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2012-13\\n4,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2014-15\\n41,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2019-20\\n21,Department of Public,Public Works Adminis,4510B,Percent rating exper,5,YES,YES,NO,FY 2015-16\\n \\n CSV Table B: aWH6IJ5IjF4,hMlFRB3b0OU,6TBG45I7TLk,UCUt++OaxnM,Gu1a6Jx2RSE,0dfsuiTLoSQ,tTar7XACrwc,53NiJOr4DrA,T2n+8bg76ww\\nDepartment of Public,NO,2024-04-23T05:00:01.,FY 2015-16,0,4510B,5,YES,No\\nDepartment of Public,NO,2024-04-23T05:00:01.,FY 2013-14,1,4510B,5,YES,No\\nDepartment of Public,NO,2024-04-23T05:00:01.,FY 2013-14,2,4510B,5,YES,No\\nDepartment of Public,NO,2024-04-23T05:00:01.,FY 2013-14,3,4510B,5,YES,No\\nDepartment of Public,NO,2024-04-23T05:00:01.,FY 2018-19,4,4510B,5,YES,No\\nDepartment of Public,NO,2024-04-23T05:00:01.,FY 2011-12,5,4510B,5,YES,Si\\nDepartment of Public,NO,2024-04-23T05:00:01.,FY 2011-12,6,4510B,5,YES,Si\\nDepartment of Public,NO,2024-04-23T05:00:01.,FY 2018-19,7,4510B,5,YES,Si\\nDepartment of Public,NO,2024-04-23T05:00:01.,FY 2019-20,8,4510B,5,YES,No\\nDepartment of Public,NO,2024-04-23T05:00:01.,FY 2013-14,9,4510B,5,YES,No\\n \\n Output: \\n"
] | {"dept_name": "aWH6IJ5IjF4", "fiscal_year": "UCUt++OaxnM", "measure_id": "tTar7XACrwc", "priority_measure": "53NiJOr4DrA", "budget_book": "hMlFRB3b0OU", "org_number": "0dfsuiTLoSQ"} | tablejoin |
a8995a220d4b23e751dded30067eb09897b7269b0ec3632762c9e97d41b80c95 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: Date,Open,High,Low,Close,Volume\\n2013-01-04,42.459999,42.5,41.82,41.970001,15428500\\n2013-12-18,47.869999,48.93,47.650002,48.900002,13549700\\n2013-09-18,47.810001,48.709999,47.630001,48.400002,14008700\\n2015-04-27,57.830002,58.029999,56.880001,57.099998,10599600\\n2015-07-06,57.240002,57.84,56.639999,57.549999,8054100\\n2015-11-16,52.189999,53.810001,52.130001,53.700001,6907800\\n2014-03-10,57.439999,57.619999,57.0,57.32,7383200\\n2014-12-16,56.970001,58.290001,56.779999,56.799999,11214000\\n2015-12-15,52.48,53.189999,52.23,52.900002,11585900\\n2013-11-20,47.98,48.419998,47.75,48.130001,8251900\\n2014-08-08,55.869999,56.610001,55.580002,56.549999,7081500\\n2014-11-04,58.869999,59.709999,58.869999,59.369999,11338400\\n2012-11-12,44.470001,44.52,43.880001,44.02,7329800\\n2014-12-22,59.119999,59.560001,58.549999,58.959999,10010500\\n2014-01-27,52.860001,54.099998,52.529999,52.529999,31002000\\n2014-02-07,53.650002,54.82,53.439999,54.77,14497100\\n2013-07-05,46.93,47.299999,46.610001,47.16,8103000\\n \\n CSV Table B: uUeSJYWTyDY,sK4/vfuebl0,9etcI5xa42c\\n14656200,No,2024-04-23T05:00:01.\\n11893000,No,2024-04-23T05:00:01.\\n7429500,No,2024-04-23T05:00:01.\\n14065400,No,2024-04-23T05:00:01.\\n14165400,No,2024-04-23T05:00:01.\\n8649500,Si,2024-04-23T05:00:01.\\n12117800,Si,2024-04-23T05:00:01.\\n9935100,Si,2024-04-23T05:00:01.\\n5187600,No,2024-04-23T05:00:01.\\n14206900,No,2024-04-23T05:00:01.\\n6900000,Si,2024-04-23T05:00:01.\\n8981200,No,2024-04-23T05:00:01.\\n9639700,Si,2024-04-23T05:00:01.\\n8654800,Si,2024-04-23T05:00:01.\\n7914600,No,2024-04-23T05:00:01.\\n7533400,No,2024-04-23T05:00:01.\\n8617800,No,2024-04-23T05:00:01.\\n \\n Output: \\n"
] | {"Volume": "uUeSJYWTyDY"} | tablejoin |
8b842182b7cbb2b961d8cdc64a1b4b28aff1f8ed4f4dd3fb58e3533baa754043 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: DeviceTimeStamp,WL1,WL2,WL3,VAL1,VAL2,VAL3,RVAL1,RVAL2,RVAL3\\n2019-09-12T16:45,32.1,27.7,34.0,32.9,28.1,34.4,7.0,4.5,0.0057\\n2020-02-23T03:00,9.6,3.4,11.0,9.6,3.4,11.1,0.2,0.2,0.0017\\n2020-03-26T03:15,10.9,7.5,12.0,10.9,7.8,12.1,0.4,2.0,0.0011\\n2019-08-12T20:15,32.0,37.3,36.4,32.1,37.4,36.8,2.1,2.6,0.0051\\n2020-04-04T08:30,11.6,8.9,11.4,11.7,9.5,12.1,1.9,3.3,0.004\\n2019-08-22T09:45,16.2,13.2,17.6,16.2,13.7,18.4,0.8,3.5,0.0053\\n2019-09-17T23:00,21.6,19.2,30.2,21.9,19.3,30.3,3.5,1.9,0.0012\\n2019-12-05T06:45,8.3,6.1,12.0,8.4,6.2,12.7,-0.4,1.5,0.004\\n2019-09-14T21:15,24.6,25.9,27.9,24.8,25.9,28.1,2.5,1.7,0.0035\\n2019-10-25T23:43,14.5,10.1,15.8,14.7,10.3,16.2,2.0,1.7,0.0036\\n2019-12-14T08:00,7.6,8.1,11.8,7.7,8.6,12.4,0.9,2.8,0.0037\\n2020-03-30T23:15,21.3,12.5,19.7,21.4,12.7,20.0,1.7,2.2,0.0034\\n2020-04-13T12:15,11.9,6.7,15.5,12.0,7.1,16.1,0.8,2.2,0.0043\\n2020-04-09T00:45,13.4,10.1,16.3,13.5,10.3,16.4,1.0,1.9,0.0022\\n2019-08-14T19:30,27.9,32.3,39.6,27.9,32.4,40.0,1.1,3.2,0.0054\\n2020-04-07T05:15,13.1,7.5,15.2,13.1,7.7,15.4,-0.2,1.7,0.0024\\n2020-01-28T13:45,17.1,11.3,20.6,17.2,11.5,21.0,1.4,2.3,0.0043\\n2020-04-08T01:30,15.6,10.4,19.2,15.6,10.5,19.3,0.0,1.4,0.002\\n2019-10-19T12:45,35.7,24.3,28.2,35.9,24.5,28.9,3.8,3.2,0.0066\\n \\n CSV Table B: 5VcgIh9wM7I,S3GJlnNyunE,v3NEVV2Owbs,pQZDnCfGEk4,ega9e6/dBuw,mlTxGdesaBg,09ii68KGAcU\\n25.7,25.0,0,gas,22.1,No,6040452\\n13.4,13.2,1,gas,9.5,No,6038888\\n26.7,26.4,2,gas,19.8,No,5941356\\n27.0,26.2,3,gas,20.7,No,6040452\\n13.6,13.3,4,gas,9.8,No,5941356\\n21.6,21.6,5,gas,19.3,Si,5510456\\n18.9,18.7,6,gas,20.7,Si,6040452\\n7.6,7.1,7,gas,9.7,Si,5510456\\n27.7,26.5,8,gas,34.3,No,6038888\\n13.7,13.5,9,gas,9.8,No,5026787\\n21.4,20.9,10,gas,15.0,Si,6040452\\n14.1,13.9,11,gas,12.7,No,5510456\\n12.0,11.7,12,gas,10.6,Si,6038888\\n12.4,12.2,13,gas,9.3,Si,5941356\\n26.4,26.0,14,gas,19.2,No,5510456\\n9.9,9.6,15,gas,7.8,No,5026787\\n23.5,23.1,16,gas,14.4,No,5510456\\n0.0,0.0,17,gas,0.0,No,5026787\\n16.1,16.1,18,gas,12.9,No,5510456\\n15.8,15.4,19,gas,12.4,No,6038888\\n \\n Output: \\n"
] | {"WL1": "ega9e6/dBuw", "VAL3": "5VcgIh9wM7I", "WL3": "S3GJlnNyunE"} | tablejoin |
dc753a46614f7f4d1c839d06ec864324f8b6142e30bf804dae6aae8b6eb91941 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: source_name,source_link,event_id,event_date,event_title,event_description,location_description,location_accuracy,landslide_category,landslide_trigger\\nstuff,{\\'url\\': \\'http://www.,3931,2011-08-17T23:45:00.,\"Belvedere Road, Hata\",\"landslide, about 15m\",\"Belvedere Road, Hata\",exact,landslide,unknown\\ncnn,{\\'url\\': \\'http://www.,1621,2010-04-06T00:00:00.,other slides in Rio ,Brazilian President ,other slides in Rio ,50km,complex,downpour\\nCBS News,{\\'url\\': \\'https://www,973,2007-01-19T00:00:00.,\"San Ramon district, \",(CBS/AP) At least 10,\"San Ramon district, \",10km,landslide,downpour\\ngoogle,{\\'url\\': \\'http://www.,1594,2010-03-26T00:00:00.,\"Carabaya Province, P\",Peruvian police say ,\"Carabaya Province, P\",unknown,landslide,downpour\\nthecitizen.co,{\\'url\\': \\'http://thec,1293,2009-11-10T00:00:00.,\"Goha village, Same d\",A landslide on a mou,\"Goha village, Same d\",25km,landslide,downpour\\nAP.google.com,{\\'url\\': \\'http://ap.g,325,2007-10-26T00:00:00.,Kinshasa,heavy flooding and l,Kinshasa,25km,mudslide,rain\\nthejakartapost,{\\'url\\': \\'http://www.,3384,2011-04-20T01:00:00.,\"Rengganis(?), Cintam\",\"Wed, 04/20/2011 1:19\",\"Rengganis(?), Cintam\",50km,landslide,downpour\\nantaranews,{\\'url\\': \\'http://www.,4617,2012-11-18T00:00:00.,\"Caringin, Sukabumi\",Landslides have hit ,\"Caringin, Sukabumi\",5km,landslide,rain\\nLa depeche de Madaga,{\\'url\\': \\'http://www.,9648,2016-05-13T00:00:00.,\"Manjavela, in the di\",\"On Friday, a tragedy\",\"Manjavela, in the di\",50km,other,unknown\\nStandard Digital,{\\'url\\': \\'http://www.,7101,2015-05-01T18:00:00.,Maganyakulo area of ,\"\"\"It was around 6p.m.\",Maganyakulo area of ,5km,landslide,continuous_rain\\nnews.bbc,{\\'url\\': \\'http://news,1376,2009-12-31T00:00:00.,Greater Rio de Janei,Heavy rains have cau,Greater Rio de Janei,5km,mudslide,downpour\\nStuff,{\\'url\\': \\'http://www.,1881,2010-05-20T09:00:00.,\"the narrows, near Bo\",A landslide that dum,\"the narrows, near Bo\",5km,rock_fall,continuous_rain\\nNTD Television,{\\'url\\': \\'https://web,1476,2010-02-06T00:00:00.,Zurite district,Mud and rocks piled ,Zurite district,10km,mudslide,downpour\\necr,{\\'url\\': \\'http://www.,4542,2012-09-06T00:00:00.,Amanzimtoti,Clean-up operations ,Amanzimtoti,10km,landslide,downpour\\nlivinginperu,{\\'url\\': \\'http://www.,1366,2009-12-17T00:00:00.,\"Huamanga, Ayacucho, \",The Presidency of Pe,\"Huamanga, Ayacucho, \",25km,mudslide,downpour\\nwellington.scoop.co.,{\\'url\\': \\'http://well,4816,2013-04-21T00:00:00.,\"Takaka Hill Highway,\",Torrential rain has ,\"Takaka Hill Highway,\",25km,landslide,rain\\n \\n CSV Table B: yYHA7vnvIBw,Zmb1BRco8l4,IbcRFtTB0wI,0F0qIGz9/W4,6kw4WhkPpNQ,5AxJyCWgWsc,o9rYtCP+WBg,jgFx2gX5+sM,vhKccO94mOM\\nNo,gas,unknown,Landslides have clos,Rex Highway between ,abc,15.6466,{\\'url\\': \\'http://www.,0\\nNo,gas,1km,PARTS of the Souther,\"New England Hwy, 800\",Warwick Daily News,15.6466,{\\'url\\': \\'http://www.,0\\nNo,gas,1km,O mapa da devastação,Cocota,maps.google.com,15.6466,{\\'url\\': \\'http://maps,0\\nNo,gas,10km,over 200 slips in pa,Manukau,3news.co,15.6466,{\\'url\\': \\'http://3new,0\\nNo,gas,25km,8 month old baby kil,\"Danyon village, Slah\",antara,15.6466,{\\'url\\': \\'http://www.,0\\nSi,gas,5km,The worst hit area w,Teresópolis,guardian,15.6466,{\\'url\\': \\'http://www.,0\\nSi,gas,250km,Heavy rains slammed ,Quellouno,RT,15.6466,,0\\nSi,gas,1km,A landslide in La Pa,Auquisamaña Area Lan,Buzz Videos,15.6466,{\\'url\\': \\'http://www.,0\\nNo,gas,1km,The landslip that ha,Snowy Mountains High,abc,15.6466,{\\'url\\': \\'http://www.,0\\nNo,gas,25km,The government yeste,Bikita Landslide Kil,Newsday,15.6466,{\\'url\\': \\'https://www,0\\nSi,gas,5km,A landslide in Bogor,\"Sempur, Bogor, West \",www.thejakartaglobe.,15.6466,{\\'url\\': \\'http://www.,0\\nNo,gas,5km,A LIFE could have be,\"Waimanu road, near S\",fijitimes,15.6466,{\\'url\\': \\'http://www.,0\\nSi,gas,1km,landslides on the ro,Estrada da Froes Nit,maps.google.com,15.6466,{\\'url\\': \\'http://maps,0\\nSi,gas,100km,The central jungle o,Satipo Province,Living In Peru,15.6466,{\\'url\\': \\'http://www.,0\\nNo,gas,1km,A remote village com,\"Biche, Gatokae, Moro\",Solomon Star,15.6466,{\\'url\\': \\'http://www.,0\\nNo,gas,10km,Eight people were ki,Resifi(Recife) north,english.ruvr,15.6466,{\\'url\\': \\'http://engl,0\\n \\n Output: \\n"
] | {"source_name": "5AxJyCWgWsc", "location_accuracy": "IbcRFtTB0wI", "event_description": "0F0qIGz9/W4", "source_link": "jgFx2gX5+sM", "event_title": "6kw4WhkPpNQ"} | tablejoin |
4840c0c5075383274db75d8610087c3a725f4be885832e5fa97a46933e7485ae | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: Areas,freq_1,freq_2,freq_3,freq_4,freq_5,freq_6\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n52.69691934980033,1.0,0.3066003914775975,0.1245689303063943,0.1054524435622401,0.0417304339140407,0.0547108674678267\\n7.185992410601374,1.0,0.2999206528073539,0.1222511487682431,0.0772947974051657,0.0487553884339519,0.0353324096055299\\n32.7291864913512,1.0,0.213146090194573,0.1183964102800875,0.0704606572262718,0.0441183363159674,0.033178644798613\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n6.446951236371171,1.0,0.4262288438201601,0.1916872539057724,0.1156817194523204,0.044848274171492,0.0222903737771126\\n1.957639593458942,1.0,0.533393886177141,0.1893246349211403,0.0714277935184967,0.0284848249671974,0.0238569282251618\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n71.00332161496897,1.0,0.2740220004756795,0.1278905256445208,0.0692331631443914,0.0482897713293649,0.0357922581591704\\n0.0,0.0,0.0,0.0,0.0,0.0,0.0\\n3.301667962759854,1.0,0.1091959612260343,0.0454704054003767,0.0344613292581027,0.025557057115189,0.0129898029281604\\n16.754123508406163,0.2856924485187471,0.1709920569783453,0.1496525553644551,0.0982513539490028,0.1027482655787128,0.1590234249293817\\n \\n CSV Table B: 7dYptJU3eKE,7raemdfhCtY,oSIrzv9LNvo,NDJjzG/U34g,j5ilz2RtsY4\\n24591000,No,15.6466,0.0,0.0\\n8334800,No,15.6466,0.0,0.0\\n9875400,No,15.6466,0.0,0.0\\n8338300,No,15.6466,0.0,0.0\\n8995500,No,15.6466,0.0,0.0\\n8564500,Si,15.6466,0.1795146403862751,0.5059258063362236\\n8948500,Si,15.6466,0.05852812458766,0.0248499329639729\\n11859900,Si,15.6466,0.0,0.0\\n16537400,No,15.6466,0.0571120579565183,0.030578336333865\\n11010400,No,15.6466,0.1357617818231772,0.091585463814462\\n7534000,Si,15.6466,0.1409075536548341,0.0658817937143762\\n9818100,No,15.6466,0.0,0.0\\n9965000,Si,15.6466,0.0,0.0\\n20254600,Si,15.6466,0.3648607143842685,0.148324977324336\\n9989300,No,15.6466,0.0,0.0\\n \\n Output: \\n"
] | {"freq_6": "j5ilz2RtsY4", "freq_4": "NDJjzG/U34g"} | tablejoin |
da9f424fc770103fa6b2639920d84fd8be3c448031ed96d13b975289356f4a67 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: gender,age,profession,occupation,country_of_residence,urban_rural,owns_car,salary,cost_of_living,marital_status\\nFemale,29,Musician,Full-Time,United States,Rural,No,71672,Medium,Single\\nFemale,29,Chef,Full-Time,United States,Rural,No,52829,Medium,Married\\nFemale,40,Architect,Full-Time,United States,Urban,Yes (Loan),62303,High,Single\\nMale,28,Pilot,Full-Time,United States,Urban,Yes (Owned),73258,High,Married\\nFemale,40,Doctor,Full-Time,United States,Rural,No,59573,Medium,Single\\nMale,26,Musician,Full-Time,United States,Urban,No,88218,High,Single\\nMale,29,Marketing Specialist,Full-Time,United States,Urban,Yes (Loan),78838,Medium,Married\\nMale,39,Pilot,Full-Time,United States,Urban,Yes (Loan),74197,High,Single\\nMale,29,Writer,Full-Time,United States,Rural,Yes (Owned),88437,High,Married\\nFemale,38,Pilot,Full-Time,United States,Urban,No,115931,High,Married\\nMale,31,Doctor,Full-Time,United States,Rural,No,111470,High,Single\\nFemale,40,Doctor,Full-Time,United States,Rural,Yes (Loan),103918,High,Single\\nFemale,23,Firefighter,Full-Time,United States,Urban,No,67955,High,Married\\nMale,38,Teacher,Full-Time,United States,Urban,No,84761,Medium,Married\\nFemale,36,Doctor,Full-Time,United States,Rural,No,89057,High,Single\\nFemale,27,Pilot,Full-Time,United States,Rural,Yes (Owned),119808,Medium,Single\\nMale,22,Pilot,Full-Time,United States,Urban,No,112298,Medium,Single\\nMale,23,Marketing Specialist,Full-Time,United States,Urban,Yes (Loan),71946,Medium,Single\\n \\n CSV Table B: 8UKIX1iMOZg,lsTuaMKy100,q9mixw71rsY,NWoi+UEeAUY,Krl1e9fqzyc,LB1c5bVtloU,+3hdejHnpQE,x+dSLMV/+GA\\n2024-04-23T05:00:01.,76515,32,0,Male,6040452,5.0 out of 5 stars,Architect\\n2024-04-23T05:00:01.,99155,28,1,Female,6038888,5.0 out of 5 stars,Architect\\n2024-04-23T05:00:01.,49782,32,2,Male,5941356,5.0 out of 5 stars,Pilot\\n2024-04-23T05:00:01.,116517,33,3,Female,6040452,5.0 out of 5 stars,Pilot\\n2024-04-23T05:00:01.,82120,25,4,Male,5941356,5.0 out of 5 stars,Chef\\n2024-04-23T05:00:01.,89186,32,5,Female,5510456,4.0 out of 5 stars,Pilot\\n2024-04-23T05:00:01.,61713,38,6,Female,6040452,5.0 out of 5 stars,Firefighter\\n2024-04-23T05:00:01.,109924,35,7,Female,5510456,5.0 out of 5 stars,Teacher\\n2024-04-23T05:00:01.,70534,25,8,Male,6038888,5.0 out of 5 stars,Doctor\\n2024-04-23T05:00:01.,71039,28,9,Male,5026787,5.0 out of 5 stars,Firefighter\\n2024-04-23T05:00:01.,103669,39,10,Male,6040452,5.0 out of 5 stars,Writer\\n2024-04-23T05:00:01.,107400,40,11,Female,5510456,5.0 out of 5 stars,Doctor\\n2024-04-23T05:00:01.,42569,33,12,Male,6038888,5.0 out of 5 stars,Marketing Specialist\\n2024-04-23T05:00:01.,57466,27,13,Female,5941356,5.0 out of 5 stars,Teacher\\n2024-04-23T05:00:01.,49245,37,14,Female,5510456,5.0 out of 5 stars,Writer\\n2024-04-23T05:00:01.,111461,34,15,Male,5026787,5.0 out of 5 stars,Chef\\n2024-04-23T05:00:01.,100164,34,16,Female,5510456,5.0 out of 5 stars,Marketing Specialist\\n2024-04-23T05:00:01.,106415,26,17,Female,5026787,5.0 out of 5 stars,Writer\\n2024-04-23T05:00:01.,102207,36,18,Female,5510456,5.0 out of 5 stars,Doctor\\n \\n Output: \\n"
] | {"profession": "x+dSLMV/+GA", "salary": "lsTuaMKy100", "gender": "Krl1e9fqzyc", "age": "q9mixw71rsY"} | tablejoin |
ae4654298c694908b994dd999e784904f1c22e2978e6e958d71cf0e5d5ab5975 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: time,power,temp,humidity,light,CO2,dust\\n2015-08-09 22:38:21,0.55,34,34,0,1963,8.99\\n2015-08-11 13:02:42,0.638,31,36,27,2000,23.53\\n2015-08-31 14:23:02,0.0,35,28,12,2000,1.23\\n2015-08-16 19:11:54,0.066,33,31,0,2000,4.33\\n2015-08-31 07:32:28,-1.0,33,29,0,2000,3.06\\n2015-08-16 09:11:40,0.0,35,31,0,2000,44.52\\n2015-08-27 01:46:24,-1.0,31,31,0,2000,4.9\\n2015-08-16 08:05:55,0.0,34,32,0,2000,33.12\\n2015-08-13 18:28:38,0.528,35,30,27,2000,11.39\\n2015-08-12 04:59:51,-1.0,33,33,0,2000,23.56\\n2015-08-26 14:22:16,-1.0,32,30,35,2000,2.71\\n2015-08-05 08:32:58,0.0,32,40,9,1190,17.35\\n2015-08-17 08:40:28,-1.0,32,32,3,2000,8.11\\n2015-08-12 10:32:45,-1.0,34,33,10,2000,41.84\\n2015-08-30 12:47:11,-1.0,34,29,22,2000,8.04\\n2015-08-15 13:14:12,0.0,35,30,6,2000,22.01\\n \\n CSV Table B: 9etcI5xa42c,JJY6KSu5yhg,zh000AR22V8,sK4/vfuebl0,ws35g9DHMug\\n2024-04-23T05:00:01.,0,2015-08-22 21:49:59,No,0.0\\n2024-04-23T05:00:01.,0,2015-08-31 05:14:27,No,-1.0\\n2024-04-23T05:00:01.,17,2015-08-18 12:38:48,No,-1.0\\n2024-04-23T05:00:01.,0,2015-08-30 06:22:12,No,-1.0\\n2024-04-23T05:00:01.,0,2015-08-31 22:40:53,No,0.572\\n2024-04-23T05:00:01.,0,2015-08-03 04:43:17,Si,0.0\\n2024-04-23T05:00:01.,0,2015-08-12 22:58:13,Si,-1.0\\n2024-04-23T05:00:01.,26,2015-08-25 07:49:46,Si,-1.0\\n2024-04-23T05:00:01.,14,2015-08-17 13:14:00,No,0.528\\n2024-04-23T05:00:01.,0,2015-08-02 06:52:53,No,0.0\\n2024-04-23T05:00:01.,2,2015-08-08 08:37:11,Si,0.0\\n2024-04-23T05:00:01.,0,2015-08-22 21:56:01,No,0.0\\n2024-04-23T05:00:01.,0,2015-08-22 04:23:01,Si,-1.0\\n2024-04-23T05:00:01.,0,2015-08-09 22:00:43,Si,0.0\\n2024-04-23T05:00:01.,12,2015-08-03 17:18:37,No,0.638\\n2024-04-23T05:00:01.,35,2015-08-14 21:37:41,No,0.0\\n2024-04-23T05:00:01.,13,2015-08-31 10:45:43,No,-1.0\\n \\n Output: \\n"
] | {"time": "zh000AR22V8", "light": "JJY6KSu5yhg", "power": "ws35g9DHMug"} | tablejoin |
587e13e04d18246f787cc8d41da67701eb1343795150a63b1996c5ec8270b20e | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: cleanup_site_name,location,zipcode,city,responsible_section,:@computed_region_fny7_vc3j,:@computed_region_x4ys_rtnd,region,latitude,cleanup_site_id\\nBland Property,{'latitude': '45.728,98685,VANCOUVER,Southwest,3,2977.0,Southwest,45.72869,14645\\nCOUNTRY STORE MINI M,{'latitude': '47.598,98826-1455,LEAVENWORTH,Central,8,2956.0,Central,47.598419,6698\\nL & L Exxon,{'latitude': '46.274,99352,RICHLAND,Central,4,2955.0,Central,46.27471,7128\\nBURKS BROS CONOCO,{'latitude': '46.207,99336-3931,KENNEWICK,Central,4,2955.0,Central,46.2078,8264\\nHEISSON STORE,{'latitude': '45.824,98622,HEISSON,Southwest,3,2977.0,Southwest,45.82483,8814\\nKAMAN BEARING & SUPP,{'latitude': '46.969,98520,ABERDEEN,Southwest,6,2983.0,Southwest,46.96953,8704\\nLUCKYS SERVICE,{'latitude': '47.684,98822,ENTIAT,Central,8,2956.0,Central,47.684441,9917\\nPacific Pride Tanker,{'latitude': '47.483,98836,MONITOR,Central,8,2956.0,Central,47.483057,4757\\nWolfkill Feed and Fe,{'latitude': '46.893,99357,ROYAL CITY,Eastern,4,2982.0,Eastern,46.893581,4587\\nUS DOE 200-WA-1,{'latitude': '46.556,99352,RICHLAND,Nuclear Waste Prgm,4,2955.0,Central,46.5562,11562\\nA G EDWARDS INC,{'latitude': '46.151,99336,KENNEWICK,Central,4,2955.0,Central,46.151438,10122\\nUS DOE 100-KR-1,{'latitude': '46.656,99352,RICHLAND,Nuclear Waste Prgm,4,2955.0,Central,46.656433,3975\\nSHOTWELL INDUSTRIES,{'latitude': '48.017,98362,PORT ANGELES,Southwest,6,2976.0,Southwest,48.017589,9260\\nMoore Wrecking Yard,{'latitude': '45.879,98675,YACOLT,Southwest,3,2977.0,Southwest,45.87945,14639\\nElectro Tech Metal F,{'latitude': '45.673,98682,VANCOUVER,Southwest,3,2977.0,Southwest,45.673507,4351\\nSCHMELZER WELL SITE,{'latitude': '46.190,99336,KENNEWICK,Central,4,2955.0,Central,46.190922,3102\\nJR Simplot Co Othell,{'latitude': '46.838,99344,OTHELLO,Eastern,4,2953.0,Eastern,46.838177,2350\\n \\n CSV Table B: +TcFRhetc3o,93uWjlrnDi8,IBOO7n66j2I,0tAjwzEbXgc,zSt62OHmjJ8,9etcI5xa42c,xEEeWKcl26k,O82C1HeOr40\\n6040452,4747,Weak,ANATONE,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '46.133\\n6038888,1504,Weak,CLARKSTON,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '46.402\\n5941356,6157,Weak,PORT ANGELES,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '48.104\\n6040452,10905,New,RICHLAND,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '46.253\\n5941356,2762,Weak,YACOLT,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '45.731\\n5510456,11504,New,WENATCHEE,4.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '47.480\\n6040452,8329,New,ELMA,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '47.004\\n5510456,12622,New,FORKS,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '47.949\\n6038888,3877,Weak,RICHLAND,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '46.695\\n5026787,4273,New,PORT ANGELES,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '48.105\\n6040452,3572,New,SEQUIM,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '48.092\\n5510456,9612,Weak,LEAVENWORTH,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '47.556\\n6038888,2872,Weak,MOSES LAKE,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '47.187\\n5941356,10466,Good,KENNEWICK,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '46.187\\n5510456,7992,New,PORT ANGELES,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '48.116\\n5026787,8293,Weak,PROSSER,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '46.382\\n5510456,8437,New,WENATCHEE,5.0 out of 5 stars,2024-04-23T05:00:01.,0,{'latitude': '47.416\\n \\n Output: \\n"
] | {"city": "0tAjwzEbXgc", "cleanup_site_id": "93uWjlrnDi8", "location": "O82C1HeOr40"} | tablejoin |
bd4b2031ad50538f365ac3312534d813fb7326fd90cf5056ac80b31d189cbb15 | data_analysis | [
"Please create a valid join mapping between CSV Table A and CSV Table B. Each column in A maps to 0 or 1 columns in B. Return your response as a Python dictionary, formatted as {col_name_in_df_a : col_name_in_df_b}. Please return only the dictionary. \\n CSV Table A: center,center_search_status,facility,occupied,record_date,last_update,country,contact,phone,location\\nMarshall Space Fligh,Public,ET Flight Environmen,1962-01-01T00:00:00.,1996-03-01T00:00:00.,2015-02-26T00:00:00.,US,Pam Caruso,256-544-7795,{'latitude': '34.729\\nKennedy Space Center,Public,Airlock/M7-360/SSPF ,1995-01-01T00:00:00.,1996-03-01T00:00:00.,2015-06-22T00:00:00.,US,Sheryl Chaffee,321-867-8047,{'latitude': '28.538\\nKennedy Space Center,Public,Payload Shipping Con,1986-01-01T00:00:00.,1996-03-01T00:00:00.,2015-06-22T00:00:00.,US,Sheryl Chaffee,321-867-8047,{'latitude': '28.538\\nKennedy Space Center,Public,High Bay 4 Cell/K6-8,1966-01-01T00:00:00.,1996-03-01T00:00:00.,2015-06-22T00:00:00.,US,Sheryl Chaffee,321-867-8047,{'latitude': '28.538\\nMarshall Space Fligh,Public,EH SRB-TPS (Thermal ,1956-01-01T00:00:00.,1996-03-01T00:00:00.,2014-06-02T00:00:00.,US,Pam Caruso,256-544-7795,{'latitude': '34.729\\nMarshall Space Fligh,Public,ES Earth Science & A,1991-01-01T00:00:00.,1996-03-01T00:00:00.,2014-03-31T00:00:00.,US,Pam Caruso,256-544-7795,{'latitude': '34.729\\nMarshall Space Fligh,Public,EL Ground Control Ex,1958-01-01T00:00:00.,1996-03-01T00:00:00.,2014-06-02T00:00:00.,US,Pam Caruso,256-544-7795,{'latitude': '34.729\\nAmes Research Center,Public,N229 - EXPER. AEROTH,1961-01-01T00:00:00.,1996-03-01T00:00:00.,2014-06-13T00:00:00.,US,Rocci Caringello,650 603-9506,{'latitude': '37.414\\nMarshall Space Fligh,Public,ES Low Energy Ion Fa,1974-01-01T00:00:00.,1996-03-01T00:00:00.,2014-03-31T00:00:00.,US,Pam Caruso,256-544-7795,{'latitude': '34.729\\nJohnson Space Center,Public,Vibration Acoustic T,,2012-09-26T00:00:00.,2012-09-26T00:00:00.,US,Charles Noel,281.483.3219,{'latitude': '29.559\\nJet Propulsion Lab,Public,DSS 43 Antenna,1963-01-01T00:00:00.,1996-03-01T00:00:00.,2013-08-07T00:00:00.,US,Gary Gray,818.354.0701,{'latitude': '34.178\\nMarshall Space Fligh,Public,EI Manned Habitat EC,1985-01-01T00:00:00.,1996-05-17T00:00:00.,2014-06-02T00:00:00.,US,Pam Caruso,256-544-7795,{'latitude': '34.729\\nKennedy Space Center,Public,Engineering Developm,1966-01-01T00:00:00.,1996-03-01T00:00:00.,2015-06-22T00:00:00.,US,Sheryl Chaffee,321-867-8047,{'latitude': '28.538\\nStennis Space Center,Public,Sensor Laboratory #1,1966-01-01T00:00:00.,1996-03-01T00:00:00.,2015-04-06T00:00:00.,US,Robert Bruce,228-688-1646,{'latitude': '30.385\\n \\n CSV Table B: k1vXu+r6Ouc,GDenm4WiBpQ,pmjzbvItDZo,Bezp8Kegeiw,pg09D/VHAjI,+xkGOBJYDCk,BkPad8F1Zfw\\ngas,Langley Research Cen,1946-01-01T00:00:00.,24591000,1996-03-01T00:00:00.,{'latitude': '37.086,Weak\\ngas,Wallops Flight Facil,1994-01-01T00:00:00.,8334800,1996-03-01T00:00:00.,{'latitude': '37.911,Weak\\ngas,Kennedy Space Center,1966-01-01T00:00:00.,9875400,1996-03-01T00:00:00.,{'latitude': '28.538,Weak\\ngas,Kennedy Space Center,1962-01-01T00:00:00.,8338300,1996-03-01T00:00:00.,{'latitude': '28.538,New\\ngas,Jet Propulsion Lab,1963-01-01T00:00:00.,8995500,1996-03-01T00:00:00.,{'latitude': '34.178,Weak\\ngas,Armstrong Flight Res,,8564500,2010-04-13T00:00:00.,{'latitude': '35.000,New\\ngas,Goddard Space Flight,,8948500,1996-03-01T00:00:00.,{'latitude': '38.995,New\\ngas,NASA Aircraft Manage,,11859900,2009-11-04T00:00:00.,{'latitude': '38.883,New\\ngas,Marshall Space Fligh,1995-01-01T00:00:00.,16537400,1996-03-01T00:00:00.,{'latitude': '34.729,Weak\\ngas,Wallops Flight Facil,1959-01-01T00:00:00.,11010400,1996-03-01T00:00:00.,{'latitude': '37.911,New\\ngas,Glenn Research Cente,1993-01-01T00:00:00.,7534000,1996-03-01T00:00:00.,{'latitude': '41.430,New\\ngas,Jet Propulsion Lab,1992-01-01T00:00:00.,9818100,1996-03-01T00:00:00.,{'latitude': '34.178,Weak\\ngas,Marshall Space Fligh,1965-01-01T00:00:00.,9965000,1996-03-01T00:00:00.,{'latitude': '34.729,Weak\\ngas,Goddard Space Flight,1966-01-01T00:00:00.,20254600,1996-03-01T00:00:00.,{'latitude': '38.995,Good\\n \\n Output: \\n"
] | {"location": "+xkGOBJYDCk", "center": "GDenm4WiBpQ", "record_date": "pg09D/VHAjI", "occupied": "pmjzbvItDZo"} | tablejoin |
e114ea800daa6938bd7bbc29c6fde32844324662764b5cb63d7e4e78c3b66c65 | data_analysis | [
"Please convert the Input Table from CSV format to JSON format. Please respond only with the table. \n Input Table: swsLengthHR,swsTimeHR,swsLengthT,swsTimeT,decreasePercentageT,swsTimeM,swsLengthM,decreasePercentageM\n0.40387,0.125,0.08702,0.03448,0.6986,0.05263,-0.73889,0.74472\n0.0,0.0,0.0,0.0,0.68042,0.18421,0.21577,0.79564\n0.35725,0.125,0.17935,0.10345,0.75992,0.28947,1.02812,0.88919\n0.08659,0.04167,0.0,0.0,0.7441,0.0,-1.00196,0.61898\n0.33737,0.16667,0.12945,0.06897,0.64663,0.05263,-0.79333,0.62288\n0.05548,0.04167,0.11269,0.03448,0.7798,0.23684,1.20461,0.71585\n0.32591,0.58333,0.02467,0.03448,0.66134,0.55263,0.73997,0.53467\n0.0,0.0,0.03896,0.03448,0.66269,0.15789,2.84312,0.65916\n0.06369,0.04167,0.39228,0.13793,0.73069,0.18421,0.45976,0.67106\n0.0,0.0,0.43818,0.13793,0.68326,0.13158,-0.3926,0.81514\n0.0,0.0,0.0,0.0,0.67266,0.0,-1.00196,0.96306\n \n Output: \n"
] | {"69":{"swsLengthHR":0.40387,"swsTimeHR":0.125,"swsLengthT":0.08702,"swsTimeT":0.03448,"decreasePercentageT":0.6986,"swsTimeM":0.05263,"swsLengthM":-0.73889,"decreasePercentageM":0.74472},"88":{"swsLengthHR":0.0,"swsTimeHR":0.0,"swsLengthT":0.0,"swsTimeT":0.0,"decreasePercentageT":0.68042,"swsTimeM":0.18421,"swsLengthM":0.21577,"decreasePercentageM":0.79564},"73":{"swsLengthHR":0.35725,"swsTimeHR":0.125,"swsLengthT":0.17935,"swsTimeT":0.10345,"decreasePercentageT":0.75992,"swsTimeM":0.28947,"swsLengthM":1.02812,"decreasePercentageM":0.88919},"54":{"swsLengthHR":0.08659,"swsTimeHR":0.04167,"swsLengthT":0.0,"swsTimeT":0.0,"decreasePercentageT":0.7441,"swsTimeM":0.0,"swsLengthM":-1.00196,"decreasePercentageM":0.61898},"23":{"swsLengthHR":0.33737,"swsTimeHR":0.16667,"swsLengthT":0.12945,"swsTimeT":0.06897,"decreasePercentageT":0.64663,"swsTimeM":0.05263,"swsLengthM":-0.79333,"decreasePercentageM":0.62288},"201":{"swsLengthHR":0.05548,"swsTimeHR":0.04167,"swsLengthT":0.11269,"swsTimeT":0.03448,"decreasePercentageT":0.7798,"swsTimeM":0.23684,"swsLengthM":1.20461,"decreasePercentageM":0.71585},"211":{"swsLengthHR":0.32591,"swsTimeHR":0.58333,"swsLengthT":0.02467,"swsTimeT":0.03448,"decreasePercentageT":0.66134,"swsTimeM":0.55263,"swsLengthM":0.73997,"decreasePercentageM":0.53467},"198":{"swsLengthHR":0.0,"swsTimeHR":0.0,"swsLengthT":0.03896,"swsTimeT":0.03448,"decreasePercentageT":0.66269,"swsTimeM":0.15789,"swsLengthM":2.84312,"decreasePercentageM":0.65916},"35":{"swsLengthHR":0.06369,"swsTimeHR":0.04167,"swsLengthT":0.39228,"swsTimeT":0.13793,"decreasePercentageT":0.73069,"swsTimeM":0.18421,"swsLengthM":0.45976,"decreasePercentageM":0.67106},"79":{"swsLengthHR":0.0,"swsTimeHR":0.0,"swsLengthT":0.43818,"swsTimeT":0.13793,"decreasePercentageT":0.68326,"swsTimeM":0.13158,"swsLengthM":-0.3926,"decreasePercentageM":0.81514},"44":{"swsLengthHR":0.0,"swsTimeHR":0.0,"swsLengthT":0.0,"swsTimeT":0.0,"decreasePercentageT":0.67266,"swsTimeM":0.0,"swsLengthM":-1.00196,"decreasePercentageM":0.96306}} | tablereformat |
09752b3d3e355017282301de1735bd903221368e1fadf3e64aa9594ef7730e62 | data_analysis | [
"Please convert the Input Table from HTML format to JSON format. Please respond only with the table. \n Input Table: <table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>Country</th>\n <th>Inequality HDI</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>Indonesia</td>\n <td>2</td>\n </tr>\n <tr>\n <td>Azerbaijan</td>\n <td>1</td>\n </tr>\n <tr>\n <td>Denmark</td>\n <td>0</td>\n </tr>\n <tr>\n <td>North Macedonia</td>\n <td>2</td>\n </tr>\n <tr>\n <td>Canada</td>\n <td>0</td>\n </tr>\n <tr>\n <td>Palau</td>\n <td>2</td>\n </tr>\n <tr>\n <td>Papua New Guinea</td>\n <td>3</td>\n </tr>\n <tr>\n <td>Samoa</td>\n <td>2</td>\n </tr>\n <tr>\n <td>Marshall Islands</td>\n <td>2</td>\n </tr>\n <tr>\n <td>Lebanon</td>\n <td>2</td>\n </tr>\n </tbody>\n</table> \n Output: \n"
] | {"111":{"Country":"Indonesia","Inequality HDI":2},"88":{"Country":"Azerbaijan","Inequality HDI":1},"4":{"Country":"Denmark","Inequality HDI":0},"83":{"Country":"North Macedonia","Inequality HDI":2},"17":{"Country":"Canada","Inequality HDI":0},"70":{"Country":"Palau","Inequality HDI":2},"153":{"Country":"Papua New Guinea","Inequality HDI":3},"115":{"Country":"Samoa","Inequality HDI":2},"101":{"Country":"Marshall Islands","Inequality HDI":2},"108":{"Country":"Lebanon","Inequality HDI":2}} | tablereformat |
eb8aebddc3e1eff35a92de9e8306dfcfebd25201eefda2921b830226b5347dc5 | data_analysis | [
"Please convert the Input Table from HTML format to JSON format. Please respond only with the table. \n Input Table: <table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>country</th>\n <th>code country</th>\n <th>Year</th>\n <th>Maize yield</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>Low-income countries</td>\n <td>0</td>\n <td>1993</td>\n <td>1.675.252</td>\n </tr>\n <tr>\n <td>Lebanon</td>\n <td>LBN</td>\n <td>1971</td>\n <td>82.339.996</td>\n </tr>\n <tr>\n <td>United Kingdom</td>\n <td>GBR</td>\n <td>1999</td>\n <td>0</td>\n </tr>\n <tr>\n <td>Small Island Develop</td>\n <td>0</td>\n <td>1972</td>\n <td>1.0519</td>\n </tr>\n <tr>\n <td>Cuba</td>\n <td>CUB</td>\n <td>1964</td>\n <td>9.474</td>\n </tr>\n </tbody>\n</table> \n Output: \n"
] | {"6792":{"country":"Low-income countries","code country":"0","Year":1993,"Maize yield":"1.675.252"},"2266":{"country":"Lebanon","code country":"LBN","Year":1971,"Maize yield":"82.339.996"},"8256":{"country":"United Kingdom","code country":"GBR","Year":1999,"Maize yield":"0"},"2530":{"country":"Small Island Develop","code country":"0","Year":1972,"Maize yield":"1.0519"},"799":{"country":"Cuba","code country":"CUB","Year":1964,"Maize yield":"9.474"}} | tablereformat |
57772b38ab8d4f9f2e30d5e0cca6e007f228786523ed027bdbf37b59fe20e3b1 | data_analysis | [
"Please convert the Input Table from TSV format to JSONL format. Please respond only with the table. \n Input Table: plan_strategy\trtpid\ttitle\tscope\topen_period\tfunding_millions_yoe\tcounty\nRegional Rail\t21-T11-107\tRail | Service Frequ\tThis program include\t2036 - 2050\t2840\tVarious\nRegional Rail\t21-T11-097\tFerry | Service Expa\tThis program include\t2021 - 2035\t271\tSan Francisco\nInterchanges and Bot\t21-T06-041\tCorridor & Interchan\tThis program include\t2021 - 2035\t40\tAlameda\nRegional Rail\t21-T11-101\tRail | Modernization\tThis program include\t2021 - 2035\t1980\tVarious\nRegional Rail\t21-T11-201\tRail | New Station |\tThis program include\t2021 - 2035\t14\tSonoma\nInterchanges and Bot\t21-T06-020\tCorridor & Interchan\tThis program include\t2021 - 2035\t173\tVarious\n \n Output: \n"
] | {"plan_strategy":"Regional Rail","rtpid":"21-T11-107","title":"Rail | Service Frequ","scope":"This program include","open_period":"2036 - 2050","funding_millions_yoe":2840,"county":"Various"}
{"plan_strategy":"Regional Rail","rtpid":"21-T11-097","title":"Ferry | Service Expa","scope":"This program include","open_period":"2021 - 2035","funding_millions_yoe":271,"county":"San Francisco"}
{"plan_strategy":"Interchanges and Bot","rtpid":"21-T06-041","title":"Corridor & Interchan","scope":"This program include","open_period":"2021 - 2035","funding_millions_yoe":40,"county":"Alameda"}
{"plan_strategy":"Regional Rail","rtpid":"21-T11-101","title":"Rail | Modernization","scope":"This program include","open_period":"2021 - 2035","funding_millions_yoe":1980,"county":"Various"}
{"plan_strategy":"Regional Rail","rtpid":"21-T11-201","title":"Rail | New Station |","scope":"This program include","open_period":"2021 - 2035","funding_millions_yoe":14,"county":"Sonoma"}
{"plan_strategy":"Interchanges and Bot","rtpid":"21-T06-020","title":"Corridor & Interchan","scope":"This program include","open_period":"2021 - 2035","funding_millions_yoe":173,"county":"Various"}
| tablereformat |
8b25c07592f100c7c61af45d3de6eec07f4996d0a211f613cc1e224db02bba4c | data_analysis | [
"Please convert the Input Table from JSONL format to JSON format. Please respond only with the table. \n Input Table: {\"Profanity\":\"tongue fucker\",\"Severity Rating\":2.4,\"Severity Description\":\"Strong\"}\n{\"Profanity\":\"arse-shagger\",\"Severity Rating\":2.4,\"Severity Description\":\"Strong\"}\n{\"Profanity\":\"s.o.b.s\",\"Severity Rating\":1.6,\"Severity Description\":\"Strong\"}\n{\"Profanity\":\"bollocktician\",\"Severity Rating\":1.4,\"Severity Description\":\"Mild\"}\n{\"Profanity\":\"d1ck\",\"Severity Rating\":1.0,\"Severity Description\":\"Mild\"}\n{\"Profanity\":\"goddamned\",\"Severity Rating\":1.8,\"Severity Description\":\"Strong\"}\n{\"Profanity\":\"shittydick\",\"Severity Rating\":2.0,\"Severity Description\":\"Strong\"}\n{\"Profanity\":\"groper\",\"Severity Rating\":1.4,\"Severity Description\":\"Mild\"}\n \n Output: \n"
] | {"14":{"Profanity":"tongue fucker","Severity Rating":2.4,"Severity Description":"Strong"},"1541":{"Profanity":"arse-shagger","Severity Rating":2.4,"Severity Description":"Strong"},"199":{"Profanity":"s.o.b.s","Severity Rating":1.6,"Severity Description":"Strong"},"1477":{"Profanity":"bollocktician","Severity Rating":1.4,"Severity Description":"Mild"},"1154":{"Profanity":"d1ck","Severity Rating":1.0,"Severity Description":"Mild"},"857":{"Profanity":"goddamned","Severity Rating":1.8,"Severity Description":"Strong"},"123":{"Profanity":"shittydick","Severity Rating":2.0,"Severity Description":"Strong"},"846":{"Profanity":"groper","Severity Rating":1.4,"Severity Description":"Mild"}} | tablereformat |
ce1fa8f673c3d33b3986e4f4f148ab1a11e42cd2b7f15390f0f4f2acb530a6e3 | data_analysis | [
"Please convert the Input Table from TSV format to JSONL format. Please respond only with the table. \n Input Table: Calendar Year\tActive Duty\tFull-Time (est) Guard+Reserve\tSelected Reserve FTE\tTotal Military FTE\tTotal Deaths\tAccident \tHostile Action\n2008\t1402227\t73000\t207917\t1683144\t1440\t506.0\t352\n1988\t2121659\t72000\t115836\t2309495\t1819\t1080.0\t0\n1981\t2093032\t22000\t91719\t2206751\t2380\t1524.0\t0\n2003\t1423348\t66000\t243284\t1732632\t1399\t597.0\t312\n1984\t2138339\t55000\t104583\t2297922\t1999\t1293.0\t1\n2004\t1411287\t66000\t234629\t1711916\t1847\t605.0\t735\n1995\t1502343\t65000\t94585\t1661928\t1040\t538.0\t0\n1982\t2112609\t41000\t97458\t2251067\t2319\t1493.0\t0\n1994\t1581649\t65000\t99833\t1746482\t1075\t544.0\t0\n1980\t2050758\t22000\t86872\t2159630\t2392\t1556.0\t0\n1997\t1418773\t65000\t94609\t1578382\t817\t433.0\t0\n1999\t1367838\t65000\t93104\t1525942\t796\t439.0\t0\n \n Output: \n"
] | {"Calendar Year":2008,"Active Duty":1402227,"Full-Time (est) Guard+Reserve":73000,"Selected Reserve FTE":207917,"Total Military FTE":1683144,"Total Deaths":1440,"Accident ":506.0,"Hostile Action":352}
{"Calendar Year":1988,"Active Duty":2121659,"Full-Time (est) Guard+Reserve":72000,"Selected Reserve FTE":115836,"Total Military FTE":2309495,"Total Deaths":1819,"Accident ":1080.0,"Hostile Action":0}
{"Calendar Year":1981,"Active Duty":2093032,"Full-Time (est) Guard+Reserve":22000,"Selected Reserve FTE":91719,"Total Military FTE":2206751,"Total Deaths":2380,"Accident ":1524.0,"Hostile Action":0}
{"Calendar Year":2003,"Active Duty":1423348,"Full-Time (est) Guard+Reserve":66000,"Selected Reserve FTE":243284,"Total Military FTE":1732632,"Total Deaths":1399,"Accident ":597.0,"Hostile Action":312}
{"Calendar Year":1984,"Active Duty":2138339,"Full-Time (est) Guard+Reserve":55000,"Selected Reserve FTE":104583,"Total Military FTE":2297922,"Total Deaths":1999,"Accident ":1293.0,"Hostile Action":1}
{"Calendar Year":2004,"Active Duty":1411287,"Full-Time (est) Guard+Reserve":66000,"Selected Reserve FTE":234629,"Total Military FTE":1711916,"Total Deaths":1847,"Accident ":605.0,"Hostile Action":735}
{"Calendar Year":1995,"Active Duty":1502343,"Full-Time (est) Guard+Reserve":65000,"Selected Reserve FTE":94585,"Total Military FTE":1661928,"Total Deaths":1040,"Accident ":538.0,"Hostile Action":0}
{"Calendar Year":1982,"Active Duty":2112609,"Full-Time (est) Guard+Reserve":41000,"Selected Reserve FTE":97458,"Total Military FTE":2251067,"Total Deaths":2319,"Accident ":1493.0,"Hostile Action":0}
{"Calendar Year":1994,"Active Duty":1581649,"Full-Time (est) Guard+Reserve":65000,"Selected Reserve FTE":99833,"Total Military FTE":1746482,"Total Deaths":1075,"Accident ":544.0,"Hostile Action":0}
{"Calendar Year":1980,"Active Duty":2050758,"Full-Time (est) Guard+Reserve":22000,"Selected Reserve FTE":86872,"Total Military FTE":2159630,"Total Deaths":2392,"Accident ":1556.0,"Hostile Action":0}
{"Calendar Year":1997,"Active Duty":1418773,"Full-Time (est) Guard+Reserve":65000,"Selected Reserve FTE":94609,"Total Military FTE":1578382,"Total Deaths":817,"Accident ":433.0,"Hostile Action":0}
{"Calendar Year":1999,"Active Duty":1367838,"Full-Time (est) Guard+Reserve":65000,"Selected Reserve FTE":93104,"Total Military FTE":1525942,"Total Deaths":796,"Accident ":439.0,"Hostile Action":0}
| tablereformat |
56ace477670fa1527771dc1a4f2babac3b704f1c313b8981a53ce892f55b6c05 | data_analysis | [
"Please convert the Input Table from TSV format to CSV format. Please respond only with the table. \n Input Table: species\tquantity\nLAKE TROUT\t2931\nKOKANEE\t716220\nGRAYLING ARCTIC\t84211\nSUNFISH BLUEGILL\t47840\nWIPER\t386460\nSUCKER JUNE\t80510\nTIGER TROUT\t401119\nRAINBOW\t3904196\nBROOK TROUT\t232058\nCUTTHROAT\t1506513\nCHUB\t34740\nALL TROUT\t1650\nBROWN TROUT\t245553\nGOLDEN TROUT\t4581\n \n Output: \n"
] | species,quantity
LAKE TROUT,2931
KOKANEE,716220
GRAYLING ARCTIC,84211
SUNFISH BLUEGILL,47840
WIPER,386460
SUCKER JUNE,80510
TIGER TROUT,401119
RAINBOW,3904196
BROOK TROUT,232058
CUTTHROAT,1506513
CHUB,34740
ALL TROUT,1650
BROWN TROUT,245553
GOLDEN TROUT,4581
| tablereformat |
3f9091ebb24ea69d1e9ad7a20e3a617f47548595e9b0b0a46a95061ec3e81740 | data_analysis | [
"Please convert the Input Table from HTML format to TSV format. Please respond only with the table. \n Input Table: <table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>license_description</th>\n <th>zip_code</th>\n <th>license_id</th>\n <th>location</th>\n <th>date_issued</th>\n <th>city</th>\n <th>ward_precinct</th>\n <th>address</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>Retail Food Establis</td>\n <td>60607</td>\n <td>2959264</td>\n <td>{'latitude': '41.885</td>\n <td>2024-04-16T00:00:00.</td>\n <td>CHICAGO</td>\n <td>27-1</td>\n <td>205-209 N PEORIA ST</td>\n </tr>\n <tr>\n <td>Pharmaceutical Repre</td>\n <td>60031</td>\n <td>2960784</td>\n <td>NaN</td>\n <td>2024-03-22T00:00:00.</td>\n <td>GURNEE</td>\n <td>NaN</td>\n <td>[REDACTED FOR PRIVAC</td>\n </tr>\n <tr>\n <td>Wholesale Food Estab</td>\n <td>60640</td>\n <td>2964234</td>\n <td>{'latitude': '41.964</td>\n <td>2024-04-16T00:00:00.</td>\n <td>CHICAGO</td>\n <td>47-36</td>\n <td>4527 N RAVENSWOOD AV</td>\n </tr>\n <tr>\n <td>Limited Business Lic</td>\n <td>60613</td>\n <td>2957581</td>\n <td>{'latitude': '41.955</td>\n <td>2024-04-19T00:00:00.</td>\n <td>CHICAGO</td>\n <td>46-10</td>\n <td>4025 N SHERIDAN RD 1</td>\n </tr>\n <tr>\n <td>Tavern</td>\n <td>60613</td>\n <td>2962989</td>\n <td>{'latitude': '41.949</td>\n <td>2024-04-16T00:00:00.</td>\n <td>CHICAGO</td>\n <td>44-32</td>\n <td>3714 N CLARK ST 1ST</td>\n </tr>\n <tr>\n <td>Pawnbroker</td>\n <td>60639</td>\n <td>2959235</td>\n <td>{'latitude': '41.931</td>\n <td>2024-03-18T00:00:00.</td>\n <td>CHICAGO</td>\n <td>31-40</td>\n <td>5401 - 5405 W DIVERS</td>\n </tr>\n </tbody>\n</table> \n Output: \n"
] | license_description zip_code license_id location date_issued city ward_precinct address
Retail Food Establis 60607 2959264 {'latitude': '41.885 2024-04-16T00:00:00. CHICAGO 27-1 205-209 N PEORIA ST
Pharmaceutical Repre 60031 2960784 2024-03-22T00:00:00. GURNEE [REDACTED FOR PRIVAC
Wholesale Food Estab 60640 2964234 {'latitude': '41.964 2024-04-16T00:00:00. CHICAGO 47-36 4527 N RAVENSWOOD AV
Limited Business Lic 60613 2957581 {'latitude': '41.955 2024-04-19T00:00:00. CHICAGO 46-10 4025 N SHERIDAN RD 1
Tavern 60613 2962989 {'latitude': '41.949 2024-04-16T00:00:00. CHICAGO 44-32 3714 N CLARK ST 1ST
Pawnbroker 60639 2959235 {'latitude': '41.931 2024-03-18T00:00:00. CHICAGO 31-40 5401 - 5405 W DIVERS
| tablereformat |
35b3098f43e9129b80af5263066ffad973670da10a3bbc4d350fa88cea4d980f | data_analysis | [
"Please convert the Input Table from TSV format to CSV format. Please respond only with the table. \n Input Table: credentialnumber\tlastname\tfirstname\tmiddlename\tcredentialtype\tstatus\tbirthyear\tfirstissuedate\nNA00164281\tJones\tSusan\tMary\tNursing Assistant Re\tACTIVE\t1963.0\t20040426.0\nLP60805320\tOlson\tChristina\tMarie\tLicensed Practical N\tCLOSED\t1978.0\t\nES61380905\tHawks\tWilliam\tJonathan\tEmergency Medical Te\tCLOSED\t1997.0\t\nNC10102413\tBlount\tJoyce\tL\tNursing Assistant Ce\tEXPIRED\t1971.0\t20080206.0\nVA60030789\tGrubich\tAshley\tNichole\tPharmacy Technician \tACTIVE\t1989.0\t20080815.0\nOL61464825\tWyer\tKyle\tDavid\tOsteopathic Physicia\tACTIVE\t1990.0\t20230725.0\nCP60969079\tMullin\tTiffany\tAnn\tSubstance Use Disord\tACTIVE\t1967.0\t20200114.0\nCG61289229\tOrtiz\tNicole\tLynne\tCounselor Agency Aff\tPENDING\t1968.0\t\nMC61191565\tCapozzolo\tMerry\tAlexandra\tMental Health Counse\tSUPERSEDED\t1991.0\t20210628.0\n \n Output: \n"
] | credentialnumber,lastname,firstname,middlename,credentialtype,status,birthyear,firstissuedate
NA00164281,Jones,Susan,Mary,Nursing Assistant Re,ACTIVE,1963.0,20040426.0
LP60805320,Olson,Christina,Marie,Licensed Practical N,CLOSED,1978.0,
ES61380905,Hawks,William,Jonathan,Emergency Medical Te,CLOSED,1997.0,
NC10102413,Blount,Joyce,L,Nursing Assistant Ce,EXPIRED,1971.0,20080206.0
VA60030789,Grubich,Ashley,Nichole,Pharmacy Technician ,ACTIVE,1989.0,20080815.0
OL61464825,Wyer,Kyle,David,Osteopathic Physicia,ACTIVE,1990.0,20230725.0
CP60969079,Mullin,Tiffany,Ann,Substance Use Disord,ACTIVE,1967.0,20200114.0
CG61289229,Ortiz,Nicole,Lynne,Counselor Agency Aff,PENDING,1968.0,
MC61191565,Capozzolo,Merry,Alexandra,Mental Health Counse,SUPERSEDED,1991.0,20210628.0
| tablereformat |
0cbc79763d1930cd7c78821f52cbb8c368eacbee9ea3b9bb9ece1e79167deb4a | data_analysis | [
"Please convert the Input Table from JSONL format to JSON format. Please respond only with the table. \n Input Table: {\"app_no\":6067396,\"type\":\"HDR\",\"app_date\":\"2024-02-05T00:00:00.\",\"status\":\"Incomplete\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Complete\",\"wav_course\":\"Complete\",\"defensive_driving\":\"Complete\"}\n{\"app_no\":6070694,\"type\":\"HDR\",\"app_date\":\"2024-03-20T00:00:00.\",\"status\":\"Incomplete\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Needed\",\"wav_course\":\"Needed\",\"defensive_driving\":\"Complete\"}\n{\"app_no\":6068735,\"type\":\"HDR\",\"app_date\":\"2024-02-22T00:00:00.\",\"status\":\"Incomplete\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Needed\",\"wav_course\":\"Complete\",\"defensive_driving\":\"Complete\"}\n{\"app_no\":6070320,\"type\":\"HDR\",\"app_date\":\"2024-03-14T00:00:00.\",\"status\":\"Incomplete\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Complete\",\"wav_course\":\"Complete\",\"defensive_driving\":\"Complete\"}\n{\"app_no\":6071839,\"type\":\"HDR\",\"app_date\":\"2024-04-04T00:00:00.\",\"status\":\"Approved - License I\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Complete\",\"wav_course\":\"Complete\",\"defensive_driving\":\"Complete\"}\n{\"app_no\":6070328,\"type\":\"HDR\",\"app_date\":\"2024-03-14T00:00:00.\",\"status\":\"Incomplete\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Needed\",\"wav_course\":\"Needed\",\"defensive_driving\":\"Needed\"}\n{\"app_no\":6070076,\"type\":\"HDR\",\"app_date\":\"2024-03-11T00:00:00.\",\"status\":\"Incomplete\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Complete\",\"wav_course\":\"Complete\",\"defensive_driving\":\"Needed\"}\n{\"app_no\":6070287,\"type\":\"HDR\",\"app_date\":\"2024-03-14T00:00:00.\",\"status\":\"Approved - License I\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Complete\",\"wav_course\":\"Complete\",\"defensive_driving\":\"Complete\"}\n{\"app_no\":6070315,\"type\":\"HDR\",\"app_date\":\"2024-03-14T00:00:00.\",\"status\":\"Approved - License I\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Complete\",\"wav_course\":\"Complete\",\"defensive_driving\":\"Complete\"}\n{\"app_no\":6067471,\"type\":\"HDR\",\"app_date\":\"2024-02-06T00:00:00.\",\"status\":\"Approved - License I\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Complete\",\"wav_course\":\"Complete\",\"defensive_driving\":\"Complete\"}\n{\"app_no\":6071018,\"type\":\"HDR\",\"app_date\":\"2024-03-24T00:00:00.\",\"status\":\"Incomplete\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Needed\",\"wav_course\":\"Complete\",\"defensive_driving\":\"Complete\"}\n{\"app_no\":6069877,\"type\":\"HDR\",\"app_date\":\"2024-03-08T00:00:00.\",\"status\":\"Incomplete\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Complete\",\"wav_course\":\"Complete\",\"defensive_driving\":\"Complete\"}\n{\"app_no\":6070400,\"type\":\"HDR\",\"app_date\":\"2024-03-16T00:00:00.\",\"status\":\"Incomplete\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Complete\",\"wav_course\":\"Complete\",\"defensive_driving\":\"Complete\"}\n{\"app_no\":6067570,\"type\":\"HDR\",\"app_date\":\"2024-02-07T00:00:00.\",\"status\":\"Approved - License I\",\"fru_interview_scheduled\":\"Not Applicable\",\"drug_test\":\"Complete\",\"wav_course\":\"Complete\",\"defensive_driving\":\"Complete\"}\n \n Output: \n"
] | {"188":{"app_no":6067396,"type":"HDR","app_date":"2024-02-05T00:00:00.","status":"Incomplete","fru_interview_scheduled":"Not Applicable","drug_test":"Complete","wav_course":"Complete","defensive_driving":"Complete"},"951":{"app_no":6070694,"type":"HDR","app_date":"2024-03-20T00:00:00.","status":"Incomplete","fru_interview_scheduled":"Not Applicable","drug_test":"Needed","wav_course":"Needed","defensive_driving":"Complete"},"650":{"app_no":6068735,"type":"HDR","app_date":"2024-02-22T00:00:00.","status":"Incomplete","fru_interview_scheduled":"Not Applicable","drug_test":"Needed","wav_course":"Complete","defensive_driving":"Complete"},"823":{"app_no":6070320,"type":"HDR","app_date":"2024-03-14T00:00:00.","status":"Incomplete","fru_interview_scheduled":"Not Applicable","drug_test":"Complete","wav_course":"Complete","defensive_driving":"Complete"},"118":{"app_no":6071839,"type":"HDR","app_date":"2024-04-04T00:00:00.","status":"Approved - License I","fru_interview_scheduled":"Not Applicable","drug_test":"Complete","wav_course":"Complete","defensive_driving":"Complete"},"62":{"app_no":6070328,"type":"HDR","app_date":"2024-03-14T00:00:00.","status":"Incomplete","fru_interview_scheduled":"Not Applicable","drug_test":"Needed","wav_course":"Needed","defensive_driving":"Needed"},"115":{"app_no":6070076,"type":"HDR","app_date":"2024-03-11T00:00:00.","status":"Incomplete","fru_interview_scheduled":"Not Applicable","drug_test":"Complete","wav_course":"Complete","defensive_driving":"Needed"},"549":{"app_no":6070287,"type":"HDR","app_date":"2024-03-14T00:00:00.","status":"Approved - License I","fru_interview_scheduled":"Not Applicable","drug_test":"Complete","wav_course":"Complete","defensive_driving":"Complete"},"531":{"app_no":6070315,"type":"HDR","app_date":"2024-03-14T00:00:00.","status":"Approved - License I","fru_interview_scheduled":"Not Applicable","drug_test":"Complete","wav_course":"Complete","defensive_driving":"Complete"},"495":{"app_no":6067471,"type":"HDR","app_date":"2024-02-06T00:00:00.","status":"Approved - License I","fru_interview_scheduled":"Not Applicable","drug_test":"Complete","wav_course":"Complete","defensive_driving":"Complete"},"654":{"app_no":6071018,"type":"HDR","app_date":"2024-03-24T00:00:00.","status":"Incomplete","fru_interview_scheduled":"Not Applicable","drug_test":"Needed","wav_course":"Complete","defensive_driving":"Complete"},"908":{"app_no":6069877,"type":"HDR","app_date":"2024-03-08T00:00:00.","status":"Incomplete","fru_interview_scheduled":"Not Applicable","drug_test":"Complete","wav_course":"Complete","defensive_driving":"Complete"},"720":{"app_no":6070400,"type":"HDR","app_date":"2024-03-16T00:00:00.","status":"Incomplete","fru_interview_scheduled":"Not Applicable","drug_test":"Complete","wav_course":"Complete","defensive_driving":"Complete"},"874":{"app_no":6067570,"type":"HDR","app_date":"2024-02-07T00:00:00.","status":"Approved - License I","fru_interview_scheduled":"Not Applicable","drug_test":"Complete","wav_course":"Complete","defensive_driving":"Complete"}} | tablereformat |
2379e1e2586eacdf0c9ea0b9385f3c679ff72aa7e2a74fb741aa42018b4d78ea | data_analysis | [
"Please convert the Input Table from TSV format to HTML format. Please respond only with the table. \n Input Table: id\toriginal_text\trewritten_text\trewrite_prompt\n181\tTitle: The Growing P\tTitle: Exploring the\tThe key difference i\n237\tSarah was shopping f\tSarah was browsing f\tConvey a more lighth\n102\tHey Marcelle! Just w\tMarcelle, beware the\tEncourage practice a\n7\tThe ovary is an esse\tThe ovary, a mystica\tEmploy a whimsical, \n109\tMildred woke up feel\tMildred woke up feel\tRevise the text with\n301\tLee used the pruner \tRephrase: Lee tidied\tRephrase the text by\n330\tRebecca eagerly awai\tRebecca cautiously a\tReflect a more cauti\n38\tTitle: The Schnitzel\tTitle: The Schnitzel\tRevise the text in a\n351\tJoseph, a young boy \tIn a world where dre\tRevise the text with\n \n Output: \n"
] | <table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>id</th>
<th>original_text</th>
<th>rewritten_text</th>
<th>rewrite_prompt</th>
</tr>
</thead>
<tbody>
<tr>
<td>181</td>
<td>Title: The Growing P</td>
<td>Title: Exploring the</td>
<td>The key difference i</td>
</tr>
<tr>
<td>237</td>
<td>Sarah was shopping f</td>
<td>Sarah was browsing f</td>
<td>Convey a more lighth</td>
</tr>
<tr>
<td>102</td>
<td>Hey Marcelle! Just w</td>
<td>Marcelle, beware the</td>
<td>Encourage practice a</td>
</tr>
<tr>
<td>7</td>
<td>The ovary is an esse</td>
<td>The ovary, a mystica</td>
<td>Employ a whimsical,</td>
</tr>
<tr>
<td>109</td>
<td>Mildred woke up feel</td>
<td>Mildred woke up feel</td>
<td>Revise the text with</td>
</tr>
<tr>
<td>301</td>
<td>Lee used the pruner</td>
<td>Rephrase: Lee tidied</td>
<td>Rephrase the text by</td>
</tr>
<tr>
<td>330</td>
<td>Rebecca eagerly awai</td>
<td>Rebecca cautiously a</td>
<td>Reflect a more cauti</td>
</tr>
<tr>
<td>38</td>
<td>Title: The Schnitzel</td>
<td>Title: The Schnitzel</td>
<td>Revise the text in a</td>
</tr>
<tr>
<td>351</td>
<td>Joseph, a young boy</td>
<td>In a world where dre</td>
<td>Revise the text with</td>
</tr>
</tbody>
</table> | tablereformat |
23369207a1755bd3b7cea52155e7cbbd7ab1e2fa79015d1f8776111b8648f8ed | data_analysis | [
"Please convert the Input Table from JSON format to CSV format. Please respond only with the table. \n Input Table: {\"620\":{\"fecha_reporte_web\":\"2020-09-03 00:00:00\",\"id_de_caso\":640674,\"fecha_de_notificaci_n\":\"2020-08-21 00:00:00\",\"departamento\":5,\"departamento_nom\":\"ANTIOQUIA\",\"ciudad_municipio\":5001,\"ciudad_municipio_nom\":\"MEDELLIN\",\"edad\":62},\"664\":{\"fecha_reporte_web\":\"2020-09-03 00:00:00\",\"id_de_caso\":640639,\"fecha_de_notificaci_n\":\"2020-08-19 00:00:00\",\"departamento\":5,\"departamento_nom\":\"ANTIOQUIA\",\"ciudad_municipio\":5360,\"ciudad_municipio_nom\":\"ITAGUI\",\"edad\":19},\"381\":{\"fecha_reporte_web\":\"2020-07-09 00:00:00\",\"id_de_caso\":133383,\"fecha_de_notificaci_n\":\"2020-06-29 00:00:00\",\"departamento\":11,\"departamento_nom\":\"BOGOTA\",\"ciudad_municipio\":11001,\"ciudad_municipio_nom\":\"BOGOTA\",\"edad\":31},\"643\":{\"fecha_reporte_web\":\"2020-10-23 00:00:00\",\"id_de_caso\":993946,\"fecha_de_notificaci_n\":\"2020-10-20 00:00:00\",\"departamento\":17,\"departamento_nom\":\"CALDAS\",\"ciudad_municipio\":17001,\"ciudad_municipio_nom\":\"MANIZALES\",\"edad\":28},\"221\":{\"fecha_reporte_web\":\"2021-01-14 00:00:00\",\"id_de_caso\":1841877,\"fecha_de_notificaci_n\":\"2021-01-04 00:00:00\",\"departamento\":11,\"departamento_nom\":\"BOGOTA\",\"ciudad_municipio\":11001,\"ciudad_municipio_nom\":\"BOGOTA\",\"edad\":35},\"379\":{\"fecha_reporte_web\":\"2020-07-09 00:00:00\",\"id_de_caso\":133381,\"fecha_de_notificaci_n\":\"2020-07-01 00:00:00\",\"departamento\":11,\"departamento_nom\":\"BOGOTA\",\"ciudad_municipio\":11001,\"ciudad_municipio_nom\":\"BOGOTA\",\"edad\":50},\"771\":{\"fecha_reporte_web\":\"2020-06-25 00:00:00\",\"id_de_caso\":78503,\"fecha_de_notificaci_n\":\"2020-06-19 00:00:00\",\"departamento\":70,\"departamento_nom\":\"SUCRE\",\"ciudad_municipio\":70001,\"ciudad_municipio_nom\":\"SINCELEJO\",\"edad\":64},\"944\":{\"fecha_reporte_web\":\"2020-09-03 00:00:00\",\"id_de_caso\":640279,\"fecha_de_notificaci_n\":\"2020-08-14 00:00:00\",\"departamento\":5,\"departamento_nom\":\"ANTIOQUIA\",\"ciudad_municipio\":5088,\"ciudad_municipio_nom\":\"BELLO\",\"edad\":32},\"318\":{\"fecha_reporte_web\":\"2021-01-13 00:00:00\",\"id_de_caso\":1830859,\"fecha_de_notificaci_n\":\"2020-12-29 00:00:00\",\"departamento\":68,\"departamento_nom\":\"SANTANDER\",\"ciudad_municipio\":68001,\"ciudad_municipio_nom\":\"BUCARAMANGA\",\"edad\":51},\"871\":{\"fecha_reporte_web\":\"2020-07-18 00:00:00\",\"id_de_caso\":186772,\"fecha_de_notificaci_n\":\"2020-06-30 00:00:00\",\"departamento\":5,\"departamento_nom\":\"ANTIOQUIA\",\"ciudad_municipio\":5088,\"ciudad_municipio_nom\":\"BELLO\",\"edad\":23},\"843\":{\"fecha_reporte_web\":\"2021-01-07 00:00:00\",\"id_de_caso\":1734645,\"fecha_de_notificaci_n\":\"2021-01-04 00:00:00\",\"departamento\":76,\"departamento_nom\":\"VALLE\",\"ciudad_municipio\":76001,\"ciudad_municipio_nom\":\"CALI\",\"edad\":73}} \n Output: \n"
] | fecha_reporte_web,id_de_caso,fecha_de_notificaci_n,departamento,departamento_nom,ciudad_municipio,ciudad_municipio_nom,edad
2020-09-03 00:00:00,640674,2020-08-21 00:00:00,5,ANTIOQUIA,5001,MEDELLIN,62
2020-09-03 00:00:00,640639,2020-08-19 00:00:00,5,ANTIOQUIA,5360,ITAGUI,19
2020-07-09 00:00:00,133383,2020-06-29 00:00:00,11,BOGOTA,11001,BOGOTA,31
2020-10-23 00:00:00,993946,2020-10-20 00:00:00,17,CALDAS,17001,MANIZALES,28
2021-01-14 00:00:00,1841877,2021-01-04 00:00:00,11,BOGOTA,11001,BOGOTA,35
2020-07-09 00:00:00,133381,2020-07-01 00:00:00,11,BOGOTA,11001,BOGOTA,50
2020-06-25 00:00:00,78503,2020-06-19 00:00:00,70,SUCRE,70001,SINCELEJO,64
2020-09-03 00:00:00,640279,2020-08-14 00:00:00,5,ANTIOQUIA,5088,BELLO,32
2021-01-13 00:00:00,1830859,2020-12-29 00:00:00,68,SANTANDER,68001,BUCARAMANGA,51
2020-07-18 00:00:00,186772,2020-06-30 00:00:00,5,ANTIOQUIA,5088,BELLO,23
2021-01-07 00:00:00,1734645,2021-01-04 00:00:00,76,VALLE,76001,CALI,73
| tablereformat |
9dd93740e50a0913119103c1212284600703756ff930d2a8fa46d3dc97912d96 | data_analysis | [
"Please convert the Input Table from CSV format to TSV format. Please respond only with the table. \n Input Table: Age ,Gender,BMI,Fever,Nausea/Vomting,Headache ,Diarrhea ,Fatigue & generalized bone ache \n41,2,28,2,2,2,1,2\n61,2,32,2,2,2,1,1\n44,2,32,2,1,2,2,1\n50,2,25,2,2,2,2,1\n42,1,35,2,1,2,1,2\n61,1,24,1,2,1,2,1\n35,2,32,2,1,1,2,2\n45,2,24,1,2,1,2,2\n33,2,22,1,2,2,2,1\n51,1,28,1,1,1,2,1\n32,2,28,1,1,1,1,1\n38,1,25,2,2,2,2,2\n53,2,29,2,1,1,2,2\n50,1,27,2,2,1,1,1\n \n Output: \n"
] | Age Gender BMI Fever Nausea/Vomting Headache Diarrhea Fatigue & generalized bone ache
41 2 28 2 2 2 1 2
61 2 32 2 2 2 1 1
44 2 32 2 1 2 2 1
50 2 25 2 2 2 2 1
42 1 35 2 1 2 1 2
61 1 24 1 2 1 2 1
35 2 32 2 1 1 2 2
45 2 24 1 2 1 2 2
33 2 22 1 2 2 2 1
51 1 28 1 1 1 2 1
32 2 28 1 1 1 1 1
38 1 25 2 2 2 2 2
53 2 29 2 1 1 2 2
50 1 27 2 2 1 1 1
| tablereformat |
8f3ca4d439a2167eda91a41deaecf48838405ce32967097d2ec7e931b1313cf4 | data_analysis | [
"Please convert the Input Table from TSV format to CSV format. Please respond only with the table. \n Input Table: Unnamed: 0\tfecha\thora\tsistema\tbandera\tprecio\ttipo_moneda\torigen_dato\n21150\t2012-12-18\t6\tHU\t0\t35.0\t1\t6\n830180\t2017-06-19\t3\tPT\t1\t45.8\t1\t1\n285124\t2014-07-30\t7\tSE3\t1\t32.17\t1\t2\n906469\t2017-11-10\t14\tLT\t0\t36.9\t1\t2\n148609\t2013-10-22\t8\tNO5\t1\t39.57\t1\t2\n1311561\t2019-11-22\t3\tNO5\t0\t37.48\t1\t2\n281792\t2014-07-23\t17\tFI\t1\t46.84\t1\t2\n702672\t2016-10-20\t15\tSE3\t1\t43.22\t1\t2\n788303\t2017-03-31\t20\tFI\t1\t33.9\t1\t2\n214985\t2014-03-13\t2\tSE4\t0\t25.57\t1\t2\n900240\t2017-10-29\t19\tFR\t0\t59.58\t1\t1\n1413759\t2020-05-02\t18\tDK1\t1\t8.5\t1\t2\n996520\t2018-04-30\t4\tNO4\t1\t35.17\t1\t2\n \n Output: \n"
] | Unnamed: 0,fecha,hora,sistema,bandera,precio,tipo_moneda,origen_dato
21150,2012-12-18,6,HU,0,35.0,1,6
830180,2017-06-19,3,PT,1,45.8,1,1
285124,2014-07-30,7,SE3,1,32.17,1,2
906469,2017-11-10,14,LT,0,36.9,1,2
148609,2013-10-22,8,NO5,1,39.57,1,2
1311561,2019-11-22,3,NO5,0,37.48,1,2
281792,2014-07-23,17,FI,1,46.84,1,2
702672,2016-10-20,15,SE3,1,43.22,1,2
788303,2017-03-31,20,FI,1,33.9,1,2
214985,2014-03-13,2,SE4,0,25.57,1,2
900240,2017-10-29,19,FR,0,59.58,1,1
1413759,2020-05-02,18,DK1,1,8.5,1,2
996520,2018-04-30,4,NO4,1,35.17,1,2
| tablereformat |
31b5500fbd88c9b6087f15229a84578b6863700ef5b4bf2c645d8927a4723a77 | data_analysis | [
"Please convert the Input Table from TSV format to HTML format. Please respond only with the table. \n Input Table: name\tid\tnametype\trecclass\tmass (g)\tfall\tyear\treclat\nRamlat as Sahmah 307\t51908\tValid\tH4-6\t327.5\tFound\t2009.0\t20.52627\nHammadah al Hamra 20\t11685\tValid\tLL6\t386.0\tFound\t1997.0\t28.633\nElephant Moraine 909\t9395\tValid\tCM2\t1.2\tFound\t1990.0\t-76.2675\nMacKay Glacier 05241\t36380\tValid\tL5\t2.5\tFound\t2005.0\t\nWisconsin Range 9161\t24301\tValid\tL5\t1.5\tFound\t1991.0\t-86.48087\n \n Output: \n"
] | <table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>name</th>
<th>id</th>
<th>nametype</th>
<th>recclass</th>
<th>mass (g)</th>
<th>fall</th>
<th>year</th>
<th>reclat</th>
</tr>
</thead>
<tbody>
<tr>
<td>Ramlat as Sahmah 307</td>
<td>51908</td>
<td>Valid</td>
<td>H4-6</td>
<td>327.5</td>
<td>Found</td>
<td>2009.0</td>
<td>20.52627</td>
</tr>
<tr>
<td>Hammadah al Hamra 20</td>
<td>11685</td>
<td>Valid</td>
<td>LL6</td>
<td>386.0</td>
<td>Found</td>
<td>1997.0</td>
<td>28.63300</td>
</tr>
<tr>
<td>Elephant Moraine 909</td>
<td>9395</td>
<td>Valid</td>
<td>CM2</td>
<td>1.2</td>
<td>Found</td>
<td>1990.0</td>
<td>-76.26750</td>
</tr>
<tr>
<td>MacKay Glacier 05241</td>
<td>36380</td>
<td>Valid</td>
<td>L5</td>
<td>2.5</td>
<td>Found</td>
<td>2005.0</td>
<td>NaN</td>
</tr>
<tr>
<td>Wisconsin Range 9161</td>
<td>24301</td>
<td>Valid</td>
<td>L5</td>
<td>1.5</td>
<td>Found</td>
<td>1991.0</td>
<td>-86.48087</td>
</tr>
</tbody>
</table> | tablereformat |
9b8b3c1cdfbadc5d248114dcd74f7376fe3e24db964f3acb8d12159404199aac | data_analysis | [
"Please convert the Input Table from JSON format to HTML format. Please respond only with the table. \n Input Table: {\"137\":{\"res_geo_short\":\"Lake\",\"work_geo_short\":\"Colusa\",\"year\":2016,\"total\":25,\"drove_alone\":25,\"_2_person_carpool\":0,\"_3_person_carpool\":0,\"_4_person_carpool\":0},\"855\":{\"res_geo_short\":\"Napa\",\"work_geo_short\":\"Riverside\",\"year\":2016,\"total\":30,\"drove_alone\":4,\"_2_person_carpool\":15,\"_3_person_carpool\":0,\"_4_person_carpool\":0},\"456\":{\"res_geo_short\":\"Fresno\",\"work_geo_short\":\"Los Angeles\",\"year\":2016,\"total\":675,\"drove_alone\":420,\"_2_person_carpool\":95,\"_3_person_carpool\":75,\"_4_person_carpool\":0},\"207\":{\"res_geo_short\":\"Alameda\",\"work_geo_short\":\"El Dorado\",\"year\":2016,\"total\":25,\"drove_alone\":0,\"_2_person_carpool\":0,\"_3_person_carpool\":25,\"_4_person_carpool\":0},\"921\":{\"res_geo_short\":\"Trinity\",\"work_geo_short\":\"Sacramento\",\"year\":2016,\"total\":4,\"drove_alone\":4,\"_2_person_carpool\":0,\"_3_person_carpool\":0,\"_4_person_carpool\":0},\"788\":{\"res_geo_short\":\"Colusa\",\"work_geo_short\":\"Placer\",\"year\":2016,\"total\":45,\"drove_alone\":45,\"_2_person_carpool\":0,\"_3_person_carpool\":0,\"_4_person_carpool\":0},\"940\":{\"res_geo_short\":\"San Luis Obispo\",\"work_geo_short\":\"San Benito\",\"year\":2016,\"total\":15,\"drove_alone\":15,\"_2_person_carpool\":0,\"_3_person_carpool\":0,\"_4_person_carpool\":0},\"820\":{\"res_geo_short\":\"Sutter\",\"work_geo_short\":\"Placer\",\"year\":2016,\"total\":1475,\"drove_alone\":1105,\"_2_person_carpool\":120,\"_3_person_carpool\":95,\"_4_person_carpool\":45},\"881\":{\"res_geo_short\":\"El Dorado\",\"work_geo_short\":\"Sacramento\",\"year\":2016,\"total\":21690,\"drove_alone\":18355,\"_2_person_carpool\":2005,\"_3_person_carpool\":195,\"_4_person_carpool\":105},\"877\":{\"res_geo_short\":\"Butte\",\"work_geo_short\":\"Sacramento\",\"year\":2016,\"total\":630,\"drove_alone\":490,\"_2_person_carpool\":60,\"_3_person_carpool\":0,\"_4_person_carpool\":0},\"509\":{\"res_geo_short\":\"Riverside\",\"work_geo_short\":\"Madera\",\"year\":2016,\"total\":4,\"drove_alone\":4,\"_2_person_carpool\":0,\"_3_person_carpool\":0,\"_4_person_carpool\":0},\"477\":{\"res_geo_short\":\"Sacramento\",\"work_geo_short\":\"Los Angeles\",\"year\":2016,\"total\":500,\"drove_alone\":315,\"_2_person_carpool\":50,\"_3_person_carpool\":30,\"_4_person_carpool\":40}} \n Output: \n"
] | <table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>res_geo_short</th>
<th>work_geo_short</th>
<th>year</th>
<th>total</th>
<th>drove_alone</th>
<th>_2_person_carpool</th>
<th>_3_person_carpool</th>
<th>_4_person_carpool</th>
</tr>
</thead>
<tbody>
<tr>
<td>Lake</td>
<td>Colusa</td>
<td>2016</td>
<td>25</td>
<td>25</td>
<td>0</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<td>Napa</td>
<td>Riverside</td>
<td>2016</td>
<td>30</td>
<td>4</td>
<td>15</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<td>Fresno</td>
<td>Los Angeles</td>
<td>2016</td>
<td>675</td>
<td>420</td>
<td>95</td>
<td>75</td>
<td>0</td>
</tr>
<tr>
<td>Alameda</td>
<td>El Dorado</td>
<td>2016</td>
<td>25</td>
<td>0</td>
<td>0</td>
<td>25</td>
<td>0</td>
</tr>
<tr>
<td>Trinity</td>
<td>Sacramento</td>
<td>2016</td>
<td>4</td>
<td>4</td>
<td>0</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<td>Colusa</td>
<td>Placer</td>
<td>2016</td>
<td>45</td>
<td>45</td>
<td>0</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<td>San Luis Obispo</td>
<td>San Benito</td>
<td>2016</td>
<td>15</td>
<td>15</td>
<td>0</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<td>Sutter</td>
<td>Placer</td>
<td>2016</td>
<td>1475</td>
<td>1105</td>
<td>120</td>
<td>95</td>
<td>45</td>
</tr>
<tr>
<td>El Dorado</td>
<td>Sacramento</td>
<td>2016</td>
<td>21690</td>
<td>18355</td>
<td>2005</td>
<td>195</td>
<td>105</td>
</tr>
<tr>
<td>Butte</td>
<td>Sacramento</td>
<td>2016</td>
<td>630</td>
<td>490</td>
<td>60</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<td>Riverside</td>
<td>Madera</td>
<td>2016</td>
<td>4</td>
<td>4</td>
<td>0</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<td>Sacramento</td>
<td>Los Angeles</td>
<td>2016</td>
<td>500</td>
<td>315</td>
<td>50</td>
<td>30</td>
<td>40</td>
</tr>
</tbody>
</table> | tablereformat |
1f592e4e1a40499fb15905f6badb7c507a643106aec8d907f34de9cd200cb3fa | data_analysis | [
"Please convert the Input Table from HTML format to TSV format. Please respond only with the table. \n Input Table: <table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>Areas</th>\n <th>freq_1</th>\n <th>freq_2</th>\n <th>freq_3</th>\n <th>freq_4</th>\n <th>freq_5</th>\n <th>freq_6</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>21.011988</td>\n <td>1.0</td>\n <td>0.500439</td>\n <td>0.251738</td>\n <td>0.078005</td>\n <td>0.093293</td>\n <td>0.018903</td>\n </tr>\n <tr>\n <td>10.337971</td>\n <td>1.0</td>\n <td>0.466725</td>\n <td>0.419106</td>\n <td>0.274681</td>\n <td>0.267607</td>\n <td>0.157107</td>\n </tr>\n <tr>\n <td>10.849468</td>\n <td>1.0</td>\n <td>0.202631</td>\n <td>0.085090</td>\n <td>0.045319</td>\n <td>0.033782</td>\n <td>0.024511</td>\n </tr>\n <tr>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <td>32.851421</td>\n <td>1.0</td>\n <td>0.254474</td>\n <td>0.120420</td>\n <td>0.074471</td>\n <td>0.045632</td>\n <td>0.031202</td>\n </tr>\n </tbody>\n</table> \n Output: \n"
] | Areas freq_1 freq_2 freq_3 freq_4 freq_5 freq_6
21.011987996801384 1.0 0.5004387263728519 0.2517378735892901 0.078005199375179 0.093293367604831 0.0189026940475218
10.337970555779648 1.0 0.4667245036083286 0.4191063338191223 0.2746805132472518 0.2676071164217446 0.1571065760449514
10.84946821575966 1.0 0.2026312336424063 0.0850897545416327 0.0453185688575391 0.0337823596808117 0.0245107766664011
0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0
32.85142142890733 1.0 0.2544744562396613 0.1204201767574232 0.0744708623829048 0.0456319411571197 0.031201680845393
| tablereformat |
e852443f6993386ec44106f68bee0f7f278cfd9fb116228e55a50713257692b2 | data_analysis | [
"Please convert the Input Table from JSONL format to TSV format. Please respond only with the table. \n Input Table: {\"Outlook\":\"Rain\",\"Temperature\":\"Cool\",\"Humidity\":\"Normal\",\"Wind\":\"Weak\",\"Play_Badminton\":\"No\"}\n{\"Outlook\":\"Overcast\",\"Temperature\":\"Cool\",\"Humidity\":\"Normal\",\"Wind\":\"Weak\",\"Play_Badminton\":\"Yes\"}\n{\"Outlook\":\"Sunny\",\"Temperature\":\"Mild\",\"Humidity\":\"Normal\",\"Wind\":\"Strong\",\"Play_Badminton\":\"No\"}\n{\"Outlook\":\"Rain\",\"Temperature\":\"Mild\",\"Humidity\":\"High\",\"Wind\":\"Strong\",\"Play_Badminton\":\"No\"}\n{\"Outlook\":\"Overcast\",\"Temperature\":\"Mild\",\"Humidity\":\"High\",\"Wind\":\"Weak\",\"Play_Badminton\":\"Yes\"}\n{\"Outlook\":\"Rain\",\"Temperature\":\"Cool\",\"Humidity\":\"Normal\",\"Wind\":\"Strong\",\"Play_Badminton\":\"No\"}\n{\"Outlook\":\"Rain\",\"Temperature\":\"Cool\",\"Humidity\":\"High\",\"Wind\":\"Weak\",\"Play_Badminton\":\"No\"}\n{\"Outlook\":\"Overcast\",\"Temperature\":\"Hot\",\"Humidity\":\"High\",\"Wind\":\"Strong\",\"Play_Badminton\":\"No\"}\n{\"Outlook\":\"Overcast\",\"Temperature\":\"Hot\",\"Humidity\":\"High\",\"Wind\":\"Weak\",\"Play_Badminton\":\"Yes\"}\n{\"Outlook\":\"Rain\",\"Temperature\":\"Hot\",\"Humidity\":\"High\",\"Wind\":\"Strong\",\"Play_Badminton\":\"No\"}\n{\"Outlook\":\"Rain\",\"Temperature\":\"Cool\",\"Humidity\":\"High\",\"Wind\":\"Strong\",\"Play_Badminton\":\"No\"}\n{\"Outlook\":\"Sunny\",\"Temperature\":\"Hot\",\"Humidity\":\"High\",\"Wind\":\"Strong\",\"Play_Badminton\":\"No\"}\n{\"Outlook\":\"Rain\",\"Temperature\":\"Mild\",\"Humidity\":\"Normal\",\"Wind\":\"Weak\",\"Play_Badminton\":\"No\"}\n{\"Outlook\":\"Rain\",\"Temperature\":\"Hot\",\"Humidity\":\"Normal\",\"Wind\":\"Weak\",\"Play_Badminton\":\"No\"}\n \n Output: \n"
] | Outlook Temperature Humidity Wind Play_Badminton
Rain Cool Normal Weak No
Overcast Cool Normal Weak Yes
Sunny Mild Normal Strong No
Rain Mild High Strong No
Overcast Mild High Weak Yes
Rain Cool Normal Strong No
Rain Cool High Weak No
Overcast Hot High Strong No
Overcast Hot High Weak Yes
Rain Hot High Strong No
Rain Cool High Strong No
Sunny Hot High Strong No
Rain Mild Normal Weak No
Rain Hot Normal Weak No
| tablereformat |
f44bcc507aa7a438c07f435c70e687868c07af785cc257410780ff861c54c646 | data_analysis | [
"Please convert the Input Table from JSONL format to JSON format. Please respond only with the table. \n Input Table: {\"name\":\"Roosevelt County 050\",\"id\":22705,\"nametype\":\"Valid\",\"recclass\":\"L4\",\"mass (g)\":13.1,\"fall\":\"Found\",\"year\":1971.0,\"reclat\":34.08333}\n{\"name\":\"Asuka 881632\",\"id\":4341,\"nametype\":\"Valid\",\"recclass\":\"CO3\",\"mass (g)\":138.11,\"fall\":\"Found\",\"year\":1988.0,\"reclat\":-72.0}\n{\"name\":\"Asuka 87345\",\"id\":2702,\"nametype\":\"Valid\",\"recclass\":\"H4\",\"mass (g)\":73.78,\"fall\":\"Found\",\"year\":1987.0,\"reclat\":-72.0}\n{\"name\":\"Queen Alexandra Rang\",\"id\":19882,\"nametype\":\"Valid\",\"recclass\":\"L6\",\"mass (g)\":71.8,\"fall\":\"Found\",\"year\":1994.0,\"reclat\":-84.0}\n{\"name\":\"Northwest Africa 827\",\"id\":17856,\"nametype\":\"Valid\",\"recclass\":\"H3.9\",\"mass (g)\":48.7,\"fall\":\"Found\",\"year\":2000.0,\"reclat\":null}\n \n Output: \n"
] | {"36341":{"name":"Roosevelt County 050","id":22705,"nametype":"Valid","recclass":"L4","mass (g)":13.1,"fall":"Found","year":1971.0,"reclat":34.08333},"4568":{"name":"Asuka 881632","id":4341,"nametype":"Valid","recclass":"CO3","mass (g)":138.11,"fall":"Found","year":1988.0,"reclat":-72.0},"3707":{"name":"Asuka 87345","id":2702,"nametype":"Valid","recclass":"H4","mass (g)":73.78,"fall":"Found","year":1987.0,"reclat":-72.0},"33052":{"name":"Queen Alexandra Rang","id":19882,"nametype":"Valid","recclass":"L6","mass (g)":71.8,"fall":"Found","year":1994.0,"reclat":-84.0},"30803":{"name":"Northwest Africa 827","id":17856,"nametype":"Valid","recclass":"H3.9","mass (g)":48.7,"fall":"Found","year":2000.0,"reclat":null}} | tablereformat |
0bedfad80bcaab18b0ab15531247a61a8b75f42c6e87c40f05d398dc25984d35 | data_analysis | [
"Please convert the Input Table from TSV format to CSV format. Please respond only with the table. \n Input Table: longitude\tlatitude\tstart_date\tend_date\tsource\thorizon_lower\thorizon_upper\taluminium_extractable\n34.32938\t-24.17005\t01/01/2008\t31/12/2018\tafsis_spectral\t20\t0\t392.113\n31.84637\t-8.19007\t01/01/2008\t31/12/2018\tafsis_spectral\t50\t20\t1148.256\n37.44746\t-5.31403\t01/01/2008\t31/12/2018\tafsis_spectral\t50\t20\t967.844\n37.08281\t-6.91857\t01/01/2008\t31/12/2018\tafsis_spectral\t50\t20\t999.199\n33.01138\t-3.06592\t01/01/2008\t31/12/2018\tafsis_spectral\t50\t20\t586.904\n-7.81056\t14.83462\t01/01/2008\t31/12/2018\tafsis_spectral\t50\t20\t550.305\n-2.40365\t6.98108\t01/01/2008\t31/12/2018\tafsis_spectral\t50\t20\t704.25\n35.36507\t-7.94579\t01/01/2008\t31/12/2018\tafsis_spectral\t50\t20\t904.558\n7.69961\t11.55999\t01/01/2008\t31/12/2018\tafsis_wetchem\t20\t0\t578.975\n31.22275\t-7.85177\t01/01/2008\t31/12/2018\tafsis_spectral\t20\t0\t745.065\n-13.42865\t10.53617\t01/01/2008\t31/12/2018\tafsis_spectral\t20\t0\t1861.265\n32.18869\t-2.47482\t01/01/2008\t31/12/2018\tafsis_spectral\t50\t20\t566.69\n \n Output: \n"
] | longitude,latitude,start_date,end_date,source,horizon_lower,horizon_upper,aluminium_extractable
34.32938,-24.17005,01/01/2008,31/12/2018,afsis_spectral,20,0,392.113
31.84637,-8.19007,01/01/2008,31/12/2018,afsis_spectral,50,20,1148.256
37.44746,-5.31403,01/01/2008,31/12/2018,afsis_spectral,50,20,967.844
37.08281,-6.91857,01/01/2008,31/12/2018,afsis_spectral,50,20,999.199
33.01138,-3.06592,01/01/2008,31/12/2018,afsis_spectral,50,20,586.904
-7.81056,14.83462,01/01/2008,31/12/2018,afsis_spectral,50,20,550.305
-2.40365,6.98108,01/01/2008,31/12/2018,afsis_spectral,50,20,704.25
35.36507,-7.94579,01/01/2008,31/12/2018,afsis_spectral,50,20,904.558
7.69961,11.55999,01/01/2008,31/12/2018,afsis_wetchem,20,0,578.975
31.22275,-7.85177,01/01/2008,31/12/2018,afsis_spectral,20,0,745.065
-13.42865,10.53617,01/01/2008,31/12/2018,afsis_spectral,20,0,1861.265
32.18869,-2.47482,01/01/2008,31/12/2018,afsis_spectral,50,20,566.69
| tablereformat |
9cd37119651a821e2695ee073ddf004d50d9add830f4e7f3bc469f9b0d4ddbe3 | data_analysis | [
"Please convert the Input Table from JSON format to HTML format. Please respond only with the table. \n Input Table: {\"963\":{\"fecha_reporte_web\":\"2020-09-03 00:00:00\",\"id_de_caso\":640218,\"fecha_de_notificaci_n\":\"2020-08-10 00:00:00\",\"departamento\":5,\"departamento_nom\":\"ANTIOQUIA\",\"ciudad_municipio\":5631,\"ciudad_municipio_nom\":\"SABANETA\",\"edad\":53},\"777\":{\"fecha_reporte_web\":\"2020-06-25 00:00:00\",\"id_de_caso\":78509,\"fecha_de_notificaci_n\":\"2020-06-19 00:00:00\",\"departamento\":70,\"departamento_nom\":\"SUCRE\",\"ciudad_municipio\":70001,\"ciudad_municipio_nom\":\"SINCELEJO\",\"edad\":31},\"495\":{\"fecha_reporte_web\":\"2020-07-18 00:00:00\",\"id_de_caso\":186899,\"fecha_de_notificaci_n\":\"2020-06-30 00:00:00\",\"departamento\":13001,\"departamento_nom\":\"CARTAGENA\",\"ciudad_municipio\":13001,\"ciudad_municipio_nom\":\"CARTAGENA\",\"edad\":62},\"618\":{\"fecha_reporte_web\":\"2020-09-03 00:00:00\",\"id_de_caso\":640672,\"fecha_de_notificaci_n\":\"2020-08-21 00:00:00\",\"departamento\":5,\"departamento_nom\":\"ANTIOQUIA\",\"ciudad_municipio\":5088,\"ciudad_municipio_nom\":\"BELLO\",\"edad\":67},\"331\":{\"fecha_reporte_web\":\"2020-07-18 00:00:00\",\"id_de_caso\":186936,\"fecha_de_notificaci_n\":\"2020-06-29 00:00:00\",\"departamento\":47001,\"departamento_nom\":\"STA MARTA D.E.\",\"ciudad_municipio\":47001,\"ciudad_municipio_nom\":\"SANTA MARTA\",\"edad\":48},\"220\":{\"fecha_reporte_web\":\"2021-01-14 00:00:00\",\"id_de_caso\":1841876,\"fecha_de_notificaci_n\":\"2021-01-12 00:00:00\",\"departamento\":11,\"departamento_nom\":\"BOGOTA\",\"ciudad_municipio\":11001,\"ciudad_municipio_nom\":\"BOGOTA\",\"edad\":34},\"497\":{\"fecha_reporte_web\":\"2020-07-18 00:00:00\",\"id_de_caso\":186901,\"fecha_de_notificaci_n\":\"2020-06-30 00:00:00\",\"departamento\":25,\"departamento_nom\":\"CUNDINAMARCA\",\"ciudad_municipio\":25473,\"ciudad_municipio_nom\":\"MOSQUERA\",\"edad\":18},\"51\":{\"fecha_reporte_web\":\"2020-12-24 00:00:00\",\"id_de_caso\":1556950,\"fecha_de_notificaci_n\":\"2020-12-18 00:00:00\",\"departamento\":76,\"departamento_nom\":\"VALLE\",\"ciudad_municipio\":76001,\"ciudad_municipio_nom\":\"CALI\",\"edad\":78},\"115\":{\"fecha_reporte_web\":\"2020-08-05 00:00:00\",\"id_de_caso\":338086,\"fecha_de_notificaci_n\":\"2020-07-30 00:00:00\",\"departamento\":76,\"departamento_nom\":\"VALLE\",\"ciudad_municipio\":76001,\"ciudad_municipio_nom\":\"CALI\",\"edad\":25},\"865\":{\"fecha_reporte_web\":\"2021-01-07 00:00:00\",\"id_de_caso\":1734667,\"fecha_de_notificaci_n\":\"2021-01-02 00:00:00\",\"departamento\":76,\"departamento_nom\":\"VALLE\",\"ciudad_municipio\":76001,\"ciudad_municipio_nom\":\"CALI\",\"edad\":36},\"186\":{\"fecha_reporte_web\":\"2021-01-14 00:00:00\",\"id_de_caso\":1841916,\"fecha_de_notificaci_n\":\"2021-01-11 00:00:00\",\"departamento\":11,\"departamento_nom\":\"BOGOTA\",\"ciudad_municipio\":11001,\"ciudad_municipio_nom\":\"BOGOTA\",\"edad\":23}} \n Output: \n"
] | <table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>fecha_reporte_web</th>
<th>id_de_caso</th>
<th>fecha_de_notificaci_n</th>
<th>departamento</th>
<th>departamento_nom</th>
<th>ciudad_municipio</th>
<th>ciudad_municipio_nom</th>
<th>edad</th>
</tr>
</thead>
<tbody>
<tr>
<td>2020-09-03 00:00:00</td>
<td>640218</td>
<td>2020-08-10 00:00:00</td>
<td>5</td>
<td>ANTIOQUIA</td>
<td>5631</td>
<td>SABANETA</td>
<td>53</td>
</tr>
<tr>
<td>2020-06-25 00:00:00</td>
<td>78509</td>
<td>2020-06-19 00:00:00</td>
<td>70</td>
<td>SUCRE</td>
<td>70001</td>
<td>SINCELEJO</td>
<td>31</td>
</tr>
<tr>
<td>2020-07-18 00:00:00</td>
<td>186899</td>
<td>2020-06-30 00:00:00</td>
<td>13001</td>
<td>CARTAGENA</td>
<td>13001</td>
<td>CARTAGENA</td>
<td>62</td>
</tr>
<tr>
<td>2020-09-03 00:00:00</td>
<td>640672</td>
<td>2020-08-21 00:00:00</td>
<td>5</td>
<td>ANTIOQUIA</td>
<td>5088</td>
<td>BELLO</td>
<td>67</td>
</tr>
<tr>
<td>2020-07-18 00:00:00</td>
<td>186936</td>
<td>2020-06-29 00:00:00</td>
<td>47001</td>
<td>STA MARTA D.E.</td>
<td>47001</td>
<td>SANTA MARTA</td>
<td>48</td>
</tr>
<tr>
<td>2021-01-14 00:00:00</td>
<td>1841876</td>
<td>2021-01-12 00:00:00</td>
<td>11</td>
<td>BOGOTA</td>
<td>11001</td>
<td>BOGOTA</td>
<td>34</td>
</tr>
<tr>
<td>2020-07-18 00:00:00</td>
<td>186901</td>
<td>2020-06-30 00:00:00</td>
<td>25</td>
<td>CUNDINAMARCA</td>
<td>25473</td>
<td>MOSQUERA</td>
<td>18</td>
</tr>
<tr>
<td>2020-12-24 00:00:00</td>
<td>1556950</td>
<td>2020-12-18 00:00:00</td>
<td>76</td>
<td>VALLE</td>
<td>76001</td>
<td>CALI</td>
<td>78</td>
</tr>
<tr>
<td>2020-08-05 00:00:00</td>
<td>338086</td>
<td>2020-07-30 00:00:00</td>
<td>76</td>
<td>VALLE</td>
<td>76001</td>
<td>CALI</td>
<td>25</td>
</tr>
<tr>
<td>2021-01-07 00:00:00</td>
<td>1734667</td>
<td>2021-01-02 00:00:00</td>
<td>76</td>
<td>VALLE</td>
<td>76001</td>
<td>CALI</td>
<td>36</td>
</tr>
<tr>
<td>2021-01-14 00:00:00</td>
<td>1841916</td>
<td>2021-01-11 00:00:00</td>
<td>11</td>
<td>BOGOTA</td>
<td>11001</td>
<td>BOGOTA</td>
<td>23</td>
</tr>
</tbody>
</table> | tablereformat |
af06a250c4d58799cd7bf0f73df94134106098b21d8b0a3a3e61dd9eacda6724 | data_analysis | [
"Please convert the Input Table from CSV format to TSV format. Please respond only with the table. \n Input Table: :@computed_region_43wa_7qmu,location,case_,date_of_occurrence,block,y_coordinate,_primary_decsription,latitude\n5.0,{'latitude': '42.018,JG481551,2023-10-28T00:07:00.,075XX N PAULINA ST,1950023,CRIMINAL DAMAGE,42.018498254\n22.0,{'latitude': '41.705,JG513212,2023-11-21T19:28:00.,010XX W 103RD PL,1836186,ASSAULT,41.70595701\n36.0,{'latitude': '41.876,JG526062,2023-11-30T21:00:00.,002XX W VAN BUREN ST,1898485,CRIMINAL DAMAGE,41.87683815\n8.0,{'latitude': '41.807,JG519147,2023-11-21T12:30:00.,046XX W 47TH ST,1873061,THEFT,41.807662149\n46.0,{'latitude': '41.909,JG561296,2023-12-31T22:34:00.,015XX N SEDGWICK ST,1910545,BATTERY,41.909959349\n24.0,{'latitude': '41.979,JG496701,2023-11-08T16:39:00.,025XX W BALMORAL AVE,1935772,OTHER OFFENSE,41.979505088\n23.0,{'latitude': '41.878,JG512547,2023-11-21T08:45:00.,040XX W WILCOX ST,1899030,NARCOTICS,41.878858482\n31.0,{'latitude': '41.749,JG492993,2023-11-05T22:04:00.,079XX S SANGAMON ST,1852130,BATTERY,41.749707624\n40.0,{'latitude': '41.937,JG542128,2023-12-15T00:00:00.,030XX N ASHLAND AVE,1920425,THEFT,41.937249995\n43.0,{'latitude': '41.707,JH117137,2024-01-16T10:52:00.,102XX S MICHIGAN AVE,1836918,OTHER OFFENSE,41.707793505\n38.0,{'latitude': '41.997,JG496744,2023-11-08T16:41:00.,054XX W DEVON AVE,1942130,BATTERY,41.997327626\n36.0,{'latitude': '41.890,JG560653,2023-12-31T09:30:00.,004XX N ORLEANS ST,1903356,THEFT,41.890221601\n \n Output: \n"
] | :@computed_region_43wa_7qmu location case_ date_of_occurrence block y_coordinate _primary_decsription latitude
5.0 {'latitude': '42.018 JG481551 2023-10-28T00:07:00. 075XX N PAULINA ST 1950023 CRIMINAL DAMAGE 42.018498254
22.0 {'latitude': '41.705 JG513212 2023-11-21T19:28:00. 010XX W 103RD PL 1836186 ASSAULT 41.70595701
36.0 {'latitude': '41.876 JG526062 2023-11-30T21:00:00. 002XX W VAN BUREN ST 1898485 CRIMINAL DAMAGE 41.87683815
8.0 {'latitude': '41.807 JG519147 2023-11-21T12:30:00. 046XX W 47TH ST 1873061 THEFT 41.807662149
46.0 {'latitude': '41.909 JG561296 2023-12-31T22:34:00. 015XX N SEDGWICK ST 1910545 BATTERY 41.909959349
24.0 {'latitude': '41.979 JG496701 2023-11-08T16:39:00. 025XX W BALMORAL AVE 1935772 OTHER OFFENSE 41.979505088
23.0 {'latitude': '41.878 JG512547 2023-11-21T08:45:00. 040XX W WILCOX ST 1899030 NARCOTICS 41.878858482
31.0 {'latitude': '41.749 JG492993 2023-11-05T22:04:00. 079XX S SANGAMON ST 1852130 BATTERY 41.749707624
40.0 {'latitude': '41.937 JG542128 2023-12-15T00:00:00. 030XX N ASHLAND AVE 1920425 THEFT 41.937249995
43.0 {'latitude': '41.707 JH117137 2024-01-16T10:52:00. 102XX S MICHIGAN AVE 1836918 OTHER OFFENSE 41.707793505
38.0 {'latitude': '41.997 JG496744 2023-11-08T16:41:00. 054XX W DEVON AVE 1942130 BATTERY 41.997327626
36.0 {'latitude': '41.890 JG560653 2023-12-31T09:30:00. 004XX N ORLEANS ST 1903356 THEFT 41.890221601
| tablereformat |
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