Common/shared_datasets/titanic.csv

+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
+37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
+75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
+76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
+77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
+79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
+80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
+81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
+82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
+83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
+84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
+85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
+87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
+88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
+89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
+90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
+91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
+92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
+93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
+94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
+95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
+96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
+97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
+98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
+99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
+100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
+101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
+102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
+103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
+104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
+105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
+106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
+107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
+108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
+109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
+110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
+111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
+112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
+113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
+114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
+115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
+116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
+117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
+118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
+119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
+120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
+121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
+122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
+123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
+124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
+125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
+126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
+127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
+128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
+129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
+130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
+131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
+132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
+133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
+134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
+135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
+136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
+137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
+138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
+139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
+140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
+141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
+142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
+143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
+144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
+145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
+146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
+147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
+148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
+149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
+150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
+151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
+152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
+153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
+154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
+155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
+156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
+157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
+158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
+159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
+160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
+161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
+162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
+163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
+164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
+165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
+166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
+167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
+168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
+169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
+170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
+171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S
+172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q
+173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S
+174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
+175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
+176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S
+177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
+178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
+179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
+180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
+181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
+182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
+183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
+184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
+185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S
+186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S
+187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
+188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
+189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
+190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
+191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
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+193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S
+194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S
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+196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C
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+198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S
+199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
+200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
+201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S
+202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
+203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
+204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
+205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
+206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S
+207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
+208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
+209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
+210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
+211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
+212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
+213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
+214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
+215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
+216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
+217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
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+883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
+884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
+885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
+886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
+888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
+889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
+890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
+891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q

f22/andy_lec_notes/lec35_Dec02_Pandas3/lec_35_pandas3_data_transformation_completed.ipynb

+%% Cell type:code id: tags:
+
+``` python
+# known import statements
+import pandas as pd
+import sqlite3 as sql # note that we are renaming to sql
+import os
+
+# new import statement
+import numpy as np
+```
+
+%% Cell type:markdown id: tags:
+
+# Lecture 35 Pandas 3: Data Transformation
+* Data transformation is the process of changing the format, structure, or values of data.
+* Often needed during data cleaning and sometimes during data analysis
+
+Possible data transformation:
+* Parsing/Extraction
+  * Parse CSV to Pandas DataFrame
+* Missing Value Manipulations
+  * Dropping
+  * Imputation: replace missing value with substitute values
+* Typecasting, Formating, Renaming
+  * Typecasting: covert one column from int to float
+  * Formating: format the time column to datatime object
+  * Renaming: rename column and index names
+* Applying/Mapping
+* Filtering, Aggregation, Grouping, and Summarization
+  * Covered in Pandas 1 & 2 lectures
+
+%% Cell type:markdown id: tags:
+
+# Today's Learning Objectives:
+
+* Identify, drop, or fill missing values with Pandas .isna, .dropna, and .fillna
+* Apply a function to Pandas Series and DataFrame rows/columns
+* Replace all target values to Pandas Series and DataFrame rows/columns
+* Filter, Aggregate, Group, and Summarize information in a DataFrame with .groupby
+* Convert .groupby examples to SQL
+
+%% Cell type:markdown id: tags:
+
+# The dataset: Spotify songs
+Adapted from https://www.kaggle.com/datasets/mrmorj/dataset-of-songs-in-spotify.
+
+If you are interested in digging deeper in this dataset, here's a [blog post](https://medium.com/@boplantinga/what-do-spotifys-audio-features-tell-us-about-this-year-s-eurovision-song-contest-66ad188e112a) that explain each column in details.
+
+%% Cell type:markdown id: tags:
+
+### WARMUP 1: Establish a connection to the spotify.db database
+
+%% Cell type:code id: tags:
+
+``` python
+# open up the spotify database
+db_pathname = "spotify.db"
+assert os.path.exists(db_pathname)
+conn = sql.connect(db_pathname)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def qry(sql):
+    return pd.read_sql(sql, conn)
+```
+
+%% Cell type:markdown id: tags:
+
+### WARMUP 2: Identify the table name(s) inside the database
+
+%% Cell type:code id: tags:
+
+``` python
+qry("select * from sqlite_master")
+```
+
+%% Output
+
+        type                        name tbl_name  rootpage  \
+    0  table                     spotify  spotify      1527
+    1  index  sqlite_autoindex_spotify_1  spotify      1528
+    
+                                                     sql
+    0  CREATE TABLE spotify(\nid TEXT PRIMARY KEY,\nt...
+    1                                               None
+
+%% Cell type:markdown id: tags:
+
+### WARMUP 3: Use pandas lookup expression to identify the column names and the types: use .iloc
+
+%% Cell type:code id: tags:
+
+``` python
+print(qry("select * from sqlite_master")["sql"].iloc[0])
+```
+
+%% Output
+
+    CREATE TABLE spotify(
+    id TEXT PRIMARY KEY,
+    title BLOB,
+    song_name BLOB,
+    genre TEXT,
+    duration_ms INTEGER,
+    key INTEGER,
+    mode INTEGER,
+    time_signature INTEGER,
+    tempo REAL,
+    acousticness REAL,
+    danceability REAL,
+    energy REAL,
+    instrumentalness REAL,
+    liveness REAL,
+    loudness REAL,
+    speechiness REAL,
+    valence REAL)
+
+%% Cell type:markdown id: tags:
+
+### WARMUP 4: Store the data inside `spotify` table inside a variable called `df`
+
+%% Cell type:code id: tags:
+
+``` python
+df = qry("select * from spotify")
+df
+```
+
+%% Output
+
+                               id                        title  song_name  \
+    0      7pgJBLVz5VmnL7uGHmRj6p                               Pathology
+    1      0vSWgAlfpye0WCGeNmuNhy                                Symbiote
+    2      7EL7ifncK2PWFYThJjzR25                               BRAINFOOD
+    3      1umsRbM7L4ju7rn9aU8Ju6                               Sacrifice
+    4      4SKqOHKYU5pgHr5UiVKiQN                                Backpack
+    ...                       ...                          ...        ...
+    35872  46bXU7Sgj7104ZoXxzz9tM           Euphoric Hardstyle
+    35873  0he2ViGMUO3ajKTxLOfWVT  Greatest Hardstyle Playlist
+    35874  72DAt9Lbpy9EUS29OzQLob       Best of Hardstyle 2020
+    35875  6HXgExFVuE1c3cq9QjFCcU           Euphoric Hardstyle
+    35876  6MAAMZImxcvYhRnxDLTufD       Best of Hardstyle 2020
+    
+               genre  duration_ms  key  mode  time_signature    tempo  \
+    0      Dark Trap       224427    8     1               4  115.080
+    1      Dark Trap        98821    5     1               4  218.050
+    2      Dark Trap       101172    8     1               4  189.938
+    3      Dark Trap        96062   10     0               4  139.990
+    4      Dark Trap       135079    5     1               4  128.014
+    ...          ...          ...  ...   ...             ...      ...
+    35872  hardstyle       269208    4     1               4  150.013
+    35873  hardstyle       210112    0     0               4  149.928
+    35874  hardstyle       234823    8     1               4  154.935
+    35875  hardstyle       323200    6     0               4  150.042
+    35876  hardstyle       162161    9     1               4  155.047
+    
+           acousticness  danceability  energy  instrumentalness  liveness  \
+    0          0.401000         0.719   0.493          0.000000    0.1180
+    1          0.013800         0.850   0.893          0.000004    0.3720
+    2          0.187000         0.864   0.365          0.000000    0.1160
+    3          0.145000         0.767   0.576          0.000003    0.0968
+    4          0.007700         0.765   0.726          0.000000    0.6190
+    ...             ...           ...     ...               ...       ...
+    35872      0.031500         0.528   0.693          0.000345    0.1210
+    35873      0.022500         0.517   0.768          0.000018    0.2050
+    35874      0.026000         0.361   0.821          0.000242    0.3850
+    35875      0.000551         0.477   0.921          0.029600    0.0575
+    35876      0.001890         0.529   0.945          0.000055    0.4140
+    
+           loudness  speechiness  valence
+    0        -7.230       0.0794   0.1240
+    1        -4.783       0.0623   0.0391
+    2       -10.219       0.0655   0.0478
+    3        -9.683       0.2560   0.1870
+    4        -5.580       0.1910   0.2700
+    ...         ...          ...      ...
+    35872    -5.148       0.0304   0.3940
+    35873    -7.922       0.0479   0.3830
+    35874    -3.102       0.0505   0.1240
+    35875    -4.777       0.0392   0.4880
+    35876    -5.862       0.0615   0.1340
+    
+    [35877 rows x 17 columns]
+
+%% Cell type:markdown id: tags:
+
+### Setting a column as row indices for the `DataFrame`
+
+- Syntax: `df.set_index("<COLUMN>")`
+- Returns a new DataFrame object instance reference.
+- WARNING: executing this twice will result in `KeyError` being thrown. Once you set a column as row index, it will no longer be a column within the `DataFrame`. If you tried this, go back and execute the above cell and update `df` once more and then execute the below cell exactly once.
+
+%% Cell type:code id: tags:
+
+``` python
+# Set the id column as row indices
+df = df.set_index("id")
+df
+```
+
+%% Output
+
+                                                  title  song_name      genre  \
+    id
+    7pgJBLVz5VmnL7uGHmRj6p                               Pathology  Dark Trap
+    0vSWgAlfpye0WCGeNmuNhy                                Symbiote  Dark Trap
+    7EL7ifncK2PWFYThJjzR25                               BRAINFOOD  Dark Trap
+    1umsRbM7L4ju7rn9aU8Ju6                               Sacrifice  Dark Trap
+    4SKqOHKYU5pgHr5UiVKiQN                                Backpack  Dark Trap
+    ...                                             ...        ...        ...
+    46bXU7Sgj7104ZoXxzz9tM           Euphoric Hardstyle             hardstyle
+    0he2ViGMUO3ajKTxLOfWVT  Greatest Hardstyle Playlist             hardstyle
+    72DAt9Lbpy9EUS29OzQLob       Best of Hardstyle 2020             hardstyle
+    6HXgExFVuE1c3cq9QjFCcU           Euphoric Hardstyle             hardstyle
+    6MAAMZImxcvYhRnxDLTufD       Best of Hardstyle 2020             hardstyle
+    
+                            duration_ms  key  mode  time_signature    tempo  \
+    id
+    7pgJBLVz5VmnL7uGHmRj6p       224427    8     1               4  115.080
+    0vSWgAlfpye0WCGeNmuNhy        98821    5     1               4  218.050
+    7EL7ifncK2PWFYThJjzR25       101172    8     1               4  189.938
+    1umsRbM7L4ju7rn9aU8Ju6        96062   10     0               4  139.990
+    4SKqOHKYU5pgHr5UiVKiQN       135079    5     1               4  128.014
+    ...                             ...  ...   ...             ...      ...
+    46bXU7Sgj7104ZoXxzz9tM       269208    4     1               4  150.013
+    0he2ViGMUO3ajKTxLOfWVT       210112    0     0               4  149.928
+    72DAt9Lbpy9EUS29OzQLob       234823    8     1               4  154.935
+    6HXgExFVuE1c3cq9QjFCcU       323200    6     0               4  150.042
+    6MAAMZImxcvYhRnxDLTufD       162161    9     1               4  155.047
+    
+                            acousticness  danceability  energy  instrumentalness  \
+    id
+    7pgJBLVz5VmnL7uGHmRj6p      0.401000         0.719   0.493          0.000000
+    0vSWgAlfpye0WCGeNmuNhy      0.013800         0.850   0.893          0.000004
+    7EL7ifncK2PWFYThJjzR25      0.187000         0.864   0.365          0.000000
+    1umsRbM7L4ju7rn9aU8Ju6      0.145000         0.767   0.576          0.000003
+    4SKqOHKYU5pgHr5UiVKiQN      0.007700         0.765   0.726          0.000000
+    ...                              ...           ...     ...               ...
+    46bXU7Sgj7104ZoXxzz9tM      0.031500         0.528   0.693          0.000345
+    0he2ViGMUO3ajKTxLOfWVT      0.022500         0.517   0.768          0.000018
+    72DAt9Lbpy9EUS29OzQLob      0.026000         0.361   0.821          0.000242
+    6HXgExFVuE1c3cq9QjFCcU      0.000551         0.477   0.921          0.029600
+    6MAAMZImxcvYhRnxDLTufD      0.001890         0.529   0.945          0.000055
+    
+                            liveness  loudness  speechiness  valence
+    id
+    7pgJBLVz5VmnL7uGHmRj6p    0.1180    -7.230       0.0794   0.1240
+    0vSWgAlfpye0WCGeNmuNhy    0.3720    -4.783       0.0623   0.0391
+    7EL7ifncK2PWFYThJjzR25    0.1160   -10.219       0.0655   0.0478
+    1umsRbM7L4ju7rn9aU8Ju6    0.0968    -9.683       0.2560   0.1870
+    4SKqOHKYU5pgHr5UiVKiQN    0.6190    -5.580       0.1910   0.2700
+    ...                          ...       ...          ...      ...
+    46bXU7Sgj7104ZoXxzz9tM    0.1210    -5.148       0.0304   0.3940
+    0he2ViGMUO3ajKTxLOfWVT    0.2050    -7.922       0.0479   0.3830
+    72DAt9Lbpy9EUS29OzQLob    0.3850    -3.102       0.0505   0.1240
+    6HXgExFVuE1c3cq9QjFCcU    0.0575    -4.777       0.0392   0.4880
+    6MAAMZImxcvYhRnxDLTufD    0.4140    -5.862       0.0615   0.1340
+    
+    [35877 rows x 16 columns]
+
+%% Cell type:markdown id: tags:
+
+### Not a Number
+
+- `np.NaN` is the floating point representation of Not a Number
+- You do not need to know / learn the details about the `numpy` package
+
+### Replacing / modifying values within the `DataFrame`
+
+Syntax: `df.replace(<TARGET>, <REPLACE>)`
+- Your target can be `str`, `int`, `float`, `None` (there are other possiblities, but those are too advanced for this course)
+- Returns a new DataFrame object instance reference.
+
+Let's now replace the missing values (empty strings) with `np.NAN`
+
+%% Cell type:code id: tags:
+
+``` python
+df = df.replace("", np.NaN)
+df.head(10) # title is the album name
+```
+
+%% Output
+
+                           title             song_name      genre  duration_ms  \
+    id
+    7pgJBLVz5VmnL7uGHmRj6p   NaN             Pathology  Dark Trap       224427
+    0vSWgAlfpye0WCGeNmuNhy   NaN              Symbiote  Dark Trap        98821
+    7EL7ifncK2PWFYThJjzR25   NaN             BRAINFOOD  Dark Trap       101172
+    1umsRbM7L4ju7rn9aU8Ju6   NaN             Sacrifice  Dark Trap        96062
+    4SKqOHKYU5pgHr5UiVKiQN   NaN              Backpack  Dark Trap       135079
+    3uE1swbcRp5BrO64UNy6Ex   NaN     TakingOutTheTrash  Dark Trap       192833
+    3KJrwOuqiEwHq6QTreZT61   NaN           Io sono qui  Dark Trap       180880
+    4QhUXx4ON40TIBrZIlnIke   NaN                Murder  Dark Trap       186261
+    09320vyX4qHd4GjHIpy5w0   NaN        High 'N Mighty  Dark Trap       124676
+    6xEnbXM1us9fDJy2LC0lru   NaN  Bang Ya Fucking Head  Dark Trap       154929
+    
+                            key  mode  time_signature    tempo  acousticness  \
+    id
+    7pgJBLVz5VmnL7uGHmRj6p    8     1               4  115.080        0.4010
+    0vSWgAlfpye0WCGeNmuNhy    5     1               4  218.050        0.0138
+    7EL7ifncK2PWFYThJjzR25    8     1               4  189.938        0.1870
+    1umsRbM7L4ju7rn9aU8Ju6   10     0               4  139.990        0.1450
+    4SKqOHKYU5pgHr5UiVKiQN    5     1               4  128.014        0.0077
+    3uE1swbcRp5BrO64UNy6Ex   11     1               4  120.004        0.1720
+    3KJrwOuqiEwHq6QTreZT61   10     0               4  128.066        0.0987
+    4QhUXx4ON40TIBrZIlnIke    0     1               4  114.956        0.0343
+    09320vyX4qHd4GjHIpy5w0    7     1               5  111.958        0.1120
+    6xEnbXM1us9fDJy2LC0lru    1     1               4  125.013        0.0525
+    
+                            danceability  energy  instrumentalness  liveness  \
+    id
+    7pgJBLVz5VmnL7uGHmRj6p         0.719   0.493          0.000000    0.1180
+    0vSWgAlfpye0WCGeNmuNhy         0.850   0.893          0.000004    0.3720
+    7EL7ifncK2PWFYThJjzR25         0.864   0.365          0.000000    0.1160
+    1umsRbM7L4ju7rn9aU8Ju6         0.767   0.576          0.000003    0.0968
+    4SKqOHKYU5pgHr5UiVKiQN         0.765   0.726          0.000000    0.6190
+    3uE1swbcRp5BrO64UNy6Ex         0.814   0.575          0.000291    0.1090
+    3KJrwOuqiEwHq6QTreZT61         0.812   0.813          0.000150    0.0758
+    4QhUXx4ON40TIBrZIlnIke         0.602   0.578          0.000000    0.1640
+    09320vyX4qHd4GjHIpy5w0         0.876   0.768          0.000012    0.2830
+    6xEnbXM1us9fDJy2LC0lru         0.690   0.760          0.000000    0.1340
+    
+                            loudness  speechiness  valence
+    id
+    7pgJBLVz5VmnL7uGHmRj6p    -7.230       0.0794   0.1240
+    0vSWgAlfpye0WCGeNmuNhy    -4.783       0.0623   0.0391
+    7EL7ifncK2PWFYThJjzR25   -10.219       0.0655   0.0478
+    1umsRbM7L4ju7rn9aU8Ju6    -9.683       0.2560   0.1870
+    4SKqOHKYU5pgHr5UiVKiQN    -5.580       0.1910   0.2700
+    3uE1swbcRp5BrO64UNy6Ex    -9.635       0.0635   0.2880
+    3KJrwOuqiEwHq6QTreZT61    -5.583       0.0984   0.3480
+    4QhUXx4ON40TIBrZIlnIke    -5.610       0.0283   0.1560
+    09320vyX4qHd4GjHIpy5w0    -6.606       0.2010   0.7200
+    6xEnbXM1us9fDJy2LC0lru    -5.431       0.0895   0.0797
+
+%% Cell type:markdown id: tags:
+
+### Checking for missing values
+
+Syntax: `Series.isna()`
+- Returns a boolean Series
+
+Let's check if any of the "song_name"(s) are missing
+
+%% Cell type:code id: tags:
+
+``` python
+df["song_name"].isna()
+```
+
+%% Output
+
+    id
+    7pgJBLVz5VmnL7uGHmRj6p    False
+    0vSWgAlfpye0WCGeNmuNhy    False
+    7EL7ifncK2PWFYThJjzR25    False
+    1umsRbM7L4ju7rn9aU8Ju6    False
+    4SKqOHKYU5pgHr5UiVKiQN    False
+                              ...
+    46bXU7Sgj7104ZoXxzz9tM     True
+    0he2ViGMUO3ajKTxLOfWVT     True
+    72DAt9Lbpy9EUS29OzQLob     True
+    6HXgExFVuE1c3cq9QjFCcU     True
+    6MAAMZImxcvYhRnxDLTufD     True
+    Name: song_name, Length: 35877, dtype: bool
+
+%% Cell type:markdown id: tags:
+
+### Review: `Pandas.Series.value_counts()`
+- Returns a new `Series` with unique values from the original `Series` as keys and the count of those unique values as values.
+- Return value `Series` is ordered using descending order of counts
+
+%% Cell type:code id: tags:
+
+``` python
+# count the number of missing values for song name
+df["song_name"].isna().value_counts()
+```
+
+%% Output
+
+    False    18342
+    True     17535
+    Name: song_name, dtype: int64
+
+%% Cell type:markdown id: tags:
+
+### Missing value manipulation
+Syntax: `df.fillna(<REPLACE>)`
+- Returns a new DataFrame object instance reference.
+
+%% Cell type:code id: tags:
+
+``` python
+# use .fillna to replace missing values
+df["song_name"].fillna("No Song Name")
+
+# to replace the original DataFrame's column, you need to explicitly update that object instance
+# TODO: uncomment the below lines and update the code
+df["song_name"] = df["song_name"].fillna("No Song Name")
+df
+```
+
+%% Output
+
+                                                  title     song_name      genre  \
+    id
+    7pgJBLVz5VmnL7uGHmRj6p                          NaN     Pathology  Dark Trap
+    0vSWgAlfpye0WCGeNmuNhy                          NaN      Symbiote  Dark Trap
+    7EL7ifncK2PWFYThJjzR25                          NaN     BRAINFOOD  Dark Trap
+    1umsRbM7L4ju7rn9aU8Ju6                          NaN     Sacrifice  Dark Trap
+    4SKqOHKYU5pgHr5UiVKiQN                          NaN      Backpack  Dark Trap
+    ...                                             ...           ...        ...
+    46bXU7Sgj7104ZoXxzz9tM           Euphoric Hardstyle  No Song Name  hardstyle
+    0he2ViGMUO3ajKTxLOfWVT  Greatest Hardstyle Playlist  No Song Name  hardstyle
+    72DAt9Lbpy9EUS29OzQLob       Best of Hardstyle 2020  No Song Name  hardstyle
+    6HXgExFVuE1c3cq9QjFCcU           Euphoric Hardstyle  No Song Name  hardstyle
+    6MAAMZImxcvYhRnxDLTufD       Best of Hardstyle 2020  No Song Name  hardstyle
+    
+                            duration_ms  key  mode  time_signature    tempo  \
+    id
+    7pgJBLVz5VmnL7uGHmRj6p       224427    8     1               4  115.080
+    0vSWgAlfpye0WCGeNmuNhy        98821    5     1               4  218.050
+    7EL7ifncK2PWFYThJjzR25       101172    8     1               4  189.938
+    1umsRbM7L4ju7rn9aU8Ju6        96062   10     0               4  139.990
+    4SKqOHKYU5pgHr5UiVKiQN       135079    5     1               4  128.014
+    ...                             ...  ...   ...             ...      ...
+    46bXU7Sgj7104ZoXxzz9tM       269208    4     1               4  150.013
+    0he2ViGMUO3ajKTxLOfWVT       210112    0     0               4  149.928
+    72DAt9Lbpy9EUS29OzQLob       234823    8     1               4  154.935
+    6HXgExFVuE1c3cq9QjFCcU       323200    6     0               4  150.042
+    6MAAMZImxcvYhRnxDLTufD       162161    9     1               4  155.047
+    
+                            acousticness  danceability  energy  instrumentalness  \
+    id
+    7pgJBLVz5VmnL7uGHmRj6p      0.401000         0.719   0.493          0.000000
+    0vSWgAlfpye0WCGeNmuNhy      0.013800         0.850   0.893          0.000004
+    7EL7ifncK2PWFYThJjzR25      0.187000         0.864   0.365          0.000000
+    1umsRbM7L4ju7rn9aU8Ju6      0.145000         0.767   0.576          0.000003
+    4SKqOHKYU5pgHr5UiVKiQN      0.007700         0.765   0.726          0.000000
+    ...                              ...           ...     ...               ...
+    46bXU7Sgj7104ZoXxzz9tM      0.031500         0.528   0.693          0.000345
+    0he2ViGMUO3ajKTxLOfWVT      0.022500         0.517   0.768          0.000018
+    72DAt9Lbpy9EUS29OzQLob      0.026000         0.361   0.821          0.000242
+    6HXgExFVuE1c3cq9QjFCcU      0.000551         0.477   0.921          0.029600
+    6MAAMZImxcvYhRnxDLTufD      0.001890         0.529   0.945          0.000055
+    
+                            liveness  loudness  speechiness  valence
+    id
+    7pgJBLVz5VmnL7uGHmRj6p    0.1180    -7.230       0.0794   0.1240
+    0vSWgAlfpye0WCGeNmuNhy    0.3720    -4.783       0.0623   0.0391
+    7EL7ifncK2PWFYThJjzR25    0.1160   -10.219       0.0655   0.0478
+    1umsRbM7L4ju7rn9aU8Ju6    0.0968    -9.683       0.2560   0.1870
+    4SKqOHKYU5pgHr5UiVKiQN    0.6190    -5.580       0.1910   0.2700
+    ...                          ...       ...          ...      ...
+    46bXU7Sgj7104ZoXxzz9tM    0.1210    -5.148       0.0304   0.3940
+    0he2ViGMUO3ajKTxLOfWVT    0.2050    -7.922       0.0479   0.3830
+    72DAt9Lbpy9EUS29OzQLob    0.3850    -3.102       0.0505   0.1240
+    6HXgExFVuE1c3cq9QjFCcU    0.0575    -4.777       0.0392   0.4880
+    6MAAMZImxcvYhRnxDLTufD    0.4140    -5.862       0.0615   0.1340
+    
+    [35877 rows x 16 columns]
+
+%% Cell type:markdown id: tags:
+
+### Dropping missing values
+Syntax: `df.dropna()`
+- Returns a new DataFrame object instance reference.
+
+%% Cell type:code id: tags:
+
+``` python
+# .dropna will drop all rows that contain NaN in them
+df.dropna()
+```
+
+%% Output
+
+                                                  title     song_name      genre  \
+    id
+    5LzAV6KfjN8VhWCedeygfY            Dirtybird Players  No Song Name  techhouse
+    3TsCb6ueD678XBJDiRrvhr                   tech house  No Song Name  techhouse
+    6Y0Fy2buEis7bEOlG0QET1           Tech House Bangerz  No Song Name  techhouse
+    4EJI2XGViSQp6WscLKgYDD                   tech house  No Song Name  techhouse
+    4x6VzOQTLIrkkCWcDPh5Y0           blanc | Tech House  No Song Name  techhouse
+    ...                                             ...           ...        ...
+    46bXU7Sgj7104ZoXxzz9tM           Euphoric Hardstyle  No Song Name  hardstyle
+    0he2ViGMUO3ajKTxLOfWVT  Greatest Hardstyle Playlist  No Song Name  hardstyle
+    72DAt9Lbpy9EUS29OzQLob       Best of Hardstyle 2020  No Song Name  hardstyle
+    6HXgExFVuE1c3cq9QjFCcU           Euphoric Hardstyle  No Song Name  hardstyle
+    6MAAMZImxcvYhRnxDLTufD       Best of Hardstyle 2020  No Song Name  hardstyle
+    
+                            duration_ms  key  mode  time_signature    tempo  \
+    id
+    5LzAV6KfjN8VhWCedeygfY       197499    7     1               4  127.997
+    3TsCb6ueD678XBJDiRrvhr       206000   10     1               4  124.994
+    6Y0Fy2buEis7bEOlG0QET1       199839    4     0               4  124.006
+    4EJI2XGViSQp6WscLKgYDD       173861    8     1               4  125.031
+    4x6VzOQTLIrkkCWcDPh5Y0       394960    8     0               4  127.029
+    ...                             ...  ...   ...             ...      ...
+    46bXU7Sgj7104ZoXxzz9tM       269208    4     1               4  150.013
+    0he2ViGMUO3ajKTxLOfWVT       210112    0     0               4  149.928
+    72DAt9Lbpy9EUS29OzQLob       234823    8     1               4  154.935
+    6HXgExFVuE1c3cq9QjFCcU       323200    6     0               4  150.042
+    6MAAMZImxcvYhRnxDLTufD       162161    9     1               4  155.047
+    
+                            acousticness  danceability  energy  instrumentalness  \
+    id
+    5LzAV6KfjN8VhWCedeygfY      0.000957         0.806   0.950          0.920000
+    3TsCb6ueD678XBJDiRrvhr      0.062300         0.729   0.978          0.908000
+    6Y0Fy2buEis7bEOlG0QET1      0.019100         0.724   0.792          0.812000
+    4EJI2XGViSQp6WscLKgYDD      0.053000         0.700   0.898          0.418000
+    4x6VzOQTLIrkkCWcDPh5Y0      0.000301         0.803   0.919          0.926000
+    ...                              ...           ...     ...               ...
+    46bXU7Sgj7104ZoXxzz9tM      0.031500         0.528   0.693          0.000345
+    0he2ViGMUO3ajKTxLOfWVT      0.022500         0.517   0.768          0.000018
+    72DAt9Lbpy9EUS29OzQLob      0.026000         0.361   0.821          0.000242
+    6HXgExFVuE1c3cq9QjFCcU      0.000551         0.477   0.921          0.029600
+    6MAAMZImxcvYhRnxDLTufD      0.001890         0.529   0.945          0.000055
+    
+                            liveness  loudness  speechiness  valence
+    id
+    5LzAV6KfjN8VhWCedeygfY    0.1130    -6.782       0.0811    0.580
+    3TsCb6ueD678XBJDiRrvhr    0.0353    -6.645       0.0420    0.778
+    6Y0Fy2buEis7bEOlG0QET1    0.1080    -8.555       0.0405    0.346
+    4EJI2XGViSQp6WscLKgYDD    0.5740    -6.099       0.2570    0.791
+    4x6VzOQTLIrkkCWcDPh5Y0    0.1020    -8.667       0.0702    0.754
+    ...                          ...       ...          ...      ...
+    46bXU7Sgj7104ZoXxzz9tM    0.1210    -5.148       0.0304    0.394
+    0he2ViGMUO3ajKTxLOfWVT    0.2050    -7.922       0.0479    0.383
+    72DAt9Lbpy9EUS29OzQLob    0.3850    -3.102       0.0505    0.124
+    6HXgExFVuE1c3cq9QjFCcU    0.0575    -4.777       0.0392    0.488
+    6MAAMZImxcvYhRnxDLTufD    0.4140    -5.862       0.0615    0.134
+    
+    [17529 rows x 16 columns]
+
+%% Cell type:markdown id: tags:
+
+### Review: `Pandas.Series.apply(...)`
+Syntax: `Series.apply(<FUNCTION OBJECT REFERENCE>)`
+- applies input function to every element of the Series.
+- Returns a new `Series` object instance reference.
+
+Let's apply transformation function to `mode` column `Series`:
+- mode = 1 means major modality (sounds happy)
+- mode = 0 means minor modality (sounds sad)
+
+%% Cell type:code id: tags:
+
+``` python
+def replace_mode(m):
+    if m == 1:
+        return "major"
+    else:
+        return "minor"
+```
+
+%% Cell type:code id: tags:
+
+``` python
+df["mode"].apply(replace_mode)
+```
+
+%% Output
+
+    id
+    7pgJBLVz5VmnL7uGHmRj6p    major
+    0vSWgAlfpye0WCGeNmuNhy    major
+    7EL7ifncK2PWFYThJjzR25    major
+    1umsRbM7L4ju7rn9aU8Ju6    minor
+    4SKqOHKYU5pgHr5UiVKiQN    major
+                              ...
+    46bXU7Sgj7104ZoXxzz9tM    major
+    0he2ViGMUO3ajKTxLOfWVT    minor
+    72DAt9Lbpy9EUS29OzQLob    major
+    6HXgExFVuE1c3cq9QjFCcU    minor
+    6MAAMZImxcvYhRnxDLTufD    major
+    Name: mode, Length: 35877, dtype: object
+
+%% Cell type:markdown id: tags:
+
+### `lambda` recap
+
+Let's write a `lambda` function instead of the `replace_mode` function
+
+%% Cell type:code id: tags:
+
+``` python
+df["mode"].apply(lambda m: "major" if m == 1 else "minor")
+```
+
+%% Output
+
+    id
+    7pgJBLVz5VmnL7uGHmRj6p    major
+    0vSWgAlfpye0WCGeNmuNhy    major
+    7EL7ifncK2PWFYThJjzR25    major
+    1umsRbM7L4ju7rn9aU8Ju6    minor
+    4SKqOHKYU5pgHr5UiVKiQN    major
+                              ...
+    46bXU7Sgj7104ZoXxzz9tM    major
+    0he2ViGMUO3ajKTxLOfWVT    minor
+    72DAt9Lbpy9EUS29OzQLob    major
+    6HXgExFVuE1c3cq9QjFCcU    minor
+    6MAAMZImxcvYhRnxDLTufD    major
+    Name: mode, Length: 35877, dtype: object
+
+%% Cell type:markdown id: tags:
+
+Typically transformed columns are added as new columns within the DataFrame.
+Let's add a new `modified_mode` column.
+
+%% Cell type:code id: tags:
+
+``` python
+df["modified_mode"] = df["mode"].apply(lambda m: "major" if m == 1 else "minor")
+df
+```
+
+%% Output
+
+                                                  title     song_name      genre  \
+    id
+    7pgJBLVz5VmnL7uGHmRj6p                          NaN     Pathology  Dark Trap
+    0vSWgAlfpye0WCGeNmuNhy                          NaN      Symbiote  Dark Trap
+    7EL7ifncK2PWFYThJjzR25                          NaN     BRAINFOOD  Dark Trap
+    1umsRbM7L4ju7rn9aU8Ju6                          NaN     Sacrifice  Dark Trap
+    4SKqOHKYU5pgHr5UiVKiQN                          NaN      Backpack  Dark Trap
+    ...                                             ...           ...        ...
+    46bXU7Sgj7104ZoXxzz9tM           Euphoric Hardstyle  No Song Name  hardstyle
+    0he2ViGMUO3ajKTxLOfWVT  Greatest Hardstyle Playlist  No Song Name  hardstyle
+    72DAt9Lbpy9EUS29OzQLob       Best of Hardstyle 2020  No Song Name  hardstyle
+    6HXgExFVuE1c3cq9QjFCcU           Euphoric Hardstyle  No Song Name  hardstyle
+    6MAAMZImxcvYhRnxDLTufD       Best of Hardstyle 2020  No Song Name  hardstyle
+    
+                            duration_ms  key  mode  time_signature    tempo  \
+    id
+    7pgJBLVz5VmnL7uGHmRj6p       224427    8     1               4  115.080
+    0vSWgAlfpye0WCGeNmuNhy        98821    5     1               4  218.050
+    7EL7ifncK2PWFYThJjzR25       101172    8     1               4  189.938
+    1umsRbM7L4ju7rn9aU8Ju6        96062   10     0               4  139.990
+    4SKqOHKYU5pgHr5UiVKiQN       135079    5     1               4  128.014
+    ...                             ...  ...   ...             ...      ...
+    46bXU7Sgj7104ZoXxzz9tM       269208    4     1               4  150.013
+    0he2ViGMUO3ajKTxLOfWVT       210112    0     0               4  149.928
+    72DAt9Lbpy9EUS29OzQLob       234823    8     1               4  154.935
+    6HXgExFVuE1c3cq9QjFCcU       323200    6     0               4  150.042
+    6MAAMZImxcvYhRnxDLTufD       162161    9     1               4  155.047
+    
+                            acousticness  danceability  energy  instrumentalness  \
+    id
+    7pgJBLVz5VmnL7uGHmRj6p      0.401000         0.719   0.493          0.000000
+    0vSWgAlfpye0WCGeNmuNhy      0.013800         0.850   0.893          0.000004
+    7EL7ifncK2PWFYThJjzR25      0.187000         0.864   0.365          0.000000
+    1umsRbM7L4ju7rn9aU8Ju6      0.145000         0.767   0.576          0.000003
+    4SKqOHKYU5pgHr5UiVKiQN      0.007700         0.765   0.726          0.000000
+    ...                              ...           ...     ...               ...
+    46bXU7Sgj7104ZoXxzz9tM      0.031500         0.528   0.693          0.000345
+    0he2ViGMUO3ajKTxLOfWVT      0.022500         0.517   0.768          0.000018
+    72DAt9Lbpy9EUS29OzQLob      0.026000         0.361   0.821          0.000242
+    6HXgExFVuE1c3cq9QjFCcU      0.000551         0.477   0.921          0.029600
+    6MAAMZImxcvYhRnxDLTufD      0.001890         0.529   0.945          0.000055
+    
+                            liveness  loudness  speechiness  valence modified_mode
+    id
+    7pgJBLVz5VmnL7uGHmRj6p    0.1180    -7.230       0.0794   0.1240         major
+    0vSWgAlfpye0WCGeNmuNhy    0.3720    -4.783       0.0623   0.0391         major
+    7EL7ifncK2PWFYThJjzR25    0.1160   -10.219       0.0655   0.0478         major
+    1umsRbM7L4ju7rn9aU8Ju6    0.0968    -9.683       0.2560   0.1870         minor
+    4SKqOHKYU5pgHr5UiVKiQN    0.6190    -5.580       0.1910   0.2700         major
+    ...                          ...       ...          ...      ...           ...
+    46bXU7Sgj7104ZoXxzz9tM    0.1210    -5.148       0.0304   0.3940         major
+    0he2ViGMUO3ajKTxLOfWVT    0.2050    -7.922       0.0479   0.3830         minor
+    72DAt9Lbpy9EUS29OzQLob    0.3850    -3.102       0.0505   0.1240         major
+    6HXgExFVuE1c3cq9QjFCcU    0.0575    -4.777       0.0392   0.4880         minor
+    6MAAMZImxcvYhRnxDLTufD    0.4140    -5.862       0.0615   0.1340         major
+    
+    [35877 rows x 17 columns]
+
+%% Cell type:markdown id: tags:
+
+#### Let's go back to the original table from the SQL database
+
+%% Cell type:code id: tags:
+
+``` python
+df = qry("SELECT * FROM spotify")
+df
+```
+
+%% Output
+
+                               id                        title  song_name  \
+    0      7pgJBLVz5VmnL7uGHmRj6p                               Pathology
+    1      0vSWgAlfpye0WCGeNmuNhy                                Symbiote
+    2      7EL7ifncK2PWFYThJjzR25                               BRAINFOOD
+    3      1umsRbM7L4ju7rn9aU8Ju6                               Sacrifice
+    4      4SKqOHKYU5pgHr5UiVKiQN                                Backpack
+    ...                       ...                          ...        ...
+    35872  46bXU7Sgj7104ZoXxzz9tM           Euphoric Hardstyle
+    35873  0he2ViGMUO3ajKTxLOfWVT  Greatest Hardstyle Playlist
+    35874  72DAt9Lbpy9EUS29OzQLob       Best of Hardstyle 2020
+    35875  6HXgExFVuE1c3cq9QjFCcU           Euphoric Hardstyle
+    35876  6MAAMZImxcvYhRnxDLTufD       Best of Hardstyle 2020
+    
+               genre  duration_ms  key  mode  time_signature    tempo  \
+    0      Dark Trap       224427    8     1               4  115.080
+    1      Dark Trap        98821    5     1               4  218.050
+    2      Dark Trap       101172    8     1               4  189.938
+    3      Dark Trap        96062   10     0               4  139.990
+    4      Dark Trap       135079    5     1               4  128.014
+    ...          ...          ...  ...   ...             ...      ...
+    35872  hardstyle       269208    4     1               4  150.013
+    35873  hardstyle       210112    0     0               4  149.928
+    35874  hardstyle       234823    8     1               4  154.935
+    35875  hardstyle       323200    6     0               4  150.042
+    35876  hardstyle       162161    9     1               4  155.047
+    
+           acousticness  danceability  energy  instrumentalness  liveness  \
+    0          0.401000         0.719   0.493          0.000000    0.1180
+    1          0.013800         0.850   0.893          0.000004    0.3720
+    2          0.187000         0.864   0.365          0.000000    0.1160
+    3          0.145000         0.767   0.576          0.000003    0.0968
+    4          0.007700         0.765   0.726          0.000000    0.6190
+    ...             ...           ...     ...               ...       ...
+    35872      0.031500         0.528   0.693          0.000345    0.1210
+    35873      0.022500         0.517   0.768          0.000018    0.2050
+    35874      0.026000         0.361   0.821          0.000242    0.3850
+    35875      0.000551         0.477   0.921          0.029600    0.0575
+    35876      0.001890         0.529   0.945          0.000055    0.4140
+    
+           loudness  speechiness  valence
+    0        -7.230       0.0794   0.1240
+    1        -4.783       0.0623   0.0391
+    2       -10.219       0.0655   0.0478
+    3        -9.683       0.2560   0.1870
+    4        -5.580       0.1910   0.2700
+    ...         ...          ...      ...
+    35872    -5.148       0.0304   0.3940
+    35873    -7.922       0.0479   0.3830
+    35874    -3.102       0.0505   0.1240
+    35875    -4.777       0.0392   0.4880
+    35876    -5.862       0.0615   0.1340
+    
+    [35877 rows x 17 columns]
+
+%% Cell type:markdown id: tags:
+
+Extract just the "genre" and "duration_ms" columns from `df`.
+
+%% Cell type:code id: tags:
+
+``` python
+df[["genre", 'duration_ms']]
+```
+
+%% Output
+
+               genre  duration_ms
+    0      Dark Trap       224427
+    1      Dark Trap        98821
+    2      Dark Trap       101172
+    3      Dark Trap        96062
+    4      Dark Trap       135079
+    ...          ...          ...
+    35872  hardstyle       269208
+    35873  hardstyle       210112
+    35874  hardstyle       234823
+    35875  hardstyle       323200
+    35876  hardstyle       162161
+    
+    [35877 rows x 2 columns]
+
+%% Cell type:markdown id: tags:
+
+### `Pandas.DataFrame.groupby(...)`
+
+Syntax: `DataFrame.groupby(<COLUMN>)`
+- Returns a `groupby` object instance reference
+- Need to apply aggregation methods to use the return value of `groupby`
+
+%% Cell type:code id: tags:
+
+``` python
+df[["genre", "duration_ms"]].groupby("genre")
+```
+
+%% Output
+
+    <pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f82f8026e50>
+
+%% Cell type:markdown id: tags:
+
+### What is the average duration for each genre ordered based on decreasing order of averages?
+#### v1: using `df` (`pandas`) to answer the question
+
+%% Cell type:code id: tags:
+
+``` python
+df[["genre", "duration_ms"]].groupby("genre").mean().sort_values(by = "duration_ms", ascending = False)
+```
+
+%% Output
+
+                       duration_ms
+    genre
+    psytrance        445770.492075
+    techno           399123.187453
+    techhouse        298395.587596
+    dnb              288860.138811
+    trance           288729.366262
+    hardstyle        232828.626542
+    Hiphop           227885.028411
+    RnB              225628.556955
+    trap             225149.277731
+    Emo              218370.989519
+    Pop              211558.052980
+    Rap              200816.798836
+    Dark Trap        196059.938997
+    Underground Rap  175506.191224
+    Trap Metal       145940.519467
+
+%% Cell type:code id: tags:
+
+``` python
+df[["genre"]].value_counts()
+```
+
+%% Output
+
+    genre
+    Underground Rap    4330
+    Dark Trap          3590
+    Hiphop             3027
+    trance             2804
+    psytrance          2650
+    techno             2646
+    dnb                2507
+    trap               2362
+    hardstyle          2351
+    techhouse          2209
+    RnB                1905
+    Trap Metal         1875
+    Emo                1622
+    Rap                1546
+    Pop                 453
+    dtype: int64
+
+%% Cell type:markdown id: tags:
+
+One way to check whether `groupby` works would be to use `value_counts` on the same column `Series`.
+
+%% Cell type:code id: tags:
+
+``` python
+df["genre"].value_counts()
+```
+
+%% Cell type:markdown id: tags:
+
+### What is the average duration for each genre ordered based on decreasing order of averages?
+#### v2: using SQL query to answer the question
+
+%% Cell type:code id: tags:
+
+``` python
+# SQL equivalent query of the above Pandas query
+avg_duration_per_genre = qry("""
+select genre, avg(duration_ms)
+from spotify
+group by genre
+order by avg(duration_ms) desc
+
+""")
+
+# How can we get make the SQL query output to be exactly same as df.groupby?
+avg_duration_per_genre = avg_duration_per_genre.set_index("genre")
+avg_duration_per_genre
+```
+
+%% Output
+
+                     avg(duration_ms)
+    genre
+    psytrance           445770.492075
+    techno              399123.187453
+    techhouse           298395.587596
+    dnb                 288860.138811
+    trance              288729.366262
+    hardstyle           232828.626542
+    Hiphop              227885.028411
+    RnB                 225628.556955
+    trap                225149.277731
+    Emo                 218370.989519
+    Pop                 211558.052980
+    Rap                 200816.798836
+    Dark Trap           196059.938997
+    Underground Rap     175506.191224
+    Trap Metal          145940.519467
+
+%% Cell type:markdown id: tags:
+
+### You can groupby more than one column
+
+%% Cell type:code id: tags:
+
+``` python
+# select mode, time_signature, and speechiness
+df[["mode", "time_signature", "speechiness"]]
+```
+
+%% Output
+
+           mode  time_signature  speechiness
+    0         1               4       0.0794
+    1         1               4       0.0623
+    2         1               4       0.0655
+    3         0               4       0.2560
+    4         1               4       0.1910
+    ...     ...             ...          ...
+    35872     1               4       0.0304
+    35873     0               4       0.0479
+    35874     1               4       0.0505
+    35875     0               4       0.0392
+    35876     1               4       0.0615
+    
+    [35877 rows x 3 columns]
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:markdown id: tags:
+
+### What is the average speechiness for each mode, time signature pair?
+#### v1: pandas
+
+%% Cell type:code id: tags:
+
+``` python
+df[["mode", "time_signature", "speechiness"]].groupby(["mode", "time_signature"]).mean()
+```
+
+%% Output
+
+                         speechiness
+    mode time_signature
+    0    1                  0.181224
+         3                  0.121837
+         4                  0.126688
+         5                  0.204890
+    1    1                  0.173138
+         3                  0.129512
+         4                  0.139170
+         5                  0.220177
+
+%% Cell type:code id: tags:
+
+``` python
+# SQL equivalent query of the above Pandas query
+qry("""
+SELECT mode, time_signature, AVG(speechiness) as avg_speechiness
+FROM spotify
+GROUP BY mode, time_signature
+""")
+```
+
+%% Output
+
+       mode  time_signature  avg_speechiness
+    0     0               1         0.181224
+    1     0               3         0.121837
+    2     0               4         0.126688
+    3     0               5         0.204890
+    4     1               1         0.173138
+    5     1               3         0.129512
+    6     1               4         0.139170
+    7     1               5         0.220177
+
+%% Cell type:markdown id: tags:
+
+### Self-practice
+
+%% Cell type:markdown id: tags:
+
+### Which songs have a tempo greater than 150 and what are their genre?
+
+%% Cell type:code id: tags:
+
+``` python
+# v1: pandas
+fast_songs =
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# v2: SQL
+
+qry("""
+
+""")
+```
+
+%% Cell type:markdown id: tags:
+
+### What is the sum of danceability and liveness for "Hiphop" genre songs?
+
+%% Cell type:code id: tags:
+
+``` python
+# v1: pandas
+hiphop_songs =
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# v2: SQL
+hiphop_songs = qry("""
+
+""")
+hiphop_songs
+```
+
+%% Cell type:markdown id: tags:
+
+### Find all song_name ordered by ascending order of duration_ms. Eliminate songs which don't have a song_name
+
+%% Cell type:code id: tags:
+
+``` python
+# v1: pandas
+songs_by_duration =
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# v2
+songs_by_duration = qry("""
+
+""")
+songs_by_duration
+```
+
+%% Cell type:markdown id: tags:
+
+### How many distinct "genre"s are there in the dataset?
+
+%% Cell type:code id: tags:
+
+``` python
+# v1: pandas
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# v2: SQL
+genres = qry("""
+
+""")
+```
+
+%% Cell type:markdown id: tags:
+
+### Considering only songs with energy greater than 0.5, what is the maximum energy for each "genre" with song count greater than 2000?
+
+%% Cell type:code id: tags:
+
+``` python
+genre_groups =
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# v1: pandas
+high_energy_songs = ???
+genre_groups = ???
+max_energy = ???
+max_energy["energy"]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+genre_counts = ???
+genre_counts["energy_max"] = max_energy["energy"]
+filtered_genre_counts = ???
+filtered_genre_counts
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# v2: SQL
+qry("""
+
+""")
+```

f22/andy_lec_notes/lec35_Dec02_Pandas3/lec_35_pandas3_data_transformation_template.ipynb

+%% Cell type:code id: tags:
+
+``` python
+# known import statements
+import pandas as pd
+import sqlite3 as sql # note that we are renaming to sql
+import os
+
+# new import statement
+import numpy as np
+```
+
+%% Cell type:markdown id: tags:
+
+# Lecture 35 Pandas 3: Data Transformation
+* Data transformation is the process of changing the format, structure, or values of data.
+* Often needed during data cleaning and sometimes during data analysis
+
+Possible data transformation:
+* Parsing/Extraction
+  * Parse CSV to Pandas DataFrame
+* Missing Value Manipulations
+  * Dropping
+  * Imputation: replace missing value with substitute values
+* Typecasting, Formating, Renaming
+  * Typecasting: covert one column from int to float
+  * Formating: format the time column to datatime object
+  * Renaming: rename column and index names
+* Applying/Mapping
+* Filtering, Aggregation, Grouping, and Summarization
+  * Covered in Pandas 1 & 2 lectures
+
+%% Cell type:markdown id: tags:
+
+# Today's Learning Objectives:
+
+* Identify, drop, or fill missing values with Pandas .isna, .dropna, and .fillna
+* Apply a function to Pandas Series and DataFrame rows/columns
+* Replace all target values to Pandas Series and DataFrame rows/columns
+* Filter, Aggregate, Group, and Summarize information in a DataFrame with .groupby
+* Convert .groupby examples to SQL
+
+%% Cell type:markdown id: tags:
+
+# The dataset: Spotify songs
+Adapted from https://www.kaggle.com/datasets/mrmorj/dataset-of-songs-in-spotify.
+
+If you are interested in digging deeper in this dataset, here's a [blog post](https://medium.com/@boplantinga/what-do-spotifys-audio-features-tell-us-about-this-year-s-eurovision-song-contest-66ad188e112a) that explain each column in details.
+
+%% Cell type:markdown id: tags:
+
+### WARMUP 1: Establish a connection to the spotify.db database
+
+%% Cell type:code id: tags:
+
+``` python
+# open up the spotify database
+db_pathname = "spotify.db"
+assert os.path.exists(db_pathname)
+conn = sql.connect(db_pathname)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def qry(sql):
+    return pd.read_sql(sql, conn)
+```
+
+%% Cell type:markdown id: tags:
+
+### WARMUP 2: Identify the table name(s) inside the database
+
+%% Cell type:code id: tags:
+
+``` python
+qry("select * from sqlite_master")
+```
+
+%% Output
+
+        type                        name tbl_name  rootpage  \
+    0  table                     spotify  spotify      1527
+    1  index  sqlite_autoindex_spotify_1  spotify      1528
+    
+                                                     sql
+    0  CREATE TABLE spotify(\nid TEXT PRIMARY KEY,\nt...
+    1                                               None
+
+%% Cell type:markdown id: tags:
+
+### WARMUP 3: Use pandas lookup expression to identify the column names and the types: use .iloc
+
+%% Cell type:code id: tags:
+
+``` python
+print(qry("select * from sqlite_master")["sql"].iloc[0])
+```
+
+%% Output
+
+    CREATE TABLE spotify(
+    id TEXT PRIMARY KEY,
+    title BLOB,
+    song_name BLOB,
+    genre TEXT,
+    duration_ms INTEGER,
+    key INTEGER,
+    mode INTEGER,
+    time_signature INTEGER,
+    tempo REAL,
+    acousticness REAL,
+    danceability REAL,
+    energy REAL,
+    instrumentalness REAL,
+    liveness REAL,
+    loudness REAL,
+    speechiness REAL,
+    valence REAL)
+
+%% Cell type:markdown id: tags:
+
+### WARMUP 4: Store the data inside `spotify` table inside a variable called `df`
+
+%% Cell type:code id: tags:
+
+``` python
+df = qry("select * from spotify")
+df
+```
+
+%% Output
+
+                               id                        title  song_name  \
+    0      7pgJBLVz5VmnL7uGHmRj6p                               Pathology
+    1      0vSWgAlfpye0WCGeNmuNhy                                Symbiote
+    2      7EL7ifncK2PWFYThJjzR25                               BRAINFOOD
+    3      1umsRbM7L4ju7rn9aU8Ju6                               Sacrifice
+    4      4SKqOHKYU5pgHr5UiVKiQN                                Backpack
+    ...                       ...                          ...        ...
+    35872  46bXU7Sgj7104ZoXxzz9tM           Euphoric Hardstyle
+    35873  0he2ViGMUO3ajKTxLOfWVT  Greatest Hardstyle Playlist
+    35874  72DAt9Lbpy9EUS29OzQLob       Best of Hardstyle 2020
+    35875  6HXgExFVuE1c3cq9QjFCcU           Euphoric Hardstyle
+    35876  6MAAMZImxcvYhRnxDLTufD       Best of Hardstyle 2020
+    
+               genre  duration_ms  key  mode  time_signature    tempo  \
+    0      Dark Trap       224427    8     1               4  115.080
+    1      Dark Trap        98821    5     1               4  218.050
+    2      Dark Trap       101172    8     1               4  189.938
+    3      Dark Trap        96062   10     0               4  139.990
+    4      Dark Trap       135079    5     1               4  128.014
+    ...          ...          ...  ...   ...             ...      ...
+    35872  hardstyle       269208    4     1               4  150.013
+    35873  hardstyle       210112    0     0               4  149.928
+    35874  hardstyle       234823    8     1               4  154.935
+    35875  hardstyle       323200    6     0               4  150.042
+    35876  hardstyle       162161    9     1               4  155.047
+    
+           acousticness  danceability  energy  instrumentalness  liveness  \
+    0          0.401000         0.719   0.493          0.000000    0.1180
+    1          0.013800         0.850   0.893          0.000004    0.3720
+    2          0.187000         0.864   0.365          0.000000    0.1160
+    3          0.145000         0.767   0.576          0.000003    0.0968
+    4          0.007700         0.765   0.726          0.000000    0.6190
+    ...             ...           ...     ...               ...       ...
+    35872      0.031500         0.528   0.693          0.000345    0.1210
+    35873      0.022500         0.517   0.768          0.000018    0.2050
+    35874      0.026000         0.361   0.821          0.000242    0.3850
+    35875      0.000551         0.477   0.921          0.029600    0.0575
+    35876      0.001890         0.529   0.945          0.000055    0.4140
+    
+           loudness  speechiness  valence
+    0        -7.230       0.0794   0.1240
+    1        -4.783       0.0623   0.0391
+    2       -10.219       0.0655   0.0478
+    3        -9.683       0.2560   0.1870
+    4        -5.580       0.1910   0.2700
+    ...         ...          ...      ...
+    35872    -5.148       0.0304   0.3940
+    35873    -7.922       0.0479   0.3830
+    35874    -3.102       0.0505   0.1240
+    35875    -4.777       0.0392   0.4880
+    35876    -5.862       0.0615   0.1340
+    
+    [35877 rows x 17 columns]
+
+%% Cell type:markdown id: tags:
+
+### Setting a column as row indices for the `DataFrame`
+
+- Syntax: `df.set_index("<COLUMN>")`
+- Returns a new DataFrame object instance reference.
+- WARNING: executing this twice will result in `KeyError` being thrown. Once you set a column as row index, it will no longer be a column within the `DataFrame`. If you tried this, go back and execute the above cell and update `df` once more and then execute the below cell exactly once.
+
+%% Cell type:code id: tags:
+
+``` python
+# Set the id column as row indices
+df =
+df
+```
+
+%% Cell type:markdown id: tags:
+
+### Not a Number
+
+- `np.NaN` is the floating point representation of Not a Number
+- You do not need to know / learn the details about the `numpy` package
+
+### Replacing / modifying values within the `DataFrame`
+
+Syntax: `df.replace(<TARGET>, <REPLACE>)`
+- Your target can be `str`, `int`, `float`, `None` (there are other possiblities, but those are too advanced for this course)
+- Returns a new DataFrame object instance reference.
+
+Let's now replace the missing values (empty strings) with `np.NAN`
+
+%% Cell type:code id: tags:
+
+``` python
+df =
+df.head(10) # title is the album name
+```
+
+%% Cell type:markdown id: tags:
+
+### Checking for missing values
+
+Syntax: `Series.isna()`
+- Returns a boolean Series
+
+Let's check if any of the "song_name"(s) are missing
+
+%% Cell type:code id: tags:
+
+``` python
+df["song_name"]
+```
+
+%% Cell type:markdown id: tags:
+
+### Review: `Pandas.Series.value_counts()`
+- Returns a new `Series` with unique values from the original `Series` as keys and the count of those unique values as values.
+- Return value `Series` is ordered using descending order of counts
+
+%% Cell type:code id: tags:
+
+``` python
+# count the number of missing values for song name
+df["song_name"]
+```
+
+%% Cell type:markdown id: tags:
+
+### Missing value manipulation
+Syntax: `df.fillna(<REPLACE>)`
+- Returns a new DataFrame object instance reference.
+
+%% Cell type:code id: tags:
+
+``` python
+# use .fillna to replace missing values
+df["song_name"]
+
+# to replace the original DataFrame's column, you need to explicitly update that object instance
+# TODO: uncomment the below lines and update the code
+#df["song_name"] = ???
+#df
+```
+
+%% Cell type:markdown id: tags:
+
+### Dropping missing values
+Syntax: `df.dropna()`
+- Returns a new DataFrame object instance reference.
+
+%% Cell type:code id: tags:
+
+``` python
+# .dropna will drop all rows that contain NaN in them
+df.dropna()
+```
+
+%% Cell type:markdown id: tags:
+
+### Review: `Pandas.Series.apply(...)`
+Syntax: `Series.apply(<FUNCTION OBJECT REFERENCE>)`
+- applies input function to every element of the Series.
+- Returns a new `Series` object instance reference.
+
+Let's apply transformation function to `mode` column `Series`:
+- mode = 1 means major modality (sounds happy)
+- mode = 0 means minor modality (sounds sad)
+
+%% Cell type:code id: tags:
+
+``` python
+def replace_mode(m):
+    if m == 1:
+        return "major"
+    else:
+        return "minor"
+```
+
+%% Cell type:code id: tags:
+
+``` python
+df["mode"]
+```
+
+%% Cell type:markdown id: tags:
+
+### `lambda` recap
+
+Let's write a `lambda` function instead of the `replace_mode` function
+
+%% Cell type:code id: tags:
+
+``` python
+df["mode"].apply(???)
+```
+
+%% Cell type:markdown id: tags:
+
+Typically transformed columns are added as new columns within the DataFrame.
+Let's add a new `modified_mode` column.
+
+%% Cell type:code id: tags:
+
+``` python
+df["modified_mode"] = df["mode"].apply(lambda m: "major" if m == 1 else "minor")
+df
+```
+
+%% Cell type:markdown id: tags:
+
+#### Let's go back to the original table from the SQL database
+
+%% Cell type:code id: tags:
+
+``` python
+df = qry("SELECT * FROM spotify")
+df
+```
+
+%% Cell type:markdown id: tags:
+
+Extract just the "genre" and "duration_ms" columns from `df`.
+
+%% Cell type:code id: tags:
+
+``` python
+df[???]
+```
+
+%% Cell type:markdown id: tags:
+
+### `Pandas.DataFrame.groupby(...)`
+
+Syntax: `DataFrame.groupby(<COLUMN>)`
+- Returns a `groupby` object instance reference
+- Need to apply aggregation methods to use the return value of `groupby`
+
+%% Cell type:code id: tags:
+
+``` python
+df[["genre", "duration_ms"]]
+```
+
+%% Cell type:markdown id: tags:
+
+### What is the average duration for each genre ordered based on decreasing order of averages?
+#### v1: using `df` (`pandas`) to answer the question
+
+%% Cell type:code id: tags:
+
+``` python
+df[["genre", "duration_ms"]]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+df[["genre", "duration_ms"]]
+```
+
+%% Cell type:markdown id: tags:
+
+One way to check whether `groupby` works would be to use `value_counts` on the same column `Series`.
+
+%% Cell type:code id: tags:
+
+``` python
+df["genre"].value_counts()
+```
+
+%% Cell type:markdown id: tags:
+
+### What is the average duration for each genre ordered based on decreasing order of averages?
+#### v2: using SQL query to answer the question
+
+%% Cell type:code id: tags:
+
+``` python
+# SQL equivalent query of the above Pandas query
+avg_duration_per_genre = qry("""
+
+""")
+
+# How can we get make the SQL query output to be exactly same as df.groupby?
+avg_duration_per_genre = avg_duration_per_genre.set_index("genre")
+avg_duration_per_genre
+```
+
+%% Cell type:markdown id: tags:
+
+### What is the average speechiness for each mode, time signature pair?
+#### v1: pandas
+
+%% Cell type:code id: tags:
+
+``` python
+# use a list to indicate all the columns you want to groupby
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# SQL equivalent query of the above Pandas query
+qry("""
+
+""")
+```
+
+%% Cell type:markdown id: tags:
+
+### Self-practice
+
+%% Cell type:markdown id: tags:
+
+### Which songs have a tempo greater than 150 and what are their genre?
+
+%% Cell type:code id: tags:
+
+``` python
+# v1: pandas
+fast_songs =
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# v2: SQL
+
+qry("""
+
+""")
+```
+
+%% Cell type:markdown id: tags:
+
+### What is the sum of danceability and liveness for "Hiphop" genre songs?
+
+%% Cell type:code id: tags:
+
+``` python
+# v1: pandas
+hiphop_songs =
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# v2: SQL
+hiphop_songs = qry("""
+
+""")
+hiphop_songs
+```
+
+%% Cell type:markdown id: tags:
+
+### Find all song_name ordered by ascending order of duration_ms. Eliminate songs which don't have a song_name
+
+%% Cell type:code id: tags:
+
+``` python
+# v1: pandas
+songs_by_duration =
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# v2
+songs_by_duration = qry("""
+
+""")
+songs_by_duration
+```
+
+%% Cell type:markdown id: tags:
+
+### How many distinct "genre"s are there in the dataset?
+
+%% Cell type:code id: tags:
+
+``` python
+# v1: pandas
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# v2: SQL
+genres = qry("""
+
+""")
+```
+
+%% Cell type:markdown id: tags:
+
+### Considering only songs with energy greater than 0.5, what is the maximum energy for each "genre" with song count greater than 2000?
+
+%% Cell type:code id: tags:
+
+``` python
+genre_groups =
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# v1: pandas
+high_energy_songs = ???
+genre_groups = ???
+max_energy = ???
+max_energy["energy"]
+```
+
+%% Cell type:code id: tags:
+
+``` python
+genre_counts = ???
+genre_counts["energy_max"] = max_energy["energy"]
+filtered_genre_counts = ???
+filtered_genre_counts
+```
+
+%% Cell type:code id: tags:
+
+``` python
+# v2: SQL
+qry("""
+
+""")
+```

f22/andy_lec_notes/lec35_Dec02_Pandas3/link_to_notebook.md

+Today's lecture notes will be shared via a Google Doc shared in lecture