├── Datasets ├── Coffee_and_code.csv ├── car_crashes.csv ├── data_2020-Sep-07 (1).csv ├── data_2020-Sep-07.csv ├── data_by_genres.csv ├── data_ign_scores.csv ├── flights.csv └── insurance.csv ├── Dataviz.pdf ├── Images ├── Watson_Studio.png └── file.txt ├── LICENSE ├── Notebook └── Data_Viz.ipynb └── README.md /Datasets/Coffee_and_code.csv: -------------------------------------------------------------------------------- 1 | CodingHours,CoffeeCupsPerDay,CoffeeTime,CodingWithoutCoffee,CoffeeType,CoffeeSolveBugs,Gender,Country,AgeRange 2 | 8,2,Before coding,Yes,Caffè latte,Sometimes,Female,Lebanon,18 to 29 3 | 3,2,Before coding,Yes,Americano,Yes,Female,Lebanon,30 to 39 4 | 5,3,While coding,No,Nescafe,Yes,Female,Lebanon,18 to 29 5 | 8,2,Before coding,No,Nescafe,Yes,Male,Lebanon,NA 6 | 10,3,While coding,Sometimes,Turkish,No,Male,Lebanon,18 to 29 7 | 8,2,While coding,Sometimes,Nescafe,Yes,Male,Lebanon,30 to 39 8 | 5,2,While coding,Yes,Nescafe,Sometimes,Male,Lebanon,NA 9 | 10,4,Before coding,Sometimes,Turkish,Sometimes,Male,Lebanon,18 to 29 10 | 10,2,While coding,Yes,American Coffee,Sometimes,Male,Lebanon,30 to 39 11 | 10,2,While coding,Yes,Nescafe,No,Male,Lebanon,30 to 39 12 | 10,3,While coding,Sometimes,American Coffee,Sometimes,Male,Lebanon,30 to 39 13 | 2,3,Before coding,Sometimes,Turkish,Yes,Male,Lebanon,30 to 39 14 | 8,2,Before and while coding,No,Nescafe,Yes,Male,Lebanon,30 to 39 15 | 9,3,While coding,Sometimes,Nescafe,Yes,Male,Lebanon,30 to 39 16 | 6,1,Before coding,Yes,Nescafe,Sometimes,Male,Lebanon,18 to 29 17 | 6,3,While coding,No,Americano,Yes,Female,Lebanon,30 to 39 18 | 10,3,While coding,Sometimes,American Coffee,Sometimes,Male,Lebanon,30 to 39 19 | 6,4,While coding,Yes,Turkish,Sometimes,Male,Lebanon,30 to 39 20 | 8,3,While coding,No,Nescafe,No,Male,Lebanon,18 to 29 21 | 3,1,In the morning,Yes,Nescafe,Sometimes,Female,Lebanon,30 to 39 22 | 3,3,While coding,Yes,Espresso (Short Black),Sometimes,Male,Lebanon,18 to 29 23 | 9,5,While coding,Sometimes,Nescafe,Yes,Male,Lebanon,18 to 29 24 | 4,2,While coding,Sometimes,American Coffee,Sometimes,Male,Lebanon,18 to 29 25 | 8,3,While coding,No,Turkish,No,Male,Lebanon,40 to 49 26 | 5,3,While coding,Sometimes,Cappuccino,Sometimes,Male,Lebanon,Under 18 27 | 6,3,While coding,Yes,Nescafe,No,Female,Lebanon,30 to 39 28 | 7,4,Before coding,Sometimes,Espresso (Short Black),No,Male,Lebanon,30 to 39 29 | 8,4,While coding,Sometimes,Turkish,Yes,Male,Lebanon,30 to 39 30 | 7,1,Before coding,Yes,Nescafe,Yes,Male,Lebanon,30 to 39 31 | 2,2,While coding,Sometimes,American Coffee,Sometimes,Male,Lebanon,40 to 49 32 | 10,6,All the time,No,American Coffee,Yes,Male,Lebanon,30 to 39 33 | 2,2,While coding,No,Nescafe,Sometimes,Female,Lebanon,18 to 29 34 | 9,2,Before coding,Sometimes,Nescafe,Sometimes,Female,Lebanon,18 to 29 35 | 4,1,Before coding,Yes,Nescafe,No,Male,Lebanon,30 to 39 36 | 6,2,While coding,Sometimes,Espresso (Short Black),Sometimes,Male,Lebanon,18 to 29 37 | 2,2,While coding,Yes,American Coffee,No,Male,Lebanon,30 to 39 38 | 2,3,All the time,Sometimes,Turkish,Sometimes,Male,Lebanon,30 to 39 39 | 7,7,After coding,Yes,Turkish,Yes,Female,Lebanon,40 to 49 40 | 1,1,While coding,Yes,Cappuccino,Yes,Male,Lebanon,40 to 49 41 | 4,2,While coding,Yes,Turkish,No,Male,Lebanon,50 to 59 42 | 8,2,While coding,Sometimes,Turkish,No,Male,Lebanon,30 to 39 43 | 8,4,While coding,Sometimes,Turkish,Yes,Male,Lebanon,30 to 39 44 | 3,2,While coding,Sometimes,American Coffee,Sometimes,Female,Lebanon,30 to 39 45 | 10,6,While coding,Sometimes,NA,No,Male,Lebanon,18 to 29 46 | 3,2,After coding,Yes,Espresso (Short Black),Sometimes,Male,Lebanon,40 to 49 47 | 7,2,While coding,Sometimes,American Coffee,No,Female,Lebanon,18 to 29 48 | 2,1,While coding,Sometimes,Caffè latte,No,Female,Lebanon,18 to 29 49 | 9,1,In the morning,Yes,Nescafe,No,Female,Lebanon,18 to 29 50 | 8,2,Before coding,No,Nescafe,No,Female,Lebanon,18 to 29 51 | 6,1,Before coding,Yes,Nescafe,No,Male,Lebanon,30 to 39 52 | 6,1,Before coding,Yes,Nescafe,No,Male,Lebanon,30 to 39 53 | 8,3,While coding,Sometimes,American Coffee,Yes,Male,Lebanon,18 to 29 54 | 8,2,Before coding,Sometimes,American Coffee,Yes,Male,Lebanon,18 to 29 55 | 3,3,While coding,No,Turkish,No,Male,Lebanon,18 to 29 56 | 7,4,No specific time,Yes,Cappuccino,Sometimes,Male,Lebanon,18 to 29 57 | 4,7,Before and while coding,No,Double Espresso (Doppio),Sometimes,Male,Lebanon,18 to 29 58 | 6,4,While coding,Sometimes,Espresso (Short Black),Sometimes,Male,Lebanon,18 to 29 59 | 10,8,While coding,No,Turkish,Sometimes,Male,Lebanon,18 to 29 60 | 8,2,While coding,Sometimes,Turkish,No,Male,Lebanon,18 to 29 61 | 2,4,While coding,Sometimes,Cappuccino,Sometimes,Male,Lebanon,40 to 49 62 | 6,2,Before coding,Yes,Caffè latte,No,Male,Lebanon,18 to 29 63 | 7,4,While coding,No,American Coffee,Sometimes,Male,Lebanon,18 to 29 64 | 8,2,While coding,Sometimes,Turkish,Sometimes,Male,Lebanon,18 to 29 65 | 7,4,Before coding,No,Turkish,Yes,Male,Lebanon,18 to 29 66 | 8,4,While coding,No,American Coffee,Sometimes,Male,Lebanon,18 to 29 67 | 8,2,Before coding,Sometimes,American Coffee,Sometimes,Female,Lebanon,18 to 29 68 | 6,1,In the morning,Yes,Nescafe,No,Female,Lebanon,30 to 39 69 | 10,2,While coding,Sometimes,American Coffee,Yes,Male,Lebanon,18 to 29 70 | 8,4,Before and while coding,No,Turkish,Yes,Male,Lebanon,18 to 29 71 | 10,6,While coding,Sometimes,Double Espresso (Doppio),No,Male,Lebanon,30 to 39 72 | 6,4,While coding,Sometimes,Espresso (Short Black),Yes,Male,Lebanon,18 to 29 73 | 10,8,All the time,No,American Coffee,Yes,Male,Lebanon,18 to 29 74 | 5,5,While coding,Sometimes,American Coffee,Yes,Male,Lebanon,18 to 29 75 | 8,4,Before and while coding,Sometimes,Turkish,Sometimes,Male,Lebanon,30 to 39 76 | 4,2,While coding,No,Nescafe,Yes,Female,Lebanon,18 to 29 77 | 6,6,While coding,Sometimes,Cappuccino,No,Male,Lebanon,18 to 29 78 | 8,2,While coding,Sometimes,Nescafe,Sometimes,Male,Lebanon,30 to 39 79 | 9,3,While coding,Sometimes,Nescafe,Sometimes,Female,Lebanon,18 to 29 80 | 7,2,While coding,Sometimes,Caffè latte,No,Male,Lebanon,18 to 29 81 | 4,5,While coding,Sometimes,American Coffee,Yes,Male,Lebanon,18 to 29 82 | 2,1,While coding,Yes,Nescafe,No,Male,Lebanon,18 to 29 83 | 7,3,While coding,Sometimes,American Coffee,Sometimes,Female,Lebanon,18 to 29 84 | 6,3,While coding,Yes,Nescafe,Yes,Female,Lebanon,18 to 29 85 | 3,3,While coding,Sometimes,Caffè latte,Sometimes,Male,Lebanon,18 to 29 86 | 10,2,While coding,Yes,American Coffee,No,Male,Lebanon,18 to 29 87 | 10,3,While coding,Sometimes,American Coffee,Sometimes,Male,Lebanon,18 to 29 88 | 3,1,While coding,Sometimes,Nescafe,Sometimes,Male,Lebanon,18 to 29 89 | 3,3,While coding,Sometimes,American Coffee,Sometimes,Female,Lebanon,18 to 29 90 | 9,7,All the time,No,Espresso (Short Black),Yes,Male,Lebanon,18 to 29 91 | 10,1,Before coding,Sometimes,American Coffee,Sometimes,Male,Lebanon,18 to 29 92 | 7,1,Before coding,Sometimes,Cappuccino,Sometimes,Male,Lebanon,18 to 29 93 | 5,3,While coding,Sometimes,Turkish,Sometimes,Female,Lebanon,18 to 29 94 | 5,2,Before coding,Sometimes,Nescafe,Yes,Female,Lebanon,18 to 29 95 | 3,1,Before coding,Yes,Nescafe,No,Female,Lebanon,18 to 29 96 | 4,2,While coding,Yes,Nescafe,Sometimes,Male,Lebanon,18 to 29 97 | 6,2,Before coding,Yes,Nescafe,Yes,Male,Lebanon,18 to 29 98 | 4,1,Before coding,Sometimes,Nescafe,Sometimes,Female,Lebanon,18 to 29 99 | 10,3,Before coding,Yes,Cappuccino,Yes,Male,Lebanon,Under 18 100 | 2,2,While coding,Sometimes,Espresso (Short Black),Sometimes,Female,Lebanon,18 to 29 101 | 10,4,Before coding,Sometimes,Double Espresso (Doppio),Sometimes,Male,Lebanon,18 to 29 102 | -------------------------------------------------------------------------------- /Datasets/car_crashes.csv: -------------------------------------------------------------------------------- 1 | total,speeding,alcohol,not_distracted,no_previous,ins_premium,ins_losses,abbrev 2 | 18.8,7.332000000000001,5.64,18.048000000000002,15.04,784.55,145.08,AL 3 | 18.1,7.421,4.525,16.290000000000003,17.014,1053.48,133.93,AK 4 | 18.6,6.51,5.208000000000001,15.624,17.856,899.47,110.35,AZ 5 | 22.4,4.032,5.824,21.055999999999997,21.28,827.34,142.39,AR 6 | 12.0,4.2,3.36,10.92,10.68,878.41,165.63,CA 7 | 13.6,5.032,3.8080000000000003,10.743999999999998,12.92,835.5,139.91,CO 8 | 10.8,4.968,3.888,9.396,8.856,1068.73,167.02,CT 9 | 16.2,6.156000000000001,4.86,14.094,16.038,1137.87,151.48,DE 10 | 5.9,2.0060000000000002,1.5930000000000002,5.9,5.9,1273.89,136.05,DC 11 | 17.9,3.759,5.190999999999999,16.468,16.826,1160.13,144.18,FL 12 | 15.6,2.964,3.9,14.82,14.508,913.15,142.8,GA 13 | 17.5,9.45,7.175,14.35,15.225,861.18,120.92,HI 14 | 15.3,5.508000000000001,4.437,13.005,14.994000000000002,641.96,82.75,ID 15 | 12.8,4.6080000000000005,4.352,12.032,12.288000000000002,803.11,139.15,IL 16 | 14.5,3.625,4.205,13.775,13.775,710.46,108.92,IN 17 | 15.7,2.6689999999999996,3.925,15.229,13.658999999999999,649.06,114.47,IA 18 | 17.8,4.806,4.272,13.706000000000001,15.13,780.45,133.8,KS 19 | 21.4,4.066,4.922,16.691999999999997,16.264,872.51,137.13,KY 20 | 20.5,7.175,6.765,14.965,20.09,1281.55,194.78,LA 21 | 15.1,5.7379999999999995,4.53,13.137,12.684,661.88,96.57,ME 22 | 12.5,4.25,4.0,8.875,12.375,1048.78,192.7,MD 23 | 8.2,1.886,2.87,7.1339999999999995,6.56,1011.14,135.63,MA 24 | 14.1,3.384,3.948,13.395,10.857000000000001,1110.61,152.26,MI 25 | 9.6,2.2079999999999997,2.784,8.448,8.448,777.18,133.35,MN 26 | 17.6,2.64,5.456,1.76,17.6,896.07,155.77,MS 27 | 16.1,6.923000000000001,5.474000000000001,14.812000000000001,13.524000000000001,790.32,144.45,MO 28 | 21.4,8.345999999999998,9.415999999999999,17.976,18.189999999999998,816.21,85.15,MT 29 | 14.9,1.9370000000000003,5.215,13.857000000000001,13.41,732.28,114.82,NE 30 | 14.7,5.439,4.704,13.965,14.552999999999999,1029.87,138.71,NV 31 | 11.6,4.06,3.48,10.091999999999999,9.628,746.54,120.21,NH 32 | 11.2,1.7919999999999998,3.1359999999999997,9.632,8.735999999999999,1301.52,159.85,NJ 33 | 18.4,3.4959999999999996,4.968,12.328,18.031999999999996,869.85,120.75,NM 34 | 12.3,3.9360000000000004,3.5670000000000006,10.824000000000002,9.84,1234.31,150.01,NY 35 | 16.8,6.5520000000000005,5.208000000000001,15.792,13.607999999999999,708.24,127.82,NC 36 | 23.9,5.496999999999999,10.038,23.660999999999998,20.554000000000002,688.75,109.72,ND 37 | 14.1,3.948,4.794,13.958999999999998,11.562000000000001,697.73,133.52,OH 38 | 19.9,6.367999999999999,5.770999999999999,18.308,18.706,881.51,178.86,OK 39 | 12.8,4.224,3.3280000000000003,8.576,11.52,804.71,104.61,OR 40 | 18.2,9.1,5.6419999999999995,17.471999999999998,16.016,905.99,153.86,PA 41 | 11.1,3.7739999999999996,4.218,10.212,8.769,1148.99,148.58,RI 42 | 23.9,9.081999999999999,9.799,22.943999999999996,19.358999999999998,858.97,116.29,SC 43 | 19.4,6.013999999999999,6.401999999999999,19.011999999999997,16.683999999999997,669.31,96.87,SD 44 | 19.5,4.095,5.655,15.99,15.795,767.91,155.57,TN 45 | 19.4,7.76,7.371999999999999,17.654,16.878,1004.75,156.83,TX 46 | 11.3,4.859,1.808,9.944,10.848000000000003,809.38,109.48,UT 47 | 13.6,4.08,4.08,13.056,12.92,716.2,109.61,VT 48 | 12.7,2.413,3.429,11.049,11.175999999999998,768.95,153.72,VA 49 | 10.6,4.452,3.498,8.692,9.116,890.03,111.62,WA 50 | 23.8,8.092,6.664,23.086,20.706,992.61,152.56,WV 51 | 13.8,4.968,4.554,5.382000000000001,11.592,670.31,106.62,WI 52 | 17.4,7.308,5.568,14.094,15.659999999999998,791.14,122.04,WY 53 | -------------------------------------------------------------------------------- /Datasets/data_2020-Sep-07 (1).csv: -------------------------------------------------------------------------------- 1 | areaType,areaName,areaCode,date,newCasesBySpecimenDate,cumCasesBySpecimenDate 2 | nation,England,E92000001,2020-09-07,0,302175 3 | nation,England,E92000001,2020-09-06,16,302175 4 | nation,England,E92000001,2020-09-05,1149,302159 5 | 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nation,Wales,W92000004,2020-05-18,1931,59908 565 | nation,Wales,W92000004,2020-05-17,1743,57977 566 | nation,Wales,W92000004,2020-05-16,1652,56234 567 | nation,Wales,W92000004,2020-05-15,1421,54584 568 | nation,Wales,W92000004,2020-05-14,1300,53163 569 | nation,Wales,W92000004,2020-05-13,1087,51863 570 | nation,Wales,W92000004,2020-05-12,1193,50776 571 | nation,Wales,W92000004,2020-05-11,1314,49583 572 | nation,Wales,W92000004,2020-05-10,2017,48269 573 | nation,Wales,W92000004,2020-05-09,1096,46252 574 | nation,Wales,W92000004,2020-05-08,1134,45156 575 | nation,Wales,W92000004,2020-05-07,933,44022 576 | nation,Wales,W92000004,2020-05-06,743,43089 577 | nation,Wales,W92000004,2020-05-05,892,42346 578 | nation,Wales,W92000004,2020-05-04,1504,41454 579 | nation,Wales,W92000004,2020-05-03,1198,39950 580 | nation,Wales,W92000004,2020-05-02,1438,38752 581 | nation,Wales,W92000004,2020-05-01,1090,37314 582 | nation,Wales,W92000004,2020-04-30,1042,36224 583 | nation,Wales,W92000004,2020-04-29,734,35182 584 | nation,Wales,W92000004,2020-04-28,1191,34448 585 | nation,Wales,W92000004,2020-04-27,1250,33257 586 | nation,Wales,W92000004,2020-04-26,775,32007 587 | nation,Wales,W92000004,2020-04-25,1301,31232 588 | nation,Wales,W92000004,2020-04-24,1027,29931 589 | nation,Wales,W92000004,2020-04-23,816,28904 590 | nation,Wales,W92000004,2020-04-22,964,28088 591 | nation,Wales,W92000004,2020-04-21,1033,27124 592 | nation,Wales,W92000004,2020-04-20,921,26091 593 | nation,Wales,W92000004,2020-04-19,1056,25170 594 | nation,Wales,W92000004,2020-04-18,783,24114 595 | nation,Wales,W92000004,2020-04-17,705,23332 596 | nation,Wales,W92000004,2020-04-16,755,22627 597 | nation,Wales,W92000004,2020-04-15,703,21872 598 | nation,Wales,W92000004,2020-04-14,678,21169 599 | nation,Wales,W92000004,2020-04-13,770,20491 600 | nation,Wales,W92000004,2020-04-12,1005,19721 601 | nation,Wales,W92000004,2020-04-11,912,18716 602 | nation,Wales,W92000004,2020-04-10,1254,17804 603 | nation,Wales,W92000004,2020-04-09,21,16550 604 | nation,Wales,W92000004,2020-04-08,794,16529 605 | nation,Wales,W92000004,2020-04-07,696,15735 606 | nation,Wales,W92000004,2020-04-06,910,15039 607 | nation,Wales,W92000004,2020-04-05,1000,14129 608 | nation,Wales,W92000004,2020-04-04,1110,13129 609 | nation,Wales,W92000004,2020-04-03,1090,12019 610 | nation,Wales,W92000004,2020-04-02,942,10929 611 | nation,Wales,W92000004,2020-04-01,855,9987 612 | -------------------------------------------------------------------------------- /Datasets/data_ign_scores.csv: -------------------------------------------------------------------------------- 1 | Platform,Action,"Action, Adventure",Adventure,Fighting,Platformer,Puzzle,RPG,Racing,Shooter,Simulation,Sports,Strategy 2 | DrCast,6.882857143,7.511111111,6.281818182,8.2,8.34,8.088888889,7.7,7.0425,7.616666667,7.628571429,7.272222222,6.433333333 3 | GBoyAd,6.373076923,7.507692308,6.057142857,6.226315789,6.970588235,6.532142857,7.542857143,6.657142857,6.444444444,6.928571429,6.694444444,7.175 4 | GBoyC,6.272727273,8.166666667,5.307692308,4.5,6.352941176,6.583333333,7.285714286,5.897435897,4.5,5.9,5.790697674,7.4 5 | Gcube,6.53258427,7.608333333,6.753846154,7.422222222,6.665714286,6.133333333,7.890909091,6.852631579,6.981818182,8.028571429,7.481318681,7.116666667 6 | Nin3DS,6.670833333,7.481818182,7.414285714,6.614285714,7.503448276,8,7.719230769,6.9,7.033333333,7.7,6.388888889,7.9 7 | N64,6.649056604,8.25,7,5.68125,6.889655172,7.461538462,6.05,6.939622642,8.042857143,5.675,6.967857143,6.9 8 | NDS,5.903608247,7.24,6.259803922,6.32,6.84,6.604615385,7.222619048,6.038636364,6.965217391,5.874358974,5.936666667,6.644736842 9 | NinDSi,6.827027027,8.5,6.090909091,7.5,7.25,6.810526316,7.166666667,6.563636364,6.5,5.195652174,5.644444444,6.566666667 10 | PC,6.805790646,7.334745763,7.136797753,7.166666667,7.4109375,6.924705882,7.759930314,7.032417582,7.084878049,7.104888889,6.902424242,7.310207337 11 | PS,6.01640625,7.933333333,6.31372549,6.553731343,6.579069767,6.757894737,7.91,6.773387097,6.424,6.918181818,6.751219512,6.496875 12 | PS2,6.467361111,7.25,6.315151515,7.306349206,7.068421053,6.354545455,7.473076923,6.585064935,6.641666667,7.152631579,7.197826087,7.238888889 13 | PS3,6.853819444,7.306153846,6.820987654,7.7109375,7.735714286,7.35,7.436111111,6.978571429,7.219553073,7.142857143,7.485815603,7.355172414 14 | PS4,7.55,7.835294118,7.388571429,7.28,8.390909091,7.4,7.944,7.59,7.804444444,9.25,7.43,6.566666667 15 | PSPort,6.46779661,7,6.938095238,6.822222222,7.194736842,6.726666667,6.817777778,6.401960784,7.071052632,6.761538462,6.956790123,6.55 16 | PSVi,7.173076923,6.133333333,8.057142857,7.527272727,8.56875,8.25,7.3375,6.3,7.66,5.725,7.13,8.9 17 | Wii,6.26271777,7.294642857,6.234042553,6.733333333,7.054255319,6.426984127,7.410344828,5.011666667,6.47979798,6.327027027,5.966901408,6.975 18 | WL,7.041698842,7.3125,6.972413793,6.74,7.509090909,7.360550459,8.26,6.898305085,6.906779661,7.802857143,7.417699115,7.542857143 19 | Xb,6.819512195,7.479032258,6.821428571,7.02962963,7.303448276,5.125,8.277777778,7.021590909,7.485416667,7.155555556,7.884397163,7.313333333 20 | Xb360,6.719047619,7.137837838,6.857352941,7.552238806,7.559574468,7.141025641,7.65,6.996153846,7.33815261,7.325,7.317857143,7.112244898 21 | XbOne,7.702857143,7.566666667,7.254545455,7.171428571,6.733333333,8.1,8.291666667,8.163636364,8.02,7.733333333,7.331818182,8.5 22 | iPhone,6.865445026,7.764285714,7.745833333,6.0875,7.471929825,7.810784314,7.185185185,7.315789474,6.995588235,7.328571429,7.152173913,7.534920635 -------------------------------------------------------------------------------- /Datasets/flights.csv: -------------------------------------------------------------------------------- 1 | year,month,passengers 2 | 1949,January,112 3 | 1949,February,118 4 | 1949,March,132 5 | 1949,April,129 6 | 1949,May,121 7 | 1949,June,135 8 | 1949,July,148 9 | 1949,August,148 10 | 1949,September,136 11 | 1949,October,119 12 | 1949,November,104 13 | 1949,December,118 14 | 1950,January,115 15 | 1950,February,126 16 | 1950,March,141 17 | 1950,April,135 18 | 1950,May,125 19 | 1950,June,149 20 | 1950,July,170 21 | 1950,August,170 22 | 1950,September,158 23 | 1950,October,133 24 | 1950,November,114 25 | 1950,December,140 26 | 1951,January,145 27 | 1951,February,150 28 | 1951,March,178 29 | 1951,April,163 30 | 1951,May,172 31 | 1951,June,178 32 | 1951,July,199 33 | 1951,August,199 34 | 1951,September,184 35 | 1951,October,162 36 | 1951,November,146 37 | 1951,December,166 38 | 1952,January,171 39 | 1952,February,180 40 | 1952,March,193 41 | 1952,April,181 42 | 1952,May,183 43 | 1952,June,218 44 | 1952,July,230 45 | 1952,August,242 46 | 1952,September,209 47 | 1952,October,191 48 | 1952,November,172 49 | 1952,December,194 50 | 1953,January,196 51 | 1953,February,196 52 | 1953,March,236 53 | 1953,April,235 54 | 1953,May,229 55 | 1953,June,243 56 | 1953,July,264 57 | 1953,August,272 58 | 1953,September,237 59 | 1953,October,211 60 | 1953,November,180 61 | 1953,December,201 62 | 1954,January,204 63 | 1954,February,188 64 | 1954,March,235 65 | 1954,April,227 66 | 1954,May,234 67 | 1954,June,264 68 | 1954,July,302 69 | 1954,August,293 70 | 1954,September,259 71 | 1954,October,229 72 | 1954,November,203 73 | 1954,December,229 74 | 1955,January,242 75 | 1955,February,233 76 | 1955,March,267 77 | 1955,April,269 78 | 1955,May,270 79 | 1955,June,315 80 | 1955,July,364 81 | 1955,August,347 82 | 1955,September,312 83 | 1955,October,274 84 | 1955,November,237 85 | 1955,December,278 86 | 1956,January,284 87 | 1956,February,277 88 | 1956,March,317 89 | 1956,April,313 90 | 1956,May,318 91 | 1956,June,374 92 | 1956,July,413 93 | 1956,August,405 94 | 1956,September,355 95 | 1956,October,306 96 | 1956,November,271 97 | 1956,December,306 98 | 1957,January,315 99 | 1957,February,301 100 | 1957,March,356 101 | 1957,April,348 102 | 1957,May,355 103 | 1957,June,422 104 | 1957,July,465 105 | 1957,August,467 106 | 1957,September,404 107 | 1957,October,347 108 | 1957,November,305 109 | 1957,December,336 110 | 1958,January,340 111 | 1958,February,318 112 | 1958,March,362 113 | 1958,April,348 114 | 1958,May,363 115 | 1958,June,435 116 | 1958,July,491 117 | 1958,August,505 118 | 1958,September,404 119 | 1958,October,359 120 | 1958,November,310 121 | 1958,December,337 122 | 1959,January,360 123 | 1959,February,342 124 | 1959,March,406 125 | 1959,April,396 126 | 1959,May,420 127 | 1959,June,472 128 | 1959,July,548 129 | 1959,August,559 130 | 1959,September,463 131 | 1959,October,407 132 | 1959,November,362 133 | 1959,December,405 134 | 1960,January,417 135 | 1960,February,391 136 | 1960,March,419 137 | 1960,April,461 138 | 1960,May,472 139 | 1960,June,535 140 | 1960,July,622 141 | 1960,August,606 142 | 1960,September,508 143 | 1960,October,461 144 | 1960,November,390 145 | 1960,December,432 146 | -------------------------------------------------------------------------------- /Datasets/insurance.csv: -------------------------------------------------------------------------------- 1 | age,sex,bmi,children,smoker,region,expenses 19,female,27.9,0,yes,southwest,16884.92 18,male,33.8,1,no,southeast,1725.55 28,male,33.0,3,no,southeast,4449.46 33,male,22.7,0,no,northwest,21984.47 32,male,28.9,0,no,northwest,3866.86 31,female,25.7,0,no,southeast,3756.62 46,female,33.4,1,no,southeast,8240.59 37,female,27.7,3,no,northwest,7281.51 37,male,29.8,2,no,northeast,6406.41 60,female,25.8,0,no,northwest,28923.14 25,male,26.2,0,no,northeast,2721.32 62,female,26.3,0,yes,southeast,27808.73 23,male,34.4,0,no,southwest,1826.84 56,female,39.8,0,no,southeast,11090.72 27,male,42.1,0,yes,southeast,39611.76 19,male,24.6,1,no,southwest,1837.24 52,female,30.8,1,no,northeast,10797.34 23,male,23.8,0,no,northeast,2395.17 56,male,40.3,0,no,southwest,10602.39 30,male,35.3,0,yes,southwest,36837.47 60,female,36.0,0,no,northeast,13228.85 30,female,32.4,1,no,southwest,4149.74 18,male,34.1,0,no,southeast,1137.01 34,female,31.9,1,yes,northeast,37701.88 37,male,28.0,2,no,northwest,6203.9 59,female,27.7,3,no,southeast,14001.13 63,female,23.1,0,no,northeast,14451.84 55,female,32.8,2,no,northwest,12268.63 23,male,17.4,1,no,northwest,2775.19 31,male,36.3,2,yes,southwest,38711 22,male,35.6,0,yes,southwest,35585.58 18,female,26.3,0,no,northeast,2198.19 19,female,28.6,5,no,southwest,4687.8 63,male,28.3,0,no,northwest,13770.1 28,male,36.4,1,yes,southwest,51194.56 19,male,20.4,0,no,northwest,1625.43 62,female,33.0,3,no,northwest,15612.19 26,male,20.8,0,no,southwest,2302.3 35,male,36.7,1,yes,northeast,39774.28 60,male,39.9,0,yes,southwest,48173.36 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64,male,23.8,0,yes,southeast,26926.51 55,female,30.5,0,no,southwest,10704.47 24,male,31.1,0,yes,northeast,34254.05 20,female,33.3,0,no,southwest,1880.49 45,male,27.5,3,no,southwest,8615.3 26,male,33.9,1,no,northwest,3292.53 25,female,34.5,0,no,northwest,3021.81 43,male,25.5,5,no,southeast,14478.33 35,male,27.6,1,no,southeast,4747.05 26,male,27.1,0,yes,southeast,17043.34 57,male,23.7,0,no,southwest,10959.33 22,female,30.4,0,no,northeast,2741.95 32,female,29.7,0,no,northwest,4357.04 39,male,29.9,1,yes,northeast,22462.04 25,female,26.8,2,no,northwest,4189.11 48,female,33.3,0,no,southeast,8283.68 47,female,27.6,2,yes,northwest,24535.7 18,female,21.7,0,yes,northeast,14283.46 18,male,30.0,1,no,southeast,1720.35 61,male,36.3,1,yes,southwest,47403.88 47,female,24.3,0,no,northeast,8534.67 28,female,17.3,0,no,northeast,3732.63 36,female,25.9,1,no,southwest,5472.45 20,male,39.4,2,yes,southwest,38344.57 44,male,34.3,1,no,southeast,7147.47 38,female,20.0,2,no,northeast,7133.9 19,male,34.9,0,yes,southwest,34828.65 21,male,23.2,0,no,southeast,1515.34 46,male,25.7,3,no,northwest,9301.89 58,male,25.2,0,no,northeast,11931.13 20,male,22.0,1,no,southwest,1964.78 18,male,26.1,0,no,northeast,1708.93 28,female,26.5,2,no,southeast,4340.44 33,male,27.5,2,no,northwest,5261.47 19,female,25.7,1,no,northwest,2710.83 45,male,30.4,0,yes,southeast,62592.87 62,male,30.9,3,yes,northwest,46718.16 25,female,20.8,1,no,southwest,3208.79 43,male,27.8,0,yes,southwest,37829.72 42,male,24.6,2,yes,northeast,21259.38 24,female,27.7,0,no,southeast,2464.62 29,female,21.9,0,yes,northeast,16115.3 32,male,28.1,4,yes,northwest,21472.48 25,female,30.2,0,yes,southwest,33900.65 41,male,32.2,2,no,southwest,6875.96 42,male,26.3,1,no,northwest,6940.91 33,female,26.7,0,no,northwest,4571.41 34,male,42.9,1,no,southwest,4536.26 19,female,34.7,2,yes,southwest,36397.58 30,female,23.7,3,yes,northwest,18765.88 18,male,28.3,1,no,northeast,11272.33 19,female,20.6,0,no,southwest,1731.68 18,male,53.1,0,no,southeast,1163.46 35,male,39.7,4,no,northeast,19496.72 39,female,26.3,2,no,northwest,7201.7 31,male,31.1,3,no,northwest,5425.02 62,male,26.7,0,yes,northeast,28101.33 62,male,38.8,0,no,southeast,12981.35 42,female,40.4,2,yes,southeast,43896.38 31,male,25.9,1,no,northwest,4239.89 61,male,33.5,0,no,northeast,13143.34 42,female,32.9,0,no,northeast,7050.02 51,male,30.0,1,no,southeast,9377.9 23,female,24.2,2,no,northeast,22395.74 52,male,38.6,2,no,southwest,10325.21 57,female,25.7,2,no,southeast,12629.17 23,female,33.4,0,no,southwest,10795.94 52,female,44.7,3,no,southwest,11411.69 50,male,31.0,3,no,northwest,10600.55 18,female,31.9,0,no,northeast,2205.98 18,female,36.9,0,no,southeast,1629.83 21,female,25.8,0,no,southwest,2007.95 61,female,29.1,0,yes,northwest,29141.36 -------------------------------------------------------------------------------- /Dataviz.pdf: -------------------------------------------------------------------------------- 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associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Notebook/Data_Viz.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "collapsed": true 7 | }, 8 | "source": [ 9 | "# Data Visualisation with Python " 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "\n", 17 | "
  • Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars).
    \n", 18 | "
  • The main goal of data visualization is to communicate information clearly and effectively through graphical means" 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": {}, 24 | "source": [ 25 | "**Check out this workshop for an introdcution to Pandas** \n", 26 | "\n", 27 | "* [Pandas Workshop](https://github.com/IBMDeveloperUK/python-pandas-workshop)\n", 28 | "* [Code Pattern](https://developer.ibm.com/technologies/data-science/tutorials/data-analysis-in-python-using-pandas)" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "## DataViz using Pandas, Matplotlib and Seaborn" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": null, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "import numpy as np\n", 45 | "import pandas as pd\n", 46 | "import matplotlib as mpl\n", 47 | "import matplotlib.pyplot as plt" 48 | ] 49 | }, 50 | { 51 | "cell_type": "code", 52 | "execution_count": null, 53 | "metadata": {}, 54 | "outputs": [], 55 | "source": [ 56 | "x = [1,4,7]\n", 57 | "y = [1,6,9]\n", 58 | "\n", 59 | "plt.plot(x,y)\n", 60 | "plt.show()\n", 61 | "\n", 62 | "#plt.title(\"Test Plot\")" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": null, 68 | "metadata": {}, 69 | "outputs": [], 70 | "source": [ 71 | "df= pd.DataFrame({'xval': range(1,11), 'yval': np.random.randn(10)})\n", 72 | "\n", 73 | "# plot\n", 74 | "plt.plot('xval','yval', data=df)\n", 75 | "plt.show()\n" 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": {}, 81 | "source": [ 82 | "### [Lineplot](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.plot.html)" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": null, 88 | "metadata": {}, 89 | "outputs": [], 90 | "source": [ 91 | "StudentID = [192,193,194,195,196,197,198,199,200,201]\n", 92 | "Scores = [9.2,8.6,8,7.2,6.9,7,6.5,8.2,7.5,6.3]\n", 93 | "\n", 94 | "\n", 95 | "Data = {'StudentID' : [192,193,194,195,196,197,198,199,200,201], 'Scores' : [9.2,8.6,8,7.2,6.9,7,6.5,8.2,7.5,6.3]}\n", 96 | "df = pd.DataFrame(Data,columns=['StudentID','Scores']) \n", 97 | "\n", 98 | "plt.plot(StudentID,Scores)\n", 99 | "\n", 100 | "\n", 101 | "plt.plot(StudentID,Scores, color='red', marker='o')\n", 102 | "plt.title('Exam Scores', fontsize=14)\n", 103 | "plt.xlabel('Student ID', fontsize=14)\n", 104 | "plt.ylabel('Scores', fontsize=14)\n", 105 | "plt.grid(True)\n", 106 | "plt.show()\n", 107 | "\n" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": null, 113 | "metadata": {}, 114 | "outputs": [], 115 | "source": [ 116 | "StudID = [192,193,194,195,196,197,198,199,200,201]\n", 117 | "Theory_Scores = [9.2,8.6,8,7.2,6.9,7,6.5,8.2,7.5,6.3]\n", 118 | "\n", 119 | "StudID = [192,193,194,195,196,197,198,199,200,201]\n", 120 | "Prac_Scores = [9,8.2,7.5,7,7.5,6.8,7,8.6,7.9,6.5]\n", 121 | "\n", 122 | "plt.plot(StudID, Theory_Scores, label =\"Theory Scores\", color='red', marker='o')\n", 123 | "plt.plot(StudID, Prac_Scores, label =\"Practical Scores\", color='Blue', marker='x')\n", 124 | "\n", 125 | "\n", 126 | "plt.title('Exam Scores', fontsize=14)\n", 127 | "plt.xlabel('Student ID', fontsize=14)\n", 128 | "plt.ylabel('Scores', fontsize=14)\n", 129 | "plt.legend()\n", 130 | "plt.grid(True)\n", 131 | "plt.show()" 132 | ] 133 | }, 134 | { 135 | "cell_type": "markdown", 136 | "metadata": {}, 137 | "source": [ 138 | "## Linspace \n", 139 | "\n", 140 | "**Tool in Python for creating numeric sequences. Linspace creates Sequences of Evenly spaced values within an Interval.**\n", 141 | "\n", 142 | "**Read More here : [Linspace](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html)**" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": null, 148 | "metadata": {}, 149 | "outputs": [], 150 | "source": [ 151 | "plt.figure(figsize=(10,5))\n", 152 | "\n", 153 | "x = np.linspace(0, 10, 100) \n", 154 | "y = np.sin(x) # Sine Graph\n", 155 | "plt.grid(True)\n", 156 | "\n", 157 | "plt.plot(x,y,'-r')\n", 158 | "plt.xlabel(\"X - Axis\")\n", 159 | "plt.ylabel(\"Y - Axis\")\n", 160 | "\n", 161 | "\n", 162 | "plt.show()" 163 | ] 164 | }, 165 | { 166 | "cell_type": "markdown", 167 | "metadata": {}, 168 | "source": [ 169 | "
    You can use the following commands to change your plots \n", 170 | " \n", 171 | "
    \n", 172 | " \n", 173 | "
  • 'b-' - Solid Blue Line\n", 174 | "
  • 'r^' - Red Traingles\n", 175 | "
  • 'go' - Green Dots \n", 176 | "
  • 'ro' - Red Dots\n", 177 | "
  • 'rv' - Reverse Traingles\n", 178 | " \n", 179 | "
    \n", 180 | " \n", 181 | " Try your own plots using these different combinations \n", 182 | "\n", 183 | "" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": null, 189 | "metadata": {}, 190 | "outputs": [], 191 | "source": [ 192 | "plt.figure(figsize=(14,6))\n", 193 | "plt.grid(True)\n", 194 | "x = np.linspace(0, 10, 100) \n", 195 | "y1 = np.sin(x) \n", 196 | "y2 = np.cos(x)\n", 197 | "\n", 198 | "plt.plot(x,y1, linewidth =4, label='SinX')\n", 199 | "plt.plot(x,y2, linewidth =4, label='CosX')\n", 200 | "plt.legend(loc = 'upper right')\n", 201 | "plt.xlabel(\"X - Axis\")\n", 202 | "plt.ylabel(\"Y - Axis\") \n", 203 | "plt.show()\n", 204 | "\n", 205 | "\n", 206 | "plt.figure(figsize = (12,6))\n", 207 | "plt.subplot(1,2,1)\n", 208 | "plt.plot(x,y1,'-r')\n", 209 | "\n", 210 | "plt.subplot(1,2,2)\n", 211 | "plt.plot(x,y2,'-b')\n", 212 | "\n", 213 | "plt.show()" 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "metadata": {}, 219 | "source": [ 220 | "### [SubPlots](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.subplot.html?highlight=subplot#matplotlib.pyplot.subplot)" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": null, 226 | "metadata": {}, 227 | "outputs": [], 228 | "source": [ 229 | "plt.style.use(['dark_background'])\n", 230 | "\n", 231 | "plt.figure(figsize = (12,6))\n", 232 | "plt.subplot(1,2,1)\n", 233 | "plt.plot(x,y1,'-r')\n", 234 | "plt.grid(True)\n", 235 | "plt.subplot(1,2,2)\n", 236 | "plt.plot(x,y2,'-b')\n", 237 | "plt.grid(True)" 238 | ] 239 | }, 240 | { 241 | "cell_type": "markdown", 242 | "metadata": {}, 243 | "source": [ 244 | "## Customise your charts further using [Style Sheets](https://matplotlib.org/tutorials/introductory/customizing.html)" 245 | ] 246 | }, 247 | { 248 | "cell_type": "code", 249 | "execution_count": null, 250 | "metadata": {}, 251 | "outputs": [], 252 | "source": [ 253 | "plt.style.use('seaborn-darkgrid')\n", 254 | "\n", 255 | "\n", 256 | "x1= ['Australia','Hungary','Japan'] \n", 257 | "y1= np.array([3,3,2])\n", 258 | "y2 =np.array([4,2,2])\n", 259 | "y3 =np.array([3,2,3])\n", 260 | "\n", 261 | "plt.figure(figsize=(6,8))\n", 262 | "plt.bar(x1,y1,label = \"Gold Medals\",width = 0.5,color = '#0040ff')\n", 263 | "plt.bar(x1,y2,label = \"Silver Medals\",width = 0.5 ,bottom = y1 , color = '#0080ff')\n", 264 | "plt.bar(x1,y3,label = \"Bronze Medals\",width = 0.5 ,bottom = y1+y2 , color = '#00bfff')\n", 265 | "plt.xlabel('$ Countries $')\n", 266 | "plt.ylabel('$ Medals $')\n", 267 | "plt.title(\"Medal Summary\")\n", 268 | "plt.legend()\n", 269 | "\n", 270 | "plt.grid(linestyle='-', linewidth=0.5)\n", 271 | "\n", 272 | "plt.show()" 273 | ] 274 | }, 275 | { 276 | "cell_type": "code", 277 | "execution_count": null, 278 | "metadata": {}, 279 | "outputs": [], 280 | "source": [ 281 | "Age = [28,33,43,45,55]\n", 282 | "Name = [\"Joe\", \"Priya\", 'Donna', \"Shankar\", \"Mo\"]\n", 283 | "plt.barh(Name,Age, color =\"#FF6F00\")\n", 284 | "plt.show()\n" 285 | ] 286 | }, 287 | { 288 | "cell_type": "code", 289 | "execution_count": null, 290 | "metadata": {}, 291 | "outputs": [], 292 | "source": [ 293 | "x1 = [\"Joe\", \"Priya\", 'Donna', \"Shankar\", \"Mo\"]\n", 294 | "\n", 295 | "y1 = [9,7,8,5,6]\n", 296 | "y2 = [4,3,1,2,9]\n", 297 | "\n", 298 | "plt.figure (figsize = (8,4))\n", 299 | "\n", 300 | "\n", 301 | "plt.barh(x1,y1,label = \"Fiction\",color = '#ff00bf')\n", 302 | "plt.barh(x1,y2,label = \"Non Fiction\", left = y1 , color = '#bf00ff')\n", 303 | "\n", 304 | "plt.title('Books Read')\n", 305 | "plt.legend()\n", 306 | "\n", 307 | "plt.show()" 308 | ] 309 | }, 310 | { 311 | "cell_type": "markdown", 312 | "metadata": {}, 313 | "source": [ 314 | "### Let us now upload a Dataset to continue creating charts using Matplotlib and Pandas Visualisation" 315 | ] 316 | }, 317 | { 318 | "cell_type": "code", 319 | "execution_count": null, 320 | "metadata": {}, 321 | "outputs": [], 322 | "source": [ 323 | "ign = pd.read_csv('https://raw.githubusercontent.com/YaminiRao/Data-Visualisation-with-Python/master/Datasets/data_ign_scores.csv',encoding = 'unicode_escape')\n", 324 | "ign.head()" 325 | ] 326 | }, 327 | { 328 | "cell_type": "code", 329 | "execution_count": null, 330 | "metadata": {}, 331 | "outputs": [], 332 | "source": [] 333 | }, 334 | { 335 | "cell_type": "code", 336 | "execution_count": null, 337 | "metadata": {}, 338 | "outputs": [], 339 | "source": [ 340 | "plt.figure (figsize = (20,4))\n", 341 | "\n", 342 | "plt.scatter(ign ['Platform'], ign['Action'],color =\"#ff4d4d\")\n", 343 | "\n", 344 | "plt.title('Ign Scroes')\n", 345 | "plt.xlabel('$ Platform $ ')\n", 346 | "plt.ylabel('$ Action Ratings $')" 347 | ] 348 | }, 349 | { 350 | "cell_type": "code", 351 | "execution_count": null, 352 | "metadata": {}, 353 | "outputs": [], 354 | "source": [ 355 | "plt.figure(figsize=(20,6))\n", 356 | "\n", 357 | "# \"alpha\" is used for softnening colors\n", 358 | "\n", 359 | "plt.rcParams['text.color'] = 'red' # Label Color\n", 360 | "plt.scatter(ign['Platform'], ign['Action'],c='r', s=50 , alpha=0.8 , label = 'Action' )\n", 361 | "plt.scatter(ign['Platform'], ign['Fighting'],c='b', s=100 , alpha=0.8 , label = 'Fighting')\n", 362 | "plt.scatter(ign['Platform'], ign['Racing'],c='g', s=150 , alpha=0.8 , label = 'Racing')\n", 363 | "plt.scatter(ign['Platform'], ign['Puzzle'],c='y', s=200 , alpha=0.8 , label = 'Puzzle')\n", 364 | "plt.legend(bbox_to_anchor=(1.0, 1.0) , shadow=True, fontsize='x-large')\n", 365 | "\n", 366 | "plt.show()" 367 | ] 368 | }, 369 | { 370 | "cell_type": "markdown", 371 | "metadata": {}, 372 | "source": [ 373 | "## Refer this link for more information on customising your legends [Legend Guide](https://matplotlib.org/3.1.1/tutorials/intermediate/legend_guide.html)" 374 | ] 375 | }, 376 | { 377 | "cell_type": "markdown", 378 | "metadata": {}, 379 | "source": [ 380 | "#### Creating Histograms can be done by using the *Hist* command, for Categorical data" 381 | ] 382 | }, 383 | { 384 | "cell_type": "code", 385 | "execution_count": null, 386 | "metadata": {}, 387 | "outputs": [], 388 | "source": [ 389 | "fig, ax = plt.subplots()\n", 390 | "# plot histogram\n", 391 | "ax.hist(ign['Fighting'])\n", 392 | "\n", 393 | "\n", 394 | "ax.set_xlabel('Points')\n", 395 | "ax.set_ylabel('Frequency')" 396 | ] 397 | }, 398 | { 399 | "cell_type": "markdown", 400 | "metadata": {}, 401 | "source": [ 402 | "## Pandas Visualization\n", 403 | "\n", 404 | "### Pandas Visualization makes it really easy to create plots out of a pandas dataframe and series. It also has a higher level API than Matplotlib and therefore we need less code" 405 | ] 406 | }, 407 | { 408 | "cell_type": "code", 409 | "execution_count": null, 410 | "metadata": {}, 411 | "outputs": [], 412 | "source": [ 413 | "ign.plot.hist(subplots=True, layout=(4,4), figsize=(20, 20), bins=20)\n", 414 | "\n", 415 | "plt.show()" 416 | ] 417 | }, 418 | { 419 | "cell_type": "code", 420 | "execution_count": null, 421 | "metadata": {}, 422 | "outputs": [], 423 | "source": [ 424 | "ign.groupby(\"Platform\").Racing.mean().sort_values(ascending=False)[:15].plot.bar(color ='#4D1A3B')" 425 | ] 426 | }, 427 | { 428 | "cell_type": "code", 429 | "execution_count": null, 430 | "metadata": {}, 431 | "outputs": [], 432 | "source": [ 433 | "ign['Strategy'].plot.hist()" 434 | ] 435 | }, 436 | { 437 | "cell_type": "code", 438 | "execution_count": null, 439 | "metadata": {}, 440 | "outputs": [], 441 | "source": [ 442 | "df = pd.DataFrame({'count': {0: 500, 1: 600, 2: 726, 3: 326, 4: 410}})\n", 443 | "\n", 444 | "ax = df.T.plot(kind='bar', color=['C0', 'C1', 'C2', 'C3', 'C4'])\n", 445 | "\n", 446 | "plt.show()" 447 | ] 448 | }, 449 | { 450 | "cell_type": "markdown", 451 | "metadata": {}, 452 | "source": [ 453 | "## Area Plots\n", 454 | "\n", 455 | "Area charts are commonly used to showcase data that depicts a time-series relationship\n", 456 | "\n", 457 | "\n", 458 | "### You can create area plots with Series or Data Frames. Area plots are stacked by default.
    \n", 459 | "\n", 460 | "
  • For Area Plots, column must either have all positive or all negative values.\n", 461 | "\n", 462 | "
  • When input data contains NaN, it will be automatically filled by 0. If you want to drop or fill by different values, use dataframe.dropna() or dataframe.fillna() before calling plot." 463 | ] 464 | }, 465 | { 466 | "cell_type": "code", 467 | "execution_count": null, 468 | "metadata": {}, 469 | "outputs": [], 470 | "source": [ 471 | "df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])\n", 472 | "\n", 473 | "df.plot.area();" 474 | ] 475 | }, 476 | { 477 | "cell_type": "code", 478 | "execution_count": null, 479 | "metadata": {}, 480 | "outputs": [], 481 | "source": [ 482 | "df.plot.area(stacked =False)" 483 | ] 484 | }, 485 | { 486 | "cell_type": "markdown", 487 | "metadata": {}, 488 | "source": [ 489 | "## Hexagonal Bin Plots
    \n", 490 | "\n", 491 | "###
  • These plots can be a useful alternative to scatter plots if your data are too dense to plot each point individually." 492 | ] 493 | }, 494 | { 495 | "cell_type": "code", 496 | "execution_count": null, 497 | "metadata": {}, 498 | "outputs": [], 499 | "source": [ 500 | "n = 10000\n", 501 | "df = pd.DataFrame({'x': np.random.randn(n),\n", 502 | " 'y': np.random.randn(n)})\n", 503 | "ax = df.plot.hexbin(x='x', y='y', gridsize=25)" 504 | ] 505 | }, 506 | { 507 | "cell_type": "markdown", 508 | "metadata": {}, 509 | "source": [ 510 | "
  • By default, a histogram of the counts around each (x, y) point is computed.
    \n", 511 | "\n", 512 | "
  • You can specify alternative aggregations by passing values to the C and reduce_C_function arguments. C specifies the value at each (x, y) point and reduce_C_function is a function of one argument that reduces all the values in a bin to a single number (e.g. mean, max, sum, std). In this example the positions are given by columns a and b, while the value is given by column z. The bins are aggregated with NumPy’s max function.\n", 513 | "\n", 514 | "\n", 515 | "numpy.random.uniform(low=0.0, high=1.0, size=None)\n", 516 | "Draw samples from a uniform distribution." 517 | ] 518 | }, 519 | { 520 | "cell_type": "code", 521 | "execution_count": null, 522 | "metadata": {}, 523 | "outputs": [], 524 | "source": [ 525 | "df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])\n", 526 | "\n", 527 | "df['b'] = df['b'] + np.arange(1000)\n", 528 | "df['z'] = np.random.uniform(0, 3, 1000)\n", 529 | "\n", 530 | "df.plot(kind='hexbin', x='a', y='b', C='z', reduce_C_function=np.max,\n", 531 | " gridsize=20, color = 'red')\n" 532 | ] 533 | }, 534 | { 535 | "cell_type": "markdown", 536 | "metadata": {}, 537 | "source": [ 538 | "## [Useful Guide for Specifying Colors](https://matplotlib.org/3.1.1/tutorials/colors/colors.html)" 539 | ] 540 | }, 541 | { 542 | "cell_type": "markdown", 543 | "metadata": {}, 544 | "source": [ 545 | "# Seaborn\n", 546 | "\n", 547 | "\n", 548 | "###
  • Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.\n", 549 | "\n" 550 | ] 551 | }, 552 | { 553 | "cell_type": "code", 554 | "execution_count": null, 555 | "metadata": {}, 556 | "outputs": [], 557 | "source": [ 558 | "import seaborn as sns\n", 559 | "\n", 560 | "mpl.rcParams.update(mpl.rcParamsDefault)\n", 561 | "%matplotlib inline" 562 | ] 563 | }, 564 | { 565 | "cell_type": "code", 566 | "execution_count": null, 567 | "metadata": {}, 568 | "outputs": [], 569 | "source": [ 570 | "cars = pd.read_csv('https://raw.githubusercontent.com/YaminiRao/Data-Visualisation-with-Python/master/Datasets/car_crashes.csv',encoding = 'unicode_escape')" 571 | ] 572 | }, 573 | { 574 | "cell_type": "code", 575 | "execution_count": null, 576 | "metadata": {}, 577 | "outputs": [], 578 | "source": [ 579 | "cases = pd.read_csv('https://raw.githubusercontent.com/YaminiRao/Data-Visualisation-with-Python/master/Datasets/data_2020-Sep-07%20(1).csv',encoding = 'unicode_escape')" 580 | ] 581 | }, 582 | { 583 | "cell_type": "code", 584 | "execution_count": null, 585 | "metadata": {}, 586 | "outputs": [], 587 | "source": [ 588 | "insurance = pd.read_csv('https://raw.githubusercontent.com/YaminiRao/Data-Visualisation-with-Python/master/Datasets/insurance.csv',encoding = 'unicode_escape')" 589 | ] 590 | }, 591 | { 592 | "cell_type": "code", 593 | "execution_count": null, 594 | "metadata": {}, 595 | "outputs": [], 596 | "source": [ 597 | "cars.describe()" 598 | ] 599 | }, 600 | { 601 | "cell_type": "code", 602 | "execution_count": null, 603 | "metadata": {}, 604 | "outputs": [], 605 | "source": [ 606 | "plt.figure(figsize=(20,6))\n", 607 | "sns.lineplot(data=cars['speeding'],linewidth = 1.5 , label = 'Speeding')\n", 608 | "sns.lineplot(data=cars['alcohol'],linewidth = 1.5 , label = 'Alcohol')\n", 609 | "sns.lineplot(data=cars['not_distracted'],linewidth = 1.5 , label = 'Not Distracted') \n", 610 | "plt.show()" 611 | ] 612 | }, 613 | { 614 | "cell_type": "code", 615 | "execution_count": null, 616 | "metadata": {}, 617 | "outputs": [], 618 | "source": [ 619 | "plt.figure(figsize=(10,10))\n", 620 | "sns.scatterplot(x=\"speeding\", y=\"alcohol\", data=cars, color=\"Blue\")\n", 621 | "plt.title(\"collisions due to speeding across states\\n\", fontsize=17)\n", 622 | "plt.show()" 623 | ] 624 | }, 625 | { 626 | "cell_type": "code", 627 | "execution_count": null, 628 | "metadata": {}, 629 | "outputs": [], 630 | "source": [ 631 | "coffee = pd.read_csv('https://raw.githubusercontent.com/YaminiRao/Data-Visualisation-with-Python/master/Datasets/Coffee_and_code.csv',encoding = 'unicode_escape')\n", 632 | "coffee.head()" 633 | ] 634 | }, 635 | { 636 | "cell_type": "code", 637 | "execution_count": null, 638 | "metadata": {}, 639 | "outputs": [], 640 | "source": [ 641 | "coffee.columns" 642 | ] 643 | }, 644 | { 645 | "cell_type": "markdown", 646 | "metadata": {}, 647 | "source": [ 648 | "## Pairplot
    \n", 649 | "\n", 650 | "
  • Plot pairwise relationships in a dataset.\n", 651 | "\n", 652 | "
  • By default, this function will create a grid of Axes such that each numeric variable in data will by shared in the y-axis across a single row and in the x-axis across a single column. The diagonal Axes are treated differently, drawing a plot to show the univariate distribution of the data for the variable in that column.
    \n", 653 | " \n", 654 | "\n", 655 | " [Read More here](https://seaborn.pydata.org/generated/seaborn.pairplot.html)" 656 | ] 657 | }, 658 | { 659 | "cell_type": "code", 660 | "execution_count": null, 661 | "metadata": {}, 662 | "outputs": [], 663 | "source": [ 664 | "sns.set_style(\"darkgrid\")\n", 665 | "sns.pairplot(coffee,height=4)\n" 666 | ] 667 | }, 668 | { 669 | "cell_type": "markdown", 670 | "metadata": {}, 671 | "source": [ 672 | "## Catplots
    \n", 673 | "\n", 674 | "
  • This function provides access to several axes-level functions that show the relationship between a numerical and one or more categorical variables using one of several visual representations.
    \n", 675 | " \n", 676 | " Refer this link for [More Info](https://seaborn.pydata.org/generated/seaborn.catplot.html) \n", 677 | "\n", 678 | "The kind parameter selects the underlying axes-level function to use:\n", 679 | "\n", 680 | " Categorical scatterplots: \n", 681 | "\n", 682 | "
  • stripplot() (with kind=\"strip\"; the default)\n", 683 | "
  • swarmplot() \n", 684 | "\n", 685 | " Categorical distribution plots: \n", 686 | "\n", 687 | "
  • boxplot() \n", 688 | "
  • violinplot() \n", 689 | "
  • boxenplot()\n", 690 | "\n", 691 | " Categorical estimate plots: \n", 692 | "\n", 693 | "
  • pointplot()\n", 694 | "
  • barplot() \n", 695 | "
  • countplot()" 696 | ] 697 | }, 698 | { 699 | "cell_type": "code", 700 | "execution_count": null, 701 | "metadata": {}, 702 | "outputs": [], 703 | "source": [ 704 | "sns.catplot(x=\"AgeRange\",y=\"CoffeeCupsPerDay\",data=coffee,hue=\"Gender\",aspect=2,kind=\"point\")" 705 | ] 706 | }, 707 | { 708 | "cell_type": "code", 709 | "execution_count": null, 710 | "metadata": {}, 711 | "outputs": [], 712 | "source": [ 713 | "plt.figure(figsize=(15,5))\n", 714 | "\n", 715 | "sns.countplot(coffee[\"CoffeeTime\"], palette=sns.color_palette(\"rainbow\",7))" 716 | ] 717 | }, 718 | { 719 | "cell_type": "code", 720 | "execution_count": null, 721 | "metadata": {}, 722 | "outputs": [], 723 | "source": [ 724 | "plt.figure(figsize=(15,5))\n", 725 | "\n", 726 | "sns.boxplot(x=\"CoffeeTime\",y=\"CoffeeCupsPerDay\",data=coffee)\n", 727 | "plt.show()" 728 | ] 729 | }, 730 | { 731 | "cell_type": "markdown", 732 | "metadata": {}, 733 | "source": [ 734 | "### [Understanding Box Plots](https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51)" 735 | ] 736 | }, 737 | { 738 | "cell_type": "code", 739 | "execution_count": null, 740 | "metadata": {}, 741 | "outputs": [], 742 | "source": [ 743 | "sns.set(rc={\"figure.figsize\":(10,5)},style=\"darkgrid\")\n", 744 | "\n", 745 | "sns.stripplot(x=\"AgeRange\", y=\"CoffeeCupsPerDay\", data=coffee)" 746 | ] 747 | }, 748 | { 749 | "cell_type": "code", 750 | "execution_count": null, 751 | "metadata": {}, 752 | "outputs": [], 753 | "source": [ 754 | "plt.figure(figsize=(16,7))\n", 755 | "sns.violinplot(x= insurance.region , y = insurance.expenses , hue= insurance.smoker , palette=\"Set2\")\n", 756 | "plt.show()" 757 | ] 758 | }, 759 | { 760 | "cell_type": "code", 761 | "execution_count": null, 762 | "metadata": {}, 763 | "outputs": [], 764 | "source": [ 765 | "plt.figure(figsize=(20,10))\n", 766 | "sns.violinplot(x= coffee.CoffeeType, y = coffee.CoffeeCupsPerDay , hue= coffee.Gender , palette=\"bright\")\n", 767 | "plt.show()" 768 | ] 769 | }, 770 | { 771 | "cell_type": "code", 772 | "execution_count": null, 773 | "metadata": { 774 | "scrolled": true 775 | }, 776 | "outputs": [], 777 | "source": [ 778 | "sns.boxenplot(x=insurance.smoker , y = insurance.expenses ,palette=\"Set1\")\n", 779 | "plt.show()" 780 | ] 781 | }, 782 | { 783 | "cell_type": "markdown", 784 | "metadata": {}, 785 | "source": [ 786 | " ## [Choosing Color Palettes in Seaborn](https://seaborn.pydata.org/tutorial/color_palettes.html)" 787 | ] 788 | }, 789 | { 790 | "cell_type": "markdown", 791 | "metadata": {}, 792 | "source": [ 793 | "## FacetGrid\n", 794 | "\n", 795 | "[More Info:](https://seaborn.pydata.org/generated/seaborn.FacetGrid.html?highlight=facetgrid#seaborn.FacetGrid)" 796 | ] 797 | }, 798 | { 799 | "cell_type": "code", 800 | "execution_count": null, 801 | "metadata": { 802 | "scrolled": true 803 | }, 804 | "outputs": [], 805 | "source": [ 806 | "i = sns.FacetGrid(insurance, col=\"region\",height=4)\n", 807 | "i = i.map(plt.hist,\"age\", linewidth=2)" 808 | ] 809 | }, 810 | { 811 | "cell_type": "code", 812 | "execution_count": null, 813 | "metadata": {}, 814 | "outputs": [], 815 | "source": [ 816 | "i = sns.FacetGrid(insurance, col=\"region\" , row = \"sex\" , hue=\"smoker\" ,height=4, aspect=1)\n", 817 | "i = i.map(plt.scatter, \"bmi\" , \"expenses\" , edgecolor=\"w\",s=80)\n", 818 | "i.add_legend()" 819 | ] 820 | }, 821 | { 822 | "cell_type": "markdown", 823 | "metadata": {}, 824 | "source": [ 825 | "### PairGrid " 826 | ] 827 | }, 828 | { 829 | "cell_type": "code", 830 | "execution_count": null, 831 | "metadata": {}, 832 | "outputs": [], 833 | "source": [ 834 | "ca = sns.PairGrid(cases , hue='areaName' ,vars=[\"newCasesBySpecimenDate\" , \"cumCasesBySpecimenDate\"],height=6, aspect=2)\n", 835 | "ca = ca.map_offdiag(plt.scatter , edgecolor=\"w\", s=130)\n", 836 | "ca = ca.map_diag(plt.hist , edgecolor ='w', linewidth=2)\n", 837 | "ca = ca.add_legend()\n" 838 | ] 839 | }, 840 | { 841 | "cell_type": "markdown", 842 | "metadata": {}, 843 | "source": [ 844 | "## KDE Plot\n", 845 | "\n", 846 | "KDE Plot described as Kernel Density Estimate is used for visualizing the Probability Density of a continuous variable. It depicts the probability density at different values in a continuous variable" 847 | ] 848 | }, 849 | { 850 | "cell_type": "code", 851 | "execution_count": null, 852 | "metadata": {}, 853 | "outputs": [], 854 | "source": [ 855 | "\n", 856 | "sns.kdeplot(insurance.bmi,insurance.expenses,shade=True,cmap=\"Blues\", shade_lowest=False)\n", 857 | "plt.show()" 858 | ] 859 | }, 860 | { 861 | "cell_type": "markdown", 862 | "metadata": {}, 863 | "source": [ 864 | "### [Cmap Reference](https://matplotlib.org/examples/color/colormaps_reference.html)" 865 | ] 866 | }, 867 | { 868 | "cell_type": "markdown", 869 | "metadata": {}, 870 | "source": [ 871 | "## HeatMap" 872 | ] 873 | }, 874 | { 875 | "cell_type": "code", 876 | "execution_count": null, 877 | "metadata": {}, 878 | "outputs": [], 879 | "source": [ 880 | "data = np.random.rand(4, 6)\n", 881 | "\n", 882 | "heat_map = sns.heatmap(data)" 883 | ] 884 | }, 885 | { 886 | "cell_type": "code", 887 | "execution_count": null, 888 | "metadata": { 889 | "scrolled": true 890 | }, 891 | "outputs": [], 892 | "source": [ 893 | "flights= pd.read_csv('https://raw.githubusercontent.com/YaminiRao/Data-Visualisation-with-Python/master/Datasets/flights.csv',encoding = 'unicode_escape')\n" 894 | ] 895 | }, 896 | { 897 | "cell_type": "code", 898 | "execution_count": null, 899 | "metadata": {}, 900 | "outputs": [], 901 | "source": [ 902 | "flights = flights.pivot(\"month\", \"year\", \"passengers\")\n", 903 | "ax = sns.heatmap(flights) " 904 | ] 905 | }, 906 | { 907 | "cell_type": "code", 908 | "execution_count": null, 909 | "metadata": {}, 910 | "outputs": [], 911 | "source": [ 912 | "ax = sns.heatmap(flights, linewidths=.5,annot=True, fmt='d') " 913 | ] 914 | }, 915 | { 916 | "cell_type": "code", 917 | "execution_count": null, 918 | "metadata": {}, 919 | "outputs": [], 920 | "source": [ 921 | "ax = sns.heatmap(flights, linewidths=0.5, cmap = 'cubehelix')" 922 | ] 923 | }, 924 | { 925 | "cell_type": "code", 926 | "execution_count": null, 927 | "metadata": {}, 928 | "outputs": [], 929 | "source": [ 930 | "data = np.random.randn(50, 20)\n", 931 | "ax = sns.heatmap(data, xticklabels=2, yticklabels=False)" 932 | ] 933 | }, 934 | { 935 | "cell_type": "markdown", 936 | "metadata": {}, 937 | "source": [ 938 | "
    \n", 939 | " OPTIONAL EXERCISE
    \n", 940 | " \n", 941 | " Use the below dataset to create your own plots using the examples in the notebook as a reference
    \n", 942 | "
      \n", 943 | " \n", 944 | " \n", 945 | "Useful Links : \n", 946 | " \n", 947 | "[Plotting with Matplotlib](https://matplotlib.org/)
      \n", 948 | "[Pandas Visualisation](https://pandas.pydata.org/docs/user_guide/visualization.html)
      \n", 949 | "[Seaborn](https://seaborn.pydata.org/examples/index.html)\n", 950 | " " 951 | ] 952 | }, 953 | { 954 | "cell_type": "code", 955 | "execution_count": null, 956 | "metadata": {}, 957 | "outputs": [], 958 | "source": [ 959 | "music = pd.read_csv('https://raw.githubusercontent.com/YaminiRao/Data-Visualisation-with-Python/master/Datasets/data_by_genres.csv')" 960 | ] 961 | }, 962 | { 963 | "cell_type": "markdown", 964 | "metadata": {}, 965 | "source": [ 966 | "\n", 967 | "#### There is a lot to explore with Data Visualisation. Let us know if you find anything interesting! You can find us on twitter @yaminigrao or @CodeMeetup \n", 968 | "
      \n", 969 | "
      \n", 970 | "
      \n", 971 | " \n", 972 | "\n", 973 | "\n", 974 | "\n", 975 | "\n" 976 | ] 977 | }, 978 | { 979 | "cell_type": "markdown", 980 | "metadata": {}, 981 | "source": [ 982 | " Author \n", 983 | "\n", 984 | "Yamini Rao is a Developer Advocate for IBM, focusing on Data Science and AI. She compiles developer scenarios, workshops and training material based on IBM Cloud technologies. She works with local developer communities, presenting workshops and organising meetups and conferences.\n", 985 | "She has a background in computer science and has worked extensively as an Implementation Engineer for various IBM Analytical tools." 986 | ] 987 | } 988 | ], 989 | "metadata": { 990 | "kernelspec": { 991 | "display_name": "Python 3.6", 992 | "language": "python", 993 | "name": "python3" 994 | }, 995 | "language_info": { 996 | "codemirror_mode": { 997 | "name": "ipython", 998 | "version": 3 999 | }, 1000 | "file_extension": ".py", 1001 | "mimetype": "text/x-python", 1002 | "name": "python", 1003 | "nbconvert_exporter": "python", 1004 | "pygments_lexer": "ipython3", 1005 | "version": "3.6.9" 1006 | } 1007 | }, 1008 | "nbformat": 4, 1009 | "nbformat_minor": 1 1010 | } 1011 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Data-Visualisation-with-Python 2 | 3 | 4 | Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars). 5 | 6 | 7 | The main goal of data visualization is to communicate information clearly and effectively through graphical means. 8 | 9 | 10 | ## Getting Started with Jupyter Notebooks 11 | 12 | Jupyter notebooks are an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. 13 | 14 | In this workshop we will use IBM Watson Studio to run a notebook. For this you will need an IBM Cloud account. The following steps will show you how sign up and get started. When you have the notebook up and running we will go through the notebook. 15 | 16 | ## IBM Cloud 17 | 18 | - [Sign up]( http://ibm.biz/datavisualisation_yr) for an IBM Cloud account 19 | 20 | - When you are signed up click `Create Resource` at the top of the Resources page. You can find the resources under the hamburger menu at the top left: 21 | 22 | ![](https://github.com/IBMDeveloperUK/python-geopandas-workshop/blob/master/images/Create_resource.png) 23 | 24 | - Search for Watson Studio and click on the tile: 25 | 26 | ![](https://github.com/IBMDeveloperUK/jupyter-notebooks-101/blob/master/images/studio.png) 27 | 28 | - Select the Lite plan and click `Create`. 29 | - Go back to the Resources list and click on your Watson Studio service and then click `Get Started`. 30 | 31 | ![](https://github.com/IBMDeveloperUK/jupyter-notebooks-101/blob/master/images/launch.png) 32 | 33 | ## IBM Watson Studio 34 | 35 | ### 1. Create a new Project 36 | 37 | - You should now be in Watson Studio. 38 | - Click on the Projects option to create a New project. 39 | 40 | ![](https://github.com/YaminiRao/Data-Visualisation-with-Python/blob/master/Images/Watson_Studio.png) 41 | 42 | - Select an Object Storage from the drop-down menu or create a new one for free. This is used to store the notebooks and data. **Do not forget to click refresh when returning to the Project page.** 43 | - click `Create`. 44 | 45 | 46 | ## 4. Load and run a notebook 47 | 48 | - Add a new notebook. Click `Add to project` and choose `Notebook`: 49 | 50 | ![](https://github.com/IBMDeveloperUK/python-geopandas-workshop/blob/master/images/notebook.png) 51 | 52 | - Choose new notebook `From URL`. Give your notebook a name and copy the URL `https://github.com/IBMDeveloperUK/Data-Visualisation-with-Python/blob/master/Notebook/Data_Viz.ipynb` 53 | - Select the custom runtime enviroment that you created and click `Create Notebook`. 54 | - The notebook will load. 55 | 56 | You are now ready to follow along with the workshop in the notebook! 57 | 58 |
      59 |
      60 |
      61 |
      62 | 63 | Data Sources and other References. 64 | 65 | - Cases Dataset : https://coronavirus.data.gov.uk/cases 66 | - Cars Dataset : https://www.kaggle.com/fivethirtyeight/fivethirtyeight-bad-drivers-dataset 67 | - IGN Scores : https://www.kaggle.com/alexisbcook/data-for-datavis?select=ign_scores.csv 68 | - Coffee and Code Dataset : https://www.kaggle.com/devready/coffee-and-code 69 | - Flights Dataset: https://github.com/mwaskom/seaborn-data/blob/master/flights.csv 70 | - Music by Genre Dataset :https://www.kaggle.com/yamaerenay/spotify-dataset-19212020-160k-tracks?select=data_by_genres.csv 71 | - https://matplotlib.org/tutorials/index.html 72 | - https://seaborn.pydata.org/examples/index.html 73 | - https://github.com/datasciencescoop 74 | --------------------------------------------------------------------------------