├── README.md ├── 03_Working with Images and Text ├── output │ ├── menu.jpg │ └── menu.txt └── images │ ├── page1.jpg │ └── page2.jpg ├── 00_Analysis of Programming Fundamentals with Python Exam Results - May 2023 ├── .ipynb_checkpoints │ └── Untitled-checkpoint.ipynb └── data │ ├── midexam_r.csv │ └── finalexam_r.csv ├── 01_Data Tidying and Cleaning └── Data Tidying and Cleaning Lab.ipynb ├── 06_Data Science Project Architecture └── data │ └── titanic.csv └── 02_Data Visualization and EDA └── Data Visualization and EDA Lab.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # data-science 2 | data science 3 | -------------------------------------------------------------------------------- /03_Working with Images and Text/output/menu.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ip681/data-science/HEAD/03_Working with Images and Text/output/menu.jpg -------------------------------------------------------------------------------- /03_Working with Images and Text/images/page1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ip681/data-science/HEAD/03_Working with Images and Text/images/page1.jpg -------------------------------------------------------------------------------- /03_Working with Images and Text/images/page2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ip681/data-science/HEAD/03_Working with Images and Text/images/page2.jpg -------------------------------------------------------------------------------- /00_Analysis of Programming Fundamentals with Python Exam Results - May 2023/.ipynb_checkpoints/Untitled-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 5 6 | } 7 | -------------------------------------------------------------------------------- /03_Working with Images and Text/output/menu.txt: -------------------------------------------------------------------------------- 1 | APPETIZERS 2 | Southern Fried Quail with Greens, Huckleberries, Pecans & Blue Cheese 14.00 3 | Park Avenue Cafe Chopped Salad Goat Feta Cheese, Niçoise Olives, Marinated White Anchovies, Aged Balsamic Vinegar 12.75 4 | Smoked Salmon & Pastrami Salmon™ with Blini, Quail Eggs & Horseradish Crème Fraîche 15.50 5 | Artichoke Ravioli Tomato, Citrus, Frisée 12.75 6 | Salad of Mixed Organic Greens with Vine Ripened Tomatoes & Fresh Herbs 9.75 7 | Tasting of Petite Appetizers 16.00 8 | Country Salad with Walnuts & Coach Farms Goat Cheese 12.75 9 | Tuna & Salmon Tartare with Osetra Caviar 14.50 10 | Handmade Cavatelli with Wild Mushrooms & White Truffle Oil 14.50 11 | Caramelized Sweetbread Tartlette with Black Truffles & Onions 12.50 12 | Foie Gras "Hot & Cold" Sautéed with Raisin, Pecan & Oatmeal Tart Chilled with Roasted Beet & Mushroom Salad 17.50 13 | Black & White Bean Soup with Spicy Shrimp Quesadilla 11.50 14 | 15 | SHELLFISH BY THE PIECE 16 | Atlantic Oysters 2.25 17 | Littleneck Clams 2.00 18 | Jumbo Shrimp 4.25 19 | 1 lb Chilled Lobster 14.50 20 | 1/4 1b.Colossal Crab Meat 13.75 21 | Shellfish Platter 21.00 22 | 23 | ENTRÉES 24 | Horseradish Crusted Canadian Salmon Potato Fritters, Marinated Cucumbers Chive Vinaigrette 27.00 25 | Sautéed Prawns with Mushroom Tortellini, Grilled Tomato Vinaigrette & Sweet Corn 29.50 26 | Mustard Crusted Tuna Teriyaki 29.75 27 | Pan Roasted Atlantic Cod with Potato, Leek & Crab Home—fries 29.50 28 | Lobster Steak with Hash—Brown Potatoes Lemon Brown Butter 42.00 29 | Swordchop™ with Tomato Ditalini & Lemon Broth (Market Availability) 34.50 30 | Mrs. Ascher's Steamed Vegetable Torte 19.50 31 | Onion Crusted Roast Organic Chicken with Whipped Potatoes 26.50 32 | Duck, Duck, Duck ! " Breast," "Meatloaf" & "Foie Gras Corn Cake" 29.50 33 | Rack of Lamb with Lamb Shank Ravioli Roasted Tomatoes, Broccoli Rabe, Minted Balsamic Vinegar 30.50 34 | Veal Chop with Wild Mushrooms & Potato Gnocchi 34.50 35 | Grilled Dry Aged Rib Eye Steak 34.50 36 | 37 | SIDE DISHES 38 | French Fries with Truffle Mayonnaise 8.50 39 | Creamed Spinach & Leeks 8.50 40 | Bamboo Steamed Vegetables 8.50 41 | David Burke's Whipped Potatoes 8.50 42 | Wild Mushroom Hash 8.50 43 | 44 | Wine by the Glass 45 | Glass Taste 46 | 47 | Champagne, Taittinger "Cuvee Prestige", Brut NV France 12.75 48 | Wine of the Day 15.00 8.00 49 | Chardonnay, Logan "Sleepy Hollow Vineyard", 1998, Monterey 13.00 7.00 50 | Sauvignon Blanc, Goldwater "Dog Point", 1999, NZ (Wine Spectator Rating 90) 11.50 6.00 51 | Chenin Blane, Foxen, 1999, Santa Barbara 11.00 6.00 52 | Pinot Grigio, 1999, Danzante, Italy (Frescobaldi/Mondavi) 9.00 5.00 53 | Chateau de Pocé, Touraine, 1999, Sauvignon 7.75 4.00 54 | Merlot, Franciscan, Oakville Estate, 1997, Napa 12.75 7.00 55 | Syrah, McDowell, 1998, Mendocino 9.50 5.00 56 | Pinot Noir, David Bruce, 1999, "Central Coast" 13.50 7.00 57 | Zinfandel, Cline "Old Vines", 1999, Contra Costa 12.00 6.00 58 | Cabernet Sauvignon, Farallon, 1997, North Coast 8.75 4.50 59 | Vino Nobile di Montepulciano, Il Faggeto, 1997, Tuscany 13.25 7.00 60 | Chateau, Bel Air, "Haut Médoc", 1997, Bordeaux 9.75 5.00 61 | 62 | Flights 63 | The Bull's Flight 20.00 64 | David Bruce Pinot Noir, Cline "Old Vines"Zinfandel, 65 | Wine of the Day 66 | 67 | The Frog's Flight 16.00 68 | Foxen Chenin Blanc, Logan Chardonnay, 69 | Danzante Pinot Grigio 70 | 71 | Mix & Match Flight 16.00 72 | Franciscan Merlot, McDowell Syrah, Cline "Old Vines" Zinfandel 73 | 74 | 2001 The Vertical Limit 200.10 75 | 1/2 bottle of 95, 96, 97 Opus One (3 1/2 Bottles) 76 | 77 | -------------------------------------------------------------------------------- /00_Analysis of Programming Fundamentals with Python Exam Results - May 2023/data/midexam_r.csv: -------------------------------------------------------------------------------- 1 | №,User,Name,01. Disneyland Journey,02. Weaponsmith,03. Grocery Shopping,01. Gift Box Coverage,02. Book’s World,03. The Final Quest,01. Spring Vacation,02. Generating Numbers,03. Magic Cards,01. Google Searches,02. Archery Champion,03. Paintings' Numbers,01. Swimming Championship,02. Jeweler,03. Spice Shelf,Total 2 | 1,vasilvasilev1234321,,-,-,-,-,100,-,100,-,-,-,-,-,-,-,100,300 3 | 2,tsvetelina.shishmanova,Tsvetelina Shishmanova,-,-,-,-,100,-,-,-,-,-,-,-,100,-,100,300 4 | 3,Dobata092709,,-,-,-,-,100,-,100,-,-,-,-,100,-,-,-,300 5 | 4,ThundR,,-,-,-,-,-,-,-,-,-,100,100,100,-,-,-,300 6 | 5,sophiaspavlova,Sofia Gospodinova,100,-,-,-,100,-,-,-,-,-,-,100,-,-,-,300 7 | 6,Xristo03,,100,-,-,-,-,-,-,100,-,-,-,100,-,-,-,300 8 | 7,DeanVasilev,,-,-,-,-,-,-,100,-,-,-,-,-,-,100,100,300 9 | 8,Kristiyan0206,Kristiyan Nedelchev,-,-,-,-,-,-,-,100,100,100,-,-,-,-,-,300 10 | 9,mili9,,-,-,-,100,-,-,-,-,-,-,-,-,-,100,100,300 11 | 10,Kolito,,-,-,-,-,100,-,100,-,100,-,-,-,-,-,-,300 12 | 11,KrisKushchev,,100,-,100,-,-,-,-,-,-,-,-,-,-,100,-,300 13 | 12,Rossosin,,100,-,-,-,-,-,-,-,-,-,-,100,-,100,-,300 14 | 13,CvetaTdrv,Tsveta Todorova,-,-,-,-,-,-,-,100,-,100,-,100,-,-,-,300 15 | 14,BlagoiPankovski,Blagoy Pankovski,-,-,-,-,100,100,-,-,-,100,-,-,-,-,-,300 16 | 15,daniel1905,Daniel Gutsov,-,100,-,-,-,-,-,-,-,100,-,100,-,-,-,300 17 | 16,todrusinow,Todor Rusinov,100,-,-,-,-,-,-,-,100,-,-,-,-,100,-,300 18 | 17,blueshero92,Deyan Dimitrov,-,100,-,-,-,-,-,-,100,-,-,-,100,-,-,300 19 | 18,SimeonMinkov1,Simeon Minkov,-,-,-,-,-,-,100,100,-,-,-,100,-,-,-,300 20 | 19,unrxmarkable,Tsvetan Dimitrov,-,100,-,-,-,100,-,-,-,100,-,-,-,-,-,300 21 | 20,Slaycross,Valeri Yordanov,-,100,-,-,-,-,100,-,100,-,-,-,-,-,-,300 22 | 21,VeselinaKSt,Veselina Stancheva,-,-,-,-,100,-,-,-,100,100,-,-,-,-,-,300 23 | 22,AChaush,,100,-,-,-,100,-,-,-,-,-,-,-,-,-,100,300 24 | 23,lmarinova,Lilyana Marinova,-,-,-,100,-,-,-,-,-,-,-,100,-,100,-,300 25 | 24,rumen111,,100,100,-,-,-,100,-,-,-,-,-,-,-,-,-,300 26 | 25,Joro_Karparov,Georgi Karparov,100,-,100,-,-,-,-,-,-,-,-,-,-,100,-,300 27 | 26,krasi_pd_bg,Krasimir Dimitrov,-,100,-,100,-,-,-,-,100,-,-,-,-,-,-,300 28 | 27,itmpr,Mariya Marinova,-,-,-,-,-,-,-,-,100,100,-,-,-,100,-,300 29 | 28,TeodorHinov,Teodor Hinov,-,-,100,-,-,-,-,-,-,100,100,-,-,-,-,300 30 | 29,Tsvetelina_11,Tsvetelina Stoycheva,100,-,100,-,-,-,-,100,-,-,-,-,-,-,-,300 31 | 30,HristoValeriev,Hristo Gospodinov,-,-,-,-,-,-,100,100,-,-,-,100,-,-,-,300 32 | 31,rusty89,Biser Karov,100,-,-,-,-,-,-,-,-,-,-,100,-,100,-,300 33 | 32,gultenmc,,100,-,-,-,100,-,-,-,-,-,-,100,-,-,-,300 34 | 33,NikolShipochanska,,-,-,-,-,-,-,100,-,-,-,-,100,-,100,-,300 35 | 34,WakeUTR,Iliqn Zdravkov,-,-,-,-,-,100,100,-,-,-,-,-,-,100,-,300 36 | 35,Mila.Uzunova,Мила Узунова,-,-,-,-,100,-,100,-,-,-,-,-,-,-,100,300 37 | 36,nikola.nikolov123321,,100,-,-,-,-,-,-,-,-,-,-,100,-,100,-,300 38 | 37,epicvladog,,-,-,-,100,-,100,-,-,-,-,100,-,-,-,-,300 39 | 38,VictorMitev23,,-,100,100,-,-,-,100,-,-,-,-,-,-,-,-,300 40 | 39,bozhidar.aleksandrov,,100,-,-,-,-,-,-,-,100,-,100,-,-,-,-,300 41 | 40,Elllilar,Mihail Iliev,-,-,-,-,-,-,-,100,100,100,-,-,-,-,-,300 42 | 41,tyna_bg,Dimitrina Georgieva,100,-,-,-,-,-,-,-,-,-,100,100,-,-,-,300 43 | 42,petarbuhchev,,-,-,-,100,-,100,-,100,-,-,-,-,-,-,-,300 44 | 43,AtanasIv,Atanas 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Evtimov,-,-,100,-,-,-,-,100,-,-,-,-,100,-,-,300 59 | 58,aleksas74,,-,-,-,-,-,-,-,100,-,-,-,-,100,-,100,300 60 | 59,nikola.a.n,Nikola Nikolov,-,-,100,-,100,-,-,-,-,-,-,-,100,-,-,300 61 | 60,mariela17,,-,100,-,-,-,100,-,-,-,-,-,-,100,-,-,300 62 | 61,Bojidar26,,-,-,-,100,-,-,-,-,-,-,100,100,-,-,-,300 63 | 62,StilyanaN,Stilyana Nguen,100,100,-,-,-,-,-,-,-,-,-,100,-,-,-,300 64 | 63,ElifSali,,-,-,-,-,-,-,100,-,-,-,-,-,-,100,100,300 65 | 64,Eko96,Ertan Sadkaev,-,-,-,-,-,-,-,100,-,-,-,100,100,-,-,300 66 | 65,Kenny.Stilyan,Stilyan Pavlov,-,-,-,-,-,-,-,-,-,-,100,100,100,-,-,300 67 | 66,MartinI123,Martin Ivanchev,-,-,-,-,-,-,100,-,100,-,-,-,-,100,-,300 68 | 67,GeorgiHristianGeorgievPopov,Georgi-Hristian Popov,-,-,-,-,-,-,100,100,-,-,-,100,-,-,-,300 69 | 68,vs_vasilev,Vasil Vasilev,100,-,-,-,100,-,-,-,100,-,-,-,-,-,-,300 70 | 69,DecXceD,Alexander Isaev,-,-,-,100,-,100,-,100,-,-,-,-,-,-,-,300 71 | 70,Atanas.vasilev1992,Atanas Vasilev,-,-,-,100,-,-,-,-,100,-,-,-,-,100,-,300 72 | 71,GVasko,,-,100,-,-,-,-,-,-,100,100,-,-,-,-,-,300 73 | 72,Iliyan_S11,Iliyan Simeonov,100,-,-,-,100,100,-,-,-,-,-,-,-,-,-,300 74 | 73,Zaganza,,100,-,-,-,-,-,-,-,100,-,-,-,-,100,-,300 75 | 74,Stoyanh,,-,-,-,-,100,-,-,-,100,-,-,-,90,-,-,290 76 | 75,Hranimirov,Daniel Zdravkov,-,-,-,-,100,100,-,-,-,-,-,-,90,-,-,290 77 | 76,Preslav0980,,-,-,-,100,90,100,-,-,-,-,-,-,-,-,-,290 78 | 77,yoana_petkova,Yoana Petkova,-,100,-,-,-,-,-,-,-,-,-,-,90,-,100,290 79 | 78,Kadir.hasan,Kadir Hasan,-,100,88,-,-,-,100,-,-,-,-,-,-,-,-,288 80 | 79,RadostinGospodinov,Radostin Gospodinov,-,100,-,100,-,-,-,-,88,-,-,-,-,-,-,288 81 | 80,tinteto,,-,-,-,-,100,100,88,-,-,-,-,-,-,-,-,288 82 | 81,Saraalkadi,Sara Al-Kadi,-,100,-,-,-,100,88,-,-,-,-,-,-,-,-,288 83 | 82,ValAnVal2006,,-,-,-,-,-,-,100,88,100,-,-,-,-,-,-,288 84 | 83,Zhivko95,,-,-,-,-,-,-,100,88,-,-,-,100,-,-,-,288 85 | 84,martin.jordanov13,Martin Yordanov,-,-,88,-,-,-,-,-,-,100,-,-,-,100,-,288 86 | 85,Ramaxoni,Mario Arzhanov,-,-,88,-,100,-,-,-,-,100,-,-,-,-,-,288 87 | 86,P3t3to,,100,-,-,-,-,-,-,100,88,-,-,-,-,-,-,288 88 | 87,Nikola_Dakov1,Nikola Dakov,-,-,88,-,100,-,-,-,-,100,-,-,-,-,-,288 89 | 88,SubZ3r0,Darko Stoichkov,-,-,-,100,-,100,-,88,-,-,-,-,-,-,-,288 90 | 89,BlagoySpasov,Blagoy Spasov,-,100,88,100,-,-,-,-,-,-,-,-,-,-,-,288 91 | 90,sabri_kz,Sabri Selimov,100,-,100,-,-,-,-,-,-,-,-,-,-,87,-,287 92 | 91,Mert10,,-,-,-,83,-,-,-,-,100,-,-,-,-,100,-,283 93 | 92,nPashov96,Nikolay Pashov,-,-,-,-,-,83,-,100,-,-,-,-,100,-,-,283 94 | 93,IvanPython,Ivan Kamburov,100,-,-,-,-,83,-,-,-,-,-,-,-,100,-,283 95 | 94,MiroslavTs,,-,90,-,-,-,100,-,-,-,-,-,-,90,-,-,280 96 | 95,krasimir89,Krasimir Boyukov,-,-,-,-,-,-,-,-,-,-,100,-,80,-,100,280 97 | 96,brayanm,Brayan Markov,-,-,100,-,100,-,-,-,-,-,-,-,80,-,-,280 98 | 97,KirilKitanov,Kiril Kitanov,100,80,-,-,-,-,-,-,-,-,-,100,-,-,-,280 99 | 98,Dechkoo,Nedelcho Nedelchev,100,80,-,-,-,-,-,-,100,-,-,-,-,-,-,280 100 | 99,Valkoz,Valentin Angelov,-,-,100,-,-,-,-,-,-,-,100,-,80,-,-,280 101 | 100,PetqEva,Petya Kostadinova-Obushtarova,-,-,88,-,-,-,-,-,-,-,-,-,90,100,-,278 102 | 101,StefSpasov,,-,100,88,-,-,-,-,-,-,-,-,-,90,-,-,278 103 | 102,Yavor79,Yavor Iliev,100,90,88,-,-,-,-,-,-,-,-,-,-,-,-,278 104 | 103,Picata,,-,-,-,-,-,-,-,-,88,-,100,-,90,-,-,278 105 | 104,iliana0,Iliana Todorova,-,-,-,-,90,-,88,-,-,-,-,100,-,-,-,278 106 | 105,stefidimitrovaa,,-,-,77,-,100,-,-,-,-,100,-,-,-,-,-,277 107 | 106,jaklinzlatanova,Жаклин Златанова,100,-,-,-,-,-,-,77,100,-,-,-,-,-,-,277 108 | 107,Stanko82,,-,-,-,-,-,-,-,77,-,100,-,100,-,-,-,277 109 | 108,Atanas_Lambov,,100,-,-,-,90,-,-,-,-,-,-,-,-,-,85,275 110 | 109,Bobbyturboto95,,-,100,-,100,-,-,-,-,-,-,-,-,-,-,71,271 111 | 110,Happytoo,Ventsislav Ivanov,-,-,-,-,-,-,-,-,-,100,100,71,-,-,-,271 112 | 111,gabriel.angelov,,-,90,-,-,-,-,-,-,-,-,-,100,80,-,-,270 113 | 112,toni.kavalski,,-,-,88,-,100,-,-,-,-,-,-,-,80,-,-,268 114 | 113,boretore,Boris Dabov,-,-,-,-,-,-,-,-,88,100,80,-,-,-,-,268 115 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129 | 128,MihaelaKasheva,Mihaela Kasheva,-,-,-,-,-,-,-,-,100,-,60,-,100,-,-,260 130 | 129,Mincho77,Mincho Nachev,-,-,-,100,-,100,-,-,-,-,60,-,-,-,-,260 131 | 130,LillyJeleva,Lili Hristova,100,-,-,-,-,-,-,-,-,-,60,100,-,-,-,260 132 | 131,mariaburla,Mariya Burla,-,-,100,-,-,-,100,-,-,-,60,-,-,-,-,260 133 | 132,AntonNachev,Anton Nachev,-,100,-,83,-,-,-,-,77,-,-,-,-,-,-,260 134 | 133,Bubanster,Lyubomira Andreeva,-,-,-,-,100,-,88,-,-,-,-,-,-,-,71,259 135 | 134,Valentina_Sto,Valentina Stoyanova,-,-,-,-,-,-,88,100,-,-,-,-,-,-,71,259 136 | 135,Miki2005,Milan Nyagolov,-,90,-,66,-,-,-,-,-,-,-,-,-,-,100,256 137 | 136,Mina19,Mina Cvqtkova,-,90,77,-,-,-,88,-,-,-,-,-,-,-,-,255 138 | 137,ViliK,Vili Kirimova,-,90,-,-,-,100,-,-,-,62,-,-,-,-,-,252 139 | 138,georgi.altanov,Georgi Altanov,-,-,-,-,-,-,-,88,88,75,-,-,-,-,-,251 140 | 139,Irinaisheree,,100,-,-,-,-,-,-,-,-,-,50,100,-,-,-,250 141 | 140,Bozhydara5,Bozhidara Bozhanova,-,-,100,-,-,-,100,-,-,-,50,-,-,-,-,250 142 | 141,petardimitrov55,,-,90,-,-,-,-,-,-,-,-,-,100,60,-,-,250 143 | 142,Ally_kirilova,Alexandra Kirilova,-,-,-,-,-,83,100,66,-,-,-,-,-,-,-,249 144 | 143,DanislavAL,Danislav Lazarov,100,-,-,-,-,83,-,-,-,-,-,-,-,62,-,245 145 | 144,georgidaleev,Georgi Daleev,-,-,-,-,-,-,44,100,100,-,-,-,-,-,-,244 146 | 145,popovag,Galina Popova,-,-,-,-,-,100,-,44,-,-,-,-,100,-,-,244 147 | 146,Vessi.A,Vessela Atanasova,-,-,66,-,-,-,-,-,-,-,-,-,90,87,-,243 148 | 147,peza_86,Petya Doncheva,-,40,-,-,-,-,100,-,100,-,-,-,-,-,-,240 149 | 148,DimitarIvanov08,,-,-,-,-,90,-,-,-,-,-,-,-,90,-,57,237 150 | 149,forexmaster,,-,-,-,66,100,-,-,-,-,-,-,71,-,-,-,237 151 | 150,tedort,Teodor Todorov,-,-,-,-,-,-,-,-,-,-,-,-,90,87,57,234 152 | 151,StilyanaKachakova,Stilyana Kachakova,-,-,100,33,100,-,-,-,-,-,-,-,-,-,-,233 153 | 152,YMOPEH,Radoslav Georgiev,-,-,-,33,100,100,-,-,-,-,-,-,-,-,-,233 154 | 153,galin.todorov,,100,-,100,-,-,-,-,-,-,-,30,-,-,-,-,230 155 | 154,l.boyanovv,,-,100,-,-,-,-,-,-,-,-,-,100,30,-,-,230 156 | 155,Ina.Mihaylova,Ina Mihaylova,100,-,-,-,-,-,-,-,-,-,30,100,-,-,-,230 157 | 156,Raya19,,-,-,-,-,-,100,100,-,-,-,30,-,-,-,-,230 158 | 157,Denitsa125,Denitsa Sarakinova,-,-,-,-,-,-,-,88,-,100,-,-,-,-,42,230 159 | 158,cho4eca,Stefan Stefanov,-,-,-,66,-,-,-,-,-,-,-,100,-,62,-,228 160 | 159,GeorgiTG,,100,-,-,-,-,-,-,-,-,-,70,57,-,-,-,227 161 | 160,Nbalimezov,Nikola Balimezov,-,-,77,50,100,-,-,-,-,-,-,-,-,-,-,227 162 | 161,WikiJes,Виктория Димитрова,25,-,100,-,-,-,-,100,-,-,-,-,-,-,-,225 163 | 162,Gavrailov18,Nikola Gavrailov,-,-,-,-,-,-,-,-,-,-,-,-,90,62,71,223 164 | 163,ivan_gevezov,Ivan Gevezov,-,-,-,-,-,-,22,100,-,-,-,100,-,-,-,222 165 | 164,nevenakoynarcheva,,-,80,88,-,-,-,-,-,-,50,-,-,-,-,-,218 166 | 165,Victoriyaaa,,100,-,88,-,-,-,-,-,-,-,30,-,-,-,-,218 167 | 166,DenizRaim,Deniz Raim,-,-,-,-,-,-,-,-,77,-,50,-,90,-,-,217 168 | 167,Boris6,,-,-,-,-,-,-,-,-,100,75,40,-,-,-,-,215 169 | 168,DasAuge,,-,70,-,-,-,83,-,-,-,62,-,-,-,-,-,215 170 | 169,Stefan_Tsenev,,-,-,-,-,-,-,55,-,-,-,-,-,-,87,71,213 171 | 170,bhoper,Мирослав Дернев,-,100,-,-,-,-,11,-,100,-,-,-,-,-,-,211 172 | 171,Vladimir_Lilov,,-,-,33,-,-,-,-,88,-,-,-,-,90,-,-,211 173 | 172,KonstantinTarasevich,Konstantin Tarasevich,-,-,88,-,-,-,-,-,-,-,20,-,100,-,-,208 174 | 173,DesislavStefanov2003,Desislav Stefanov,-,100,-,-,-,-,-,-,88,-,-,-,20,-,-,208 175 | 174,Radostin_Tasev,,-,-,-,-,-,66,-,-,-,-,-,-,90,50,-,206 176 | 175,Krasss88,KRASIMIR FILIPOV,-,-,100,-,-,-,0,-,-,-,-,-,-,100,-,200 177 | 176,Krasimir0,,100,-,-,-,-,-,-,-,-,-,0,-,-,-,100,200 178 | 177,Grigorov8,Dimitar Grigorov,-,-,-,-,100,-,-,-,-,-,-,100,0,-,-,200 179 | 178,Tatqna123,Tatqna Vasileva,-,-,100,-,-,-,-,0,-,100,-,-,-,-,-,200 180 | 179,mahon,Пламен Михов,-,-,-,-,-,100,100,-,-,-,-,-,-,0,-,200 181 | 180,mbb_31,Martin Bumbarov,-,-,-,-,-,-,-,-,0,-,-,-,100,100,-,200 182 | 181,GerganaKrasimirova,,-,-,100,-,-,-,-,-,-,-,0,-,100,-,-,200 183 | 182,Todor_012,,-,-,-,-,-,-,88,-,-,-,40,-,-,-,71,199 184 | 183,NaskoOvardov,Nasko Ovardov,-,90,-,100,-,-,-,-,0,-,-,-,-,-,-,190 185 | 184,SinanMehmedov,,-,-,100,-,-,-,-,-,-,-,0,-,90,-,-,190 186 | 185,MIROGUMATA,Asen Kalqn,-,90,-,0,-,-,-,-,100,-,-,-,-,-,-,190 187 | 186,Mariopi7,,100,90,0,-,-,-,-,-,-,-,-,-,-,-,-,190 188 | 187,konstantinivanovivanov,Константин Иванов,-,-,100,-,-,-,88,0,-,-,-,-,-,-,-,188 189 | 188,SURCESLAV,Stefan Saev,-,-,88,-,-,-,-,-,-,100,-,-,-,0,-,188 190 | 189,Vasko77,Vasil Tsarvulanov,100,-,88,-,-,-,-,-,-,-,0,-,-,-,-,188 191 | 190,Degos,Gospodin Gospodinov,-,-,100,-,-,-,-,88,-,-,-,-,0,-,-,188 192 | 191,nikola_todorov97,Nikola Todorov,-,-,-,16,-,-,-,100,-,-,-,-,-,-,71,187 193 | 192,YordanKostadinov1,Yordan Kostadinov,-,-,-,-,-,66,-,-,-,100,20,-,-,-,-,186 194 | 193,D.K.Ivanova,Daniela Ivanova,-,-,-,-,-,66,100,-,-,-,20,-,-,-,-,186 195 | 194,VelislavM,Velislav Mutafchiyski,-,-,-,-,-,-,-,-,88,62,30,-,-,-,-,180 196 | 195,Maila,Daniela Georgieva-Ivanova,-,-,-,-,-,-,-,-,100,-,0,-,80,-,-,180 197 | 196,NikolKL,,-,-,-,-,-,-,-,-,-,-,-,28,100,50,-,178 198 | 197,LKrastev,,-,-,77,-,-,-,-,-,-,100,-,-,-,0,-,177 199 | 198,krasigel,Krasimira,-,-,88,-,-,-,88,0,-,-,-,-,-,-,-,176 200 | 199,IvanKonstantinovPravchev,,-,-,-,-,-,-,-,100,-,75,-,-,-,-,0,175 201 | 200,Malina.Dimova,Malina Dimova,-,-,-,33,-,100,-,-,-,-,40,-,-,-,-,173 202 | 201,Vasil_Imenov,,100,-,22,-,-,-,-,-,-,-,-,-,-,50,-,172 203 | 202,Maria36,,-,-,-,-,-,-,-,-,-,-,-,-,60,50,57,167 204 | 203,litek,Atanas Dimitrov,-,-,-,50,-,66,-,-,-,-,50,-,-,-,-,166 205 | 204,Blagoy86,Blagoy Penev,-,-,-,-,-,-,-,-,-,62,-,100,-,0,-,162 206 | 205,chris369,Krasena Kirilova,-,80,-,16,-,66,-,-,-,-,-,-,-,-,-,162 207 | 206,DanielTsochev,,-,60,-,-,-,0,100,-,-,-,-,-,-,-,-,160 208 | 207,belini,,-,-,77,-,0,-,-,-,-,-,-,-,80,-,-,157 209 | 208,bunnyhype,Georgi Ginduzov,-,80,-,-,-,-,77,-,-,-,-,0,-,-,-,157 210 | 209,Lubo45,Lyubomir Vasiliev,-,-,-,-,-,83,-,-,-,62,10,-,-,-,-,155 211 | 210,kari17,Kadrie Ali,-,-,-,-,90,0,-,-,-,62,-,-,-,-,-,152 212 | 211,volokin,Aleksandar Nikolov,100,-,0,-,-,-,-,-,-,-,-,-,-,50,-,150 213 | 212,apetrovefforts,Anton Petrov,0,-,88,-,-,-,-,-,-,-,-,-,-,62,-,150 214 | 213,vasilena.3,Vasilena Palasheva,-,-,-,-,-,-,100,-,-,-,0,-,-,-,42,142 215 | 214,Bobi70707023,,-,70,-,-,-,-,22,-,44,-,-,-,-,-,-,136 216 | 215,Aleksandar_Dochev,,-,-,-,50,-,-,-,44,-,-,-,-,-,-,42,136 217 | 216,Isuf90,Isuf Gorelski,-,-,-,-,-,-,88,-,-,-,-,-,-,12,28,128 218 | 217,mihailg777,Mihail Georgiev,-,-,-,-,-,-,-,-,66,50,-,-,-,12,-,128 219 | 218,NathalieV,Natalia Vasileva,100,-,-,-,-,-,-,-,-,-,0,28,-,-,-,128 220 | 219,Nadezhda.vto,Nadezhda Borislavova,-,-,-,-,-,-,100,-,-,-,0,28,-,-,-,128 221 | 220,Duly1,Ivaylo Radulov,-,-,-,-,-,-,-,-,-,-,0,-,100,-,28,128 222 | 221,Mirellapeychev,Mirella Boneva,-,-,-,-,-,16,-,-,-,87,20,-,-,-,-,123 223 | 222,B_Chovanski,Borislav Chovanski,-,0,-,16,-,100,-,-,-,-,-,-,-,-,-,116 224 | 223,VqraKostadinova,Viara Kostadinova,100,-,0,-,-,-,-,-,-,-,10,-,-,-,-,110 225 | 224,Stefan7323,Стефан 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Dakov,-,-,-,-,-,-,0,-,-,-,0,85,-,-,-,85 240 | 239,Viktoria8,,-,-,-,-,-,-,33,33,0,-,-,-,-,-,-,66 241 | 240,SimeonKost,Simeon Kostadinov,-,-,0,66,-,-,-,-,-,-,-,-,-,0,-,66 242 | 241,Li0y2,Iliya Ivanovski,-,-,-,-,-,-,-,-,-,62,-,0,-,0,-,62 243 | 242,Davik,Momchil Ivanov,-,-,-,-,-,-,-,-,0,62,0,-,-,-,-,62 244 | 243,E7r6r,Onur Syuleyma,-,-,-,-,-,0,44,-,-,-,-,-,-,0,-,44 245 | 244,AtanasPro,Atanas Profirov,-,-,-,-,-,16,22,-,-,-,0,-,-,-,-,38 246 | 245,Radoslav05,Radoslav Efremov,-,-,0,-,-,-,0,33,-,-,-,-,-,-,-,33 247 | 246,BlagovestBorukov,,-,-,-,33,-,-,-,0,-,-,-,0,-,-,-,33 248 | 247,teri1234b,Tereza Baevska,-,-,-,16,-,-,-,-,-,-,-,14,-,0,-,30 249 | 248,Georgieva_Anna,,25,-,0,-,0,-,-,-,-,-,-,-,-,-,-,25 250 | 249,LemonStefan,Stefan Kasabov,25,-,-,-,0,-,-,-,-,-,-,-,-,-,0,25 251 | 250,Ivaylo772,Ivaylo Petrov,-,-,0,-,-,-,-,-,-,-,0,-,20,-,-,20 252 | 251,D_i_m_i,Dimitar Ivakimov,-,-,-,16,0,-,-,-,-,-,-,-,-,-,0,16 253 | 252,UpstreamRace,,-,-,0,16,-,-,-,-,-,-,-,-,-,0,-,16 254 | 253,VasYord,Vasil Yordanov,-,0,0,16,-,-,-,-,-,-,-,-,-,-,-,16 255 | 254,DimitarStoev120,Dimitar Stoev,-,-,-,-,0,-,0,-,-,-,-,0,-,-,-,0 256 | 255,nadiia.ivanova,,-,-,-,-,-,-,-,0,0,0,-,-,-,-,-,0 257 | 256,Kalevra,Diyan Apostolov,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,0 258 | 257,yuli509,Yuli,-,-,-,0,-,-,-,0,-,-,-,-,-,-,0,0 259 | 258,svilen_banov,Svilen Banov,-,0,0,0,-,-,-,-,-,-,-,-,-,-,-,0 260 | 259,vasilbojilov24,Vasil Bozhilov,-,0,-,-,-,-,0,-,0,-,-,-,-,-,-,0 261 | 260,ivan_bahchevanski_95,Ivan Bahchevanski,-,-,-,-,-,0,0,-,-,-,-,-,-,0,-,0 262 | 261,Dessie007,Desislava Georgieva,-,-,-,0,-,-,-,0,-,-,-,-,-,-,0,0 263 | 262,MiroslawBonchev,Miroslav Bonchev,-,-,-,-,-,-,0,-,-,-,0,-,-,-,0,0 264 | 263,ico2662,,-,-,-,-,-,0,-,-,-,0,-,-,-,0,-,0 265 | 264,epalagacheva,Emilia Palagacheva,-,-,-,-,-,-,0,0,0,-,-,-,-,-,-,0 266 | 265,Viksyto,Viktor Yoshov,-,-,-,-,-,0,-,-,-,0,-,-,-,0,-,0 267 | 266,Stefan_Markov,Stefan Markov,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,0 268 | 267,JayTrad,ZHELYO RADANOV,-,-,-,0,0,0,-,-,-,-,-,-,-,-,-,0 269 | 268,petarpetkov,Petar Petkov,-,-,-,0,-,0,-,0,-,-,-,-,-,-,-,0 270 | 269,YordanDimitrov2,Yordan Dimitrov,-,0,0,-,-,-,-,-,-,0,-,-,-,-,-,0 271 | 270,bardsongg,Yordan Georgiev,-,-,-,-,0,-,0,-,0,-,-,-,-,-,-,0 272 | 271,Beyhan80,,0,-,-,-,-,0,-,0,-,-,-,-,-,-,-,0 273 | 272,IvanRmNikolov,,-,-,-,-,-,-,-,-,-,0,0,0,-,-,-,0 274 | 273,petkoandreev,Petko Andreev,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,0 275 | 274,Ivan_Ivanov98,Ivan Ivanov,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,0 276 | 275,MomchilBorisov,Momchil Borisov,-,-,-,-,0,-,-,-,-,0,-,-,-,-,0,0 277 | 276,VladislavNikolov80,,-,-,0,-,-,-,0,0,-,-,-,-,-,-,-,0 278 | 277,Denis771,Denis Stoyanov,0,-,-,-,-,-,-,-,0,-,-,-,-,0,-,0 279 | 278,UzunovOS,Ognyan Uzunov,-,-,-,-,-,-,0,0,0,-,-,-,-,-,-,0 280 | 279,Muzata,Plamen Timev,-,-,-,0,-,-,-,-,0,-,-,-,-,0,-,0 281 | 280,Ms.MarinaNay,Marina Naydenova,0,-,-,-,-,0,-,-,-,-,0,-,-,-,-,0 282 | 281,DarinaGospodinova,Дарина Господинова,-,0,-,-,-,-,-,-,-,-,-,-,0,-,0,0 283 | 282,Milyana,,-,0,0,0,-,-,-,-,-,-,-,-,-,-,-,0 284 | 283,g_stoichev,Georgi Stoychev,-,-,-,-,0,-,0,-,0,-,-,-,-,-,-,0 285 | 284,StefanGerin,,-,-,0,-,-,-,-,0,-,-,-,-,0,-,-,0 286 | -------------------------------------------------------------------------------- /00_Analysis of Programming Fundamentals with Python Exam Results - May 2023/data/finalexam_r.csv: -------------------------------------------------------------------------------- 1 | №,User,Name,01. Email Validator,02. Message Encrypter,03. Battle Manager,01. Easter,02. Employees,03. Bakery Shop,01. Registration,02. Registration,03. Monthly Report,02. The Race,03. Concert,03. Shopping,01. Word Developing,01. Warrior's Quest,02. Arriving in Kathmandu,Total 2 | 1,StefSpasov,,100,-,-,-,-,-,-,-,-,-,-,100,-,-,100,300 3 | 2,Victoriyaaa,,100,-,100,-,-,-,-,100,-,-,-,-,-,-,-,300 4 | 3,georgiev1916,,-,-,-,100,-,100,-,-,-,100,-,-,-,-,-,300 5 | 4,Nubris_94,David Dimitrov,-,-,-,-,100,100,-,-,-,-,-,-,-,100,-,300 6 | 5,denislavdd,Denislav Dimitrov,100,-,100,-,-,-,-,100,-,-,-,-,-,-,-,300 7 | 6,Yo_plam,Plamen Zhelev,-,-,-,-,-,-,100,-,-,-,100,-,-,-,100,300 8 | 7,Valentina_Sto,Valentina Stoyanova,-,-,100,-,-,-,100,100,-,-,-,-,-,-,-,300 9 | 8,ptktdrv,Petko Todorov,-,-,-,-,-,-,100,-,100,-,-,-,-,-,100,300 10 | 9,mavroleon,Aleksandar Dzhomakov,-,-,-,100,100,-,-,-,100,-,-,-,-,-,-,300 11 | 10,radi_taf,Radost Tafradzhiyska,100,-,100,-,100,-,-,-,-,-,-,-,-,-,-,300 12 | 11,PreslavStamatov,Preslav Stamatov,-,100,-,-,-,-,100,-,100,-,-,-,-,-,-,300 13 | 12,levvv,,-,-,100,100,-,-,-,100,-,-,-,-,-,-,-,300 14 | 13,lakmus,,-,-,-,-,100,-,-,-,100,-,-,-,100,-,-,300 15 | 14,sklirisalexander,,-,100,-,-,-,-,100,-,100,-,-,-,-,-,-,300 16 | 15,StelianY,Steliyan Yanakiev,-,-,-,-,100,100,-,-,-,-,-,-,100,-,-,300 17 | 16,Simeon0802,Simeon Rachev,-,-,-,-,-,-,-,100,100,-,-,-,100,-,-,300 18 | 17,metodi.shopov,Metodi Shopov,-,-,-,-,-,-,100,100,100,-,-,-,-,-,-,300 19 | 18,Tsvetelina_V.D,,-,100,100,-,-,-,100,-,-,-,-,-,-,-,-,300 20 | 19,lmarinova,Lilyana Marinova,-,-,-,-,100,-,-,-,-,-,100,-,-,100,-,300 21 | 20,Mert10,,100,-,-,-,-,100,-,-,-,100,-,-,-,-,-,300 22 | 21,TodorKrumov,Todor Krumov,100,-,-,-,-,100,-,-,-,100,-,-,-,-,-,300 23 | 22,kostatk,,-,-,-,-,100,100,-,-,-,-,-,-,100,-,-,300 24 | 23,vasko90,Vasko Bozhilov,-,-,-,-,-,100,-,100,-,-,-,-,-,100,-,300 25 | 24,tmnikolov,Tihomir Nikolov,-,-,-,100,-,-,-,-,-,100,100,-,-,-,-,300 26 | 25,niatanasov,Nikolay Atanasov,-,-,-,100,-,-,-,-,-,100,100,-,-,-,-,300 27 | 26,Ognyanat,Ognyana Toteva,-,-,-,-,-,100,100,100,-,-,-,-,-,-,-,300 28 | 27,stratoss,Stanimir Stoyanov,-,-,-,-,-,-,100,-,-,100,100,-,-,-,-,300 29 | 28,nPashov96,Nikolay Pashov,-,-,-,100,-,100,-,100,-,-,-,-,-,-,-,300 30 | 29,Marchety085,Maria Yaneva,-,100,-,-,-,-,-,-,-,-,-,100,100,-,-,300 31 | 30,cool1,,100,100,-,-,-,-,-,-,100,-,-,-,-,-,-,300 32 | 31,MartinRNikolov,Martin Nikolov,-,-,-,-,-,-,100,-,-,100,100,-,-,-,-,300 33 | 32,GerganaGrant,,-,-,-,-,100,100,100,-,-,-,-,-,-,-,-,300 34 | 33,DimityrA,Dimityr Aleksiev,-,-,-,-,-,-,-,-,-,100,100,-,100,-,-,300 35 | 34,vesned,Vesselin Nedialkov,-,-,100,-,-,-,100,100,-,-,-,-,-,-,-,300 36 | 35,BiserLilov,Biser Lilov,-,-,-,-,-,-,-,-,-,100,100,-,-,100,-,300 37 | 36,Arshin,,100,-,100,-,-,-,-,-,-,100,-,-,-,-,-,300 38 | 37,Eliza_Kovacheva,,-,-,-,-,-,-,100,-,-,100,-,100,-,-,-,300 39 | 38,yoana_petkova,Yoana Petkova,-,-,-,-,-,-,100,100,-,-,-,100,-,-,-,300 40 | 39,HristoValeriev,Hristo Gospodinov,100,-,100,-,100,-,-,-,-,-,-,-,-,-,-,300 41 | 40,Dimitar.T,Dimitar Tyanchev,-,-,100,-,-,-,100,-,-,-,-,-,-,-,100,300 42 | 41,Classifield,Peter Zahariev,-,-,-,100,100,-,-,-,-,-,-,100,-,-,-,300 43 | 42,iveta_ll,Iveta Atanasova,-,-,100,100,-,-,-,100,-,-,-,-,-,-,-,300 44 | 43,Ludmil_n1,,100,-,-,-,-,-,-,-,-,100,100,-,-,-,-,300 45 | 44,ahmedmatem,,100,-,100,-,-,-,-,100,-,-,-,-,-,-,-,300 46 | 45,mar.alex,Mariya Aleksandrova,-,-,-,-,-,-,-,-,100,-,-,-,-,100,100,300 47 | 46,Shecho,,-,-,-,-,100,-,-,-,100,-,-,-,100,-,-,300 48 | 47,Chelavrov,IVAN CHELAVROV,-,-,100,-,-,-,-,-,-,100,-,-,-,100,-,300 49 | 48,OnqSkonq,Fikri Harlov,-,-,-,-,100,-,-,-,100,-,-,-,100,-,-,300 50 | 49,RuslanKostov,Ruslan Kostov,-,-,100,100,-,-,-,-,-,-,-,-,-,-,100,300 51 | 50,Coldys,Diyan Ivanov,-,-,-,-,-,-,-,-,-,100,100,-,-,100,-,300 52 | 51,todrusinow,Todor Rusinov,-,-,100,100,-,-,-,-,-,-,-,-,-,-,100,300 53 | 52,gerip02,Gergana Popova,-,-,-,100,-,-,-,100,-,-,100,-,-,-,-,300 54 | 53,Bilyana_EGeorgieva,,-,-,-,100,-,-,-,-,-,100,100,-,-,-,-,300 55 | 54,Martin222,,-,-,100,-,-,-,100,-,-,-,-,-,-,-,100,300 56 | 55,tsankoz,Tsanko Zanov,-,100,-,100,-,-,-,-,-,-,-,100,-,-,-,300 57 | 56,nikola.nikolov123321,,-,-,-,100,-,-,-,100,-,-,100,-,-,-,-,300 58 | 57,Plamen_Panayotov,Plamen Panayotov,-,-,-,-,-,100,-,-,-,-,-,-,100,-,100,300 59 | 58,windowpane,Dobromir Tsolyov,-,-,-,-,-,-,100,100,100,-,-,-,-,-,-,300 60 | 59,st.petrov96,,-,-,100,-,100,-,-,-,-,-,-,-,100,-,-,300 61 | 60,paktozi,Ivaylo Vaklinov,-,100,-,-,-,-,100,-,100,-,-,-,-,-,-,300 62 | 61,SvilenIvanov77,Svilen Ivanov,100,-,-,-,-,100,-,100,-,-,-,-,-,-,-,300 63 | 62,Hranimirov,Daniel Zdravkov,-,100,100,-,-,-,-,-,-,-,-,-,100,-,-,300 64 | 63,MitkoM6,Dimitar Mitrev,-,100,-,100,-,-,-,-,-,-,-,100,-,-,-,300 65 | 64,denis237,,-,-,-,-,-,-,100,-,-,-,100,-,-,-,100,300 66 | 65,BlagoiPankovski,Blagoy Pankovski,-,-,-,100,-,-,-,100,-,-,-,100,-,-,-,300 67 | 66,LidiaSimeonova,Lidia Simeonova,100,-,100,-,100,-,-,-,-,-,-,-,-,-,-,300 68 | 67,aardenski,Aleksandar Ardenski,100,-,100,-,-,-,-,-,-,100,-,-,-,-,-,300 69 | 68,valentinvalentinovv,,-,-,-,100,-,-,-,-,100,-,-,-,-,-,100,300 70 | 69,Tihomir_Vodenicharov,РўРёС…РѕРјРёСЂ Воденичаров,-,-,-,100,-,-,-,-,-,100,100,-,-,-,-,300 71 | 70,virgo21,Inna Vatahova,-,100,-,-,-,100,100,-,-,-,-,-,-,-,-,300 72 | 71,KostadinHadzhiyski,,-,-,-,-,100,-,-,-,-,-,100,-,-,100,-,300 73 | 72,lambev_19,Momchil Lambev,-,-,-,-,100,-,-,-,-,-,100,-,100,-,-,300 74 | 73,atanas_kulov,,-,-,-,100,-,-,-,-,100,-,-,-,-,-,100,300 75 | 74,hristoford,,-,-,-,-,-,-,-,-,-,-,100,-,-,100,100,300 76 | 75,Iliyan_S11,Iliyan Simeonov,-,-,-,-,-,-,-,100,100,-,-,-,100,-,-,300 77 | 76,IvayloKartev,Ivaylo Kartev,-,-,100,-,-,-,-,-,-,100,-,-,-,100,-,300 78 | 77,D.Atanasov00,,-,-,-,-,-,-,-,-,-,-,-,100,-,100,100,300 79 | 78,Yavor79,Yavor Iliev,100,-,-,-,100,100,-,-,-,-,-,-,-,-,-,300 80 | 79,VelislavM,Velislav Mutafchiyski,100,100,-,-,-,100,-,-,-,-,-,-,-,-,-,300 81 | 80,BGVilla,Valentin Mitrofanov,-,-,-,100,100,-,-,-,-,-,100,-,-,-,-,300 82 | 81,StanislavVVasilev,Stanislav Vasilev,100,-,-,-,100,-,-,-,100,-,-,-,-,-,-,300 83 | 82,Tatqna123,Tatqna Vasileva,-,-,100,-,-,-,-,90,-,-,-,-,-,100,-,290 84 | 83,nikolay54,,-,-,100,-,-,-,-,-,-,-,-,-,100,-,90,290 85 | 84,IvanKonstantinovPravchev,,-,-,100,-,-,-,100,-,-,-,-,-,-,-,90,290 86 | 85,stefchobgbg,STEFAN RAYKOV,-,-,-,-,-,-,-,-,-,-,100,-,90,-,100,290 87 | 86,Grudijordanov,,-,-,88,-,100,-,-,-,-,-,-,-,-,100,-,288 88 | 87,stoyanharitov,Stoyan Haritov,100,-,88,-,100,-,-,-,-,-,-,-,-,-,-,288 89 | 88,VladimirCh,,100,-,88,-,-,-,-,100,-,-,-,-,-,-,-,288 90 | 89,CvetaTdrv,Tsveta Todorova,-,-,-,-,-,-,-,-,87,-,-,-,100,-,100,287 91 | 90,Andrs5700,Andrey Vishnevskiy,-,-,-,-,-,-,-,100,87,-,-,-,-,100,-,287 92 | 91,Zaganza,,-,-,-,-,-,-,100,-,87,100,-,-,-,-,-,287 93 | 92,invicious,Oktay Osman,-,-,-,100,85,100,-,-,-,-,-,-,-,-,-,285 94 | 93,BlueBG,Viktor Stanimirov,-,-,100,100,-,-,-,-,-,85,-,-,-,-,-,285 95 | 94,V.Dimov,,-,-,-,100,-,-,-,-,100,85,-,-,-,-,-,285 96 | 95,Tugay2002,Tugay Ali,-,85,-,100,-,100,-,-,-,-,-,-,-,-,-,285 97 | 96,SofiSofi,Sofia Altanova,-,100,-,-,-,100,-,-,-,-,-,-,-,85,-,285 98 | 97,rtsvetkov,,-,-,100,-,-,-,-,100,-,-,-,-,-,85,-,285 99 | 98,radiks,Radina Draganova,-,-,-,-,-,-,-,100,-,-,100,-,-,85,-,285 100 | 99,Wrake,Iliya Nikolov,-,100,100,-,-,-,-,-,-,-,-,-,-,85,-,285 101 | 100,misho.st,Mihail Stoyanov,-,-,-,-,-,-,-,-,-,-,100,-,-,85,100,285 102 | 101,YMOPEH,Radoslav Georgiev,-,85,100,100,-,-,-,-,-,-,-,-,-,-,-,285 103 | 102,HristoVanchov,,-,-,-,-,-,-,-,-,100,100,-,-,-,85,-,285 104 | 103,DesiWodi,Desislava Vodichenska,100,-,-,-,85,-,-,-,100,-,-,-,-,-,-,285 105 | 104,Vironika,,100,-,-,-,-,-,-,100,-,-,83,-,-,-,-,283 106 | 105,Makulev,Stanislav Makulev,-,100,-,-,-,-,-,-,-,-,83,-,-,100,-,283 107 | 106,aleksandar_n_d_softuni,,-,-,-,-,-,-,-,-,-,-,83,-,100,-,100,283 108 | 107,KristinaNikolova98,,-,-,-,-,100,-,100,-,-,-,83,-,-,-,-,283 109 | 108,dianaev,,-,-,-,-,-,-,-,-,-,-,100,-,100,-,80,280 110 | 109,Joro_Karparov,Georgi Karparov,100,-,100,-,-,-,-,-,-,-,-,-,-,-,80,280 111 | 110,Gal123,Galin Hristov,-,-,-,-,-,100,-,80,-,-,-,-,100,-,-,280 112 | 111,Valka77,Valentin Georgiev,100,-,88,-,-,-,-,-,-,-,-,-,-,-,90,278 113 | 112,PetyaRiz,Petya Rizova,100,-,-,-,-,-,-,-,87,-,-,-,-,-,90,277 114 | 113,Ili_Yanski,,-,100,-,77,-,100,-,-,-,-,-,-,-,-,-,277 115 | 114,krasi_pd_bg,Krasimir Dimitrov,-,-,-,-,-,-,-,100,87,-,-,-,90,-,-,277 116 | 115,k.manova,Katrin Manova,-,-,-,77,100,-,-,-,-,-,-,100,-,-,-,277 117 | 116,Croko,Rosen Hristov,77,-,-,-,-,-,-,-,-,-,100,-,-,-,100,277 118 | 117,nikola_todorov97,Nikola Todorov,88,-,88,-,-,-,-,-,-,-,-,-,-,-,100,276 119 | 118,GeorgiKirilov05,,-,-,-,-,-,100,-,90,-,-,-,-,-,85,-,275 120 | 119,fany.argirova,,-,-,-,-,100,75,-,-,-,-,-,-,100,-,-,275 121 | 120,Irinaisheree,,-,-,-,-,-,75,100,-,-,-,-,-,-,-,100,275 122 | 121,ivaylomihaylovv,,-,-,100,-,-,-,-,90,-,-,-,-,-,85,-,275 123 | 122,RumenMilenov,Rumen Milenov,100,-,-,-,-,75,-,100,-,-,-,-,-,-,-,275 124 | 123,bogi.ignatov,Bogomil Ignatov,-,-,-,-,-,-,90,-,-,-,83,-,-,-,100,273 125 | 124,dangelova61,Diana Angelova,-,85,88,-,-,-,-,-,-,-,-,-,100,-,-,273 126 | 125,luksan4o,Luka Chalkanov,-,85,88,100,-,-,-,-,-,-,-,-,-,-,-,273 127 | 126,forexmaster,,-,-,-,-,-,-,-,90,-,-,83,-,-,100,-,273 128 | 127,Arianitka,Ariana Dzhambazka,-,-,-,-,-,-,100,-,-,-,83,-,-,-,90,273 129 | 128,Aleks160707,,-,-,-,-,-,87,-,-,-,-,-,-,-,85,100,272 130 | 129,IVakov,Ivan Vakov,-,85,-,-,-,-,100,-,87,-,-,-,-,-,-,272 131 | 130,slowvik,,-,85,-,-,-,-,100,-,87,-,-,-,-,-,-,272 132 | 131,Ahmed03,Ahmed Ahmed,-,-,100,100,71,-,-,-,-,-,-,-,-,-,-,271 133 | 132,GeorgiStefanov99,George Stefanov,88,-,-,-,-,-,-,100,-,-,83,-,-,-,-,271 134 | 133,sophiaspavlova,Sofia Gospodinova,-,-,-,-,-,-,-,-,-,-,-,100,-,100,70,270 135 | 134,Picata,,-,-,-,-,100,-,70,-,100,-,-,-,-,-,-,270 136 | 135,Sando106,Sasan Zodzhnahin,100,-,-,-,-,-,-,-,100,-,-,-,-,-,70,270 137 | 136,yvette.y,Iveta Yancheva,-,-,-,-,-,-,70,-,-,100,100,-,-,-,-,270 138 | 137,Nikolay_Yonchev,,-,-,-,-,-,-,100,-,100,-,-,-,-,-,70,270 139 | 138,Ivan5503,Ivan Statelov,-,85,-,100,-,-,-,-,-,-,83,-,-,-,-,268 140 | 139,rr_sz12jn08,Velina Miteva,-,85,-,-,-,-,-,-,-,-,83,-,100,-,-,268 141 | 140,galski07,Галин Георгиев,100,85,-,-,-,-,-,-,-,-,83,-,-,-,-,268 142 | 141,sv3tlana,,-,-,-,100,-,-,-,-,87,-,-,-,-,-,80,267 143 | 142,petardimitrov55,,100,-,-,-,-,-,-,-,87,-,-,-,-,-,80,267 144 | 143,hparapunov,Hristo Parapunov,-,-,100,66,-,-,-,100,-,-,-,-,-,-,-,266 145 | 144,valentinos_tamenov,Valentino Stamenov,100,-,-,-,-,-,-,-,-,-,-,66,-,-,100,266 146 | 145,NyxK7,,-,-,-,-,-,-,-,100,-,-,66,-,100,-,-,266 147 | 146,tsvetelina.kerefeyna,,-,-,-,-,-,-,90,100,75,-,-,-,-,-,-,265 148 | 147,dobeca7,,-,-,-,-,-,75,-,-,-,-,-,-,100,-,90,265 149 | 148,Borimira,Mihail Velev,-,-,-,-,-,75,-,-,-,-,-,-,100,-,90,265 150 | 149,sterafa,Stefan Rafailov,-,-,-,-,-,-,80,100,-,-,83,-,-,-,-,263 151 | 150,AndreyZarev,Andrey Zarev,-,85,-,-,-,-,100,-,-,-,-,77,-,-,-,262 152 | 151,my_mun,,-,-,-,-,-,-,-,100,-,-,-,77,-,85,-,262 153 | 152,Mirellapeychev,Mirella Boneva,-,85,-,77,-,100,-,-,-,-,-,-,-,-,-,262 154 | 153,Paulina22,,-,85,-,88,-,-,-,-,87,-,-,-,-,-,-,260 155 | 154,StilyanaN,Stilyana Nguen,-,-,-,-,-,75,-,-,-,-,-,-,-,85,100,260 156 | 155,Idimchev,Ivaylo Dimchev,-,85,88,-,-,-,-,-,-,-,-,-,-,85,-,258 157 | 156,marioantonov,Mario Antonov,-,-,-,-,-,-,-,100,87,-,-,-,-,71,-,258 158 | 157,t.tsvetkov1415,Tsvetan Tsvetkov,88,-,-,-,-,-,-,-,100,-,-,-,-,-,70,258 159 | 158,freeflow,,-,-,-,-,-,-,-,100,-,-,100,-,-,57,-,257 160 | 159,SiyanaD,Siyana Danchilova,100,57,-,-,-,100,-,-,-,-,-,-,-,-,-,257 161 | 160,slawi1,Slavi Gatev,-,-,-,-,85,-,-,-,87,-,-,-,-,85,-,257 162 | 161,oksanakney,Oksana Kney,-,-,-,-,-,-,-,100,87,-,-,-,70,-,-,257 163 | 162,Tonsi,Antoniya Georgieva,100,-,-,-,-,-,-,-,-,-,-,77,-,-,80,257 164 | 163,ZhDobrev,Zhelyo Dobrev,-,-,-,-,100,-,-,-,-,-,-,55,100,-,-,255 165 | 164,SiskoN,Stanimir Nikolov,-,-,-,-,-,-,100,-,-,71,83,-,-,-,-,254 166 | 165,GeorgiP27,Georgi Panayotov,-,85,-,-,-,-,-,-,-,-,83,-,-,85,-,253 167 | 166,dzherekarov,,-,-,100,66,-,-,-,-,-,85,-,-,-,-,-,251 168 | 167,P3t3to,,-,-,-,-,-,-,-,100,-,-,-,66,-,85,-,251 169 | 168,Nbalimezov,Nikola Balimezov,-,-,100,100,-,-,-,-,-,-,-,-,-,-,50,250 170 | 169,Passko,,-,-,-,-,-,-,-,-,100,-,-,-,100,-,50,250 171 | 170,Preslav0980,,-,-,100,-,-,-,100,-,-,-,-,-,-,-,50,250 172 | 171,damyankamenov,Damyan Kamenov,-,71,-,-,-,-,-,-,-,-,-,77,100,-,-,248 173 | 172,Aleksandar_Zh,,-,-,-,-,-,-,-,90,-,-,-,55,100,-,-,245 174 | 173,Duly1,Ivaylo Radulov,-,-,88,-,-,-,100,-,-,57,-,-,-,-,-,245 175 | 174,Vanesa5006,Vanesa Vachkova,-,-,-,44,-,100,-,-,-,-,-,-,-,-,100,244 176 | 175,Ayshe180103,Ayshe Dzhemal,-,-,-,-,-,-,100,100,-,-,-,44,-,-,-,244 177 | 176,Hristo123456,,-,85,-,-,-,-,-,-,87,-,-,-,-,71,-,243 178 | 177,Maria.Antoinet,,-,-,77,66,-,-,-,100,-,-,-,-,-,-,-,243 179 | 178,iivanov95,Ivan Ivanov,-,-,100,-,-,-,-,-,-,57,-,-,-,85,-,242 180 | 179,didoncho,Diyan Nikolov,100,-,-,-,-,-,-,-,100,42,-,-,-,-,-,242 181 | 180,T0nka7a,,-,-,-,77,-,75,-,90,-,-,-,-,-,-,-,242 182 | 181,Kolyo2,Kolyo Kolev,-,71,-,66,-,100,-,-,-,-,-,-,-,-,-,237 183 | 182,MAYAKRASS,,-,-,-,-,100,37,-,-,-,-,-,-,100,-,-,237 184 | 183,Zhivko95,,-,-,-,-,-,-,100,-,-,57,-,77,-,-,-,234 185 | 184,DaniDim,Daniela Dimova,33,-,100,-,-,-,-,-,-,100,-,-,-,-,-,233 186 | 185,yulikivanov,Yuliyan Ivanov,-,-,-,-,-,-,-,-,-,-,100,-,100,-,30,230 187 | 186,Ivanivv,Ivan Ivanov,-,-,88,-,-,-,-,100,-,-,-,-,-,42,-,230 188 | 187,evgenimalev,,66,-,-,-,-,62,-,100,-,-,-,-,-,-,-,228 189 | 188,Bobi_ib,,88,-,-,-,-,-,-,90,-,-,50,-,-,-,-,228 190 | 189,ivanski_2,Ivan Ivanov,-,-,-,66,71,87,-,-,-,-,-,-,-,-,-,224 191 | 190,Martin_Ziberov,,-,57,-,88,-,75,-,-,-,-,-,-,-,-,-,220 192 | 191,Spasimir17,,-,-,-,-,-,-,-,-,-,-,-,66,100,-,50,216 193 | 192,LyubomiraK27,,-,-,88,-,-,-,-,-,-,57,-,-,70,-,-,215 194 | 193,DobrinSarakchiev,Dobrin Sarakchiev,-,-,-,-,100,-,80,-,-,-,-,33,-,-,-,213 195 | 194,vesko841,Veselin Ivanov,-,-,-,-,-,100,-,10,-,-,-,-,-,100,-,210 196 | 195,vtudzharska,Victoria Tudzharska,100,-,-,-,-,-,-,-,100,-,-,-,-,-,10,210 197 | 196,Krasimir0,,-,-,100,-,-,-,-,-,-,-,-,-,90,-,20,210 198 | 197,StelaChilikova,Stela Dimitrova,-,-,-,77,57,-,-,-,75,-,-,-,-,-,-,209 199 | 198,bojidara1702,,-,-,-,-,-,-,-,-,87,-,-,-,-,71,50,208 200 | 199,SURCESLAV,Stefan Saev,33,-,-,-,-,75,-,100,-,-,-,-,-,-,-,208 201 | 200,vasilena.3,Vasilena Palasheva,-,-,-,33,-,75,-,100,-,-,-,-,-,-,-,208 202 | 201,ThroatGag,,-,-,-,-,-,-,-,90,-,-,16,-,100,-,-,206 203 | 202,konstantinivanovivanov,Константин Иванов,-,71,66,66,-,-,-,-,-,-,-,-,-,-,-,203 204 | 203,Iskren002,,-,-,-,-,-,12,100,90,-,-,-,-,-,-,-,202 205 | 204,RositsaG,Rositsa Georgieva,100,0,-,-,-,100,-,-,-,-,-,-,-,-,-,200 206 | 205,ralicamanolova,,-,-,-,100,-,-,-,-,-,-,-,0,-,-,100,200 207 | 206,zornitsa_3,,-,-,-,-,-,-,-,-,-,0,100,-,100,-,-,200 208 | 207,Lilly13,Lilyana Kyuchukova,-,-,-,-,-,-,100,-,-,100,-,0,-,-,-,200 209 | 208,megy_r,Margarita Raeva,-,-,-,100,-,-,-,-,-,100,0,-,-,-,-,200 210 | 209,Chikoroto,,100,-,100,-,-,-,-,-,-,0,-,-,-,-,-,200 211 | 210,AlexanderMKolev,Alexander Kolev,-,-,-,100,100,-,-,-,-,-,-,0,-,-,-,200 212 | 211,idamyanov,,-,-,-,100,-,100,-,-,-,0,-,-,-,-,-,200 213 | 212,PavlinGalabov,Pavlin Galabov,-,100,-,100,-,-,-,-,-,-,-,0,-,-,-,200 214 | 213,vido1912,Vidolin Karadzhov,100,-,-,-,-,0,-,100,-,-,-,-,-,-,-,200 215 | 214,Stefan7323,Стефан Борисов,100,-,100,-,-,-,-,0,-,-,-,-,-,-,-,200 216 | 215,JoroCH,,-,-,-,-,-,100,-,-,-,0,-,-,100,-,-,200 217 | 216,HristiyanTsvetanov98,Hristiyan Tsvetanov,-,-,-,100,-,-,-,100,-,-,0,-,-,-,-,200 218 | 217,bobi1202,Boyan Antonov,-,-,-,-,0,-,100,-,100,-,-,-,-,-,-,200 219 | 218,mahon,Пламен РњРёС…РѕРІ,-,100,-,-,-,-,-,-,-,-,-,0,-,100,-,200 220 | 219,mstoyanov38,,-,-,0,100,-,-,-,100,-,-,-,-,-,-,-,200 221 | 220,CeKaTa_Sensei,Tsvetan Velinov,-,0,-,-,-,100,-,-,-,-,-,-,100,-,-,200 222 | 221,BenetoBoyan,Boyan Dimitrov,100,-,-,-,-,100,-,-,-,-,-,-,-,-,0,200 223 | 222,milendimov7,Milen Dimov,100,0,-,-,-,-,-,-,-,-,-,100,-,-,-,200 224 | 223,MilchoS,Milcho Sapundzhiev,-,-,0,-,-,-,100,-,-,100,-,-,-,-,-,200 225 | 224,Nevena1988,,-,-,-,-,0,100,100,-,-,-,-,-,-,-,-,200 226 | 225,iliana0,Iliana Todorova,-,-,-,66,-,-,-,-,62,-,-,-,-,-,70,198 227 | 226,Ognqn123,Ognyan Yordanov,-,-,88,66,-,-,-,-,-,42,-,-,-,-,-,196 228 | 227,ARodopska,Anzhelina Rodopska,-,-,-,-,-,-,-,-,100,-,-,-,0,-,90,190 229 | 228,NikolaiDulgerov,Nikolay Dyulgerov,-,-,-,-,0,100,90,-,-,-,-,-,-,-,-,190 230 | 229,HristoIvanov94,Hristo Ivanov,-,-,-,-,-,-,-,-,-,-,-,88,100,-,0,188 231 | 230,Iliyan_Georgiev,Iliyan Georgiev,88,-,100,-,-,-,-,-,-,0,-,-,-,-,-,188 232 | 231,bojidar8586,,-,-,100,-,0,-,-,-,-,-,-,-,-,85,-,185 233 | 232,isdraganov,Ivan Draganov,-,-,-,-,0,-,-,-,-,-,-,100,-,85,-,185 234 | 233,PetarMilushev,Petar Milushev,-,-,-,-,85,-,-,-,-,-,0,-,100,-,-,185 235 | 234,Stormy_Here,Boris Filev,-,85,-,-,-,-,100,-,-,-,0,-,-,-,-,185 236 | 235,DeDarko,Darin Zhelev,-,-,-,-,-,-,-,10,75,-,-,-,100,-,-,185 237 | 236,gooshys,Georgi Simonov,-,85,-,-,-,100,-,-,-,-,-,-,-,0,-,185 238 | 237,aycha,Aycha Kehaya,-,-,-,-,0,-,-,-,-,-,83,-,100,-,-,183 239 | 238,hr.nay,,-,-,-,100,-,-,-,-,-,0,83,-,-,-,-,183 240 | 239,Marinski98,Marin Nikolov,-,-,-,66,42,75,-,-,-,-,-,-,-,-,-,183 241 | 240,DenisBalabanov,Denis Balabanov,-,-,-,-,-,-,-,-,-,0,83,-,100,-,-,183 242 | 241,staninskidobroslav,,-,-,-,-,-,100,80,-,-,-,-,-,-,-,0,180 243 | 242,DesislavStefanov2003,Desislav Stefanov,-,0,-,100,-,-,-,-,-,-,-,77,-,-,-,177 244 | 243,ElifSali,,100,0,77,-,-,-,-,-,-,-,-,-,-,-,-,177 245 | 244,Ivan3000,,88,-,88,-,-,-,-,-,-,-,-,-,-,-,0,176 246 | 245,IvoVS,Ivailo Vasov,-,-,-,-,-,75,-,-,-,-,-,-,100,-,0,175 247 | 246,DanielTsochev,,100,-,-,-,-,75,-,-,-,-,-,-,-,-,0,175 248 | 247,Vasil_85,Vasil Vasilev,-,-,-,-,71,-,100,-,-,-,-,0,-,-,-,171 249 | 248,MihailxBorisov,Mihail Borisov,-,-,-,-,-,100,-,-,-,-,-,-,-,71,0,171 250 | 249,Petar.Kostadinov,Petar Kostadinov,-,-,-,-,-,0,-,-,-,100,-,-,-,71,-,171 251 | 250,jorko7120,Georgi Georgiev,-,-,-,-,-,-,70,-,-,100,-,0,-,-,-,170 252 | 251,peshobratmi,Victor Evgeniev,-,-,-,-,-,-,70,-,100,0,-,-,-,-,-,170 253 | 252,Zlatin27,Zlatin Dimitrov,-,-,-,66,-,-,-,-,-,71,-,33,-,-,-,170 254 | 253,MimiVracheva,Maria Vracheva,-,0,-,-,-,-,-,-,-,-,83,-,-,85,-,168 255 | 254,WikiJes,Виктория Димитрова,-,-,-,66,-,-,-,-,100,-,-,-,-,-,0,166 256 | 255,DanislavAL,Danislav Lazarov,-,-,100,66,-,-,-,-,-,0,-,-,-,-,-,166 257 | 256,Ivo959595,,-,-,-,66,0,-,-,-,-,-,100,-,-,-,-,166 258 | 257,enio72,,-,-,-,-,-,75,-,0,-,-,-,-,90,-,-,165 259 | 258,Yasena28,Yasena Petrova,-,0,-,-,-,-,-,-,75,-,-,-,90,-,-,165 260 | 259,IvanPython,Ivan Kamburov,-,-,-,-,-,-,-,-,-,-,-,0,-,85,80,165 261 | 260,nizzje,Nikolay Stoychev,-,-,-,88,0,-,-,-,75,-,-,-,-,-,-,163 262 | 261,YanevGeorgi,Georgi Yanev,100,0,-,-,-,62,-,-,-,-,-,-,-,-,-,162 263 | 262,tinteto,,-,-,-,-,0,75,-,-,-,-,-,-,-,85,-,160 264 | 263,Martin112,,-,-,-,-,0,-,60,-,-,-,100,-,-,-,-,160 265 | 264,BoyanYanakiev9312,Boyan Yanakiev,88,71,-,-,-,-,-,-,-,-,-,0,-,-,-,159 266 | 265,Bubanster,Lyubomira Andreeva,100,57,0,-,-,-,-,-,-,-,-,-,-,-,-,157 267 | 266,ValentinTrifonov,,-,-,-,-,0,-,-,-,-,-,100,-,-,57,-,157 268 | 267,aleksas74,,-,-,-,-,-,-,-,0,100,-,-,-,-,57,-,157 269 | 268,navushtanov,Ivo Navushtanov,-,-,-,-,0,-,100,-,-,-,-,55,-,-,-,155 270 | 269,DobriD,Dobri Dobrev,-,-,-,-,-,-,-,50,-,-,-,0,100,-,-,150 271 | 270,nikolai_bojkov,Nikolay Bozhkov,-,-,-,-,-,-,-,-,-,-,0,-,-,57,90,147 272 | 271,Tsvetan911,Tsvetan,-,-,77,-,-,-,70,-,-,-,-,-,-,-,0,147 273 | 272,ldadal,Penko Pirinov,-,0,-,-,-,-,80,-,-,-,-,66,-,-,-,146 274 | 273,Plamen005,,-,-,-,-,0,75,70,-,-,-,-,-,-,-,-,145 275 | 274,Valentin_Dragoev,Valentin Dragoev,-,-,-,44,100,-,-,-,0,-,-,-,-,-,-,144 276 | 275,IvKirin,Иван РљРёСЂРёРЅ,-,-,100,22,-,-,-,20,-,-,-,-,-,-,-,142 277 | 276,BeloslavBelov,,-,71,0,-,-,-,-,-,-,-,-,-,-,71,-,142 278 | 277,kari17,Kadrie Ali,-,42,-,100,-,-,-,-,0,-,-,-,-,-,-,142 279 | 278,KristiyanHristovHristov,,-,-,-,-,0,-,-,-,-,-,83,-,-,57,-,140 280 | 279,dilyanaKas,,-,-,-,-,-,0,-,100,-,-,-,-,40,-,-,140 281 | 280,Shterev9898,,-,-,-,55,0,-,-,-,-,-,83,-,-,-,-,138 282 | 281,yanitsatoncheva,Yanitsa Toncheva,-,-,66,-,-,-,70,-,-,-,-,-,-,-,0,136 283 | 282,RadoslavPeychev,,-,-,-,-,-,-,100,-,-,0,-,33,-,-,-,133 284 | 283,Malina.Dimova,Malina Dimova,-,-,-,100,-,-,-,-,-,-,-,33,-,-,0,133 285 | 284,StilyanaKachakova,Stilyana Kachakova,-,-,33,100,-,-,-,-,-,0,-,-,-,-,-,133 286 | 285,daniel.95,Daniel Nikolov,-,-,-,66,-,-,-,-,-,-,-,66,-,-,0,132 287 | 286,Lyuboslava030,,100,-,-,-,-,-,-,30,-,-,0,-,-,-,-,130 288 | 287,strazhakova,,-,-,88,-,0,-,40,-,-,-,-,-,-,-,-,128 289 | 288,SinanMehmedov,,-,-,-,-,0,100,-,-,-,-,-,-,-,28,-,128 290 | 289,Ppetkow900,Petar Petkov,-,-,-,-,-,-,100,-,-,-,-,22,-,-,0,122 291 | 290,nenki4a,Nenko Kovachev,-,-,-,-,0,-,-,-,-,-,-,77,-,42,-,119 292 | 291,Tsvetelina_11,Tsvetelina Stoycheva,88,-,-,-,-,-,-,-,-,-,-,0,-,-,30,118 293 | 292,qa4life,Ivaylo Vasilev,-,-,-,-,-,-,-,0,-,-,16,-,-,100,-,116 294 | 293,ViktorV777,Viktor Vurgov,-,-,-,-,-,-,-,-,-,-,-,44,-,57,0,101 295 | 294,Belev1926,Georgi Belev,-,-,-,-,-,-,100,-,-,-,-,0,-,-,0,100 296 | 295,VioletaSotirova,,-,-,-,-,-,-,-,-,-,0,0,-,100,-,-,100 297 | 296,sotirova.yulia,Yulia Sotirova,100,0,-,-,-,-,-,-,-,-,0,-,-,-,-,100 298 | 297,HIDakov,Hristo Dakov,100,-,-,-,0,-,-,-,-,-,-,0,-,-,-,100 299 | 298,elisaveta.stoyanovva,,-,0,-,-,-,-,-,-,-,-,100,-,-,0,-,100 300 | 299,amira_amar,Amira Ammar,-,0,-,-,-,-,-,-,-,-,0,-,100,-,-,100 301 | 300,Radostin_Tasev,,-,-,-,-,0,-,-,-,-,-,0,-,-,100,-,100 302 | 301,GertaNn,Veselin Bahchevanski,-,-,-,-,-,-,0,-,-,100,-,0,-,-,-,100 303 | 302,takovataname123,,100,-,-,-,0,-,-,-,-,-,-,0,-,-,-,100 304 | 303,Elf_Dude,,100,-,-,-,-,-,-,-,0,0,-,-,-,-,-,100 305 | 304,stoyan_1,Stoyan Stanev,100,0,-,-,-,-,-,-,-,-,0,-,-,-,-,100 306 | 305,Denis8,,-,0,-,-,-,-,-,-,-,-,-,0,100,-,-,100 307 | 306,Mariopi7,,100,-,0,-,-,-,-,-,-,0,-,-,-,-,-,100 308 | 307,resum,Nayden Naydenov,-,0,-,100,-,-,-,-,-,-,0,-,-,-,-,100 309 | 308,jen40,Elena Маринова,-,0,-,-,-,75,20,-,-,-,-,-,-,-,-,95 310 | 309,NVMARZAKOV,Nikolay Marzakov,-,-,-,-,0,0,90,-,-,-,-,-,-,-,-,90 311 | 310,N1kola,Nikola Nikolov,-,-,-,-,-,-,40,-,-,0,50,-,-,-,-,90 312 | 311,Radomir73,,-,-,88,0,0,-,-,-,-,-,-,-,-,-,-,88 313 | 312,Kremena_Stoyanova,Kremena 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356,hristomirster,Hristomir Vasilev,-,-,-,-,-,-,0,-,-,-,0,-,-,-,0,0 358 | 357,marina.grebenarova,Marina Grebenarova,-,-,0,-,0,-,-,-,-,-,-,-,-,0,-,0 359 | 358,KaterinaLuleova,Katerina Luleova,-,0,-,0,-,0,-,-,-,-,-,-,-,-,-,0 360 | 359,ico2662,,0,-,-,-,-,0,-,-,-,0,-,-,-,-,-,0 361 | 360,nadiia.ivanova,,0,-,-,-,-,-,-,-,0,0,-,-,-,-,-,0 362 | 361,luchezrkk,,-,-,0,-,0,-,-,-,-,-,-,-,0,-,-,0 363 | 362,Bilyana_EGeorgieva,,-,-,-,-,-,-,-,-,-,0,-,0,-,0,-,0 364 | 363,Kalevra,Diyan Apostolov,-,-,-,-,-,-,-,-,-,-,-,-,-,-,-,0 365 | 364,Park_The_Bus,,-,-,-,-,0,-,-,-,-,-,0,-,-,0,-,0 366 | 365,AleksStankovski,,-,-,-,-,-,-,0,-,0,0,-,-,-,-,-,0 367 | 366,NikolKL,,-,-,-,0,-,-,-,-,0,-,-,-,-,-,0,0 368 | 367,tedialex,,-,0,0,-,-,-,-,-,-,-,-,-,-,0,-,0 369 | 368,IlianaDB,Iliana Bakardzhieva,-,-,-,-,-,-,-,-,-,-,0,-,0,-,0,0 370 | 369,Ivanov_Ivan,,-,-,-,-,-,-,-,0,0,-,-,-,-,0,-,0 371 | 370,toni_7654321,,-,-,-,-,-,-,0,0,-,-,0,-,-,-,-,0 372 | 371,hr1s93,,-,-,-,-,-,0,-,-,-,0,-,-,-,0,-,0 373 | 372,tandautova,Tanya 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Dimitrova,-,0,-,-,-,-,0,-,-,-,0,-,-,-,-,0 389 | 388,vyara_zlatanova,,-,-,-,-,-,-,-,-,0,0,-,-,0,-,-,0 390 | 389,donika_gwd,,-,-,-,-,-,-,0,0,-,-,-,0,-,-,-,0 391 | 390,Stanislava1,Stanislava Dimitrova,-,-,-,-,-,0,0,0,-,-,-,-,-,-,-,0 392 | 391,Niko7,Rumen Nikolaev,-,-,-,-,-,0,-,0,-,-,-,-,0,-,-,0 393 | 392,renetakirilova94,Reneta Kirilova,0,0,-,-,-,-,-,-,-,-,0,-,-,-,-,0 394 | -------------------------------------------------------------------------------- /01_Data Tidying and Cleaning/Data Tidying and Cleaning Lab.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import nose.tools\n", 11 | "# Write your imports here" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": {}, 17 | "source": [ 18 | "# Data Tidying and Cleaning Lab\n", 19 | "## Reading, tidying and cleaning data. Preparing data for exploration, mining, analysis and learning" 20 | ] 21 | }, 22 | { 23 | "cell_type": "markdown", 24 | "metadata": { 25 | "collapsed": true 26 | }, 27 | "source": [ 28 | "### Problem 1. Read the dataset (2 points)\n", 29 | "The dataset [here](http://archive.ics.uci.edu/ml/datasets/Auto+MPG) contains information about fuel consumption in different cars.\n", 30 | "\n", 31 | "Click the \"Data Folder\" link and read `auto_mpg.data` into Python. You can download it, if you wish, or you can read it directly from the link.\n", 32 | "\n", 33 | "Give meaningful (and \"Pythonic\") column names, as per the `auto_mpg.names` file:\n", 34 | "1. mpg\n", 35 | "2. cylinders\n", 36 | "3. displacement\n", 37 | "4. horsepower\n", 38 | "5. weight\n", 39 | "6. acceleration\n", 40 | "7. model_year\n", 41 | "8. origin\n", 42 | "9. car_name" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 2, 48 | "metadata": { 49 | "nbgrader": { 50 | "grade": false, 51 | "grade_id": "read_data", 52 | "locked": false, 53 | "schema_version": 3, 54 | "solution": true 55 | } 56 | }, 57 | "outputs": [], 58 | "source": [ 59 | "mpg_data = None\n", 60 | "### BEGIN SOLUTION\n", 61 | "mpg_data = pd.read_fwf(\"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\", header = None)\n", 62 | "mpg_data.columns = [\"mpg\", \"cylinders\", \"displacement\", \"horsepower\", \"weight\", \"acceleration\", \"model_year\", \"origin\", \"car_name\"]\n", 63 | "### END SOLUTION" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 3, 69 | "metadata": { 70 | "nbgrader": { 71 | "grade": true, 72 | "grade_id": "read_data_tests", 73 | "locked": true, 74 | "points": 2, 75 | "schema_version": 3, 76 | "solution": false 77 | } 78 | }, 79 | "outputs": [], 80 | "source": [ 81 | "nose.tools.assert_is_not_none(mpg_data)\n", 82 | "### BEGIN HIDDEN TESTS\n", 83 | "nose.tools.assert_equal(mpg_data.shape, (398, 9))\n", 84 | "nose.tools.assert_list_equal(mpg_data.columns.tolist(), [\"mpg\", \"cylinders\", \"displacement\", \"horsepower\", \"weight\", \"acceleration\", \"model_year\", \"origin\", \"car_name\"])\n", 85 | "### END HIDDEN TESTS" 86 | ] 87 | }, 88 | { 89 | "cell_type": "markdown", 90 | "metadata": {}, 91 | "source": [ 92 | "Print the first 4 rows in the dataset to get a feel of what it looks like:" 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": 4, 98 | "metadata": { 99 | "nbgrader": { 100 | "grade": false, 101 | "grade_id": "cell-80f1e6004aaafef8", 102 | "locked": false, 103 | "schema_version": 3, 104 | "solution": true 105 | } 106 | }, 107 | "outputs": [ 108 | { 109 | "data": { 110 | "text/html": [ 111 | "
\n", 112 | "\n", 125 | "\n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | "
mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearorigincar_name
018.08307.0130.03504.012.0701\"chevrolet chevelle malibu\"
115.08350.0165.03693.011.5701\"buick skylark 320\"
218.08318.0150.03436.011.0701\"plymouth satellite\"
316.08304.0150.03433.012.0701\"amc rebel sst\"
\n", 191 | "
" 192 | ], 193 | "text/plain": [ 194 | " mpg cylinders displacement horsepower weight acceleration model_year \\\n", 195 | "0 18.0 8 307.0 130.0 3504.0 12.0 70 \n", 196 | "1 15.0 8 350.0 165.0 3693.0 11.5 70 \n", 197 | "2 18.0 8 318.0 150.0 3436.0 11.0 70 \n", 198 | "3 16.0 8 304.0 150.0 3433.0 12.0 70 \n", 199 | "\n", 200 | " origin car_name \n", 201 | "0 1 \"chevrolet chevelle malibu\" \n", 202 | "1 1 \"buick skylark 320\" \n", 203 | "2 1 \"plymouth satellite\" \n", 204 | "3 1 \"amc rebel sst\" " 205 | ] 206 | }, 207 | "execution_count": 4, 208 | "metadata": {}, 209 | "output_type": "execute_result" 210 | } 211 | ], 212 | "source": [ 213 | "### BEGIN SOLUTION\n", 214 | "mpg_data.head(4)\n", 215 | "### END SOLUTION" 216 | ] 217 | }, 218 | { 219 | "cell_type": "markdown", 220 | "metadata": {}, 221 | "source": [ 222 | "### Problem 2. Inspect the dataset (1 point)\n", 223 | "Write a function which accepts a dataset and returns the number of observations and features in it, like so: \n", 224 | "\n", 225 | "``` 10 observations on 15 features```\n", 226 | "\n", 227 | "Where 10 and 15 should be replaced with the real numbers. Test your function with the `auto_mpg` dataset.\n", 228 | "\n", 229 | "Make sure the function works with other datasets (don't worry about \"1 features\" or \"1 observations\", just leave it as it is)." 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "execution_count": 5, 235 | "metadata": { 236 | "nbgrader": { 237 | "grade": false, 238 | "grade_id": "get_shape_function", 239 | "locked": false, 240 | "schema_version": 3, 241 | "solution": true 242 | } 243 | }, 244 | "outputs": [], 245 | "source": [ 246 | "def observations_and_features(dataset):\n", 247 | " \"\"\"\n", 248 | " Returns the number of observations and features in the provided dataset\n", 249 | " \"\"\"\n", 250 | " observations = None\n", 251 | " features = None\n", 252 | " ### BEGIN SOLUTION\n", 253 | " observations = dataset.shape[0]\n", 254 | " features = dataset.shape[1]\n", 255 | " ### END SOLUTION\n", 256 | " return \"{} observations on {} features\".format(observations, features)" 257 | ] 258 | }, 259 | { 260 | "cell_type": "code", 261 | "execution_count": 6, 262 | "metadata": { 263 | "nbgrader": { 264 | "grade": true, 265 | "grade_id": "get_shape_function_tests", 266 | "locked": true, 267 | "points": 1, 268 | "schema_version": 3, 269 | "solution": false 270 | } 271 | }, 272 | "outputs": [ 273 | { 274 | "name": "stdout", 275 | "output_type": "stream", 276 | "text": [ 277 | "398 observations on 9 features\n" 278 | ] 279 | } 280 | ], 281 | "source": [ 282 | "print(observations_and_features(mpg_data))\n", 283 | "### BEGIN HIDDEN TESTS\n", 284 | "nose.tools.assert_equal(observations_and_features(mpg_data), \"398 observations on 9 features\")\n", 285 | "nose.tools.assert_equal(observations_and_features(pd.DataFrame({\"A\": [0, 1], \"B\": [1, 2], \"C\": [5, 10]})), \"2 observations on 3 features\")\n", 286 | "nose.tools.assert_equal(observations_and_features(pd.DataFrame({\"A\": [0, 1]})), \"2 observations on 1 features\")\n", 287 | "nose.tools.assert_equal(observations_and_features(pd.DataFrame({\"A\": [0], \"B\": [1], \"C\": [5]})), \"1 observations on 3 features\")\n", 288 | "nose.tools.assert_equal(observations_and_features(pd.DataFrame({\"A\": [0]})), \"1 observations on 1 features\")\n", 289 | "### END HIDDEN TESTS" 290 | ] 291 | }, 292 | { 293 | "cell_type": "markdown", 294 | "metadata": {}, 295 | "source": [ 296 | "Inspect the data types for each column." 297 | ] 298 | }, 299 | { 300 | "cell_type": "code", 301 | "execution_count": 7, 302 | "metadata": { 303 | "nbgrader": { 304 | "grade": false, 305 | "grade_id": "cell-152f652655c53f2a", 306 | "locked": false, 307 | "schema_version": 3, 308 | "solution": true 309 | } 310 | }, 311 | "outputs": [ 312 | { 313 | "data": { 314 | "text/plain": [ 315 | "mpg float64\n", 316 | "cylinders int64\n", 317 | "displacement float64\n", 318 | "horsepower object\n", 319 | "weight float64\n", 320 | "acceleration float64\n", 321 | "model_year int64\n", 322 | "origin int64\n", 323 | "car_name object\n", 324 | "dtype: object" 325 | ] 326 | }, 327 | "execution_count": 7, 328 | "metadata": {}, 329 | "output_type": "execute_result" 330 | } 331 | ], 332 | "source": [ 333 | "### BEGIN SOLUTION\n", 334 | "mpg_data.dtypes\n", 335 | "### END SOLUTION" 336 | ] 337 | }, 338 | { 339 | "cell_type": "markdown", 340 | "metadata": {}, 341 | "source": [ 342 | "### Problem 3. Correct errors (1 point)\n", 343 | "The `horsepower` column looks strange. It's a string but it must be a floating-point number. Find out why this is so and convert it to floating-point number." 344 | ] 345 | }, 346 | { 347 | "cell_type": "code", 348 | "execution_count": 8, 349 | "metadata": { 350 | "nbgrader": { 351 | "grade": false, 352 | "grade_id": "convert_to_numeric", 353 | "locked": false, 354 | "schema_version": 3, 355 | "solution": true 356 | } 357 | }, 358 | "outputs": [], 359 | "source": [ 360 | "### BEGIN SOLUTION\n", 361 | "mpg_data.horsepower = pd.to_numeric(mpg_data.horsepower, errors = \"coerce\")\n", 362 | "### END SOLUTION" 363 | ] 364 | }, 365 | { 366 | "cell_type": "code", 367 | "execution_count": 9, 368 | "metadata": { 369 | "nbgrader": { 370 | "grade": true, 371 | "grade_id": "convert_to_numeric_tests", 372 | "locked": true, 373 | "points": 1, 374 | "schema_version": 3, 375 | "solution": false 376 | } 377 | }, 378 | "outputs": [], 379 | "source": [ 380 | "nose.tools.assert_equal(mpg_data.horsepower.dtype, \"float64\")\n", 381 | "### BEGIN HIDDEN TESTS\n", 382 | "nose.tools.assert_equal(mpg_data.dtypes.horsepower, \"float64\")\n", 383 | "nose.tools.assert_equal(mpg_data.horsepower.isnull().sum(), 6)\n", 384 | "nose.tools.assert_almost_equal(mpg_data.horsepower.mean(), 104.46938775510205, delta = 0.01)\n", 385 | "nose.tools.assert_almost_equal(mpg_data.horsepower.std(), 38.49115993282855, delta = 0.01)\n", 386 | "### END HIDDEN TESTS" 387 | ] 388 | }, 389 | { 390 | "cell_type": "markdown", 391 | "metadata": {}, 392 | "source": [ 393 | "### Problem 4. Missing values: inspection (1 point)\n", 394 | "We saw that the `horsepower` column contained null values. Display the rows which contain those values. Assign the resulting dataframe to the `unknown_hp` variable." 395 | ] 396 | }, 397 | { 398 | "cell_type": "code", 399 | "execution_count": 10, 400 | "metadata": { 401 | "nbgrader": { 402 | "grade": false, 403 | "grade_id": "unknown_hp", 404 | "locked": false, 405 | "schema_version": 3, 406 | "solution": true 407 | } 408 | }, 409 | "outputs": [], 410 | "source": [ 411 | "def get_unknown_hp(dataframe):\n", 412 | " \"\"\"\n", 413 | " Returns the rows in the provided dataframe where the \"horsepower\" column is NaN\n", 414 | " \"\"\"\n", 415 | " unknown_hp = None\n", 416 | " ### BEGIN SOLUTION\n", 417 | " unknown_hp = mpg_data[mpg_data.horsepower.isnull()]\n", 418 | " ### END SOLUTION\n", 419 | " return unknown_hp" 420 | ] 421 | }, 422 | { 423 | "cell_type": "code", 424 | "execution_count": 11, 425 | "metadata": { 426 | "nbgrader": { 427 | "grade": true, 428 | "grade_id": "unknown_hp_tests", 429 | "locked": true, 430 | "points": 1, 431 | "schema_version": 3, 432 | "solution": false 433 | } 434 | }, 435 | "outputs": [ 436 | { 437 | "name": "stdout", 438 | "output_type": "stream", 439 | "text": [ 440 | " mpg cylinders displacement horsepower weight acceleration \\\n", 441 | "32 25.0 4 98.0 NaN 2046.0 19.0 \n", 442 | "126 21.0 6 200.0 NaN 2875.0 17.0 \n", 443 | "330 40.9 4 85.0 NaN 1835.0 17.3 \n", 444 | "336 23.6 4 140.0 NaN 2905.0 14.3 \n", 445 | "354 34.5 4 100.0 NaN 2320.0 15.8 \n", 446 | "374 23.0 4 151.0 NaN 3035.0 20.5 \n", 447 | "\n", 448 | " model_year origin car_name \n", 449 | "32 71 1 \"ford pinto\" \n", 450 | "126 74 1 \"ford maverick\" \n", 451 | "330 80 2 \"renault lecar deluxe\" \n", 452 | "336 80 1 \"ford mustang cobra\" \n", 453 | "354 81 2 \"renault 18i\" \n", 454 | "374 82 1 \"amc concord dl\" \n" 455 | ] 456 | } 457 | ], 458 | "source": [ 459 | "cars_with_unknown_hp = get_unknown_hp(mpg_data)\n", 460 | "print(cars_with_unknown_hp)\n", 461 | "### BEGIN HIDDEN TESTS\n", 462 | "import math\n", 463 | "nose.tools.assert_equal(cars_with_unknown_hp.shape, (6, 9))\n", 464 | "test_means = cars_with_unknown_hp.mean()\n", 465 | "nose.tools.assert_true(math.isnan(test_means.horsepower))\n", 466 | "nose.tools.assert_true(test_means.displacement, 129)\n", 467 | "### END HIDDEN TESTS" 468 | ] 469 | }, 470 | { 471 | "cell_type": "markdown", 472 | "metadata": {}, 473 | "source": [ 474 | "### Problem 5. Missing data: correction (1 point)\n", 475 | "It seems like the `NaN` values are a small fraction of all values. We can try one of several things:\n", 476 | "* Remove them\n", 477 | "* Replace them (e.g. with the mean power of all cars)\n", 478 | "* Look up the models on the internet and try our best guess on the power\n", 479 | "\n", 480 | "The third one is probably the best but the first one will suffice since these records are too few. Remove those values. Save the dataset in the same `mpg_data` variable. Ensure there are no more `NaN`s." 481 | ] 482 | }, 483 | { 484 | "cell_type": "code", 485 | "execution_count": 12, 486 | "metadata": { 487 | "nbgrader": { 488 | "grade": false, 489 | "grade_id": "remove_nulls", 490 | "locked": false, 491 | "schema_version": 3, 492 | "solution": true 493 | } 494 | }, 495 | "outputs": [], 496 | "source": [ 497 | "### BEGIN SOLUTION\n", 498 | "mpg_data = mpg_data[~mpg_data.horsepower.isnull()]\n", 499 | "### END SOLUTION" 500 | ] 501 | }, 502 | { 503 | "cell_type": "code", 504 | "execution_count": 13, 505 | "metadata": { 506 | "nbgrader": { 507 | "grade": true, 508 | "grade_id": "remove_nulls_test", 509 | "locked": true, 510 | "points": 1, 511 | "schema_version": 3, 512 | "solution": false 513 | } 514 | }, 515 | "outputs": [], 516 | "source": [ 517 | "nose.tools.assert_equal(len(get_unknown_hp(mpg_data)), 0)\n", 518 | "### BEGIN HIDDEN TESTS\n", 519 | "nose.tools.assert_equal(mpg_data.shape, (392, 9))\n", 520 | "nose.tools.assert_equal(mpg_data.isnull().sum().sum(), 0)\n", 521 | "### END HIDDEN TESTS" 522 | ] 523 | }, 524 | { 525 | "cell_type": "markdown", 526 | "metadata": {}, 527 | "source": [ 528 | "### Problem 6. Years of production (1 + 1 points)\n", 529 | "Display all unique model years. Assign them to the variable `model_years`." 530 | ] 531 | }, 532 | { 533 | "cell_type": "code", 534 | "execution_count": 14, 535 | "metadata": { 536 | "nbgrader": { 537 | "grade": false, 538 | "grade_id": "model_years", 539 | "locked": false, 540 | "schema_version": 3, 541 | "solution": true 542 | } 543 | }, 544 | "outputs": [], 545 | "source": [ 546 | "def get_unique_model_years(dataframe):\n", 547 | " \"\"\"\n", 548 | " Returns the unique values of the \"model_year\" column\n", 549 | " of the dataframe\n", 550 | " \"\"\"\n", 551 | " model_years = None\n", 552 | " ### BEGIN SOLUTION\n", 553 | " model_years = mpg_data.model_year.unique()\n", 554 | " ### END SOLUTION\n", 555 | " return model_years" 556 | ] 557 | }, 558 | { 559 | "cell_type": "code", 560 | "execution_count": 15, 561 | "metadata": { 562 | "nbgrader": { 563 | "grade": true, 564 | "grade_id": "model_years_test", 565 | "locked": true, 566 | "points": 1, 567 | "schema_version": 3, 568 | "solution": false 569 | } 570 | }, 571 | "outputs": [ 572 | { 573 | "name": "stdout", 574 | "output_type": "stream", 575 | "text": [ 576 | "[70 71 72 73 74 75 76 77 78 79 80 81 82]\n" 577 | ] 578 | } 579 | ], 580 | "source": [ 581 | "model_years = get_unique_model_years(mpg_data)\n", 582 | "print(model_years)\n", 583 | "### BEGIN HIDDEN TESTS\n", 584 | "nose.tools.assert_list_equal(model_years.tolist(), [70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82])\n", 585 | "### END HIDDEN TESTS" 586 | ] 587 | }, 588 | { 589 | "cell_type": "markdown", 590 | "metadata": {}, 591 | "source": [ 592 | "These don't look so good. Convert them to real years, like `70 -> 1970, 71 -> 1971`. Replace the column values in the dataframe." 593 | ] 594 | }, 595 | { 596 | "cell_type": "code", 597 | "execution_count": 16, 598 | "metadata": { 599 | "nbgrader": { 600 | "grade": false, 601 | "grade_id": "model_year", 602 | "locked": false, 603 | "schema_version": 3, 604 | "solution": true 605 | } 606 | }, 607 | "outputs": [], 608 | "source": [ 609 | "### BEGIN SOLUTION\n", 610 | "mpg_data.loc[:, \"model_year\"] = mpg_data.model_year + 1900\n", 611 | "### END SOLUTION" 612 | ] 613 | }, 614 | { 615 | "cell_type": "code", 616 | "execution_count": 17, 617 | "metadata": { 618 | "nbgrader": { 619 | "grade": true, 620 | "grade_id": "model_year_test", 621 | "locked": true, 622 | "points": 1, 623 | "schema_version": 3, 624 | "solution": false 625 | } 626 | }, 627 | "outputs": [ 628 | { 629 | "name": "stdout", 630 | "output_type": "stream", 631 | "text": [ 632 | "[1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982]\n" 633 | ] 634 | } 635 | ], 636 | "source": [ 637 | "model_years = get_unique_model_years(mpg_data)\n", 638 | "print(model_years)\n", 639 | "### BEGIN HIDDEN TESTS\n", 640 | "nose.tools.assert_list_equal(model_years.tolist(), [1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980,\n", 641 | " 1981, 1982])\n", 642 | "### END HIDDEN TESTS" 643 | ] 644 | }, 645 | { 646 | "cell_type": "markdown", 647 | "metadata": {}, 648 | "source": [ 649 | "### Problem 7. Exploration: low-power cars (1 point)\n", 650 | "The data looks quite good now. Let's try some exploration.\n", 651 | "\n", 652 | "Write a function to find the cars which have the smallest number of cylinders and print their model names. Return a list of car names." 653 | ] 654 | }, 655 | { 656 | "cell_type": "code", 657 | "execution_count": 18, 658 | "metadata": { 659 | "nbgrader": { 660 | "grade": false, 661 | "grade_id": "car_names", 662 | "locked": false, 663 | "schema_version": 3, 664 | "solution": true 665 | } 666 | }, 667 | "outputs": [], 668 | "source": [ 669 | "def get_model_names_smallest_cylinders(dataframe):\n", 670 | " \"\"\"\n", 671 | " Returns the names of the cars with the smallest number of cylinders\n", 672 | " \"\"\"\n", 673 | " car_names = None\n", 674 | " ### BEGIN SOLUTION\n", 675 | " car_names = dataframe[dataframe.cylinders == dataframe.cylinders.min()].car_name\n", 676 | " ### END SOLUTION\n", 677 | " return car_names" 678 | ] 679 | }, 680 | { 681 | "cell_type": "code", 682 | "execution_count": 19, 683 | "metadata": { 684 | "nbgrader": { 685 | "grade": true, 686 | "grade_id": "car_names_test", 687 | "locked": true, 688 | "points": 1, 689 | "schema_version": 3, 690 | "solution": false 691 | } 692 | }, 693 | "outputs": [ 694 | { 695 | "name": "stdout", 696 | "output_type": "stream", 697 | "text": [ 698 | "71 \"mazda rx2 coupe\"\n", 699 | "111 \"maxda rx3\"\n", 700 | "243 \"mazda rx-4\"\n", 701 | "334 \"mazda rx-7 gs\"\n", 702 | "Name: car_name, dtype: object\n" 703 | ] 704 | } 705 | ], 706 | "source": [ 707 | "car_names = get_model_names_smallest_cylinders(mpg_data)\n", 708 | "print(car_names)\n", 709 | "nose.tools.assert_true(car_names.shape == (4,) or car_names.shape == (4, 1))\n", 710 | "### BEGIN HIDDEN TESTS\n", 711 | "nose.tools.assert_equal(car_names.index.tolist(), [71, 111, 243, 334])\n", 712 | "mpg_data_filtered = mpg_data[mpg_data.cylinders > 3]\n", 713 | "test_car_names = get_model_names_smallest_cylinders(mpg_data_filtered)\n", 714 | "nose.tools.assert_true(test_car_names.shape == (199,) or test_car_names.shape == (199, 1))\n", 715 | "### END HIDDEN TESTS" 716 | ] 717 | }, 718 | { 719 | "cell_type": "markdown", 720 | "metadata": {}, 721 | "source": [ 722 | "### Problem 8. Exploration: correlations (1 point)\n", 723 | "Finally, let's see some connections between variables. These are also called **correlations**.\n", 724 | "\n", 725 | "Find how to calculate correlations between different columns using `pandas`.\n", 726 | "\n", 727 | "**Hint:** The correlation function in `pandas` returns a `DataFrame` by default. You need only one value from it.\n", 728 | "\n", 729 | "Create a function which accepts a dataframe and two columns and prints the correlation coefficient between those two columns." 730 | ] 731 | }, 732 | { 733 | "cell_type": "code", 734 | "execution_count": 20, 735 | "metadata": { 736 | "nbgrader": { 737 | "grade": false, 738 | "grade_id": "correlation", 739 | "locked": false, 740 | "schema_version": 3, 741 | "solution": true 742 | } 743 | }, 744 | "outputs": [], 745 | "source": [ 746 | "def calculate_correlation(dataframe, first_column, second_column):\n", 747 | " \"\"\"\n", 748 | " Calculates and returns the correlation coefficient between the two columns in the dataframe.\n", 749 | " \"\"\"\n", 750 | " correlation = None\n", 751 | " ### BEGIN SOLUTION\n", 752 | " correlation = dataframe[[first_column, second_column]].corr().iloc[0, 1]\n", 753 | " ### END SOLUTION\n", 754 | " return correlation" 755 | ] 756 | }, 757 | { 758 | "cell_type": "code", 759 | "execution_count": 21, 760 | "metadata": { 761 | "nbgrader": { 762 | "grade": true, 763 | "grade_id": "cell-457c5946f2350991", 764 | "locked": true, 765 | "points": 1, 766 | "schema_version": 3, 767 | "solution": false 768 | } 769 | }, 770 | "outputs": [ 771 | { 772 | "name": "stdout", 773 | "output_type": "stream", 774 | "text": [ 775 | "Horsepower:Weight correlation coefficient: 0.8645377375741428\n" 776 | ] 777 | } 778 | ], 779 | "source": [ 780 | "hp_weight = calculate_correlation(mpg_data, \"horsepower\", \"weight\")\n", 781 | "print(\"Horsepower:Weight correlation coefficient:\", hp_weight)\n", 782 | "nose.tools.assert_almost_equal(hp_weight, 0.864537737574, delta = 0.01)\n", 783 | "\n", 784 | "### BEGIN HIDDEN TESTS\n", 785 | "corr_test = calculate_correlation(mpg_data, \"horsepower\", \"mpg\")\n", 786 | "nose.tools.assert_almost_equal(corr_test, -0.77842678389777509, delta = 0.01)\n", 787 | "corr_test = calculate_correlation(mpg_data, \"mpg\", \"acceleration\")\n", 788 | "nose.tools.assert_almost_equal(corr_test, 0.42332853690278727, delta = 0.01)\n", 789 | "corr_test = calculate_correlation(mpg_data, \"mpg\", \"mpg\")\n", 790 | "nose.tools.assert_equal(corr_test, 1)\n", 791 | "### END HIDDEN TESTS" 792 | ] 793 | } 794 | ], 795 | "metadata": { 796 | "celltoolbar": "Create Assignment", 797 | "kernelspec": { 798 | "display_name": "Python 3 (ipykernel)", 799 | "language": "python", 800 | "name": "python3" 801 | }, 802 | "language_info": { 803 | "codemirror_mode": { 804 | "name": "ipython", 805 | "version": 3 806 | }, 807 | "file_extension": ".py", 808 | "mimetype": "text/x-python", 809 | "name": "python", 810 | "nbconvert_exporter": "python", 811 | "pygments_lexer": "ipython3", 812 | "version": "3.9.7" 813 | } 814 | }, 815 | "nbformat": 4, 816 | "nbformat_minor": 2 817 | } 818 | -------------------------------------------------------------------------------- /06_Data Science Project Architecture/data/titanic.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 3 | 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C 4 | 3,1,3,"Heikkinen, Miss. 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Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S 866 | 865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S 867 | 866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S 868 | 867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C 869 | 868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S 870 | 869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S 871 | 870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S 872 | 871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S 873 | 872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S 874 | 873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S 875 | 874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S 876 | 875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C 877 | 876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C 878 | 877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S 879 | 878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S 880 | 879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S 881 | 880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C 882 | 881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S 883 | 882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S 884 | 883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S 885 | 884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S 886 | 885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S 887 | 886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q 888 | 887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S 889 | 888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S 890 | 889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S 891 | 890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C 892 | 891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q 893 | -------------------------------------------------------------------------------- /02_Data Visualization and EDA/Data Visualization and EDA Lab.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "%matplotlib inline" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 2, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "import pandas as pd\n", 19 | "import matplotlib.pyplot as plt\n", 20 | "import nose.tools\n", 21 | "# Write your imports here" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "# Data Visualization and Exploratory Data Analysis Lab\n", 29 | "## Visualizing and exploring data. Data mining process as a whole" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "### Problem 1. Read the dataset (1 point)\n", 37 | "You'll be exploring data about people's income. Your task is to understand whether there are significant differences in the lifestyle of lower- vs. higher-income groups.\n", 38 | "\n", 39 | "Read the dataset located [here](https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data). The information file is [here](https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names). Save it into the variable `income_data`. Change the column names to what you like. The last column is related to the income class.\n", 40 | "\n", 41 | "Get acquainted with the information file well before starting work.\n", 42 | "\n", 43 | "You don't need to clean the dataset." 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 3, 49 | "metadata": { 50 | "nbgrader": { 51 | "grade": false, 52 | "grade_id": "read-dataset", 53 | "locked": false, 54 | "schema_version": 3, 55 | "solution": true 56 | } 57 | }, 58 | "outputs": [ 59 | { 60 | "name": "stderr", 61 | "output_type": "stream", 62 | "text": [ 63 | "C:\\Users\\Yordan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n", 64 | " This is separate from the ipykernel package so we can avoid doing imports until\n" 65 | ] 66 | } 67 | ], 68 | "source": [ 69 | "income_data = None\n", 70 | "### BEGIN SOLUTION\n", 71 | "income_data = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\", sep = \", \", header = None)\n", 72 | "income_data.columns = [\"age\", \"workclass\", \"fnlwgt\", \"education\", \"education-num\", \"marital-status\", \"occupation\", \"relationship\", \"race\", \"sex\", \"capital-gain\", \"capital-loss\", \"hours-per-week\", \"native-country\", \"income\"]\n", 73 | "### END SOLUTION" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": 4, 79 | "metadata": { 80 | "nbgrader": { 81 | "grade": true, 82 | "grade_id": "read-dataset-tests", 83 | "locked": true, 84 | "points": 1, 85 | "schema_version": 3, 86 | "solution": false 87 | } 88 | }, 89 | "outputs": [ 90 | { 91 | "name": "stderr", 92 | "output_type": "stream", 93 | "text": [ 94 | "C:\\Users\\Yordan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n", 95 | " This is separate from the ipykernel package so we can avoid doing imports until\n" 96 | ] 97 | } 98 | ], 99 | "source": [ 100 | "nose.tools.assert_is_not_none(income_data)\n", 101 | "### BEGIN HIDDEN TESTS\n", 102 | "income_data_test = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\", sep = \", \", header = None)\n", 103 | "income_data_test.columns = income_data.columns\n", 104 | "\n", 105 | "nose.tools.assert_equal(income_data.shape, income_data_test.shape)\n", 106 | "### END HIDDEN TESTS" 107 | ] 108 | }, 109 | { 110 | "cell_type": "markdown", 111 | "metadata": {}, 112 | "source": [ 113 | "### Problem 2. High income (1 point)\n", 114 | "How many people have high income (over 50 000 USD per year)? Write a function to return the value. The function should accept the dataframe as a parameter. Work with that parameter." 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": 5, 120 | "metadata": { 121 | "nbgrader": { 122 | "grade": false, 123 | "grade_id": "high-income", 124 | "locked": false, 125 | "schema_version": 3, 126 | "solution": true 127 | } 128 | }, 129 | "outputs": [], 130 | "source": [ 131 | "def get_num_people_with_high_income(dataframe):\n", 132 | " ### BEGIN SOLUTION\n", 133 | " return len(dataframe[dataframe.income == \">50K\"])\n", 134 | " ### END SOLUTION" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 6, 140 | "metadata": { 141 | "nbgrader": { 142 | "grade": true, 143 | "grade_id": "high-income-tests", 144 | "locked": true, 145 | "points": 1, 146 | "schema_version": 3, 147 | "solution": false 148 | } 149 | }, 150 | "outputs": [], 151 | "source": [ 152 | "# This cell contains hidden tests\n", 153 | "### BEGIN HIDDEN TESTS\n", 154 | "nose.tools.assert_equal(get_num_people_with_high_income(income_data), 7841)\n", 155 | "nose.tools.assert_equal(get_num_people_with_high_income(income_data), get_num_people_with_high_income(income_data_test))\n", 156 | "### END HIDDEN TESTS" 157 | ] 158 | }, 159 | { 160 | "cell_type": "markdown", 161 | "metadata": {}, 162 | "source": [ 163 | "### Problem 3. Capital gain: thresholding (1 point)\n", 164 | "Plot a histogram of the capital gain. You can see that there are many people with relatively low gains and a few people - with very high gains." 165 | ] 166 | }, 167 | { 168 | "cell_type": "code", 169 | "execution_count": 7, 170 | "metadata": { 171 | "nbgrader": { 172 | "grade": false, 173 | "grade_id": "high-gain", 174 | "locked": false, 175 | "schema_version": 3, 176 | "solution": true 177 | } 178 | }, 179 | "outputs": [ 180 | { 181 | "data": { 182 | "image/png": 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\n", 183 | "text/plain": [ 184 | "
" 185 | ] 186 | }, 187 | "metadata": { 188 | "needs_background": "light" 189 | }, 190 | "output_type": "display_data" 191 | } 192 | ], 193 | "source": [ 194 | "### BEGIN SOLUTION\n", 195 | "plt.hist(income_data[\"capital-gain\"])\n", 196 | "plt.show()\n", 197 | "### END SOLUTION" 198 | ] 199 | }, 200 | { 201 | "cell_type": "markdown", 202 | "metadata": {}, 203 | "source": [ 204 | "Write a function which accepts a dataframe and a capital gain value (in USD) and returns how many people are there with **greater than or equal to** that threshold gain." 205 | ] 206 | }, 207 | { 208 | "cell_type": "code", 209 | "execution_count": 8, 210 | "metadata": { 211 | "nbgrader": { 212 | "grade": false, 213 | "grade_id": "high-gain-fn", 214 | "locked": false, 215 | "schema_version": 3, 216 | "solution": true 217 | } 218 | }, 219 | "outputs": [], 220 | "source": [ 221 | "def get_num_people_with_higher_gain(dataframe, threshold_gain):\n", 222 | " ### BEGIN SOLUTION\n", 223 | " return len(dataframe[dataframe[\"capital-gain\"] >= threshold_gain])\n", 224 | " ### END SOLUTION" 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": 9, 230 | "metadata": { 231 | "nbgrader": { 232 | "grade": true, 233 | "grade_id": "high-gain-tests", 234 | "locked": true, 235 | "points": 1, 236 | "schema_version": 3, 237 | "solution": false 238 | } 239 | }, 240 | "outputs": [], 241 | "source": [ 242 | "nose.tools.assert_equal(get_num_people_with_higher_gain(income_data, 60000), 159)\n", 243 | "### BEGIN HIDDEN TESTS\n", 244 | "for threshold, expected_income in [[20000, 253], [30000, 166], [45000, 159], [60000, 159], [1000000000, 0], [0, 32561]]:\n", 245 | " nose.tools.assert_equal(get_num_people_with_higher_gain(income_data, threshold), get_num_people_with_higher_gain(income_data_test, threshold))\n", 246 | " nose.tools.assert_equal(get_num_people_with_higher_gain(income_data, threshold), expected_income)\n", 247 | " ### END HIDDEN TESTS" 248 | ] 249 | }, 250 | { 251 | "cell_type": "markdown", 252 | "metadata": {}, 253 | "source": [ 254 | "Create a pie chart of the number of people by marital status." 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": 10, 260 | "metadata": { 261 | "nbgrader": { 262 | "grade": false, 263 | "grade_id": "cell-1e91969a128f0bd6", 264 | "locked": false, 265 | "schema_version": 3, 266 | "solution": true 267 | } 268 | }, 269 | "outputs": [ 270 | { 271 | "data": { 272 | "image/png": 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\n", 273 | "text/plain": [ 274 | "
" 275 | ] 276 | }, 277 | "metadata": {}, 278 | "output_type": "display_data" 279 | } 280 | ], 281 | "source": [ 282 | "### BEGIN SOLUTION\n", 283 | "groups = income_data.groupby(\"marital-status\").count()\n", 284 | "plt.pie(groups.income, labels = groups.index)\n", 285 | "plt.gca().set_aspect(\"equal\")\n", 286 | "### END SOLUTION" 287 | ] 288 | }, 289 | { 290 | "cell_type": "markdown", 291 | "metadata": {}, 292 | "source": [ 293 | "### Problem 4. Marital status (2 points)\n", 294 | "Which type of marital status is the most prominent (i.e. has the most people)? How many are there? Write a function that **calculates and returns the two answers**. " 295 | ] 296 | }, 297 | { 298 | "cell_type": "code", 299 | "execution_count": 11, 300 | "metadata": { 301 | "nbgrader": { 302 | "grade": false, 303 | "grade_id": "cell-5c9a472102a7b452", 304 | "locked": false, 305 | "schema_version": 3, 306 | "solution": true 307 | } 308 | }, 309 | "outputs": [], 310 | "source": [ 311 | "def most_prominent_marital_status(dataframe):\n", 312 | " status = \"\"\n", 313 | " num_people = 0\n", 314 | " ### BEGIN SOLUTION\n", 315 | " groups = income_data.groupby(\"marital-status\").count().income\n", 316 | " status = groups.idxmax()\n", 317 | " num_people = groups.max()\n", 318 | " ### END SOLUTION\n", 319 | " return (status, num_people)" 320 | ] 321 | }, 322 | { 323 | "cell_type": "code", 324 | "execution_count": 12, 325 | "metadata": { 326 | "nbgrader": { 327 | "grade": true, 328 | "grade_id": "cell-0077a3c8d4339ad7", 329 | "locked": true, 330 | "points": 2, 331 | "schema_version": 3, 332 | "solution": false 333 | } 334 | }, 335 | "outputs": [], 336 | "source": [ 337 | "(status, num_people) = most_prominent_marital_status(income_data)\n", 338 | "nose.tools.assert_not_equal(status, \"\")\n", 339 | "nose.tools.assert_greater(num_people, 10000)\n", 340 | "### BEGIN HIDDEN TESTS\n", 341 | "(status_test, num_people_test) = most_prominent_marital_status(income_data_test)\n", 342 | "nose.tools.assert_equal(status, \"Married-civ-spouse\")\n", 343 | "nose.tools.assert_equal(num_people, 14976)\n", 344 | "nose.tools.assert_equal(status, status_test)\n", 345 | "nose.tools.assert_equal(num_people, num_people_test)\n", 346 | "### END HIDDEN TESTS" 347 | ] 348 | }, 349 | { 350 | "cell_type": "markdown", 351 | "metadata": {}, 352 | "source": [ 353 | "### Problem 5. Age groups (1 point)\n", 354 | "Create a histogram of all people's ages. Use the default settings. Add the label \"Age\" on the x-axis and \"Count\" on the y-axis." 355 | ] 356 | }, 357 | { 358 | "cell_type": "code", 359 | "execution_count": 13, 360 | "metadata": { 361 | "nbgrader": { 362 | "grade": false, 363 | "grade_id": "cell-3ba52bf669280861", 364 | "locked": false, 365 | "schema_version": 3, 366 | "solution": true 367 | } 368 | }, 369 | "outputs": [ 370 | { 371 | "data": { 372 | "image/png": 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\n", 373 | "text/plain": [ 374 | "
" 375 | ] 376 | }, 377 | "metadata": { 378 | "needs_background": "light" 379 | }, 380 | "output_type": "display_data" 381 | } 382 | ], 383 | "source": [ 384 | "### BEGIN SOLUTION\n", 385 | "plt.hist(income_data.age)\n", 386 | "plt.xlabel(\"Age\")\n", 387 | "plt.ylabel(\"Count\")\n", 388 | "plt.show()\n", 389 | "### END SOLUTION" 390 | ] 391 | }, 392 | { 393 | "cell_type": "markdown", 394 | "metadata": {}, 395 | "source": [ 396 | "Let's get another view of the data. Split the ages into three:\n", 397 | "1. Young people: $\\text{age} \\le 30$\n", 398 | "2. Middle-aged people: $30 < \\text{age} \\le 60$\n", 399 | "3. Old people: $60 < \\text{age}$\n", 400 | "\n", 401 | "Return the counts in the following function. Which age group has the most people? How many are there?" 402 | ] 403 | }, 404 | { 405 | "cell_type": "code", 406 | "execution_count": 14, 407 | "metadata": { 408 | "nbgrader": { 409 | "grade": false, 410 | "grade_id": "cell-b8a6cda122bf0fb3", 411 | "locked": false, 412 | "schema_version": 3, 413 | "solution": true 414 | } 415 | }, 416 | "outputs": [], 417 | "source": [ 418 | "def get_num_people_by_age_category(dataframe):\n", 419 | " young, middle_aged, old = (0, 0, 0)\n", 420 | " ### BEGIN SOLUTION\n", 421 | " young = len(dataframe[dataframe.age <= 30])\n", 422 | " middle_aged = len(dataframe[(dataframe.age > 30) & (dataframe.age <= 60)])\n", 423 | " old = len(dataframe[dataframe.age > 60])\n", 424 | " ### END SOLUTION\n", 425 | " return young, middle_aged, old" 426 | ] 427 | }, 428 | { 429 | "cell_type": "code", 430 | "execution_count": 15, 431 | "metadata": { 432 | "nbgrader": { 433 | "grade": true, 434 | "grade_id": "cell-17898d5f42dd42d5", 435 | "locked": true, 436 | "points": 1, 437 | "schema_version": 3, 438 | "solution": false 439 | } 440 | }, 441 | "outputs": [], 442 | "source": [ 443 | "young, middle_aged, old = get_num_people_by_age_category(income_data)\n", 444 | "nose.tools.assert_greater(young, 0)\n", 445 | "nose.tools.assert_greater(middle_aged, 0)\n", 446 | "nose.tools.assert_greater(old, 0)\n", 447 | "### BEGIN HIDDEN TESTS\n", 448 | "test_young, test_middle_aged, test_old = get_num_people_by_age_category(income_data_test)\n", 449 | "nose.tools.assert_equal(young, test_young)\n", 450 | "nose.tools.assert_equal(middle_aged, test_middle_aged)\n", 451 | "nose.tools.assert_equal(old, test_old)\n", 452 | "nose.tools.assert_equal(young, 10572)\n", 453 | "nose.tools.assert_equal(middle_aged, 19657)\n", 454 | "nose.tools.assert_equal(old, 2332)\n", 455 | "### END HIDDEN TESTS" 456 | ] 457 | }, 458 | { 459 | "cell_type": "markdown", 460 | "metadata": {}, 461 | "source": [ 462 | "Now we can create a bar chart. Execute the code below to see it." 463 | ] 464 | }, 465 | { 466 | "cell_type": "code", 467 | "execution_count": 16, 468 | "metadata": {}, 469 | "outputs": [ 470 | { 471 | "data": { 472 | "image/png": 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\n", 473 | "text/plain": [ 474 | "
" 475 | ] 476 | }, 477 | "metadata": { 478 | "needs_background": "light" 479 | }, 480 | "output_type": "display_data" 481 | } 482 | ], 483 | "source": [ 484 | "plt.title(\"Distribution of people by age groups\")\n", 485 | "plt.bar(range(3), [young, middle_aged, old])\n", 486 | "plt.xticks(range(3), [\"Young\", \"Middle-aged\", \"Old\"])\n", 487 | "plt.ylabel(\"Count\")\n", 488 | "plt.show()" 489 | ] 490 | }, 491 | { 492 | "cell_type": "markdown", 493 | "metadata": {}, 494 | "source": [ 495 | "### Problem 6. Native country (2 points)\n", 496 | "Have a look at the native country of the people. The highest number of people are, as expected, from the US. What country makes for **the second highest** number of people? How many are they? Write a function to **calculate** and return the answer given a dataframe. DO NOT hardcode the answer, e.g. `return \"Germany\"`." 497 | ] 498 | }, 499 | { 500 | "cell_type": "code", 501 | "execution_count": 17, 502 | "metadata": { 503 | "nbgrader": { 504 | "grade": false, 505 | "grade_id": "cell-96faf6efe52dd3d0", 506 | "locked": false, 507 | "schema_version": 3, 508 | "solution": true 509 | } 510 | }, 511 | "outputs": [], 512 | "source": [ 513 | "def get_second_highest_num_people(dataframe):\n", 514 | " num_people, country = 0, \"\"\n", 515 | " ### BEGIN SOLUTION\n", 516 | " people = income_data.groupby(\"native-country\").count().income.nlargest(2)\n", 517 | " num_people = people.min()\n", 518 | " country = people.idxmin()\n", 519 | " ### END SOLUTION\n", 520 | " return num_people, country" 521 | ] 522 | }, 523 | { 524 | "cell_type": "code", 525 | "execution_count": 18, 526 | "metadata": { 527 | "nbgrader": { 528 | "grade": true, 529 | "grade_id": "cell-923d941301d6acc8", 530 | "locked": true, 531 | "points": 2, 532 | "schema_version": 3, 533 | "solution": false 534 | } 535 | }, 536 | "outputs": [], 537 | "source": [ 538 | "num_people, country = get_second_highest_num_people(income_data)\n", 539 | "nose.tools.assert_greater(num_people, 0)\n", 540 | "nose.tools.assert_not_equal(country, \"\")\n", 541 | "### BEGIN HIDDEN TESTS\n", 542 | "nose.tools.assert_equal(num_people, 643)\n", 543 | "nose.tools.assert_equal(country, \"Mexico\")\n", 544 | "test_num_people, test_country = get_second_highest_num_people(income_data_test)\n", 545 | "nose.tools.assert_equal(num_people, test_num_people)\n", 546 | "nose.tools.assert_equal(country, test_country)\n", 547 | "### END HIDDEN TESTS" 548 | ] 549 | }, 550 | { 551 | "cell_type": "markdown", 552 | "metadata": {}, 553 | "source": [ 554 | "### Problem 7. Busiest occupations (2 points)\n", 555 | "Which people are most overworked? Group all data by occupation and calculate the mean hours per week for each group.\n", 556 | "\n", 557 | "Write a function that **calculates and returns** all mean hours per week as a `Series`. Sort the results in descending order (most hours to fewest hours)." 558 | ] 559 | }, 560 | { 561 | "cell_type": "code", 562 | "execution_count": 19, 563 | "metadata": { 564 | "nbgrader": { 565 | "grade": false, 566 | "grade_id": "cell-f93bf9800cb3bc46", 567 | "locked": false, 568 | "schema_version": 3, 569 | "solution": true 570 | } 571 | }, 572 | "outputs": [], 573 | "source": [ 574 | "def get_mean_working_hours_by_occupation(dataframe):\n", 575 | " ### BEGIN SOLUTION\n", 576 | " groups = income_data.groupby(\"occupation\").mean()[\"hours-per-week\"]\n", 577 | " return groups.sort_values(ascending = False)\n", 578 | " ### END SOLUTION" 579 | ] 580 | }, 581 | { 582 | "cell_type": "code", 583 | "execution_count": 20, 584 | "metadata": { 585 | "nbgrader": { 586 | "grade": true, 587 | "grade_id": "cell-69cd7b7f6076b0ed", 588 | "locked": true, 589 | "points": 2, 590 | "schema_version": 3, 591 | "solution": false 592 | } 593 | }, 594 | "outputs": [ 595 | { 596 | "name": "stdout", 597 | "output_type": "stream", 598 | "text": [ 599 | "occupation\n", 600 | "Farming-fishing 46.989940\n", 601 | "Exec-managerial 44.987703\n", 602 | "Transport-moving 44.656230\n", 603 | "Protective-serv 42.870570\n", 604 | "Prof-specialty 42.386715\n", 605 | "Craft-repair 42.304221\n", 606 | "Sales 40.781096\n", 607 | "Machine-op-inspct 40.755744\n", 608 | "Armed-Forces 40.666667\n", 609 | "Tech-support 39.432112\n", 610 | "Handlers-cleaners 37.947445\n", 611 | "Adm-clerical 37.558355\n", 612 | "Other-service 34.701669\n", 613 | "Priv-house-serv 32.885906\n", 614 | "? 31.906131\n", 615 | "Name: hours-per-week, dtype: float64\n" 616 | ] 617 | } 618 | ], 619 | "source": [ 620 | "hours = get_mean_working_hours_by_occupation(income_data)\n", 621 | "print(hours)\n", 622 | "nose.tools.assert_almost_equal(hours[\"Handlers-cleaners\"], 37.95, delta = 0.01)\n", 623 | "### BEGIN HIDDEN TESTS\n", 624 | "nose.tools.assert_almost_equal(hours[\"Prof-specialty\"], 42.38, delta = 0.01)\n", 625 | "nose.tools.assert_almost_equal(hours[\"Farming-fishing\"], 46.99, delta = 0.01)\n", 626 | "nose.tools.assert_almost_equal(hours[\"?\"], 31.91, delta = 0.01)\n", 627 | "### END HIDDEN TESTS" 628 | ] 629 | }, 630 | { 631 | "cell_type": "markdown", 632 | "metadata": {}, 633 | "source": [ 634 | "Finally, let's plot a bar chart. Check the values carefully. If your do not match, feel free to edit the chart generation code below." 635 | ] 636 | }, 637 | { 638 | "cell_type": "code", 639 | "execution_count": 21, 640 | "metadata": {}, 641 | "outputs": [ 642 | { 643 | "data": { 644 | "image/png": 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\n", 645 | "text/plain": [ 646 | "
" 647 | ] 648 | }, 649 | "metadata": { 650 | "needs_background": "light" 651 | }, 652 | "output_type": "display_data" 653 | } 654 | ], 655 | "source": [ 656 | "plt.figure(figsize = (10, 6))\n", 657 | "plt.title(\"Weekly hours by occupation\")\n", 658 | "plt.barh(range(len(hours)), hours)\n", 659 | "plt.yticks(list(range(len(hours))), hours.index)\n", 660 | "plt.show()" 661 | ] 662 | } 663 | ], 664 | "metadata": { 665 | "celltoolbar": "Create Assignment", 666 | "kernelspec": { 667 | "display_name": "Python 3", 668 | "language": "python", 669 | "name": "python3" 670 | }, 671 | "language_info": { 672 | "codemirror_mode": { 673 | "name": "ipython", 674 | "version": 3 675 | }, 676 | "file_extension": ".py", 677 | "mimetype": "text/x-python", 678 | "name": "python", 679 | "nbconvert_exporter": "python", 680 | "pygments_lexer": "ipython3", 681 | "version": "3.7.3" 682 | } 683 | }, 684 | "nbformat": 4, 685 | "nbformat_minor": 2 686 | } 687 | --------------------------------------------------------------------------------