├── .github └── ISSUE_TEMPLATE │ ├── Bug_report.md │ └── Custom.md ├── Box Plot Distribution.PNG ├── CODE_OF_CONDUCT.md ├── Datasets ├── Labour Data.csv ├── PoliceKillingsUS.csv ├── Score Book.csv ├── University.csv ├── anscombe.csv ├── brain_networks.csv ├── exercise.csv ├── flights.csv ├── nyc_taxi.csv ├── tips.csv └── titanic.csv ├── LICENSE ├── PULL_REQUEST_TEMPLATE.md ├── README.md ├── Seaborn - Additional Regression Plots.ipynb ├── Seaborn - Bar Plot & Count Plot.ipynb ├── Seaborn - Box Plot.ipynb ├── Seaborn - Categorical Data Plot.ipynb ├── Seaborn - Color Palettes.ipynb ├── Seaborn - Controlling Aesthetics.ipynb ├── Seaborn - FacetGrid.ipynb ├── Seaborn - Factor Plot.ipynb ├── Seaborn - LM Plot & Reg Plot.ipynb ├── Seaborn - Loading Dataset.ipynb ├── Seaborn - Matplotlib vs Seaborn.ipynb ├── Seaborn - PairGrid.ipynb ├── Seaborn - Scatter Plot & Joint Plot.ipynb ├── Seaborn - Strip Plot.ipynb ├── Seaborn - Time-Series and Letter-Value Plot.ipynb ├── Seaborn - Violin Plot.ipynb ├── Seaborn Cheat Sheet.pdf ├── _config.yml └── docs └── CONTRIBUTING /.github/ISSUE_TEMPLATE/Bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | 5 | --- 6 | 7 | **Describe the bug** 8 | A clear and concise description of what the bug is. 9 | 10 | **To Reproduce** 11 | Steps to reproduce the behavior: 12 | 1. Go to '...' 13 | 2. Click on '....' 14 | 3. Scroll down to '....' 15 | 4. See error 16 | 17 | **Expected behavior** 18 | A clear and concise description of what you expected to happen. 19 | 20 | **Screenshots** 21 | If applicable, add screenshots to help explain your problem. 22 | 23 | **Desktop (please complete the following information):** 24 | - OS: [e.g. iOS] 25 | - Browser [e.g. chrome, safari] 26 | - Version [e.g. 22] 27 | 28 | **Smartphone (please complete the following information):** 29 | - Device: [e.g. iPhone6] 30 | - OS: [e.g. iOS8.1] 31 | - Browser [e.g. stock browser, safari] 32 | - Version [e.g. 22] 33 | 34 | **Additional context** 35 | Add any other context about the problem here. 36 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/Custom.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Custom issue template 3 | about: Describe this issue template's purpose here. 4 | 5 | --- 6 | 7 | 8 | -------------------------------------------------------------------------------- /Box Plot Distribution.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/clair513/Seaborn-Tutorial/fd562f8c662b0d3805a2b0a5bd848b2e318d0f06/Box Plot Distribution.PNG -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation. 6 | 7 | ## Our Standards 8 | 9 | Examples of behavior that contributes to creating a positive environment include: 10 | 11 | * Using welcoming and inclusive language 12 | * Being respectful of differing viewpoints and experiences 13 | * Gracefully accepting constructive criticism 14 | * Focusing on what is best for the community 15 | * Showing empathy towards other community members 16 | 17 | Examples of unacceptable behavior by participants include: 18 | 19 | * The use of sexualized language or imagery and unwelcome sexual attention or advances 20 | * Trolling, insulting/derogatory comments, and personal or political attacks 21 | * Public or private harassment 22 | * Publishing others' private information, such as a physical or electronic address, without explicit permission 23 | * Other conduct which could reasonably be considered inappropriate in a professional setting 24 | 25 | ## Our Responsibilities 26 | 27 | Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. 28 | 29 | Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. 30 | 31 | ## Scope 32 | 33 | This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. 34 | 35 | ## Enforcement 36 | 37 | Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at alokjha513@gmail.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. 38 | 39 | Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. 40 | 41 | ## Attribution 42 | 43 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version] 44 | 45 | [homepage]: http://contributor-covenant.org 46 | [version]: http://contributor-covenant.org/version/1/4/ 47 | -------------------------------------------------------------------------------- /Datasets/Labour Data.csv: -------------------------------------------------------------------------------- 1 | Country,Annual Income,Average Family members,Birth Rate 2 | Lithuania,22949,2,10.1 3 | Latvia,22389,4,9.7 4 | Hungary,22911,4,9.8 5 | Luxembourg,62636,3,3.85 6 | United States,60154,3,3.9 7 | Switzerland,60124,4,4.3 8 | Denmark,52580,3,7.7 9 | Australia,52063,3,5.2 10 | Ireland,51681,4,8.5 11 | Belgium,49587,2,8.7 12 | Canada,48403,3,8.8 13 | Austria,48295,3,6.8 14 | Germany,46389,3,8.6 15 | France,42992,4,5.9 16 | United Kingdom,42835,4,4.9 17 | Sweden,42816,3,6.8 18 | New Zealand,39397,2,5.4 19 | Japan,39113,2,7.7 20 | Spain,37333,3,7.2 21 | Italy,35397,4,8.6 22 | Slovenia,34965,3,8.2 23 | Israel,34023,3,7.6 24 | South Korea,32399,3,8.3 25 | Estonia,28621,4,7.6 26 | Chile,28434,3,9.1 27 | Poland,25921,2,9.5 28 | Greece,25124,2,8.4 29 | Portugal,24529,4,9 30 | Czech Republic,23722,3,9.3 31 | Slovak Republic,23508,3,9.7 32 | Mexico,19311,4,13.5 33 | India,16580,5,19 34 | Pakistan,15800,5,16.3 35 | Bangladesh,12940,5,13.8 36 | Iceland,55984,3,5.4 37 | Norway,53643,3,4.5 38 | Netherlands,52833,4,6.4 39 | Finland,42127,3,5.9 40 | -------------------------------------------------------------------------------- /Datasets/PoliceKillingsUS.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/clair513/Seaborn-Tutorial/fd562f8c662b0d3805a2b0a5bd848b2e318d0f06/Datasets/PoliceKillingsUS.csv -------------------------------------------------------------------------------- /Datasets/Score Book.csv: -------------------------------------------------------------------------------- 1 | Student ID,Analysis,Machine Learning,Artificial Intelligence 2 | 1,2.393412671,3.324129347,0.039631096 3 | 2,3.228434178,3.109298649,5.621414891 4 | 3,6.611171616,3.603703815,4.830770027 5 | 4,4.55351888,5.030113742,4.84697594 6 | 5,4.151165323,6.555816091,4.126257144 7 | 6,9.077035502,7.478125262,3.321003992 8 | 7,7.039631096,8.201300407,2.667565198 9 | 8,3.021068686,8.306264399,1.976072963 10 | 9,5.011522874,6.622167472,1.220086585 11 | 10,2.006190799,4.272699487,0.656404386 12 | 11,3.324129347,2.393412671,0.32202138 13 | 12,3.109298649,1.228434178,2.15599814 14 | 13,3.603703815,0.611171616,0.076945636 15 | 14,5.030113742,0.30351888,0.039381467 16 | 15,6.555816091,0.151165323,0.021178522 17 | 16,7.478125262,3.077035502,8.733030097 18 | 17,8.201300407,0.039631096,0.006440428 19 | 18,8.306264399,2.021068686,0.003664553 20 | 19,6.622167472,0.011522874,3.002106869 21 | 20,4.272699487,0.006190799,0.001308056 22 | 21,2.006190799,4.272699487,0.656404386 23 | 22,3.324129347,2.393412671,0.32202138 24 | 23,4.109298649,5.228434178,2.15599814 25 | 24,3.603703815,2.611171616,0.076945636 26 | 25,6.892500004,5.85721035,6.100588246 27 | -------------------------------------------------------------------------------- /Datasets/University.csv: -------------------------------------------------------------------------------- 1 | university_name,total_students,students_enrolled,gender_dominance,education_level,time 2 | Harvard,1582568,12587,Male,High School,Day and Night 3 | Oxford,1568291,54682,Female,Graduate,Night 4 | Luke,1822565,54808,Female,Graduate,Day and Night 5 | Cambridge,785269,24865,Male,Post-Graduate,Day 6 | MIT,64154651,258745,Female,High School,Day and Night 7 | Xavier's,6611852,5698,Male,High School,Day 8 | Cornell,5455131,98547,Male,Graduate,Night 9 | Harvard,785280,42530,Female,Post-Graduate,Day 10 | Oxford,2534156,22897,Male,Post-Graduate,Day and Night 11 | Luke,5425,89,Female,Ph.D,Night 12 | Cambridge,41154894,134981,Male,Graduate,Day and Night 13 | MIT,18547802,254856,Female,Graduate,Day 14 | Xavier's,8527025,21530,Female,High School,Day and Night 15 | Cornell,89652710,72080,Female,Post-Graduate,Day 16 | Harvard,23746520,43250,Male,High School,Day 17 | Oxford,24167204,21560,Female,Graduate,Day and Night 18 | Luke,1822565,54269,Male,High School,Night 19 | Cambridge,585555,56810,Male,Post-Graduate,Day and Night 20 | MIT,9856921,64730,Female,Graduate,Day 21 | Xavier's,12345678,548925,Female,High School,Night 22 | Cornell,9802,152,Male,Ph.D,Day 23 | Harvard,2525560,23541,Male,Post-Graduate,Day 24 | Oxford,1568291,85788,Female,Graduate,Night 25 | Luke,7999952,989012,Female,Diploma,Day and Night 26 | Cambridge,24573045,54256,Male,Graduate,Night 27 | MIT,1822565,78046,Female,Post-Graduate,Day 28 | Xavier's,9078202,11135,Female,Post-Graduate,Day and Night 29 | Cornell,4562082,136542,Male,Graduate,Night 30 | Harvard,1582568,18256,Male,High School,Day and Night 31 | Oxford,4560825,14305,Female,High School,Day and Night 32 | Luke,9805412,58460,Male,Diploma,Day 33 | Cambridge,8552449,49610,Male,Post-Graduate,Night 34 | MIT,4608792,34950,Female,Diploma,Day 35 | Xavier's,24587259,35142,Female,High School,Day and Night 36 | Cornell,1822565,19872,Female,Diploma,Night 37 | Harvard,1582568,14235,Male,Graduate,Day and Night 38 | Oxford,5234,320,Male,Ph.D,Day 39 | Luke,4560872,14811,Male,Diploma,Day 40 | Cambridge,9167890,87587,Female,High School,Night 41 | MIT,8552449,89046,Male,Graduate,Night 42 | Xavier's,4658904,75802,Male,High School,Day 43 | Cornell,1582560,65810,Male,Post-Graduate,Day and Night 44 | Harvard,8629,87,Male,Ph.D,Day and Night 45 | Oxford,1568291,64735,Female,Graduate,Night 46 | Luke,1582568,24585,Male,Post-Graduate,Day 47 | Cambridge,99999990,7777852,Female,Graduate,Day 48 | MIT,8552449,254013,Male,High School,Day and Night 49 | Xavier's,24592580,45267,Female,Post-Graduate,Day and Night 50 | Cornell,1822565,98052,Male,Graduate,Day 51 | -------------------------------------------------------------------------------- /Datasets/anscombe.csv: -------------------------------------------------------------------------------- 1 | dataset,x,y 2 | I,10.0,8.04 3 | I,8.0,6.95 4 | I,13.0,7.58 5 | I,9.0,8.81 6 | I,11.0,8.33 7 | I,14.0,9.96 8 | I,6.0,7.24 9 | I,4.0,4.26 10 | I,12.0,10.84 11 | I,7.0,4.82 12 | I,5.0,5.68 13 | II,10.0,9.14 14 | II,8.0,8.14 15 | II,13.0,8.74 16 | II,9.0,8.77 17 | II,11.0,9.26 18 | II,14.0,8.1 19 | II,6.0,6.13 20 | II,4.0,3.1 21 | II,12.0,9.13 22 | II,7.0,7.26 23 | II,5.0,4.74 24 | III,10.0,7.46 25 | III,8.0,6.77 26 | III,13.0,12.74 27 | III,9.0,7.11 28 | III,11.0,7.81 29 | III,14.0,8.84 30 | III,6.0,6.08 31 | III,4.0,5.39 32 | III,12.0,8.15 33 | III,7.0,6.42 34 | III,5.0,5.73 35 | IV,8.0,6.58 36 | IV,8.0,5.76 37 | IV,8.0,7.71 38 | IV,8.0,8.84 39 | IV,8.0,8.47 40 | IV,8.0,7.04 41 | IV,8.0,5.25 42 | IV,19.0,12.5 43 | IV,8.0,5.56 44 | IV,8.0,7.91 45 | IV,8.0,6.89 46 | -------------------------------------------------------------------------------- /Datasets/exercise.csv: -------------------------------------------------------------------------------- 1 | ,id,diet,pulse,time,kind 2 | 0,1,low fat,85,1 min,rest 3 | 1,1,low fat,85,15 min,rest 4 | 2,1,low fat,88,30 min,rest 5 | 3,2,low fat,90,1 min,rest 6 | 4,2,low fat,92,15 min,rest 7 | 5,2,low fat,93,30 min,rest 8 | 6,3,low fat,97,1 min,rest 9 | 7,3,low fat,97,15 min,rest 10 | 8,3,low fat,94,30 min,rest 11 | 9,4,low fat,80,1 min,rest 12 | 10,4,low fat,82,15 min,rest 13 | 11,4,low fat,83,30 min,rest 14 | 12,5,low fat,91,1 min,rest 15 | 13,5,low fat,92,15 min,rest 16 | 14,5,low fat,91,30 min,rest 17 | 15,6,no fat,83,1 min,rest 18 | 16,6,no fat,83,15 min,rest 19 | 17,6,no fat,84,30 min,rest 20 | 18,7,no fat,87,1 min,rest 21 | 19,7,no fat,88,15 min,rest 22 | 20,7,no fat,90,30 min,rest 23 | 21,8,no fat,92,1 min,rest 24 | 22,8,no fat,94,15 min,rest 25 | 23,8,no fat,95,30 min,rest 26 | 24,9,no fat,97,1 min,rest 27 | 25,9,no fat,99,15 min,rest 28 | 26,9,no fat,96,30 min,rest 29 | 27,10,no fat,100,1 min,rest 30 | 28,10,no fat,97,15 min,rest 31 | 29,10,no fat,100,30 min,rest 32 | 30,11,low fat,86,1 min,walking 33 | 31,11,low fat,86,15 min,walking 34 | 32,11,low fat,84,30 min,walking 35 | 33,12,low fat,93,1 min,walking 36 | 34,12,low fat,103,15 min,walking 37 | 35,12,low fat,104,30 min,walking 38 | 36,13,low fat,90,1 min,walking 39 | 37,13,low fat,92,15 min,walking 40 | 38,13,low fat,93,30 min,walking 41 | 39,14,low fat,95,1 min,walking 42 | 40,14,low fat,96,15 min,walking 43 | 41,14,low fat,100,30 min,walking 44 | 42,15,low fat,89,1 min,walking 45 | 43,15,low fat,96,15 min,walking 46 | 44,15,low fat,95,30 min,walking 47 | 45,16,no fat,84,1 min,walking 48 | 46,16,no fat,86,15 min,walking 49 | 47,16,no fat,89,30 min,walking 50 | 48,17,no fat,103,1 min,walking 51 | 49,17,no fat,109,15 min,walking 52 | 50,17,no fat,90,30 min,walking 53 | 51,18,no fat,92,1 min,walking 54 | 52,18,no fat,96,15 min,walking 55 | 53,18,no fat,101,30 min,walking 56 | 54,19,no fat,97,1 min,walking 57 | 55,19,no fat,98,15 min,walking 58 | 56,19,no fat,100,30 min,walking 59 | 57,20,no fat,102,1 min,walking 60 | 58,20,no fat,104,15 min,walking 61 | 59,20,no fat,103,30 min,walking 62 | 60,21,low fat,93,1 min,running 63 | 61,21,low fat,98,15 min,running 64 | 62,21,low fat,110,30 min,running 65 | 63,22,low fat,98,1 min,running 66 | 64,22,low fat,104,15 min,running 67 | 65,22,low fat,112,30 min,running 68 | 66,23,low fat,98,1 min,running 69 | 67,23,low fat,105,15 min,running 70 | 68,23,low fat,99,30 min,running 71 | 69,24,low fat,87,1 min,running 72 | 70,24,low fat,132,15 min,running 73 | 71,24,low fat,120,30 min,running 74 | 72,25,low fat,94,1 min,running 75 | 73,25,low fat,110,15 min,running 76 | 74,25,low fat,116,30 min,running 77 | 75,26,no fat,95,1 min,running 78 | 76,26,no fat,126,15 min,running 79 | 77,26,no fat,143,30 min,running 80 | 78,27,no fat,100,1 min,running 81 | 79,27,no fat,126,15 min,running 82 | 80,27,no fat,140,30 min,running 83 | 81,28,no fat,103,1 min,running 84 | 82,28,no fat,124,15 min,running 85 | 83,28,no fat,140,30 min,running 86 | 84,29,no fat,94,1 min,running 87 | 85,29,no fat,135,15 min,running 88 | 86,29,no fat,130,30 min,running 89 | 87,30,no fat,99,1 min,running 90 | 88,30,no fat,111,15 min,running 91 | 89,30,no fat,150,30 min,running 92 | -------------------------------------------------------------------------------- /Datasets/flights.csv: -------------------------------------------------------------------------------- 1 | year,month,passengers 2 | 1949,January,112 3 | 1949,February,118 4 | 1949,March,132 5 | 1949,April,129 6 | 1949,May,121 7 | 1949,June,135 8 | 1949,July,148 9 | 1949,August,148 10 | 1949,September,136 11 | 1949,October,119 12 | 1949,November,104 13 | 1949,December,118 14 | 1950,January,115 15 | 1950,February,126 16 | 1950,March,141 17 | 1950,April,135 18 | 1950,May,125 19 | 1950,June,149 20 | 1950,July,170 21 | 1950,August,170 22 | 1950,September,158 23 | 1950,October,133 24 | 1950,November,114 25 | 1950,December,140 26 | 1951,January,145 27 | 1951,February,150 28 | 1951,March,178 29 | 1951,April,163 30 | 1951,May,172 31 | 1951,June,178 32 | 1951,July,199 33 | 1951,August,199 34 | 1951,September,184 35 | 1951,October,162 36 | 1951,November,146 37 | 1951,December,166 38 | 1952,January,171 39 | 1952,February,180 40 | 1952,March,193 41 | 1952,April,181 42 | 1952,May,183 43 | 1952,June,218 44 | 1952,July,230 45 | 1952,August,242 46 | 1952,September,209 47 | 1952,October,191 48 | 1952,November,172 49 | 1952,December,194 50 | 1953,January,196 51 | 1953,February,196 52 | 1953,March,236 53 | 1953,April,235 54 | 1953,May,229 55 | 1953,June,243 56 | 1953,July,264 57 | 1953,August,272 58 | 1953,September,237 59 | 1953,October,211 60 | 1953,November,180 61 | 1953,December,201 62 | 1954,January,204 63 | 1954,February,188 64 | 1954,March,235 65 | 1954,April,227 66 | 1954,May,234 67 | 1954,June,264 68 | 1954,July,302 69 | 1954,August,293 70 | 1954,September,259 71 | 1954,October,229 72 | 1954,November,203 73 | 1954,December,229 74 | 1955,January,242 75 | 1955,February,233 76 | 1955,March,267 77 | 1955,April,269 78 | 1955,May,270 79 | 1955,June,315 80 | 1955,July,364 81 | 1955,August,347 82 | 1955,September,312 83 | 1955,October,274 84 | 1955,November,237 85 | 1955,December,278 86 | 1956,January,284 87 | 1956,February,277 88 | 1956,March,317 89 | 1956,April,313 90 | 1956,May,318 91 | 1956,June,374 92 | 1956,July,413 93 | 1956,August,405 94 | 1956,September,355 95 | 1956,October,306 96 | 1956,November,271 97 | 1956,December,306 98 | 1957,January,315 99 | 1957,February,301 100 | 1957,March,356 101 | 1957,April,348 102 | 1957,May,355 103 | 1957,June,422 104 | 1957,July,465 105 | 1957,August,467 106 | 1957,September,404 107 | 1957,October,347 108 | 1957,November,305 109 | 1957,December,336 110 | 1958,January,340 111 | 1958,February,318 112 | 1958,March,362 113 | 1958,April,348 114 | 1958,May,363 115 | 1958,June,435 116 | 1958,July,491 117 | 1958,August,505 118 | 1958,September,404 119 | 1958,October,359 120 | 1958,November,310 121 | 1958,December,337 122 | 1959,January,360 123 | 1959,February,342 124 | 1959,March,406 125 | 1959,April,396 126 | 1959,May,420 127 | 1959,June,472 128 | 1959,July,548 129 | 1959,August,559 130 | 1959,September,463 131 | 1959,October,407 132 | 1959,November,362 133 | 1959,December,405 134 | 1960,January,417 135 | 1960,February,391 136 | 1960,March,419 137 | 1960,April,461 138 | 1960,May,472 139 | 1960,June,535 140 | 1960,July,622 141 | 1960,August,606 142 | 1960,September,508 143 | 1960,October,461 144 | 1960,November,390 145 | 1960,December,432 146 | -------------------------------------------------------------------------------- /Datasets/tips.csv: -------------------------------------------------------------------------------- 1 | "total_bill","tip","sex","smoker","day","time","size" 2 | 16.99,1.01,"Female","No","Sun","Dinner",2 3 | 10.34,1.66,"Male","No","Sun","Dinner",3 4 | 21.01,3.5,"Male","No","Sun","Dinner",3 5 | 23.68,3.31,"Male","No","Sun","Dinner",2 6 | 24.59,3.61,"Female","No","Sun","Dinner",4 7 | 25.29,4.71,"Male","No","Sun","Dinner",4 8 | 8.77,2,"Male","No","Sun","Dinner",2 9 | 26.88,3.12,"Male","No","Sun","Dinner",4 10 | 15.04,1.96,"Male","No","Sun","Dinner",2 11 | 14.78,3.23,"Male","No","Sun","Dinner",2 12 | 10.27,1.71,"Male","No","Sun","Dinner",2 13 | 35.26,5,"Female","No","Sun","Dinner",4 14 | 15.42,1.57,"Male","No","Sun","Dinner",2 15 | 18.43,3,"Male","No","Sun","Dinner",4 16 | 14.83,3.02,"Female","No","Sun","Dinner",2 17 | 21.58,3.92,"Male","No","Sun","Dinner",2 18 | 10.33,1.67,"Female","No","Sun","Dinner",3 19 | 16.29,3.71,"Male","No","Sun","Dinner",3 20 | 16.97,3.5,"Female","No","Sun","Dinner",3 21 | 20.65,3.35,"Male","No","Sat","Dinner",3 22 | 17.92,4.08,"Male","No","Sat","Dinner",2 23 | 20.29,2.75,"Female","No","Sat","Dinner",2 24 | 15.77,2.23,"Female","No","Sat","Dinner",2 25 | 39.42,7.58,"Male","No","Sat","Dinner",4 26 | 19.82,3.18,"Male","No","Sat","Dinner",2 27 | 17.81,2.34,"Male","No","Sat","Dinner",4 28 | 13.37,2,"Male","No","Sat","Dinner",2 29 | 12.69,2,"Male","No","Sat","Dinner",2 30 | 21.7,4.3,"Male","No","Sat","Dinner",2 31 | 19.65,3,"Female","No","Sat","Dinner",2 32 | 9.55,1.45,"Male","No","Sat","Dinner",2 33 | 18.35,2.5,"Male","No","Sat","Dinner",4 34 | 15.06,3,"Female","No","Sat","Dinner",2 35 | 20.69,2.45,"Female","No","Sat","Dinner",4 36 | 17.78,3.27,"Male","No","Sat","Dinner",2 37 | 24.06,3.6,"Male","No","Sat","Dinner",3 38 | 16.31,2,"Male","No","Sat","Dinner",3 39 | 16.93,3.07,"Female","No","Sat","Dinner",3 40 | 18.69,2.31,"Male","No","Sat","Dinner",3 41 | 31.27,5,"Male","No","Sat","Dinner",3 42 | 16.04,2.24,"Male","No","Sat","Dinner",3 43 | 17.46,2.54,"Male","No","Sun","Dinner",2 44 | 13.94,3.06,"Male","No","Sun","Dinner",2 45 | 9.68,1.32,"Male","No","Sun","Dinner",2 46 | 30.4,5.6,"Male","No","Sun","Dinner",4 47 | 18.29,3,"Male","No","Sun","Dinner",2 48 | 22.23,5,"Male","No","Sun","Dinner",2 49 | 32.4,6,"Male","No","Sun","Dinner",4 50 | 28.55,2.05,"Male","No","Sun","Dinner",3 51 | 18.04,3,"Male","No","Sun","Dinner",2 52 | 12.54,2.5,"Male","No","Sun","Dinner",2 53 | 10.29,2.6,"Female","No","Sun","Dinner",2 54 | 34.81,5.2,"Female","No","Sun","Dinner",4 55 | 9.94,1.56,"Male","No","Sun","Dinner",2 56 | 25.56,4.34,"Male","No","Sun","Dinner",4 57 | 19.49,3.51,"Male","No","Sun","Dinner",2 58 | 38.01,3,"Male","Yes","Sat","Dinner",4 59 | 26.41,1.5,"Female","No","Sat","Dinner",2 60 | 11.24,1.76,"Male","Yes","Sat","Dinner",2 61 | 48.27,6.73,"Male","No","Sat","Dinner",4 62 | 20.29,3.21,"Male","Yes","Sat","Dinner",2 63 | 13.81,2,"Male","Yes","Sat","Dinner",2 64 | 11.02,1.98,"Male","Yes","Sat","Dinner",2 65 | 18.29,3.76,"Male","Yes","Sat","Dinner",4 66 | 17.59,2.64,"Male","No","Sat","Dinner",3 67 | 20.08,3.15,"Male","No","Sat","Dinner",3 68 | 16.45,2.47,"Female","No","Sat","Dinner",2 69 | 3.07,1,"Female","Yes","Sat","Dinner",1 70 | 20.23,2.01,"Male","No","Sat","Dinner",2 71 | 15.01,2.09,"Male","Yes","Sat","Dinner",2 72 | 12.02,1.97,"Male","No","Sat","Dinner",2 73 | 17.07,3,"Female","No","Sat","Dinner",3 74 | 26.86,3.14,"Female","Yes","Sat","Dinner",2 75 | 25.28,5,"Female","Yes","Sat","Dinner",2 76 | 14.73,2.2,"Female","No","Sat","Dinner",2 77 | 10.51,1.25,"Male","No","Sat","Dinner",2 78 | 17.92,3.08,"Male","Yes","Sat","Dinner",2 79 | 27.2,4,"Male","No","Thur","Lunch",4 80 | 22.76,3,"Male","No","Thur","Lunch",2 81 | 17.29,2.71,"Male","No","Thur","Lunch",2 82 | 19.44,3,"Male","Yes","Thur","Lunch",2 83 | 16.66,3.4,"Male","No","Thur","Lunch",2 84 | 10.07,1.83,"Female","No","Thur","Lunch",1 85 | 32.68,5,"Male","Yes","Thur","Lunch",2 86 | 15.98,2.03,"Male","No","Thur","Lunch",2 87 | 34.83,5.17,"Female","No","Thur","Lunch",4 88 | 13.03,2,"Male","No","Thur","Lunch",2 89 | 18.28,4,"Male","No","Thur","Lunch",2 90 | 24.71,5.85,"Male","No","Thur","Lunch",2 91 | 21.16,3,"Male","No","Thur","Lunch",2 92 | 28.97,3,"Male","Yes","Fri","Dinner",2 93 | 22.49,3.5,"Male","No","Fri","Dinner",2 94 | 5.75,1,"Female","Yes","Fri","Dinner",2 95 | 16.32,4.3,"Female","Yes","Fri","Dinner",2 96 | 22.75,3.25,"Female","No","Fri","Dinner",2 97 | 40.17,4.73,"Male","Yes","Fri","Dinner",4 98 | 27.28,4,"Male","Yes","Fri","Dinner",2 99 | 12.03,1.5,"Male","Yes","Fri","Dinner",2 100 | 21.01,3,"Male","Yes","Fri","Dinner",2 101 | 12.46,1.5,"Male","No","Fri","Dinner",2 102 | 11.35,2.5,"Female","Yes","Fri","Dinner",2 103 | 15.38,3,"Female","Yes","Fri","Dinner",2 104 | 44.3,2.5,"Female","Yes","Sat","Dinner",3 105 | 22.42,3.48,"Female","Yes","Sat","Dinner",2 106 | 20.92,4.08,"Female","No","Sat","Dinner",2 107 | 15.36,1.64,"Male","Yes","Sat","Dinner",2 108 | 20.49,4.06,"Male","Yes","Sat","Dinner",2 109 | 25.21,4.29,"Male","Yes","Sat","Dinner",2 110 | 18.24,3.76,"Male","No","Sat","Dinner",2 111 | 14.31,4,"Female","Yes","Sat","Dinner",2 112 | 14,3,"Male","No","Sat","Dinner",2 113 | 7.25,1,"Female","No","Sat","Dinner",1 114 | 38.07,4,"Male","No","Sun","Dinner",3 115 | 23.95,2.55,"Male","No","Sun","Dinner",2 116 | 25.71,4,"Female","No","Sun","Dinner",3 117 | 17.31,3.5,"Female","No","Sun","Dinner",2 118 | 29.93,5.07,"Male","No","Sun","Dinner",4 119 | 10.65,1.5,"Female","No","Thur","Lunch",2 120 | 12.43,1.8,"Female","No","Thur","Lunch",2 121 | 24.08,2.92,"Female","No","Thur","Lunch",4 122 | 11.69,2.31,"Male","No","Thur","Lunch",2 123 | 13.42,1.68,"Female","No","Thur","Lunch",2 124 | 14.26,2.5,"Male","No","Thur","Lunch",2 125 | 15.95,2,"Male","No","Thur","Lunch",2 126 | 12.48,2.52,"Female","No","Thur","Lunch",2 127 | 29.8,4.2,"Female","No","Thur","Lunch",6 128 | 8.52,1.48,"Male","No","Thur","Lunch",2 129 | 14.52,2,"Female","No","Thur","Lunch",2 130 | 11.38,2,"Female","No","Thur","Lunch",2 131 | 22.82,2.18,"Male","No","Thur","Lunch",3 132 | 19.08,1.5,"Male","No","Thur","Lunch",2 133 | 20.27,2.83,"Female","No","Thur","Lunch",2 134 | 11.17,1.5,"Female","No","Thur","Lunch",2 135 | 12.26,2,"Female","No","Thur","Lunch",2 136 | 18.26,3.25,"Female","No","Thur","Lunch",2 137 | 8.51,1.25,"Female","No","Thur","Lunch",2 138 | 10.33,2,"Female","No","Thur","Lunch",2 139 | 14.15,2,"Female","No","Thur","Lunch",2 140 | 16,2,"Male","Yes","Thur","Lunch",2 141 | 13.16,2.75,"Female","No","Thur","Lunch",2 142 | 17.47,3.5,"Female","No","Thur","Lunch",2 143 | 34.3,6.7,"Male","No","Thur","Lunch",6 144 | 41.19,5,"Male","No","Thur","Lunch",5 145 | 27.05,5,"Female","No","Thur","Lunch",6 146 | 16.43,2.3,"Female","No","Thur","Lunch",2 147 | 8.35,1.5,"Female","No","Thur","Lunch",2 148 | 18.64,1.36,"Female","No","Thur","Lunch",3 149 | 11.87,1.63,"Female","No","Thur","Lunch",2 150 | 9.78,1.73,"Male","No","Thur","Lunch",2 151 | 7.51,2,"Male","No","Thur","Lunch",2 152 | 14.07,2.5,"Male","No","Sun","Dinner",2 153 | 13.13,2,"Male","No","Sun","Dinner",2 154 | 17.26,2.74,"Male","No","Sun","Dinner",3 155 | 24.55,2,"Male","No","Sun","Dinner",4 156 | 19.77,2,"Male","No","Sun","Dinner",4 157 | 29.85,5.14,"Female","No","Sun","Dinner",5 158 | 48.17,5,"Male","No","Sun","Dinner",6 159 | 25,3.75,"Female","No","Sun","Dinner",4 160 | 13.39,2.61,"Female","No","Sun","Dinner",2 161 | 16.49,2,"Male","No","Sun","Dinner",4 162 | 21.5,3.5,"Male","No","Sun","Dinner",4 163 | 12.66,2.5,"Male","No","Sun","Dinner",2 164 | 16.21,2,"Female","No","Sun","Dinner",3 165 | 13.81,2,"Male","No","Sun","Dinner",2 166 | 17.51,3,"Female","Yes","Sun","Dinner",2 167 | 24.52,3.48,"Male","No","Sun","Dinner",3 168 | 20.76,2.24,"Male","No","Sun","Dinner",2 169 | 31.71,4.5,"Male","No","Sun","Dinner",4 170 | 10.59,1.61,"Female","Yes","Sat","Dinner",2 171 | 10.63,2,"Female","Yes","Sat","Dinner",2 172 | 50.81,10,"Male","Yes","Sat","Dinner",3 173 | 15.81,3.16,"Male","Yes","Sat","Dinner",2 174 | 7.25,5.15,"Male","Yes","Sun","Dinner",2 175 | 31.85,3.18,"Male","Yes","Sun","Dinner",2 176 | 16.82,4,"Male","Yes","Sun","Dinner",2 177 | 32.9,3.11,"Male","Yes","Sun","Dinner",2 178 | 17.89,2,"Male","Yes","Sun","Dinner",2 179 | 14.48,2,"Male","Yes","Sun","Dinner",2 180 | 9.6,4,"Female","Yes","Sun","Dinner",2 181 | 34.63,3.55,"Male","Yes","Sun","Dinner",2 182 | 34.65,3.68,"Male","Yes","Sun","Dinner",4 183 | 23.33,5.65,"Male","Yes","Sun","Dinner",2 184 | 45.35,3.5,"Male","Yes","Sun","Dinner",3 185 | 23.17,6.5,"Male","Yes","Sun","Dinner",4 186 | 40.55,3,"Male","Yes","Sun","Dinner",2 187 | 20.69,5,"Male","No","Sun","Dinner",5 188 | 20.9,3.5,"Female","Yes","Sun","Dinner",3 189 | 30.46,2,"Male","Yes","Sun","Dinner",5 190 | 18.15,3.5,"Female","Yes","Sun","Dinner",3 191 | 23.1,4,"Male","Yes","Sun","Dinner",3 192 | 15.69,1.5,"Male","Yes","Sun","Dinner",2 193 | 19.81,4.19,"Female","Yes","Thur","Lunch",2 194 | 28.44,2.56,"Male","Yes","Thur","Lunch",2 195 | 15.48,2.02,"Male","Yes","Thur","Lunch",2 196 | 16.58,4,"Male","Yes","Thur","Lunch",2 197 | 7.56,1.44,"Male","No","Thur","Lunch",2 198 | 10.34,2,"Male","Yes","Thur","Lunch",2 199 | 43.11,5,"Female","Yes","Thur","Lunch",4 200 | 13,2,"Female","Yes","Thur","Lunch",2 201 | 13.51,2,"Male","Yes","Thur","Lunch",2 202 | 18.71,4,"Male","Yes","Thur","Lunch",3 203 | 12.74,2.01,"Female","Yes","Thur","Lunch",2 204 | 13,2,"Female","Yes","Thur","Lunch",2 205 | 16.4,2.5,"Female","Yes","Thur","Lunch",2 206 | 20.53,4,"Male","Yes","Thur","Lunch",4 207 | 16.47,3.23,"Female","Yes","Thur","Lunch",3 208 | 26.59,3.41,"Male","Yes","Sat","Dinner",3 209 | 38.73,3,"Male","Yes","Sat","Dinner",4 210 | 24.27,2.03,"Male","Yes","Sat","Dinner",2 211 | 12.76,2.23,"Female","Yes","Sat","Dinner",2 212 | 30.06,2,"Male","Yes","Sat","Dinner",3 213 | 25.89,5.16,"Male","Yes","Sat","Dinner",4 214 | 48.33,9,"Male","No","Sat","Dinner",4 215 | 13.27,2.5,"Female","Yes","Sat","Dinner",2 216 | 28.17,6.5,"Female","Yes","Sat","Dinner",3 217 | 12.9,1.1,"Female","Yes","Sat","Dinner",2 218 | 28.15,3,"Male","Yes","Sat","Dinner",5 219 | 11.59,1.5,"Male","Yes","Sat","Dinner",2 220 | 7.74,1.44,"Male","Yes","Sat","Dinner",2 221 | 30.14,3.09,"Female","Yes","Sat","Dinner",4 222 | 12.16,2.2,"Male","Yes","Fri","Lunch",2 223 | 13.42,3.48,"Female","Yes","Fri","Lunch",2 224 | 8.58,1.92,"Male","Yes","Fri","Lunch",1 225 | 15.98,3,"Female","No","Fri","Lunch",3 226 | 13.42,1.58,"Male","Yes","Fri","Lunch",2 227 | 16.27,2.5,"Female","Yes","Fri","Lunch",2 228 | 10.09,2,"Female","Yes","Fri","Lunch",2 229 | 20.45,3,"Male","No","Sat","Dinner",4 230 | 13.28,2.72,"Male","No","Sat","Dinner",2 231 | 22.12,2.88,"Female","Yes","Sat","Dinner",2 232 | 24.01,2,"Male","Yes","Sat","Dinner",4 233 | 15.69,3,"Male","Yes","Sat","Dinner",3 234 | 11.61,3.39,"Male","No","Sat","Dinner",2 235 | 10.77,1.47,"Male","No","Sat","Dinner",2 236 | 15.53,3,"Male","Yes","Sat","Dinner",2 237 | 10.07,1.25,"Male","No","Sat","Dinner",2 238 | 12.6,1,"Male","Yes","Sat","Dinner",2 239 | 32.83,1.17,"Male","Yes","Sat","Dinner",2 240 | 35.83,4.67,"Female","No","Sat","Dinner",3 241 | 29.03,5.92,"Male","No","Sat","Dinner",3 242 | 27.18,2,"Female","Yes","Sat","Dinner",2 243 | 22.67,2,"Male","Yes","Sat","Dinner",2 244 | 17.82,1.75,"Male","No","Sat","Dinner",2 245 | 18.78,3,"Female","No","Thur","Dinner",2 246 | -------------------------------------------------------------------------------- /Datasets/titanic.csv: -------------------------------------------------------------------------------- 1 | survived,pclass,sex,age,sibsp,parch,fare,embarked,class,who,adult_male,deck,embark_town,alive,alone 2 | 0,3,male,22.0,1,0,7.25,S,Third,man,True,,Southampton,no,False 3 | 1,1,female,38.0,1,0,71.2833,C,First,woman,False,C,Cherbourg,yes,False 4 | 1,3,female,26.0,0,0,7.925,S,Third,woman,False,,Southampton,yes,True 5 | 1,1,female,35.0,1,0,53.1,S,First,woman,False,C,Southampton,yes,False 6 | 0,3,male,35.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 7 | 0,3,male,,0,0,8.4583,Q,Third,man,True,,Queenstown,no,True 8 | 0,1,male,54.0,0,0,51.8625,S,First,man,True,E,Southampton,no,True 9 | 0,3,male,2.0,3,1,21.075,S,Third,child,False,,Southampton,no,False 10 | 1,3,female,27.0,0,2,11.1333,S,Third,woman,False,,Southampton,yes,False 11 | 1,2,female,14.0,1,0,30.0708,C,Second,child,False,,Cherbourg,yes,False 12 | 1,3,female,4.0,1,1,16.7,S,Third,child,False,G,Southampton,yes,False 13 | 1,1,female,58.0,0,0,26.55,S,First,woman,False,C,Southampton,yes,True 14 | 0,3,male,20.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 15 | 0,3,male,39.0,1,5,31.275,S,Third,man,True,,Southampton,no,False 16 | 0,3,female,14.0,0,0,7.8542,S,Third,child,False,,Southampton,no,True 17 | 1,2,female,55.0,0,0,16.0,S,Second,woman,False,,Southampton,yes,True 18 | 0,3,male,2.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False 19 | 1,2,male,,0,0,13.0,S,Second,man,True,,Southampton,yes,True 20 | 0,3,female,31.0,1,0,18.0,S,Third,woman,False,,Southampton,no,False 21 | 1,3,female,,0,0,7.225,C,Third,woman,False,,Cherbourg,yes,True 22 | 0,2,male,35.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 23 | 1,2,male,34.0,0,0,13.0,S,Second,man,True,D,Southampton,yes,True 24 | 1,3,female,15.0,0,0,8.0292,Q,Third,child,False,,Queenstown,yes,True 25 | 1,1,male,28.0,0,0,35.5,S,First,man,True,A,Southampton,yes,True 26 | 0,3,female,8.0,3,1,21.075,S,Third,child,False,,Southampton,no,False 27 | 1,3,female,38.0,1,5,31.3875,S,Third,woman,False,,Southampton,yes,False 28 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 29 | 0,1,male,19.0,3,2,263.0,S,First,man,True,C,Southampton,no,False 30 | 1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True 31 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 32 | 0,1,male,40.0,0,0,27.7208,C,First,man,True,,Cherbourg,no,True 33 | 1,1,female,,1,0,146.5208,C,First,woman,False,B,Cherbourg,yes,False 34 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 35 | 0,2,male,66.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 36 | 0,1,male,28.0,1,0,82.1708,C,First,man,True,,Cherbourg,no,False 37 | 0,1,male,42.0,1,0,52.0,S,First,man,True,,Southampton,no,False 38 | 1,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,yes,True 39 | 0,3,male,21.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 40 | 0,3,female,18.0,2,0,18.0,S,Third,woman,False,,Southampton,no,False 41 | 1,3,female,14.0,1,0,11.2417,C,Third,child,False,,Cherbourg,yes,False 42 | 0,3,female,40.0,1,0,9.475,S,Third,woman,False,,Southampton,no,False 43 | 0,2,female,27.0,1,0,21.0,S,Second,woman,False,,Southampton,no,False 44 | 0,3,male,,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True 45 | 1,2,female,3.0,1,2,41.5792,C,Second,child,False,,Cherbourg,yes,False 46 | 1,3,female,19.0,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True 47 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 48 | 0,3,male,,1,0,15.5,Q,Third,man,True,,Queenstown,no,False 49 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 50 | 0,3,male,,2,0,21.6792,C,Third,man,True,,Cherbourg,no,False 51 | 0,3,female,18.0,1,0,17.8,S,Third,woman,False,,Southampton,no,False 52 | 0,3,male,7.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False 53 | 0,3,male,21.0,0,0,7.8,S,Third,man,True,,Southampton,no,True 54 | 1,1,female,49.0,1,0,76.7292,C,First,woman,False,D,Cherbourg,yes,False 55 | 1,2,female,29.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 56 | 0,1,male,65.0,0,1,61.9792,C,First,man,True,B,Cherbourg,no,False 57 | 1,1,male,,0,0,35.5,S,First,man,True,C,Southampton,yes,True 58 | 1,2,female,21.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True 59 | 0,3,male,28.5,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 60 | 1,2,female,5.0,1,2,27.75,S,Second,child,False,,Southampton,yes,False 61 | 0,3,male,11.0,5,2,46.9,S,Third,child,False,,Southampton,no,False 62 | 0,3,male,22.0,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 63 | 1,1,female,38.0,0,0,80.0,,First,woman,False,B,,yes,True 64 | 0,1,male,45.0,1,0,83.475,S,First,man,True,C,Southampton,no,False 65 | 0,3,male,4.0,3,2,27.9,S,Third,child,False,,Southampton,no,False 66 | 0,1,male,,0,0,27.7208,C,First,man,True,,Cherbourg,no,True 67 | 1,3,male,,1,1,15.2458,C,Third,man,True,,Cherbourg,yes,False 68 | 1,2,female,29.0,0,0,10.5,S,Second,woman,False,F,Southampton,yes,True 69 | 0,3,male,19.0,0,0,8.1583,S,Third,man,True,,Southampton,no,True 70 | 1,3,female,17.0,4,2,7.925,S,Third,woman,False,,Southampton,yes,False 71 | 0,3,male,26.0,2,0,8.6625,S,Third,man,True,,Southampton,no,False 72 | 0,2,male,32.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 73 | 0,3,female,16.0,5,2,46.9,S,Third,woman,False,,Southampton,no,False 74 | 0,2,male,21.0,0,0,73.5,S,Second,man,True,,Southampton,no,True 75 | 0,3,male,26.0,1,0,14.4542,C,Third,man,True,,Cherbourg,no,False 76 | 1,3,male,32.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 77 | 0,3,male,25.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True 78 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 79 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 80 | 1,2,male,0.83,0,2,29.0,S,Second,child,False,,Southampton,yes,False 81 | 1,3,female,30.0,0,0,12.475,S,Third,woman,False,,Southampton,yes,True 82 | 0,3,male,22.0,0,0,9.0,S,Third,man,True,,Southampton,no,True 83 | 1,3,male,29.0,0,0,9.5,S,Third,man,True,,Southampton,yes,True 84 | 1,3,female,,0,0,7.7875,Q,Third,woman,False,,Queenstown,yes,True 85 | 0,1,male,28.0,0,0,47.1,S,First,man,True,,Southampton,no,True 86 | 1,2,female,17.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True 87 | 1,3,female,33.0,3,0,15.85,S,Third,woman,False,,Southampton,yes,False 88 | 0,3,male,16.0,1,3,34.375,S,Third,man,True,,Southampton,no,False 89 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 90 | 1,1,female,23.0,3,2,263.0,S,First,woman,False,C,Southampton,yes,False 91 | 0,3,male,24.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 92 | 0,3,male,29.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 93 | 0,3,male,20.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 94 | 0,1,male,46.0,1,0,61.175,S,First,man,True,E,Southampton,no,False 95 | 0,3,male,26.0,1,2,20.575,S,Third,man,True,,Southampton,no,False 96 | 0,3,male,59.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 97 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 98 | 0,1,male,71.0,0,0,34.6542,C,First,man,True,A,Cherbourg,no,True 99 | 1,1,male,23.0,0,1,63.3583,C,First,man,True,D,Cherbourg,yes,False 100 | 1,2,female,34.0,0,1,23.0,S,Second,woman,False,,Southampton,yes,False 101 | 0,2,male,34.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 102 | 0,3,female,28.0,0,0,7.8958,S,Third,woman,False,,Southampton,no,True 103 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 104 | 0,1,male,21.0,0,1,77.2875,S,First,man,True,D,Southampton,no,False 105 | 0,3,male,33.0,0,0,8.6542,S,Third,man,True,,Southampton,no,True 106 | 0,3,male,37.0,2,0,7.925,S,Third,man,True,,Southampton,no,False 107 | 0,3,male,28.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 108 | 1,3,female,21.0,0,0,7.65,S,Third,woman,False,,Southampton,yes,True 109 | 1,3,male,,0,0,7.775,S,Third,man,True,,Southampton,yes,True 110 | 0,3,male,38.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 111 | 1,3,female,,1,0,24.15,Q,Third,woman,False,,Queenstown,yes,False 112 | 0,1,male,47.0,0,0,52.0,S,First,man,True,C,Southampton,no,True 113 | 0,3,female,14.5,1,0,14.4542,C,Third,child,False,,Cherbourg,no,False 114 | 0,3,male,22.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 115 | 0,3,female,20.0,1,0,9.825,S,Third,woman,False,,Southampton,no,False 116 | 0,3,female,17.0,0,0,14.4583,C,Third,woman,False,,Cherbourg,no,True 117 | 0,3,male,21.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 118 | 0,3,male,70.5,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 119 | 0,2,male,29.0,1,0,21.0,S,Second,man,True,,Southampton,no,False 120 | 0,1,male,24.0,0,1,247.5208,C,First,man,True,B,Cherbourg,no,False 121 | 0,3,female,2.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 122 | 0,2,male,21.0,2,0,73.5,S,Second,man,True,,Southampton,no,False 123 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 124 | 0,2,male,32.5,1,0,30.0708,C,Second,man,True,,Cherbourg,no,False 125 | 1,2,female,32.5,0,0,13.0,S,Second,woman,False,E,Southampton,yes,True 126 | 0,1,male,54.0,0,1,77.2875,S,First,man,True,D,Southampton,no,False 127 | 1,3,male,12.0,1,0,11.2417,C,Third,child,False,,Cherbourg,yes,False 128 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 129 | 1,3,male,24.0,0,0,7.1417,S,Third,man,True,,Southampton,yes,True 130 | 1,3,female,,1,1,22.3583,C,Third,woman,False,F,Cherbourg,yes,False 131 | 0,3,male,45.0,0,0,6.975,S,Third,man,True,,Southampton,no,True 132 | 0,3,male,33.0,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True 133 | 0,3,male,20.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 134 | 0,3,female,47.0,1,0,14.5,S,Third,woman,False,,Southampton,no,False 135 | 1,2,female,29.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 136 | 0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 137 | 0,2,male,23.0,0,0,15.0458,C,Second,man,True,,Cherbourg,no,True 138 | 1,1,female,19.0,0,2,26.2833,S,First,woman,False,D,Southampton,yes,False 139 | 0,1,male,37.0,1,0,53.1,S,First,man,True,C,Southampton,no,False 140 | 0,3,male,16.0,0,0,9.2167,S,Third,man,True,,Southampton,no,True 141 | 0,1,male,24.0,0,0,79.2,C,First,man,True,B,Cherbourg,no,True 142 | 0,3,female,,0,2,15.2458,C,Third,woman,False,,Cherbourg,no,False 143 | 1,3,female,22.0,0,0,7.75,S,Third,woman,False,,Southampton,yes,True 144 | 1,3,female,24.0,1,0,15.85,S,Third,woman,False,,Southampton,yes,False 145 | 0,3,male,19.0,0,0,6.75,Q,Third,man,True,,Queenstown,no,True 146 | 0,2,male,18.0,0,0,11.5,S,Second,man,True,,Southampton,no,True 147 | 0,2,male,19.0,1,1,36.75,S,Second,man,True,,Southampton,no,False 148 | 1,3,male,27.0,0,0,7.7958,S,Third,man,True,,Southampton,yes,True 149 | 0,3,female,9.0,2,2,34.375,S,Third,child,False,,Southampton,no,False 150 | 0,2,male,36.5,0,2,26.0,S,Second,man,True,F,Southampton,no,False 151 | 0,2,male,42.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 152 | 0,2,male,51.0,0,0,12.525,S,Second,man,True,,Southampton,no,True 153 | 1,1,female,22.0,1,0,66.6,S,First,woman,False,C,Southampton,yes,False 154 | 0,3,male,55.5,0,0,8.05,S,Third,man,True,,Southampton,no,True 155 | 0,3,male,40.5,0,2,14.5,S,Third,man,True,,Southampton,no,False 156 | 0,3,male,,0,0,7.3125,S,Third,man,True,,Southampton,no,True 157 | 0,1,male,51.0,0,1,61.3792,C,First,man,True,,Cherbourg,no,False 158 | 1,3,female,16.0,0,0,7.7333,Q,Third,woman,False,,Queenstown,yes,True 159 | 0,3,male,30.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 160 | 0,3,male,,0,0,8.6625,S,Third,man,True,,Southampton,no,True 161 | 0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False 162 | 0,3,male,44.0,0,1,16.1,S,Third,man,True,,Southampton,no,False 163 | 1,2,female,40.0,0,0,15.75,S,Second,woman,False,,Southampton,yes,True 164 | 0,3,male,26.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 165 | 0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 166 | 0,3,male,1.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False 167 | 1,3,male,9.0,0,2,20.525,S,Third,child,False,,Southampton,yes,False 168 | 1,1,female,,0,1,55.0,S,First,woman,False,E,Southampton,yes,False 169 | 0,3,female,45.0,1,4,27.9,S,Third,woman,False,,Southampton,no,False 170 | 0,1,male,,0,0,25.925,S,First,man,True,,Southampton,no,True 171 | 0,3,male,28.0,0,0,56.4958,S,Third,man,True,,Southampton,no,True 172 | 0,1,male,61.0,0,0,33.5,S,First,man,True,B,Southampton,no,True 173 | 0,3,male,4.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False 174 | 1,3,female,1.0,1,1,11.1333,S,Third,child,False,,Southampton,yes,False 175 | 0,3,male,21.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 176 | 0,1,male,56.0,0,0,30.6958,C,First,man,True,A,Cherbourg,no,True 177 | 0,3,male,18.0,1,1,7.8542,S,Third,man,True,,Southampton,no,False 178 | 0,3,male,,3,1,25.4667,S,Third,man,True,,Southampton,no,False 179 | 0,1,female,50.0,0,0,28.7125,C,First,woman,False,C,Cherbourg,no,True 180 | 0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 181 | 0,3,male,36.0,0,0,0.0,S,Third,man,True,,Southampton,no,True 182 | 0,3,female,,8,2,69.55,S,Third,woman,False,,Southampton,no,False 183 | 0,2,male,,0,0,15.05,C,Second,man,True,,Cherbourg,no,True 184 | 0,3,male,9.0,4,2,31.3875,S,Third,child,False,,Southampton,no,False 185 | 1,2,male,1.0,2,1,39.0,S,Second,child,False,F,Southampton,yes,False 186 | 1,3,female,4.0,0,2,22.025,S,Third,child,False,,Southampton,yes,False 187 | 0,1,male,,0,0,50.0,S,First,man,True,A,Southampton,no,True 188 | 1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False 189 | 1,1,male,45.0,0,0,26.55,S,First,man,True,,Southampton,yes,True 190 | 0,3,male,40.0,1,1,15.5,Q,Third,man,True,,Queenstown,no,False 191 | 0,3,male,36.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 192 | 1,2,female,32.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 193 | 0,2,male,19.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 194 | 1,3,female,19.0,1,0,7.8542,S,Third,woman,False,,Southampton,yes,False 195 | 1,2,male,3.0,1,1,26.0,S,Second,child,False,F,Southampton,yes,False 196 | 1,1,female,44.0,0,0,27.7208,C,First,woman,False,B,Cherbourg,yes,True 197 | 1,1,female,58.0,0,0,146.5208,C,First,woman,False,B,Cherbourg,yes,True 198 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 199 | 0,3,male,42.0,0,1,8.4042,S,Third,man,True,,Southampton,no,False 200 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 201 | 0,2,female,24.0,0,0,13.0,S,Second,woman,False,,Southampton,no,True 202 | 0,3,male,28.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 203 | 0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False 204 | 0,3,male,34.0,0,0,6.4958,S,Third,man,True,,Southampton,no,True 205 | 0,3,male,45.5,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 206 | 1,3,male,18.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True 207 | 0,3,female,2.0,0,1,10.4625,S,Third,child,False,G,Southampton,no,False 208 | 0,3,male,32.0,1,0,15.85,S,Third,man,True,,Southampton,no,False 209 | 1,3,male,26.0,0,0,18.7875,C,Third,man,True,,Cherbourg,yes,True 210 | 1,3,female,16.0,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 211 | 1,1,male,40.0,0,0,31.0,C,First,man,True,A,Cherbourg,yes,True 212 | 0,3,male,24.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 213 | 1,2,female,35.0,0,0,21.0,S,Second,woman,False,,Southampton,yes,True 214 | 0,3,male,22.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 215 | 0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 216 | 0,3,male,,1,0,7.75,Q,Third,man,True,,Queenstown,no,False 217 | 1,1,female,31.0,1,0,113.275,C,First,woman,False,D,Cherbourg,yes,False 218 | 1,3,female,27.0,0,0,7.925,S,Third,woman,False,,Southampton,yes,True 219 | 0,2,male,42.0,1,0,27.0,S,Second,man,True,,Southampton,no,False 220 | 1,1,female,32.0,0,0,76.2917,C,First,woman,False,D,Cherbourg,yes,True 221 | 0,2,male,30.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 222 | 1,3,male,16.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True 223 | 0,2,male,27.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 224 | 0,3,male,51.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 225 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 226 | 1,1,male,38.0,1,0,90.0,S,First,man,True,C,Southampton,yes,False 227 | 0,3,male,22.0,0,0,9.35,S,Third,man,True,,Southampton,no,True 228 | 1,2,male,19.0,0,0,10.5,S,Second,man,True,,Southampton,yes,True 229 | 0,3,male,20.5,0,0,7.25,S,Third,man,True,,Southampton,no,True 230 | 0,2,male,18.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 231 | 0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False 232 | 1,1,female,35.0,1,0,83.475,S,First,woman,False,C,Southampton,yes,False 233 | 0,3,male,29.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 234 | 0,2,male,59.0,0,0,13.5,S,Second,man,True,,Southampton,no,True 235 | 1,3,female,5.0,4,2,31.3875,S,Third,child,False,,Southampton,yes,False 236 | 0,2,male,24.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 237 | 0,3,female,,0,0,7.55,S,Third,woman,False,,Southampton,no,True 238 | 0,2,male,44.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 239 | 1,2,female,8.0,0,2,26.25,S,Second,child,False,,Southampton,yes,False 240 | 0,2,male,19.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 241 | 0,2,male,33.0,0,0,12.275,S,Second,man,True,,Southampton,no,True 242 | 0,3,female,,1,0,14.4542,C,Third,woman,False,,Cherbourg,no,False 243 | 1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False 244 | 0,2,male,29.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 245 | 0,3,male,22.0,0,0,7.125,S,Third,man,True,,Southampton,no,True 246 | 0,3,male,30.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 247 | 0,1,male,44.0,2,0,90.0,Q,First,man,True,C,Queenstown,no,False 248 | 0,3,female,25.0,0,0,7.775,S,Third,woman,False,,Southampton,no,True 249 | 1,2,female,24.0,0,2,14.5,S,Second,woman,False,,Southampton,yes,False 250 | 1,1,male,37.0,1,1,52.5542,S,First,man,True,D,Southampton,yes,False 251 | 0,2,male,54.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 252 | 0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True 253 | 0,3,female,29.0,1,1,10.4625,S,Third,woman,False,G,Southampton,no,False 254 | 0,1,male,62.0,0,0,26.55,S,First,man,True,C,Southampton,no,True 255 | 0,3,male,30.0,1,0,16.1,S,Third,man,True,,Southampton,no,False 256 | 0,3,female,41.0,0,2,20.2125,S,Third,woman,False,,Southampton,no,False 257 | 1,3,female,29.0,0,2,15.2458,C,Third,woman,False,,Cherbourg,yes,False 258 | 1,1,female,,0,0,79.2,C,First,woman,False,,Cherbourg,yes,True 259 | 1,1,female,30.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True 260 | 1,1,female,35.0,0,0,512.3292,C,First,woman,False,,Cherbourg,yes,True 261 | 1,2,female,50.0,0,1,26.0,S,Second,woman,False,,Southampton,yes,False 262 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 263 | 1,3,male,3.0,4,2,31.3875,S,Third,child,False,,Southampton,yes,False 264 | 0,1,male,52.0,1,1,79.65,S,First,man,True,E,Southampton,no,False 265 | 0,1,male,40.0,0,0,0.0,S,First,man,True,B,Southampton,no,True 266 | 0,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True 267 | 0,2,male,36.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 268 | 0,3,male,16.0,4,1,39.6875,S,Third,man,True,,Southampton,no,False 269 | 1,3,male,25.0,1,0,7.775,S,Third,man,True,,Southampton,yes,False 270 | 1,1,female,58.0,0,1,153.4625,S,First,woman,False,C,Southampton,yes,False 271 | 1,1,female,35.0,0,0,135.6333,S,First,woman,False,C,Southampton,yes,True 272 | 0,1,male,,0,0,31.0,S,First,man,True,,Southampton,no,True 273 | 1,3,male,25.0,0,0,0.0,S,Third,man,True,,Southampton,yes,True 274 | 1,2,female,41.0,0,1,19.5,S,Second,woman,False,,Southampton,yes,False 275 | 0,1,male,37.0,0,1,29.7,C,First,man,True,C,Cherbourg,no,False 276 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 277 | 1,1,female,63.0,1,0,77.9583,S,First,woman,False,D,Southampton,yes,False 278 | 0,3,female,45.0,0,0,7.75,S,Third,woman,False,,Southampton,no,True 279 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 280 | 0,3,male,7.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False 281 | 1,3,female,35.0,1,1,20.25,S,Third,woman,False,,Southampton,yes,False 282 | 0,3,male,65.0,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 283 | 0,3,male,28.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 284 | 0,3,male,16.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 285 | 1,3,male,19.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True 286 | 0,1,male,,0,0,26.0,S,First,man,True,A,Southampton,no,True 287 | 0,3,male,33.0,0,0,8.6625,C,Third,man,True,,Cherbourg,no,True 288 | 1,3,male,30.0,0,0,9.5,S,Third,man,True,,Southampton,yes,True 289 | 0,3,male,22.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 290 | 1,2,male,42.0,0,0,13.0,S,Second,man,True,,Southampton,yes,True 291 | 1,3,female,22.0,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 292 | 1,1,female,26.0,0,0,78.85,S,First,woman,False,,Southampton,yes,True 293 | 1,1,female,19.0,1,0,91.0792,C,First,woman,False,B,Cherbourg,yes,False 294 | 0,2,male,36.0,0,0,12.875,C,Second,man,True,D,Cherbourg,no,True 295 | 0,3,female,24.0,0,0,8.85,S,Third,woman,False,,Southampton,no,True 296 | 0,3,male,24.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 297 | 0,1,male,,0,0,27.7208,C,First,man,True,,Cherbourg,no,True 298 | 0,3,male,23.5,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 299 | 0,1,female,2.0,1,2,151.55,S,First,child,False,C,Southampton,no,False 300 | 1,1,male,,0,0,30.5,S,First,man,True,C,Southampton,yes,True 301 | 1,1,female,50.0,0,1,247.5208,C,First,woman,False,B,Cherbourg,yes,False 302 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 303 | 1,3,male,,2,0,23.25,Q,Third,man,True,,Queenstown,yes,False 304 | 0,3,male,19.0,0,0,0.0,S,Third,man,True,,Southampton,no,True 305 | 1,2,female,,0,0,12.35,Q,Second,woman,False,E,Queenstown,yes,True 306 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 307 | 1,1,male,0.92,1,2,151.55,S,First,child,False,C,Southampton,yes,False 308 | 1,1,female,,0,0,110.8833,C,First,woman,False,,Cherbourg,yes,True 309 | 1,1,female,17.0,1,0,108.9,C,First,woman,False,C,Cherbourg,yes,False 310 | 0,2,male,30.0,1,0,24.0,C,Second,man,True,,Cherbourg,no,False 311 | 1,1,female,30.0,0,0,56.9292,C,First,woman,False,E,Cherbourg,yes,True 312 | 1,1,female,24.0,0,0,83.1583,C,First,woman,False,C,Cherbourg,yes,True 313 | 1,1,female,18.0,2,2,262.375,C,First,woman,False,B,Cherbourg,yes,False 314 | 0,2,female,26.0,1,1,26.0,S,Second,woman,False,,Southampton,no,False 315 | 0,3,male,28.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 316 | 0,2,male,43.0,1,1,26.25,S,Second,man,True,,Southampton,no,False 317 | 1,3,female,26.0,0,0,7.8542,S,Third,woman,False,,Southampton,yes,True 318 | 1,2,female,24.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 319 | 0,2,male,54.0,0,0,14.0,S,Second,man,True,,Southampton,no,True 320 | 1,1,female,31.0,0,2,164.8667,S,First,woman,False,C,Southampton,yes,False 321 | 1,1,female,40.0,1,1,134.5,C,First,woman,False,E,Cherbourg,yes,False 322 | 0,3,male,22.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 323 | 0,3,male,27.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 324 | 1,2,female,30.0,0,0,12.35,Q,Second,woman,False,,Queenstown,yes,True 325 | 1,2,female,22.0,1,1,29.0,S,Second,woman,False,,Southampton,yes,False 326 | 0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False 327 | 1,1,female,36.0,0,0,135.6333,C,First,woman,False,C,Cherbourg,yes,True 328 | 0,3,male,61.0,0,0,6.2375,S,Third,man,True,,Southampton,no,True 329 | 1,2,female,36.0,0,0,13.0,S,Second,woman,False,D,Southampton,yes,True 330 | 1,3,female,31.0,1,1,20.525,S,Third,woman,False,,Southampton,yes,False 331 | 1,1,female,16.0,0,1,57.9792,C,First,woman,False,B,Cherbourg,yes,False 332 | 1,3,female,,2,0,23.25,Q,Third,woman,False,,Queenstown,yes,False 333 | 0,1,male,45.5,0,0,28.5,S,First,man,True,C,Southampton,no,True 334 | 0,1,male,38.0,0,1,153.4625,S,First,man,True,C,Southampton,no,False 335 | 0,3,male,16.0,2,0,18.0,S,Third,man,True,,Southampton,no,False 336 | 1,1,female,,1,0,133.65,S,First,woman,False,,Southampton,yes,False 337 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 338 | 0,1,male,29.0,1,0,66.6,S,First,man,True,C,Southampton,no,False 339 | 1,1,female,41.0,0,0,134.5,C,First,woman,False,E,Cherbourg,yes,True 340 | 1,3,male,45.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True 341 | 0,1,male,45.0,0,0,35.5,S,First,man,True,,Southampton,no,True 342 | 1,2,male,2.0,1,1,26.0,S,Second,child,False,F,Southampton,yes,False 343 | 1,1,female,24.0,3,2,263.0,S,First,woman,False,C,Southampton,yes,False 344 | 0,2,male,28.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 345 | 0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 346 | 0,2,male,36.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 347 | 1,2,female,24.0,0,0,13.0,S,Second,woman,False,F,Southampton,yes,True 348 | 1,2,female,40.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 349 | 1,3,female,,1,0,16.1,S,Third,woman,False,,Southampton,yes,False 350 | 1,3,male,3.0,1,1,15.9,S,Third,child,False,,Southampton,yes,False 351 | 0,3,male,42.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 352 | 0,3,male,23.0,0,0,9.225,S,Third,man,True,,Southampton,no,True 353 | 0,1,male,,0,0,35.0,S,First,man,True,C,Southampton,no,True 354 | 0,3,male,15.0,1,1,7.2292,C,Third,child,False,,Cherbourg,no,False 355 | 0,3,male,25.0,1,0,17.8,S,Third,man,True,,Southampton,no,False 356 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 357 | 0,3,male,28.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 358 | 1,1,female,22.0,0,1,55.0,S,First,woman,False,E,Southampton,yes,False 359 | 0,2,female,38.0,0,0,13.0,S,Second,woman,False,,Southampton,no,True 360 | 1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True 361 | 1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True 362 | 0,3,male,40.0,1,4,27.9,S,Third,man,True,,Southampton,no,False 363 | 0,2,male,29.0,1,0,27.7208,C,Second,man,True,,Cherbourg,no,False 364 | 0,3,female,45.0,0,1,14.4542,C,Third,woman,False,,Cherbourg,no,False 365 | 0,3,male,35.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 366 | 0,3,male,,1,0,15.5,Q,Third,man,True,,Queenstown,no,False 367 | 0,3,male,30.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 368 | 1,1,female,60.0,1,0,75.25,C,First,woman,False,D,Cherbourg,yes,False 369 | 1,3,female,,0,0,7.2292,C,Third,woman,False,,Cherbourg,yes,True 370 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 371 | 1,1,female,24.0,0,0,69.3,C,First,woman,False,B,Cherbourg,yes,True 372 | 1,1,male,25.0,1,0,55.4417,C,First,man,True,E,Cherbourg,yes,False 373 | 0,3,male,18.0,1,0,6.4958,S,Third,man,True,,Southampton,no,False 374 | 0,3,male,19.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 375 | 0,1,male,22.0,0,0,135.6333,C,First,man,True,,Cherbourg,no,True 376 | 0,3,female,3.0,3,1,21.075,S,Third,child,False,,Southampton,no,False 377 | 1,1,female,,1,0,82.1708,C,First,woman,False,,Cherbourg,yes,False 378 | 1,3,female,22.0,0,0,7.25,S,Third,woman,False,,Southampton,yes,True 379 | 0,1,male,27.0,0,2,211.5,C,First,man,True,C,Cherbourg,no,False 380 | 0,3,male,20.0,0,0,4.0125,C,Third,man,True,,Cherbourg,no,True 381 | 0,3,male,19.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 382 | 1,1,female,42.0,0,0,227.525,C,First,woman,False,,Cherbourg,yes,True 383 | 1,3,female,1.0,0,2,15.7417,C,Third,child,False,,Cherbourg,yes,False 384 | 0,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 385 | 1,1,female,35.0,1,0,52.0,S,First,woman,False,,Southampton,yes,False 386 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 387 | 0,2,male,18.0,0,0,73.5,S,Second,man,True,,Southampton,no,True 388 | 0,3,male,1.0,5,2,46.9,S,Third,child,False,,Southampton,no,False 389 | 1,2,female,36.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 390 | 0,3,male,,0,0,7.7292,Q,Third,man,True,,Queenstown,no,True 391 | 1,2,female,17.0,0,0,12.0,C,Second,woman,False,,Cherbourg,yes,True 392 | 1,1,male,36.0,1,2,120.0,S,First,man,True,B,Southampton,yes,False 393 | 1,3,male,21.0,0,0,7.7958,S,Third,man,True,,Southampton,yes,True 394 | 0,3,male,28.0,2,0,7.925,S,Third,man,True,,Southampton,no,False 395 | 1,1,female,23.0,1,0,113.275,C,First,woman,False,D,Cherbourg,yes,False 396 | 1,3,female,24.0,0,2,16.7,S,Third,woman,False,G,Southampton,yes,False 397 | 0,3,male,22.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True 398 | 0,3,female,31.0,0,0,7.8542,S,Third,woman,False,,Southampton,no,True 399 | 0,2,male,46.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 400 | 0,2,male,23.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 401 | 1,2,female,28.0,0,0,12.65,S,Second,woman,False,,Southampton,yes,True 402 | 1,3,male,39.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True 403 | 0,3,male,26.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 404 | 0,3,female,21.0,1,0,9.825,S,Third,woman,False,,Southampton,no,False 405 | 0,3,male,28.0,1,0,15.85,S,Third,man,True,,Southampton,no,False 406 | 0,3,female,20.0,0,0,8.6625,S,Third,woman,False,,Southampton,no,True 407 | 0,2,male,34.0,1,0,21.0,S,Second,man,True,,Southampton,no,False 408 | 0,3,male,51.0,0,0,7.75,S,Third,man,True,,Southampton,no,True 409 | 1,2,male,3.0,1,1,18.75,S,Second,child,False,,Southampton,yes,False 410 | 0,3,male,21.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 411 | 0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False 412 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 413 | 0,3,male,,0,0,6.8583,Q,Third,man,True,,Queenstown,no,True 414 | 1,1,female,33.0,1,0,90.0,Q,First,woman,False,C,Queenstown,yes,False 415 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 416 | 1,3,male,44.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True 417 | 0,3,female,,0,0,8.05,S,Third,woman,False,,Southampton,no,True 418 | 1,2,female,34.0,1,1,32.5,S,Second,woman,False,,Southampton,yes,False 419 | 1,2,female,18.0,0,2,13.0,S,Second,woman,False,,Southampton,yes,False 420 | 0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 421 | 0,3,female,10.0,0,2,24.15,S,Third,child,False,,Southampton,no,False 422 | 0,3,male,,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True 423 | 0,3,male,21.0,0,0,7.7333,Q,Third,man,True,,Queenstown,no,True 424 | 0,3,male,29.0,0,0,7.875,S,Third,man,True,,Southampton,no,True 425 | 0,3,female,28.0,1,1,14.4,S,Third,woman,False,,Southampton,no,False 426 | 0,3,male,18.0,1,1,20.2125,S,Third,man,True,,Southampton,no,False 427 | 0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True 428 | 1,2,female,28.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 429 | 1,2,female,19.0,0,0,26.0,S,Second,woman,False,,Southampton,yes,True 430 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 431 | 1,3,male,32.0,0,0,8.05,S,Third,man,True,E,Southampton,yes,True 432 | 1,1,male,28.0,0,0,26.55,S,First,man,True,C,Southampton,yes,True 433 | 1,3,female,,1,0,16.1,S,Third,woman,False,,Southampton,yes,False 434 | 1,2,female,42.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 435 | 0,3,male,17.0,0,0,7.125,S,Third,man,True,,Southampton,no,True 436 | 0,1,male,50.0,1,0,55.9,S,First,man,True,E,Southampton,no,False 437 | 1,1,female,14.0,1,2,120.0,S,First,child,False,B,Southampton,yes,False 438 | 0,3,female,21.0,2,2,34.375,S,Third,woman,False,,Southampton,no,False 439 | 1,2,female,24.0,2,3,18.75,S,Second,woman,False,,Southampton,yes,False 440 | 0,1,male,64.0,1,4,263.0,S,First,man,True,C,Southampton,no,False 441 | 0,2,male,31.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 442 | 1,2,female,45.0,1,1,26.25,S,Second,woman,False,,Southampton,yes,False 443 | 0,3,male,20.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 444 | 0,3,male,25.0,1,0,7.775,S,Third,man,True,,Southampton,no,False 445 | 1,2,female,28.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 446 | 1,3,male,,0,0,8.1125,S,Third,man,True,,Southampton,yes,True 447 | 1,1,male,4.0,0,2,81.8583,S,First,child,False,A,Southampton,yes,False 448 | 1,2,female,13.0,0,1,19.5,S,Second,child,False,,Southampton,yes,False 449 | 1,1,male,34.0,0,0,26.55,S,First,man,True,,Southampton,yes,True 450 | 1,3,female,5.0,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False 451 | 1,1,male,52.0,0,0,30.5,S,First,man,True,C,Southampton,yes,True 452 | 0,2,male,36.0,1,2,27.75,S,Second,man,True,,Southampton,no,False 453 | 0,3,male,,1,0,19.9667,S,Third,man,True,,Southampton,no,False 454 | 0,1,male,30.0,0,0,27.75,C,First,man,True,C,Cherbourg,no,True 455 | 1,1,male,49.0,1,0,89.1042,C,First,man,True,C,Cherbourg,yes,False 456 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 457 | 1,3,male,29.0,0,0,7.8958,C,Third,man,True,,Cherbourg,yes,True 458 | 0,1,male,65.0,0,0,26.55,S,First,man,True,E,Southampton,no,True 459 | 1,1,female,,1,0,51.8625,S,First,woman,False,D,Southampton,yes,False 460 | 1,2,female,50.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True 461 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 462 | 1,1,male,48.0,0,0,26.55,S,First,man,True,E,Southampton,yes,True 463 | 0,3,male,34.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 464 | 0,1,male,47.0,0,0,38.5,S,First,man,True,E,Southampton,no,True 465 | 0,2,male,48.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 466 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 467 | 0,3,male,38.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 468 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 469 | 0,1,male,56.0,0,0,26.55,S,First,man,True,,Southampton,no,True 470 | 0,3,male,,0,0,7.725,Q,Third,man,True,,Queenstown,no,True 471 | 1,3,female,0.75,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False 472 | 0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True 473 | 0,3,male,38.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 474 | 1,2,female,33.0,1,2,27.75,S,Second,woman,False,,Southampton,yes,False 475 | 1,2,female,23.0,0,0,13.7917,C,Second,woman,False,D,Cherbourg,yes,True 476 | 0,3,female,22.0,0,0,9.8375,S,Third,woman,False,,Southampton,no,True 477 | 0,1,male,,0,0,52.0,S,First,man,True,A,Southampton,no,True 478 | 0,2,male,34.0,1,0,21.0,S,Second,man,True,,Southampton,no,False 479 | 0,3,male,29.0,1,0,7.0458,S,Third,man,True,,Southampton,no,False 480 | 0,3,male,22.0,0,0,7.5208,S,Third,man,True,,Southampton,no,True 481 | 1,3,female,2.0,0,1,12.2875,S,Third,child,False,,Southampton,yes,False 482 | 0,3,male,9.0,5,2,46.9,S,Third,child,False,,Southampton,no,False 483 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 484 | 0,3,male,50.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 485 | 1,3,female,63.0,0,0,9.5875,S,Third,woman,False,,Southampton,yes,True 486 | 1,1,male,25.0,1,0,91.0792,C,First,man,True,B,Cherbourg,yes,False 487 | 0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False 488 | 1,1,female,35.0,1,0,90.0,S,First,woman,False,C,Southampton,yes,False 489 | 0,1,male,58.0,0,0,29.7,C,First,man,True,B,Cherbourg,no,True 490 | 0,3,male,30.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 491 | 1,3,male,9.0,1,1,15.9,S,Third,child,False,,Southampton,yes,False 492 | 0,3,male,,1,0,19.9667,S,Third,man,True,,Southampton,no,False 493 | 0,3,male,21.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 494 | 0,1,male,55.0,0,0,30.5,S,First,man,True,C,Southampton,no,True 495 | 0,1,male,71.0,0,0,49.5042,C,First,man,True,,Cherbourg,no,True 496 | 0,3,male,21.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 497 | 0,3,male,,0,0,14.4583,C,Third,man,True,,Cherbourg,no,True 498 | 1,1,female,54.0,1,0,78.2667,C,First,woman,False,D,Cherbourg,yes,False 499 | 0,3,male,,0,0,15.1,S,Third,man,True,,Southampton,no,True 500 | 0,1,female,25.0,1,2,151.55,S,First,woman,False,C,Southampton,no,False 501 | 0,3,male,24.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True 502 | 0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 503 | 0,3,female,21.0,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True 504 | 0,3,female,,0,0,7.6292,Q,Third,woman,False,,Queenstown,no,True 505 | 0,3,female,37.0,0,0,9.5875,S,Third,woman,False,,Southampton,no,True 506 | 1,1,female,16.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True 507 | 0,1,male,18.0,1,0,108.9,C,First,man,True,C,Cherbourg,no,False 508 | 1,2,female,33.0,0,2,26.0,S,Second,woman,False,,Southampton,yes,False 509 | 1,1,male,,0,0,26.55,S,First,man,True,,Southampton,yes,True 510 | 0,3,male,28.0,0,0,22.525,S,Third,man,True,,Southampton,no,True 511 | 1,3,male,26.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 512 | 1,3,male,29.0,0,0,7.75,Q,Third,man,True,,Queenstown,yes,True 513 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 514 | 1,1,male,36.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True 515 | 1,1,female,54.0,1,0,59.4,C,First,woman,False,,Cherbourg,yes,False 516 | 0,3,male,24.0,0,0,7.4958,S,Third,man,True,,Southampton,no,True 517 | 0,1,male,47.0,0,0,34.0208,S,First,man,True,D,Southampton,no,True 518 | 1,2,female,34.0,0,0,10.5,S,Second,woman,False,F,Southampton,yes,True 519 | 0,3,male,,0,0,24.15,Q,Third,man,True,,Queenstown,no,True 520 | 1,2,female,36.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 521 | 0,3,male,32.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 522 | 1,1,female,30.0,0,0,93.5,S,First,woman,False,B,Southampton,yes,True 523 | 0,3,male,22.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 524 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 525 | 1,1,female,44.0,0,1,57.9792,C,First,woman,False,B,Cherbourg,yes,False 526 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 527 | 0,3,male,40.5,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 528 | 1,2,female,50.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True 529 | 0,1,male,,0,0,221.7792,S,First,man,True,C,Southampton,no,True 530 | 0,3,male,39.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 531 | 0,2,male,23.0,2,1,11.5,S,Second,man,True,,Southampton,no,False 532 | 1,2,female,2.0,1,1,26.0,S,Second,child,False,,Southampton,yes,False 533 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 534 | 0,3,male,17.0,1,1,7.2292,C,Third,man,True,,Cherbourg,no,False 535 | 1,3,female,,0,2,22.3583,C,Third,woman,False,,Cherbourg,yes,False 536 | 0,3,female,30.0,0,0,8.6625,S,Third,woman,False,,Southampton,no,True 537 | 1,2,female,7.0,0,2,26.25,S,Second,child,False,,Southampton,yes,False 538 | 0,1,male,45.0,0,0,26.55,S,First,man,True,B,Southampton,no,True 539 | 1,1,female,30.0,0,0,106.425,C,First,woman,False,,Cherbourg,yes,True 540 | 0,3,male,,0,0,14.5,S,Third,man,True,,Southampton,no,True 541 | 1,1,female,22.0,0,2,49.5,C,First,woman,False,B,Cherbourg,yes,False 542 | 1,1,female,36.0,0,2,71.0,S,First,woman,False,B,Southampton,yes,False 543 | 0,3,female,9.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 544 | 0,3,female,11.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 545 | 1,2,male,32.0,1,0,26.0,S,Second,man,True,,Southampton,yes,False 546 | 0,1,male,50.0,1,0,106.425,C,First,man,True,C,Cherbourg,no,False 547 | 0,1,male,64.0,0,0,26.0,S,First,man,True,,Southampton,no,True 548 | 1,2,female,19.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 549 | 1,2,male,,0,0,13.8625,C,Second,man,True,,Cherbourg,yes,True 550 | 0,3,male,33.0,1,1,20.525,S,Third,man,True,,Southampton,no,False 551 | 1,2,male,8.0,1,1,36.75,S,Second,child,False,,Southampton,yes,False 552 | 1,1,male,17.0,0,2,110.8833,C,First,man,True,C,Cherbourg,yes,False 553 | 0,2,male,27.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 554 | 0,3,male,,0,0,7.8292,Q,Third,man,True,,Queenstown,no,True 555 | 1,3,male,22.0,0,0,7.225,C,Third,man,True,,Cherbourg,yes,True 556 | 1,3,female,22.0,0,0,7.775,S,Third,woman,False,,Southampton,yes,True 557 | 0,1,male,62.0,0,0,26.55,S,First,man,True,,Southampton,no,True 558 | 1,1,female,48.0,1,0,39.6,C,First,woman,False,A,Cherbourg,yes,False 559 | 0,1,male,,0,0,227.525,C,First,man,True,,Cherbourg,no,True 560 | 1,1,female,39.0,1,1,79.65,S,First,woman,False,E,Southampton,yes,False 561 | 1,3,female,36.0,1,0,17.4,S,Third,woman,False,,Southampton,yes,False 562 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 563 | 0,3,male,40.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 564 | 0,2,male,28.0,0,0,13.5,S,Second,man,True,,Southampton,no,True 565 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 566 | 0,3,female,,0,0,8.05,S,Third,woman,False,,Southampton,no,True 567 | 0,3,male,24.0,2,0,24.15,S,Third,man,True,,Southampton,no,False 568 | 0,3,male,19.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 569 | 0,3,female,29.0,0,4,21.075,S,Third,woman,False,,Southampton,no,False 570 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 571 | 1,3,male,32.0,0,0,7.8542,S,Third,man,True,,Southampton,yes,True 572 | 1,2,male,62.0,0,0,10.5,S,Second,man,True,,Southampton,yes,True 573 | 1,1,female,53.0,2,0,51.4792,S,First,woman,False,C,Southampton,yes,False 574 | 1,1,male,36.0,0,0,26.3875,S,First,man,True,E,Southampton,yes,True 575 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 576 | 0,3,male,16.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 577 | 0,3,male,19.0,0,0,14.5,S,Third,man,True,,Southampton,no,True 578 | 1,2,female,34.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 579 | 1,1,female,39.0,1,0,55.9,S,First,woman,False,E,Southampton,yes,False 580 | 0,3,female,,1,0,14.4583,C,Third,woman,False,,Cherbourg,no,False 581 | 1,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True 582 | 1,2,female,25.0,1,1,30.0,S,Second,woman,False,,Southampton,yes,False 583 | 1,1,female,39.0,1,1,110.8833,C,First,woman,False,C,Cherbourg,yes,False 584 | 0,2,male,54.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 585 | 0,1,male,36.0,0,0,40.125,C,First,man,True,A,Cherbourg,no,True 586 | 0,3,male,,0,0,8.7125,C,Third,man,True,,Cherbourg,no,True 587 | 1,1,female,18.0,0,2,79.65,S,First,woman,False,E,Southampton,yes,False 588 | 0,2,male,47.0,0,0,15.0,S,Second,man,True,,Southampton,no,True 589 | 1,1,male,60.0,1,1,79.2,C,First,man,True,B,Cherbourg,yes,False 590 | 0,3,male,22.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 591 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 592 | 0,3,male,35.0,0,0,7.125,S,Third,man,True,,Southampton,no,True 593 | 1,1,female,52.0,1,0,78.2667,C,First,woman,False,D,Cherbourg,yes,False 594 | 0,3,male,47.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 595 | 0,3,female,,0,2,7.75,Q,Third,woman,False,,Queenstown,no,False 596 | 0,2,male,37.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 597 | 0,3,male,36.0,1,1,24.15,S,Third,man,True,,Southampton,no,False 598 | 1,2,female,,0,0,33.0,S,Second,woman,False,,Southampton,yes,True 599 | 0,3,male,49.0,0,0,0.0,S,Third,man,True,,Southampton,no,True 600 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 601 | 1,1,male,49.0,1,0,56.9292,C,First,man,True,A,Cherbourg,yes,False 602 | 1,2,female,24.0,2,1,27.0,S,Second,woman,False,,Southampton,yes,False 603 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 604 | 0,1,male,,0,0,42.4,S,First,man,True,,Southampton,no,True 605 | 0,3,male,44.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 606 | 1,1,male,35.0,0,0,26.55,C,First,man,True,,Cherbourg,yes,True 607 | 0,3,male,36.0,1,0,15.55,S,Third,man,True,,Southampton,no,False 608 | 0,3,male,30.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 609 | 1,1,male,27.0,0,0,30.5,S,First,man,True,,Southampton,yes,True 610 | 1,2,female,22.0,1,2,41.5792,C,Second,woman,False,,Cherbourg,yes,False 611 | 1,1,female,40.0,0,0,153.4625,S,First,woman,False,C,Southampton,yes,True 612 | 0,3,female,39.0,1,5,31.275,S,Third,woman,False,,Southampton,no,False 613 | 0,3,male,,0,0,7.05,S,Third,man,True,,Southampton,no,True 614 | 1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False 615 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 616 | 0,3,male,35.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 617 | 1,2,female,24.0,1,2,65.0,S,Second,woman,False,,Southampton,yes,False 618 | 0,3,male,34.0,1,1,14.4,S,Third,man,True,,Southampton,no,False 619 | 0,3,female,26.0,1,0,16.1,S,Third,woman,False,,Southampton,no,False 620 | 1,2,female,4.0,2,1,39.0,S,Second,child,False,F,Southampton,yes,False 621 | 0,2,male,26.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 622 | 0,3,male,27.0,1,0,14.4542,C,Third,man,True,,Cherbourg,no,False 623 | 1,1,male,42.0,1,0,52.5542,S,First,man,True,D,Southampton,yes,False 624 | 1,3,male,20.0,1,1,15.7417,C,Third,man,True,,Cherbourg,yes,False 625 | 0,3,male,21.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 626 | 0,3,male,21.0,0,0,16.1,S,Third,man,True,,Southampton,no,True 627 | 0,1,male,61.0,0,0,32.3208,S,First,man,True,D,Southampton,no,True 628 | 0,2,male,57.0,0,0,12.35,Q,Second,man,True,,Queenstown,no,True 629 | 1,1,female,21.0,0,0,77.9583,S,First,woman,False,D,Southampton,yes,True 630 | 0,3,male,26.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 631 | 0,3,male,,0,0,7.7333,Q,Third,man,True,,Queenstown,no,True 632 | 1,1,male,80.0,0,0,30.0,S,First,man,True,A,Southampton,yes,True 633 | 0,3,male,51.0,0,0,7.0542,S,Third,man,True,,Southampton,no,True 634 | 1,1,male,32.0,0,0,30.5,C,First,man,True,B,Cherbourg,yes,True 635 | 0,1,male,,0,0,0.0,S,First,man,True,,Southampton,no,True 636 | 0,3,female,9.0,3,2,27.9,S,Third,child,False,,Southampton,no,False 637 | 1,2,female,28.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 638 | 0,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 639 | 0,2,male,31.0,1,1,26.25,S,Second,man,True,,Southampton,no,False 640 | 0,3,female,41.0,0,5,39.6875,S,Third,woman,False,,Southampton,no,False 641 | 0,3,male,,1,0,16.1,S,Third,man,True,,Southampton,no,False 642 | 0,3,male,20.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 643 | 1,1,female,24.0,0,0,69.3,C,First,woman,False,B,Cherbourg,yes,True 644 | 0,3,female,2.0,3,2,27.9,S,Third,child,False,,Southampton,no,False 645 | 1,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 646 | 1,3,female,0.75,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False 647 | 1,1,male,48.0,1,0,76.7292,C,First,man,True,D,Cherbourg,yes,False 648 | 0,3,male,19.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 649 | 1,1,male,56.0,0,0,35.5,C,First,man,True,A,Cherbourg,yes,True 650 | 0,3,male,,0,0,7.55,S,Third,man,True,,Southampton,no,True 651 | 1,3,female,23.0,0,0,7.55,S,Third,woman,False,,Southampton,yes,True 652 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 653 | 1,2,female,18.0,0,1,23.0,S,Second,woman,False,,Southampton,yes,False 654 | 0,3,male,21.0,0,0,8.4333,S,Third,man,True,,Southampton,no,True 655 | 1,3,female,,0,0,7.8292,Q,Third,woman,False,,Queenstown,yes,True 656 | 0,3,female,18.0,0,0,6.75,Q,Third,woman,False,,Queenstown,no,True 657 | 0,2,male,24.0,2,0,73.5,S,Second,man,True,,Southampton,no,False 658 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 659 | 0,3,female,32.0,1,1,15.5,Q,Third,woman,False,,Queenstown,no,False 660 | 0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 661 | 0,1,male,58.0,0,2,113.275,C,First,man,True,D,Cherbourg,no,False 662 | 1,1,male,50.0,2,0,133.65,S,First,man,True,,Southampton,yes,False 663 | 0,3,male,40.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 664 | 0,1,male,47.0,0,0,25.5875,S,First,man,True,E,Southampton,no,True 665 | 0,3,male,36.0,0,0,7.4958,S,Third,man,True,,Southampton,no,True 666 | 1,3,male,20.0,1,0,7.925,S,Third,man,True,,Southampton,yes,False 667 | 0,2,male,32.0,2,0,73.5,S,Second,man,True,,Southampton,no,False 668 | 0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 669 | 0,3,male,,0,0,7.775,S,Third,man,True,,Southampton,no,True 670 | 0,3,male,43.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 671 | 1,1,female,,1,0,52.0,S,First,woman,False,C,Southampton,yes,False 672 | 1,2,female,40.0,1,1,39.0,S,Second,woman,False,,Southampton,yes,False 673 | 0,1,male,31.0,1,0,52.0,S,First,man,True,B,Southampton,no,False 674 | 0,2,male,70.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 675 | 1,2,male,31.0,0,0,13.0,S,Second,man,True,,Southampton,yes,True 676 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 677 | 0,3,male,18.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 678 | 0,3,male,24.5,0,0,8.05,S,Third,man,True,,Southampton,no,True 679 | 1,3,female,18.0,0,0,9.8417,S,Third,woman,False,,Southampton,yes,True 680 | 0,3,female,43.0,1,6,46.9,S,Third,woman,False,,Southampton,no,False 681 | 1,1,male,36.0,0,1,512.3292,C,First,man,True,B,Cherbourg,yes,False 682 | 0,3,female,,0,0,8.1375,Q,Third,woman,False,,Queenstown,no,True 683 | 1,1,male,27.0,0,0,76.7292,C,First,man,True,D,Cherbourg,yes,True 684 | 0,3,male,20.0,0,0,9.225,S,Third,man,True,,Southampton,no,True 685 | 0,3,male,14.0,5,2,46.9,S,Third,child,False,,Southampton,no,False 686 | 0,2,male,60.0,1,1,39.0,S,Second,man,True,,Southampton,no,False 687 | 0,2,male,25.0,1,2,41.5792,C,Second,man,True,,Cherbourg,no,False 688 | 0,3,male,14.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False 689 | 0,3,male,19.0,0,0,10.1708,S,Third,man,True,,Southampton,no,True 690 | 0,3,male,18.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True 691 | 1,1,female,15.0,0,1,211.3375,S,First,child,False,B,Southampton,yes,False 692 | 1,1,male,31.0,1,0,57.0,S,First,man,True,B,Southampton,yes,False 693 | 1,3,female,4.0,0,1,13.4167,C,Third,child,False,,Cherbourg,yes,False 694 | 1,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 695 | 0,3,male,25.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 696 | 0,1,male,60.0,0,0,26.55,S,First,man,True,,Southampton,no,True 697 | 0,2,male,52.0,0,0,13.5,S,Second,man,True,,Southampton,no,True 698 | 0,3,male,44.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 699 | 1,3,female,,0,0,7.7333,Q,Third,woman,False,,Queenstown,yes,True 700 | 0,1,male,49.0,1,1,110.8833,C,First,man,True,C,Cherbourg,no,False 701 | 0,3,male,42.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True 702 | 1,1,female,18.0,1,0,227.525,C,First,woman,False,C,Cherbourg,yes,False 703 | 1,1,male,35.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True 704 | 0,3,female,18.0,0,1,14.4542,C,Third,woman,False,,Cherbourg,no,False 705 | 0,3,male,25.0,0,0,7.7417,Q,Third,man,True,,Queenstown,no,True 706 | 0,3,male,26.0,1,0,7.8542,S,Third,man,True,,Southampton,no,False 707 | 0,2,male,39.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 708 | 1,2,female,45.0,0,0,13.5,S,Second,woman,False,,Southampton,yes,True 709 | 1,1,male,42.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True 710 | 1,1,female,22.0,0,0,151.55,S,First,woman,False,,Southampton,yes,True 711 | 1,3,male,,1,1,15.2458,C,Third,man,True,,Cherbourg,yes,False 712 | 1,1,female,24.0,0,0,49.5042,C,First,woman,False,C,Cherbourg,yes,True 713 | 0,1,male,,0,0,26.55,S,First,man,True,C,Southampton,no,True 714 | 1,1,male,48.0,1,0,52.0,S,First,man,True,C,Southampton,yes,False 715 | 0,3,male,29.0,0,0,9.4833,S,Third,man,True,,Southampton,no,True 716 | 0,2,male,52.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 717 | 0,3,male,19.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True 718 | 1,1,female,38.0,0,0,227.525,C,First,woman,False,C,Cherbourg,yes,True 719 | 1,2,female,27.0,0,0,10.5,S,Second,woman,False,E,Southampton,yes,True 720 | 0,3,male,,0,0,15.5,Q,Third,man,True,,Queenstown,no,True 721 | 0,3,male,33.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 722 | 1,2,female,6.0,0,1,33.0,S,Second,child,False,,Southampton,yes,False 723 | 0,3,male,17.0,1,0,7.0542,S,Third,man,True,,Southampton,no,False 724 | 0,2,male,34.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 725 | 0,2,male,50.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 726 | 1,1,male,27.0,1,0,53.1,S,First,man,True,E,Southampton,yes,False 727 | 0,3,male,20.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 728 | 1,2,female,30.0,3,0,21.0,S,Second,woman,False,,Southampton,yes,False 729 | 1,3,female,,0,0,7.7375,Q,Third,woman,False,,Queenstown,yes,True 730 | 0,2,male,25.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 731 | 0,3,female,25.0,1,0,7.925,S,Third,woman,False,,Southampton,no,False 732 | 1,1,female,29.0,0,0,211.3375,S,First,woman,False,B,Southampton,yes,True 733 | 0,3,male,11.0,0,0,18.7875,C,Third,child,False,,Cherbourg,no,True 734 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 735 | 0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 736 | 0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 737 | 0,3,male,28.5,0,0,16.1,S,Third,man,True,,Southampton,no,True 738 | 0,3,female,48.0,1,3,34.375,S,Third,woman,False,,Southampton,no,False 739 | 1,1,male,35.0,0,0,512.3292,C,First,man,True,B,Cherbourg,yes,True 740 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 741 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 742 | 1,1,male,,0,0,30.0,S,First,man,True,D,Southampton,yes,True 743 | 0,1,male,36.0,1,0,78.85,S,First,man,True,C,Southampton,no,False 744 | 1,1,female,21.0,2,2,262.375,C,First,woman,False,B,Cherbourg,yes,False 745 | 0,3,male,24.0,1,0,16.1,S,Third,man,True,,Southampton,no,False 746 | 1,3,male,31.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True 747 | 0,1,male,70.0,1,1,71.0,S,First,man,True,B,Southampton,no,False 748 | 0,3,male,16.0,1,1,20.25,S,Third,man,True,,Southampton,no,False 749 | 1,2,female,30.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 750 | 0,1,male,19.0,1,0,53.1,S,First,man,True,D,Southampton,no,False 751 | 0,3,male,31.0,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 752 | 1,2,female,4.0,1,1,23.0,S,Second,child,False,,Southampton,yes,False 753 | 1,3,male,6.0,0,1,12.475,S,Third,child,False,E,Southampton,yes,False 754 | 0,3,male,33.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 755 | 0,3,male,23.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 756 | 1,2,female,48.0,1,2,65.0,S,Second,woman,False,,Southampton,yes,False 757 | 1,2,male,0.67,1,1,14.5,S,Second,child,False,,Southampton,yes,False 758 | 0,3,male,28.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True 759 | 0,2,male,18.0,0,0,11.5,S,Second,man,True,,Southampton,no,True 760 | 0,3,male,34.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 761 | 1,1,female,33.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True 762 | 0,3,male,,0,0,14.5,S,Third,man,True,,Southampton,no,True 763 | 0,3,male,41.0,0,0,7.125,S,Third,man,True,,Southampton,no,True 764 | 1,3,male,20.0,0,0,7.2292,C,Third,man,True,,Cherbourg,yes,True 765 | 1,1,female,36.0,1,2,120.0,S,First,woman,False,B,Southampton,yes,False 766 | 0,3,male,16.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 767 | 1,1,female,51.0,1,0,77.9583,S,First,woman,False,D,Southampton,yes,False 768 | 0,1,male,,0,0,39.6,C,First,man,True,,Cherbourg,no,True 769 | 0,3,female,30.5,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True 770 | 0,3,male,,1,0,24.15,Q,Third,man,True,,Queenstown,no,False 771 | 0,3,male,32.0,0,0,8.3625,S,Third,man,True,,Southampton,no,True 772 | 0,3,male,24.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 773 | 0,3,male,48.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 774 | 0,2,female,57.0,0,0,10.5,S,Second,woman,False,E,Southampton,no,True 775 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 776 | 1,2,female,54.0,1,3,23.0,S,Second,woman,False,,Southampton,yes,False 777 | 0,3,male,18.0,0,0,7.75,S,Third,man,True,,Southampton,no,True 778 | 0,3,male,,0,0,7.75,Q,Third,man,True,F,Queenstown,no,True 779 | 1,3,female,5.0,0,0,12.475,S,Third,child,False,,Southampton,yes,True 780 | 0,3,male,,0,0,7.7375,Q,Third,man,True,,Queenstown,no,True 781 | 1,1,female,43.0,0,1,211.3375,S,First,woman,False,B,Southampton,yes,False 782 | 1,3,female,13.0,0,0,7.2292,C,Third,child,False,,Cherbourg,yes,True 783 | 1,1,female,17.0,1,0,57.0,S,First,woman,False,B,Southampton,yes,False 784 | 0,1,male,29.0,0,0,30.0,S,First,man,True,D,Southampton,no,True 785 | 0,3,male,,1,2,23.45,S,Third,man,True,,Southampton,no,False 786 | 0,3,male,25.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 787 | 0,3,male,25.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 788 | 1,3,female,18.0,0,0,7.4958,S,Third,woman,False,,Southampton,yes,True 789 | 0,3,male,8.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False 790 | 1,3,male,1.0,1,2,20.575,S,Third,child,False,,Southampton,yes,False 791 | 0,1,male,46.0,0,0,79.2,C,First,man,True,B,Cherbourg,no,True 792 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 793 | 0,2,male,16.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 794 | 0,3,female,,8,2,69.55,S,Third,woman,False,,Southampton,no,False 795 | 0,1,male,,0,0,30.6958,C,First,man,True,,Cherbourg,no,True 796 | 0,3,male,25.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 797 | 0,2,male,39.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 798 | 1,1,female,49.0,0,0,25.9292,S,First,woman,False,D,Southampton,yes,True 799 | 1,3,female,31.0,0,0,8.6833,S,Third,woman,False,,Southampton,yes,True 800 | 0,3,male,30.0,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 801 | 0,3,female,30.0,1,1,24.15,S,Third,woman,False,,Southampton,no,False 802 | 0,2,male,34.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 803 | 1,2,female,31.0,1,1,26.25,S,Second,woman,False,,Southampton,yes,False 804 | 1,1,male,11.0,1,2,120.0,S,First,child,False,B,Southampton,yes,False 805 | 1,3,male,0.42,0,1,8.5167,C,Third,child,False,,Cherbourg,yes,False 806 | 1,3,male,27.0,0,0,6.975,S,Third,man,True,,Southampton,yes,True 807 | 0,3,male,31.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 808 | 0,1,male,39.0,0,0,0.0,S,First,man,True,A,Southampton,no,True 809 | 0,3,female,18.0,0,0,7.775,S,Third,woman,False,,Southampton,no,True 810 | 0,2,male,39.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 811 | 1,1,female,33.0,1,0,53.1,S,First,woman,False,E,Southampton,yes,False 812 | 0,3,male,26.0,0,0,7.8875,S,Third,man,True,,Southampton,no,True 813 | 0,3,male,39.0,0,0,24.15,S,Third,man,True,,Southampton,no,True 814 | 0,2,male,35.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 815 | 0,3,female,6.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 816 | 0,3,male,30.5,0,0,8.05,S,Third,man,True,,Southampton,no,True 817 | 0,1,male,,0,0,0.0,S,First,man,True,B,Southampton,no,True 818 | 0,3,female,23.0,0,0,7.925,S,Third,woman,False,,Southampton,no,True 819 | 0,2,male,31.0,1,1,37.0042,C,Second,man,True,,Cherbourg,no,False 820 | 0,3,male,43.0,0,0,6.45,S,Third,man,True,,Southampton,no,True 821 | 0,3,male,10.0,3,2,27.9,S,Third,child,False,,Southampton,no,False 822 | 1,1,female,52.0,1,1,93.5,S,First,woman,False,B,Southampton,yes,False 823 | 1,3,male,27.0,0,0,8.6625,S,Third,man,True,,Southampton,yes,True 824 | 0,1,male,38.0,0,0,0.0,S,First,man,True,,Southampton,no,True 825 | 1,3,female,27.0,0,1,12.475,S,Third,woman,False,E,Southampton,yes,False 826 | 0,3,male,2.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False 827 | 0,3,male,,0,0,6.95,Q,Third,man,True,,Queenstown,no,True 828 | 0,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,no,True 829 | 1,2,male,1.0,0,2,37.0042,C,Second,child,False,,Cherbourg,yes,False 830 | 1,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,yes,True 831 | 1,1,female,62.0,0,0,80.0,,First,woman,False,B,,yes,True 832 | 1,3,female,15.0,1,0,14.4542,C,Third,child,False,,Cherbourg,yes,False 833 | 1,2,male,0.83,1,1,18.75,S,Second,child,False,,Southampton,yes,False 834 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 835 | 0,3,male,23.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 836 | 0,3,male,18.0,0,0,8.3,S,Third,man,True,,Southampton,no,True 837 | 1,1,female,39.0,1,1,83.1583,C,First,woman,False,E,Cherbourg,yes,False 838 | 0,3,male,21.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 839 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 840 | 1,3,male,32.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 841 | 1,1,male,,0,0,29.7,C,First,man,True,C,Cherbourg,yes,True 842 | 0,3,male,20.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 843 | 0,2,male,16.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 844 | 1,1,female,30.0,0,0,31.0,C,First,woman,False,,Cherbourg,yes,True 845 | 0,3,male,34.5,0,0,6.4375,C,Third,man,True,,Cherbourg,no,True 846 | 0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 847 | 0,3,male,42.0,0,0,7.55,S,Third,man,True,,Southampton,no,True 848 | 0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False 849 | 0,3,male,35.0,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True 850 | 0,2,male,28.0,0,1,33.0,S,Second,man,True,,Southampton,no,False 851 | 1,1,female,,1,0,89.1042,C,First,woman,False,C,Cherbourg,yes,False 852 | 0,3,male,4.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 853 | 0,3,male,74.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 854 | 0,3,female,9.0,1,1,15.2458,C,Third,child,False,,Cherbourg,no,False 855 | 1,1,female,16.0,0,1,39.4,S,First,woman,False,D,Southampton,yes,False 856 | 0,2,female,44.0,1,0,26.0,S,Second,woman,False,,Southampton,no,False 857 | 1,3,female,18.0,0,1,9.35,S,Third,woman,False,,Southampton,yes,False 858 | 1,1,female,45.0,1,1,164.8667,S,First,woman,False,,Southampton,yes,False 859 | 1,1,male,51.0,0,0,26.55,S,First,man,True,E,Southampton,yes,True 860 | 1,3,female,24.0,0,3,19.2583,C,Third,woman,False,,Cherbourg,yes,False 861 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 862 | 0,3,male,41.0,2,0,14.1083,S,Third,man,True,,Southampton,no,False 863 | 0,2,male,21.0,1,0,11.5,S,Second,man,True,,Southampton,no,False 864 | 1,1,female,48.0,0,0,25.9292,S,First,woman,False,D,Southampton,yes,True 865 | 0,3,female,,8,2,69.55,S,Third,woman,False,,Southampton,no,False 866 | 0,2,male,24.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 867 | 1,2,female,42.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 868 | 1,2,female,27.0,1,0,13.8583,C,Second,woman,False,,Cherbourg,yes,False 869 | 0,1,male,31.0,0,0,50.4958,S,First,man,True,A,Southampton,no,True 870 | 0,3,male,,0,0,9.5,S,Third,man,True,,Southampton,no,True 871 | 1,3,male,4.0,1,1,11.1333,S,Third,child,False,,Southampton,yes,False 872 | 0,3,male,26.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 873 | 1,1,female,47.0,1,1,52.5542,S,First,woman,False,D,Southampton,yes,False 874 | 0,1,male,33.0,0,0,5.0,S,First,man,True,B,Southampton,no,True 875 | 0,3,male,47.0,0,0,9.0,S,Third,man,True,,Southampton,no,True 876 | 1,2,female,28.0,1,0,24.0,C,Second,woman,False,,Cherbourg,yes,False 877 | 1,3,female,15.0,0,0,7.225,C,Third,child,False,,Cherbourg,yes,True 878 | 0,3,male,20.0,0,0,9.8458,S,Third,man,True,,Southampton,no,True 879 | 0,3,male,19.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 880 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 881 | 1,1,female,56.0,0,1,83.1583,C,First,woman,False,C,Cherbourg,yes,False 882 | 1,2,female,25.0,0,1,26.0,S,Second,woman,False,,Southampton,yes,False 883 | 0,3,male,33.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 884 | 0,3,female,22.0,0,0,10.5167,S,Third,woman,False,,Southampton,no,True 885 | 0,2,male,28.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 886 | 0,3,male,25.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 887 | 0,3,female,39.0,0,5,29.125,Q,Third,woman,False,,Queenstown,no,False 888 | 0,2,male,27.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 889 | 1,1,female,19.0,0,0,30.0,S,First,woman,False,B,Southampton,yes,True 890 | 0,3,female,,1,2,23.45,S,Third,woman,False,,Southampton,no,False 891 | 1,1,male,26.0,0,0,30.0,C,First,man,True,C,Cherbourg,yes,True 892 | 0,3,male,32.0,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 893 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Alok Kumar 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /PULL_REQUEST_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | Temp Format 2 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Seaborn-Tutorial: 2 | 3 | **Data Visualization** is a critical though undermined skill required in pursuit of a **Data Science** career. This repository is an attempt to help *Data Science* aspirants gain necessary **Data Visualization** skills required to progress in their career. It includes all the types of plot offered by **Seaborn**, applied on *random* or *fabricated datasets*. The knowledge gained for inference shall in no way be limited to just Seaborn. 4 | 5 | For learners who feel at ease when steps are visually explained, you may check my [YouTube channel](https://www.youtube.com/channel/UCwvHagkArilKs7QT4vqFXqQ). You may opt reading for a *written/article* mode preview on my [Medium publication](https://medium.com/@neuralnets). My algorithms shall try to ensure that these notebooks are well synchronized with video streaming but do not guarantee perfect *Speech to Text*. 6 | 7 | ## Agenda: 8 | With this series of **Seaborn notebooks**, aspirants shall achieve or be able to upgrade their skills on: 9 | - Learn to use Pandas to have a brief overview of dataset. 10 | - Learn to use various Seaborn plots. 11 | - Learn to infer the representation of data distribution on any plot. 12 | - Utilize underlying Matplotlib arguments to tweak Seaborn plots. 13 | - Statistical interpretation of plotted data. 14 | - In-depth usage & explanation of each available plotting parameter. 15 | - Advanced customization as to satisfy complex real-world business problems. 16 | - Custom codes for enhancing data visualization experience. 17 | 18 | ## Series Curriculum: 19 | - Introduction to *Data Visualization* Fundamentals 20 | - [Setting up Tools & Resources (Jupyter Notebook](https://medium.com/@neuralnets/beginners-quick-guide-for-handling-issues-launching-jupyter-notebook-for-python-using-anaconda-8be3d57a209b)) 21 | - Overview of **NumPy** and **Pandas** 22 | - Elementary Statistical Terms : [Part-1](https://medium.com/@neuralnets/probability-distribution-statistics-for-deep-learning-73a567e65dfa), [Part-2](https://medium.com/@neuralnets/elementary-statistical-terms-for-data-science-interviews-212d931ca57d) and [Part-3](https://medium.com/@neuralnets/linear-algebra-for-data-science-revisiting-high-school-9a6bbeba19c6). 23 | - Plot styling with Seaborn (With **Tableau** flavour) 24 | - Linearly spread Data Plots 25 | - Categorical Data Plots 26 | - Visualization on Grids 27 | 28 | *Please note that the content of each Curriculum topic might get segregated into multiple videos on YouTube OR multiple articles on [Medium Publication](https://medium.com/@neuralnets) so I would recommend opening it up as a playlist for better experience.* 29 | 30 | ## Note: 31 | I could've made a [Udemy](https://www.udemy.com/) course out of this and earned money but I believe in contributing to our open-source arena, so my only expectation from learners is for them to also contribute whichever way they can in due course of time. If there is any issue with the code or explanation that you would like me to look into or *advice/suggest/recommend*, please feel free to reach out. If the content is useful, a better idea would be to **Star** or **Fork** this repository for your future reference. If the content on publication seems well explained, I would really be glad to get notified about [your applause on the story](https://help.medium.com/hc/en-us/articles/115011350967-Claps). - [Alok Kumar](https://www.linkedin.com/in/alok-kumar-85455b117/) 32 | 33 | **Edit:** I am aware of the changes brought in with **Seaborn v0.9** and shall add a *Notebook* in accordance very soon. Appreciate your time! 34 | -------------------------------------------------------------------------------- /Seaborn - Loading Dataset.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Seaborn | Part-1: Loading Datasets:" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "When working with Seaborn, we can either use one of the [built-in datasets](https://github.com/mwaskom/seaborn-data) that Seaborn offers or we can load a Pandas DataFrame. Seaborn is part of the [PyData](https://pydata.org/) stack hence accepts Pandas’ data structures.\n", 15 | "\n", 16 | "Let us begin by importing few built-in datasets but before that we shall import few other libraries as well that our Seaborn would depend upon: " 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "# Importing intrinsic libraries:\n", 26 | "import pandas as pd\n", 27 | "import matplotlib.pyplot as plt\n", 28 | "import seaborn as sns" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "Once we have imported the required libraries, now it is time to load built-in dataset. The dataset we would be dealing with in this illustration is [Iris Flower Dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set)." 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 2, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "# Loading built-in Datasets:\n", 45 | "iris = sns.load_dataset(\"iris\")" 46 | ] 47 | }, 48 | { 49 | "cell_type": "markdown", 50 | "metadata": {}, 51 | "source": [ 52 | "Similarly we may load other dataset as well and for illustration sake, I shall code few of them down here (though won't be referencing to):" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": 3, 58 | "metadata": {}, 59 | "outputs": [], 60 | "source": [ 61 | "# Refer to 'Dataset Source Reference' for list of all built-in Seaborn datasets.\n", 62 | "tips = sns.load_dataset(\"tips\")\n", 63 | "exercise = sns.load_dataset(\"exercise\")\n", 64 | "titanic = sns.load_dataset(\"titanic\")\n", 65 | "flights = sns.load_dataset(\"flights\")" 66 | ] 67 | }, 68 | { 69 | "cell_type": "markdown", 70 | "metadata": {}, 71 | "source": [ 72 | "Let us take a sneak peek as to how this Iris dataset looks like and we shall be using Pandas to do so:" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 4, 78 | "metadata": {}, 79 | "outputs": [ 80 | { 81 | "data": { 82 | "text/html": [ 83 | "
\n", 84 | "\n", 97 | "\n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \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 | "
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
55.43.91.70.4setosa
64.63.41.40.3setosa
75.03.41.50.2setosa
84.42.91.40.2setosa
94.93.11.50.1setosa
\n", 191 | "
" 192 | ], 193 | "text/plain": [ 194 | " sepal_length sepal_width petal_length petal_width species\n", 195 | "0 5.1 3.5 1.4 0.2 setosa\n", 196 | "1 4.9 3.0 1.4 0.2 setosa\n", 197 | "2 4.7 3.2 1.3 0.2 setosa\n", 198 | "3 4.6 3.1 1.5 0.2 setosa\n", 199 | "4 5.0 3.6 1.4 0.2 setosa\n", 200 | "5 5.4 3.9 1.7 0.4 setosa\n", 201 | "6 4.6 3.4 1.4 0.3 setosa\n", 202 | "7 5.0 3.4 1.5 0.2 setosa\n", 203 | "8 4.4 2.9 1.4 0.2 setosa\n", 204 | "9 4.9 3.1 1.5 0.1 setosa" 205 | ] 206 | }, 207 | "execution_count": 4, 208 | "metadata": {}, 209 | "output_type": "execute_result" 210 | } 211 | ], 212 | "source": [ 213 | "iris.head(10)" 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "metadata": {}, 219 | "source": [ 220 | "Iris dataset actually has 50 samples from each of three species of Iris flower (Setosa, Virginica and Versicolor). Four features were measured (in centimetres) from each sample: Length and Width of the Sepals and Petals. Let us try to have a summarized view of this dataset:" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": 5, 226 | "metadata": {}, 227 | "outputs": [ 228 | { 229 | "data": { 230 | "text/html": [ 231 | "
\n", 232 | "\n", 245 | "\n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | "
sepal_lengthsepal_widthpetal_lengthpetal_width
count150.000000150.000000150.000000150.000000
mean5.8433333.0573333.7580001.199333
std0.8280660.4358661.7652980.762238
min4.3000002.0000001.0000000.100000
25%5.1000002.8000001.6000000.300000
50%5.8000003.0000004.3500001.300000
75%6.4000003.3000005.1000001.800000
max7.9000004.4000006.9000002.500000
\n", 314 | "
" 315 | ], 316 | "text/plain": [ 317 | " sepal_length sepal_width petal_length petal_width\n", 318 | "count 150.000000 150.000000 150.000000 150.000000\n", 319 | "mean 5.843333 3.057333 3.758000 1.199333\n", 320 | "std 0.828066 0.435866 1.765298 0.762238\n", 321 | "min 4.300000 2.000000 1.000000 0.100000\n", 322 | "25% 5.100000 2.800000 1.600000 0.300000\n", 323 | "50% 5.800000 3.000000 4.350000 1.300000\n", 324 | "75% 6.400000 3.300000 5.100000 1.800000\n", 325 | "max 7.900000 4.400000 6.900000 2.500000" 326 | ] 327 | }, 328 | "execution_count": 5, 329 | "metadata": {}, 330 | "output_type": "execute_result" 331 | } 332 | ], 333 | "source": [ 334 | "iris.describe()" 335 | ] 336 | }, 337 | { 338 | "cell_type": "markdown", 339 | "metadata": {}, 340 | "source": [ 341 | "`.describe()` is a very useful method in Pandas as it generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. Without getting in-depth into analysis here, let us try to plot something simple from this dataset:" 342 | ] 343 | }, 344 | { 345 | "cell_type": "code", 346 | "execution_count": 6, 347 | "metadata": {}, 348 | "outputs": [ 349 | { 350 | "data": { 351 | "text/plain": [ 352 | "" 353 | ] 354 | }, 355 | "execution_count": 6, 356 | "metadata": {}, 357 | "output_type": "execute_result" 358 | }, 359 | { 360 | "data": { 361 | "image/png": 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\n", 362 | "text/plain": [ 363 | "
" 364 | ] 365 | }, 366 | "metadata": {}, 367 | "output_type": "display_data" 368 | } 369 | ], 370 | "source": [ 371 | "sns.set()\n", 372 | "%matplotlib inline\n", 373 | "# Later in the course I shall explain why above 2 lines of code have been added.\n", 374 | "\n", 375 | "sns.swarmplot(x=\"species\", y=\"petal_length\", data=iris)" 376 | ] 377 | }, 378 | { 379 | "cell_type": "markdown", 380 | "metadata": {}, 381 | "source": [ 382 | "This beautiful representation of data we see above is known as a `Swarm Plot` with minimal parameters. I shall be covering this in detail later on but for now I just wanted you to have a feel of serenity we're getting into. \n", 383 | "\n", 384 | "Let us now try to load a random dataset and the one I've picked for this illustration is [PoliceKillingsUS](https://github.com/washingtonpost/data-police-shootings) dataset. This dataset has been prepared by The Washington Post (they keep updating it on runtime) with every fatal shooting in the United States by a police officer in the line of duty since Jan. 1, 2015." 385 | ] 386 | }, 387 | { 388 | "cell_type": "code", 389 | "execution_count": 10, 390 | "metadata": {}, 391 | "outputs": [], 392 | "source": [ 393 | "# Loading Pandas DataFrame:\n", 394 | "df = pd.read_csv(\"C:/Users/Alok/Downloads/PoliceKillingsUS.csv\", encoding=\"windows-1252\")" 395 | ] 396 | }, 397 | { 398 | "cell_type": "markdown", 399 | "metadata": {}, 400 | "source": [ 401 | "Just the way we looked into Iris Data set, let us know have a preview of this dataset as well. We won't be getting into deep analysis of this dataset because our agenda is only to visualize the content within. So, let's do this: " 402 | ] 403 | }, 404 | { 405 | "cell_type": "code", 406 | "execution_count": 11, 407 | "metadata": {}, 408 | "outputs": [ 409 | { 410 | "data": { 411 | "text/html": [ 412 | "
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idnamedatemanner_of_deatharmedagegenderracecitystatesigns_of_mental_illnessthreat_levelfleebody_camera
03Tim Elliot02/01/15shotgun53.0MASheltonWATrueattackNot fleeingFalse
14Lewis Lee Lembke02/01/15shotgun47.0MWAlohaORFalseattackNot fleeingFalse
25John Paul Quintero03/01/15shot and Taseredunarmed23.0MHWichitaKSFalseotherNot fleeingFalse
38Matthew Hoffman04/01/15shottoy weapon32.0MWSan FranciscoCATrueattackNot fleeingFalse
49Michael Rodriguez04/01/15shotnail gun39.0MHEvansCOFalseattackNot fleeingFalse
511Kenneth Joe Brown04/01/15shotgun18.0MWGuthrieOKFalseattackNot fleeingFalse
613Kenneth Arnold Buck05/01/15shotgun22.0MHChandlerAZFalseattackCarFalse
715Brock Nichols06/01/15shotgun35.0MWAssariaKSFalseattackNot fleeingFalse
816Autumn Steele06/01/15shotunarmed34.0FWBurlingtonIAFalseotherNot fleeingTrue
917Leslie Sapp III06/01/15shottoy weapon47.0MBKnoxvillePAFalseattackNot fleeingFalse
\n", 619 | "
" 620 | ], 621 | "text/plain": [ 622 | " id name date manner_of_death armed age \\\n", 623 | "0 3 Tim Elliot 02/01/15 shot gun 53.0 \n", 624 | "1 4 Lewis Lee Lembke 02/01/15 shot gun 47.0 \n", 625 | "2 5 John Paul Quintero 03/01/15 shot and Tasered unarmed 23.0 \n", 626 | "3 8 Matthew Hoffman 04/01/15 shot toy weapon 32.0 \n", 627 | "4 9 Michael Rodriguez 04/01/15 shot nail gun 39.0 \n", 628 | "5 11 Kenneth Joe Brown 04/01/15 shot gun 18.0 \n", 629 | "6 13 Kenneth Arnold Buck 05/01/15 shot gun 22.0 \n", 630 | "7 15 Brock Nichols 06/01/15 shot gun 35.0 \n", 631 | "8 16 Autumn Steele 06/01/15 shot unarmed 34.0 \n", 632 | "9 17 Leslie Sapp III 06/01/15 shot toy weapon 47.0 \n", 633 | "\n", 634 | " gender race city state signs_of_mental_illness threat_level \\\n", 635 | "0 M A Shelton WA True attack \n", 636 | "1 M W Aloha OR False attack \n", 637 | "2 M H Wichita KS False other \n", 638 | "3 M W San Francisco CA True attack \n", 639 | "4 M H Evans CO False attack \n", 640 | "5 M W Guthrie OK False attack \n", 641 | "6 M H Chandler AZ False attack \n", 642 | "7 M W Assaria KS False attack \n", 643 | "8 F W Burlington IA False other \n", 644 | "9 M B Knoxville PA False attack \n", 645 | "\n", 646 | " flee body_camera \n", 647 | "0 Not fleeing False \n", 648 | "1 Not fleeing False \n", 649 | "2 Not fleeing False \n", 650 | "3 Not fleeing False \n", 651 | "4 Not fleeing False \n", 652 | "5 Not fleeing False \n", 653 | "6 Car False \n", 654 | "7 Not fleeing False \n", 655 | "8 Not fleeing True \n", 656 | "9 Not fleeing False " 657 | ] 658 | }, 659 | "execution_count": 11, 660 | "metadata": {}, 661 | "output_type": "execute_result" 662 | } 663 | ], 664 | "source": [ 665 | "df.head(10)" 666 | ] 667 | }, 668 | { 669 | "cell_type": "markdown", 670 | "metadata": {}, 671 | "source": [ 672 | "This dataset is pretty self-descriptive and has limited number of features (may read as columns).\n", 673 | "\n", 674 | "`race`:\n", 675 | "`W`: White, non-Hispanic\n", 676 | "`B`: Black, non-Hispanic\n", 677 | "`A`: Asian\n", 678 | "`N`: Native American\n", 679 | "`H`: Hispanic\n", 680 | "`O`: Other\n", 681 | "`None`: unknown\n", 682 | "\n", 683 | "And, `gender` indicates:\n", 684 | "`M`: Male\n", 685 | "`F`: Female\n", 686 | "`None`: unknown\n", 687 | "The threat_level column include incidents where officers or others were shot at, threatened with a gun, attacked with other weapons or physical force, etc. The attack category is meant to flag the highest level of threat. The `other` and `undetermined` categories represent all remaining cases. `Other` includes many incidents where officers or others faced significant threats.\n", 688 | "\n", 689 | "The `threat column` and the `fleeing column` are not necessarily related. Also, `attacks` represent a status immediately before fatal shots by police; while `fleeing` could begin slightly earlier and involve a chase. Latly, `body_camera` indicates if an officer was wearing a body camera and it may have recorded some portion of the incident.\n", 690 | "\n", 691 | "Let us now look into the descriptive statistics:" 692 | ] 693 | }, 694 | { 695 | "cell_type": "code", 696 | "execution_count": 12, 697 | "metadata": {}, 698 | "outputs": [ 699 | { 700 | "data": { 701 | "text/html": [ 702 | "
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idage
count2535.0000002458.000000
mean1445.73175536.605370
std794.25949013.030774
min3.0000006.000000
25%768.50000026.000000
50%1453.00000034.000000
75%2126.50000045.000000
max2822.00000091.000000
\n", 767 | "
" 768 | ], 769 | "text/plain": [ 770 | " id age\n", 771 | "count 2535.000000 2458.000000\n", 772 | "mean 1445.731755 36.605370\n", 773 | "std 794.259490 13.030774\n", 774 | "min 3.000000 6.000000\n", 775 | "25% 768.500000 26.000000\n", 776 | "50% 1453.000000 34.000000\n", 777 | "75% 2126.500000 45.000000\n", 778 | "max 2822.000000 91.000000" 779 | ] 780 | }, 781 | "execution_count": 12, 782 | "metadata": {}, 783 | "output_type": "execute_result" 784 | } 785 | ], 786 | "source": [ 787 | "df.describe()" 788 | ] 789 | }, 790 | { 791 | "cell_type": "markdown", 792 | "metadata": {}, 793 | "source": [ 794 | "These stats in particular do not really make much sense. Instead let us try to visualize age of people who were claimed to be armed as per this dataset.\n", 795 | "\n", 796 | "Quick Note: Two special lines of code that we added earlier won't be required again. As promised, I shall reason that in upcoming lectures." 797 | ] 798 | }, 799 | { 800 | "cell_type": "code", 801 | "execution_count": 13, 802 | "metadata": {}, 803 | "outputs": [ 804 | { 805 | "data": { 806 | "text/plain": [ 807 | "" 808 | ] 809 | }, 810 | "execution_count": 13, 811 | "metadata": {}, 812 | "output_type": "execute_result" 813 | }, 814 | { 815 | "data": { 816 | "image/png": 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\n", 817 | "text/plain": [ 818 | "
" 819 | ] 820 | }, 821 | "metadata": {}, 822 | "output_type": "display_data" 823 | } 824 | ], 825 | "source": [ 826 | "sns.stripplot(x=\"armed\", y=\"age\", data=df)" 827 | ] 828 | }, 829 | { 830 | "cell_type": "markdown", 831 | "metadata": {}, 832 | "source": [ 833 | "As you would have guessed by now, this plot is known as a Strip plot and pretty ideal for categorical values. Even this shall be dealt in length later on.\n", 834 | "\n", 835 | "I hope these sample plots have intrigued you enough to dive deeper into statistical visual inference with Seaborn. And in next lecture, we shall learn to control aesthetics of our plot and few other important aspects." 836 | ] 837 | } 838 | ], 839 | "metadata": { 840 | "kernelspec": { 841 | "display_name": "Python 3", 842 | "language": "python", 843 | "name": "python3" 844 | }, 845 | "language_info": { 846 | "codemirror_mode": { 847 | "name": "ipython", 848 | "version": 3 849 | }, 850 | "file_extension": ".py", 851 | "mimetype": "text/x-python", 852 | "name": "python", 853 | "nbconvert_exporter": "python", 854 | "pygments_lexer": "ipython3", 855 | "version": "3.6.4" 856 | } 857 | }, 858 | "nbformat": 4, 859 | "nbformat_minor": 2 860 | } 861 | -------------------------------------------------------------------------------- /Seaborn Cheat Sheet.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/clair513/Seaborn-Tutorial/fd562f8c662b0d3805a2b0a5bd848b2e318d0f06/Seaborn Cheat Sheet.pdf -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-slate -------------------------------------------------------------------------------- /docs/CONTRIBUTING: -------------------------------------------------------------------------------- 1 | Steps for creating good issues or pull requests: 2 | 3 | 4 | Links to external documentation, mailing lists, or a code of conduct: 5 | --------------------------------------------------------------------------------