Participants.ipynb

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   "id": "96e5485d-3948-4962-be7a-8f9ae47a7521",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "import requests\n",
    "import textwrap\n",
    "\n",
    "states_geojson = requests.get(\n",
    "    \"https://raw.githubusercontent.com/nvkelso/natural-earth-vector/master/geojson/ne_110m_admin_1_states_provinces_lines.geojson\"\n",
    ").json()\n",
    "\n",
    "\n",
    "df = px.data.gapminder().query(\"year == 2007\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fa8c3ccc-a77f-4874-be0f-bbaca527b2d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_excel('DSO participants all years.xlsx')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e8cec5f3-db46-46d7-bf4a-19d0b78853f9",
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   "source": [
    "data['name']=data.first_name + ' ' + data.last_name\n",
    "data=data.rename(columns={\"college_name\": \"College\", \"program_year\": \"Program Year\", \"city_from\": \"From\"})\n",
    "data['Program Year']=data['Program Year'].astype(int).astype(str)"
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   "execution_count": null,
   "id": "6ed0c646-0e44-488b-9ac8-ea17bea858a2",
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   "id": "0ab0b4b2-1225-44ce-9a6b-a78d853bd642",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Testimonial']=data['Testimonial'].astype(str).apply(\n",
    "    lambda t: \"<br>\".join(textwrap.wrap(t))\n",
    ")\n",
    "data['Testimonial']=data['Testimonial'].replace('nan','')"
   ]
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           "University of Texas at Austin",
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           "2023",
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           "Lacie Levy",
           "At the end of DSO, I felt a new sense of empowerment to do truly<br>independent research. It was basically two weeks of learning how to<br>answer big questions with publicly available data and free tools like<br>Python. Plus, it got me out of my biology silo and opened my eyes to<br>other disciplines of oceanography, making them more accessible too. I<br>didn't expect students from so many different disciplines to be here,<br>but it made it awesome to hear from everyone's perspective."
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           "Besides offering a really thorough overview of Oceanography and Data<br>Science for those interested in going into the field, this program is<br>a perfect opportunity to learn more about the grad school process from<br>both professors and grad students at UW. The organizers did an amazing<br>job of putting it all together."
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           "Emma Nguyen",
           "Participating in the two-week Data Science in Oceanography program was<br>an enriching experience. This immersive journey allowed us to explore<br>various subjects of Oceanography and gain valuable insight into the<br>cutting-edge research underway in the field. The tutorials offered an<br>excellent foundation for comprehending the intricacies of data<br>analysis and data visualization.    While these tutorials posed a<br>significant challenge, the guidance and support from both our peers<br>and dedicated instructors made them manageable and immensely<br>insightful learning experiences. The collaborative environment<br>fostered within the program was a true highlight, encouraging<br>participants to pool their diverse perspectives and expertise,<br>ultimately leading to more comprehensive results.    I would like to<br>express my heartfelt appreciation to all the program's staff, with a<br>special mention of Georgy, for curating such an outstanding and<br>successful program. I am immensely grateful for this transformative<br>experience and am eager to apply the skills and insights acquired here<br>to make meaningful contributions to the field of Oceanography in the<br>future."
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           "Tebing Tinggi, Indonesia",
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           "Reynaldi Wijaya",
           "Never have I been in a program filled with individuals who are as<br>passionate in oceanography as our cohort. Never have I also had so<br>much fun yet learned so much about oceanography, data science, and<br>graduate school. This could be one of the most fulfilling experiences<br>I have had throughout my undergraduate so far."
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           "My experience being part of the 2023 Data Science in Oceanography<br>program was truly an amazing and enriching experience! By learning<br>from professors in different fields of oceanography, I gained so much<br>insight on what oceanography looks like and the many routes it<br>encompasses. Learning new skills in python and how to apply them in<br>oceanography has opened new curiosity for me in the field, growing my<br>interest in data science. My peers in the program also came from many<br>different backgrounds and we all had different strengths and skills<br>which greatly enriched my experience being able to learn from them and<br>come together to create our research project! At the end of the<br>program, I acquired new skills in Python and gained so many skills<br>that will aid and enable me to do future research. I also gained more<br>knowledge and clarity on graduate school and what future careers I am<br>able to pursue. Overall, this program has given me so many rich skills<br>and experiences that I will take with me in my work and my future<br>pursuits!"
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           "2023",
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           "Desiree Jones",
           "What an awesome program! Great way to test your programming skills.<br>The presentations were not high pressure and gave valuable experience.<br>I also enjoyed exploring Seattle particularly since it’s a high cost<br>area that might be difficult to see entirely on your own budget. The<br>University of Washington is an excellent university and you’ll<br>certainly want to apply here after this program."
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           "Lianne Leslie",
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           "2023",
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           "Janluis Rivera",
           "This experience has represented a great learning opportunity. We were<br>able to fraternize with a community of students and professionals<br>interested in and committed to science. In addition, it gave me the<br>opportunity to visualize the future towards graduate studies."
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           "I loved every minute of this program! Between lectures from experts in<br>their fields and guided data science tutorials, it was a fun<br>experience that has boosted my confidence in data science and<br>oceanography. The end of the day and weekend opportunities to explore<br>campus and Seattle were also amazing!"
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           "Jinguang Chai",
           "This program is a chance for me to learn new things. The most precious<br>was its hybrid and systematic process to lead everyone exploring a new<br>world. Although I am not a natural science, environmental science, or<br>oceanography student, I still found plenty of possibility to develop<br>in oceanography after graduate school since I learned the connections<br>between my field and oceanography in the lectures. It gave me a clear<br>start by the assistant of a professional tutorial that could help me<br>get more useful skills to analyze this field by data science. I have<br>become more confident to find more potential directions in both my<br>current field and oceanography based on the final applicable project<br>accomplished with other wise students."
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           "Ryan Wicklund",
           "This program was an excellent application of my technical skills and a<br>fantastic oppurtunity to not only learn about oceanography, but about<br>python coding, data science, and working as a team in the realm of the<br>sciences. I highly recommend this program to all others who seek<br>technical skills and cohesion therein with team and research skills."
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           "Lake Washington Institute of Technology",
           "Tegucigalpa, Honduras",
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           "Ana Herrera Cantarero",
           "The program was a great opportunity to learn how different data<br>science tools are used to facilitate research in Oceanography. The<br>daily lectures were engaging and the presentations showed us the<br>details of current research done by graduate students in the<br>oceanography program at UW. Also, we collaborated on projects that<br>gave us hands-on experience on the topics we learned the previous week<br>and applied the tools we used in the tutorials. This way we saw a bit<br>of what we would do in graduate school; they also had students and<br>professors go over the application process with us. I would highly<br>recommend this program to any student as it is a meaningful and highly<br>collaborative experience."
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           "University of North Carolina, Chapel Hill",
           "Emerald Isle, North Carolina",
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           "Max Buglisi",
           "This opportunity was absolutely fantastic. Being able to meet all<br>sorts of driven and smart individuals from the faculty, to the<br>graduate student, and even the other undergraduates who are just as<br>interested in the intersection of data science oceanography as myself<br>was a great experience. I highly recommend it to anyone interested in<br>either of these topics as its a great opportunity to not only develop<br>your computer skills in a fascinating topic but to also explore the<br>Seattle area and beautiful campus of UW."
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           "I really like the second week that we learned new data analysis tools<br>and packages. I see the strong power behind creative methods to do<br>visualization. And various panel sessions to help me know how the real<br>industry works, in which case I know deeply how data science works in<br>fun parts for oceanography. Besides, getting in touch with how data<br>analysis in general oceanography fields through tutorials is extremely<br>helpful for me to validate my skills. The mentorship is also helpful<br>and nice! I collaborated with others on clustering projects<br>effectively, which brings me a feeling of achievement when doing<br>presentations."
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           "I spent an amazing three weeks in Seattle with some great people and<br>great oceanview! I have learned the different branches of oceanography<br>as well as data visualization and machine learning which are super<br>useful for my future career. I also love meeting and working with<br>people from all around the world who share the same passion. Professor<br>Georgy and his Ph.D. students are proficient, accessible, and patient,<br>and they are always there to support and help you. I would highly<br>recommend this program to people who have interests in oceanography or<br>data science. Thank you so much for the interesting and memorable<br>experience!"
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           "The data science in oceanography program was a really meaningful<br>experience for me. I came in with an interest in data science and<br>oceanography, and the program helped develop those interests by giving<br>informative lectures on different aspects of oceanography (e.g.<br>physical, chemical, biological etc.) and tutorials where I can apply<br>my coding skills. I’m glad that the program had people from diverse<br>backgrounds so that everyone could teach others something and learn<br>something from others. The program supported and encouraged<br>collaboration, so I worked on a project in a group. I appreciate this<br>cooperative environment since we can bring together different<br>perspectives and expertise to achieve better results. I benefited a<br>lot by sharing codes, exchanging ideas with my group members, and<br>asking for their feedback and suggestions. Not only did I expand my<br>toolkit in terms of coding, but I also learned to think of problems in<br>a different and potentially more efficient way. Overall, it was a<br>meaningful experience for me and I really recommend attending this<br>program!"
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           "Thank you so much for giving us a fantastic experience!!!"
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           "The UW Data Science in Oceanography Summer Program gave the technical<br>skills to want to pursue a more tech-y career in environmental<br>science. It served a great purpose in helping me network with grad<br>students and postdocs and exploring other career options my degree<br>holds. Being in an uplifting environment like this program helped me<br>confidently flourish in learning and exploring Python. Every staff<br>member involved helped make this program an enjoyable learning<br>experience. From start to finish, every day I was given the<br>opportunity to learn something new. I came into this program with<br>little to no experience in Python, but the patience and faith Georgy<br>had really made me feel like my presence in the program did make the<br>difference. Because of this program, I am applying to a few schools in<br>their masters program in Environmental Data Science. A big thank you<br>to the staff and especially to Georgy for hosting such a great<br>program!"
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         "text": "DSO Participants"
        },
        "width": 1200
       }
      },
      "image/png": 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       "<div>                            <div id=\"a4994ee2-8521-4e7b-a69a-befb2974c469\" class=\"plotly-graph-div\" style=\"height:800px; width:1200px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"a4994ee2-8521-4e7b-a69a-befb2974c469\")) {                    Plotly.newPlot(                        \"a4994ee2-8521-4e7b-a69a-befb2974c469\",                        [{\"customdata\":[[30.0,-97.7,\"University of Texas at Austin\",\"Austin, TX\",\"2023\",30.2850203609082,-97.734056697454,\"Lacie Levy\",\"At the end of DSO, I felt a new sense of empowerment to do truly<br>independent research. It was basically two weeks of learning how to<br>answer big questions with publicly available data and free tools like<br>Python. Plus, it got me out of my biology silo and opened my eyes to<br>other disciplines of oceanography, making them more accessible too. I<br>didn't expect students from so many different disciplines to be here,<br>but it made it awesome to hear from everyone's perspective.\"],[42.711769,-88.826484,\"University of Wisconsin-Madison\",\"Johnstown, Wisconsin\",\"2023\",43.070652,-89.406755,\"Connor Steinke\",\"DSO was a truly unique and engaging program, and a delightful<br>introduction to the University of Washington and Seattle. The program<br>connected with me with leaders in the oceanography field, introduced<br>me to the broad range of oceanographic disciplines, taught me new<br>computational methods, and inspired me to pursue graduate school in<br>the future. I'm grateful for this opportunity and would recommend to<br>anyone, at any stage of their undergraduate, who is interested in<br>oceanography!\"],[-34.8714310659868,-56.1835591846032,\"Williams College\",\"Montevideo, Uruguay\",\"2023\",42.7097439677555,-73.206511217879,\"Martina Berrutti\",\"Besides offering a really thorough overview of Oceanography and Data<br>Science for those interested in going into the field, this program is<br>a perfect opportunity to learn more about the grad school process from<br>both professors and grad students at UW. The organizers did an amazing<br>job of putting it all together.\"],[35.880448,-106.30235,\"University of Washington \",\"Los Alamos, New Mexico \",\"2023\",47.65107,-122.32167,\"Robin Chartrand\",\"A great opportunity to learn about cutting edge research in<br>oceanography and meet amazing people!\"],[38.97427,-77.01025,\"Stanford University\",\"Takoma Park, Maryland\",\"2023\",37.4277876010327,-122.170070721506,\"Anna Fisher Lopez\",\"\"],[55.7586973313509,37.625310132891,\"Massachusetts Institute of Technology\",\"Moscow, Russia\",\"2023\",42.3601622924374,-71.0941492683159,\"Gosha Geogdzhayeva\",\"\"],[47.3074,-122.23012,\"University of Washington, Seattle, WA\",\"Auburn, Washinton\",\"2023\",47.65107,-122.32167,\"Emma Nguyen\",\"Participating in the two-week Data Science in Oceanography program was<br>an enriching experience. This immersive journey allowed us to explore<br>various subjects of Oceanography and gain valuable insight into the<br>cutting-edge research underway in the field. The tutorials offered an<br>excellent foundation for comprehending the intricacies of data<br>analysis and data visualization.    While these tutorials posed a<br>significant challenge, the guidance and support from both our peers<br>and dedicated instructors made them manageable and immensely<br>insightful learning experiences. The collaborative environment<br>fostered within the program was a true highlight, encouraging<br>participants to pool their diverse perspectives and expertise,<br>ultimately leading to more comprehensive results.    I would like to<br>express my heartfelt appreciation to all the program's staff, with a<br>special mention of Georgy, for curating such an outstanding and<br>successful program. I am immensely grateful for this transformative<br>experience and am eager to apply the skills and insights acquired here<br>to make meaningful contributions to the field of Oceanography in the<br>future.\"],[3.33372225508045,99.1599629871458,\"UC Berkeley\",\"Tebing Tinggi, Indonesia\",\"2023\",37.8712709847561,-122.259544729937,\"Reynaldi Wijaya\",\"Never have I been in a program filled with individuals who are as<br>passionate in oceanography as our cohort. Never have I also had so<br>much fun yet learned so much about oceanography, data science, and<br>graduate school. This could be one of the most fulfilling experiences<br>I have had throughout my undergraduate so far.\"],[41.88266,-87.623329,\"George Washington University\",\"Chicago, IL\",\"2023\",38.89845,-77.04943,\"Ashley Gonzales\",\"My experience being part of the 2023 Data Science in Oceanography<br>program was truly an amazing and enriching experience! By learning<br>from professors in different fields of oceanography, I gained so much<br>insight on what oceanography looks like and the many routes it<br>encompasses. Learning new skills in python and how to apply them in<br>oceanography has opened new curiosity for me in the field, growing my<br>interest in data science. My peers in the program also came from many<br>different backgrounds and we all had different strengths and skills<br>which greatly enriched my experience being able to learn from them and<br>come together to create our research project! At the end of the<br>program, I acquired new skills in Python and gained so many skills<br>that will aid and enable me to do future research. I also gained more<br>knowledge and clarity on graduate school and what future careers I am<br>able to pursue. Overall, this program has given me so many rich skills<br>and experiences that I will take with me in my work and my future<br>pursuits!\"],[42.636944,-76.178611,\"Columbia University\",\"Homer, NY\",\"2023\",40.712778,-74.006111,\"Andrew Fagerheim\",\"\"],[29.228945,-81.0611432,\"Florida State University \",\"Daytona Beach, Florida\",\"2023\",30.4419,-84.2984567123058,\"Desiree Jones\",\"What an awesome program! Great way to test your programming skills.<br>The presentations were not high pressure and gave valuable experience.<br>I also enjoyed exploring Seattle particularly since it\\u2019s a high cost<br>area that might be difficult to see entirely on your own budget. The<br>University of Washington is an excellent university and you\\u2019ll<br>certainly want to apply here after this program.\"],[41.8,-71.4,\"Brown University\",\"Providence, RI\",\"2023\",41.8268515419722,-71.4025481947131,\"Samah Alam\",\"\"],[40.7201,-73.8448,\"SUNY Westchester Community College\",\"Queens, New York\",\"2023\",41.06614304095,-73.7887006054758,\"Lianne Leslie\",\"\"],[18.2108367815413,-67.1409117459915,\"University of Puerto Rico - Mayag\\u00fcez\",\"Mayag\\u00fcez, Puerto Rico\",\"2023\",18.2108367815413,-67.1409117459915,\"Janluis Rivera\",\"This experience has represented a great learning opportunity. We were<br>able to fraternize with a community of students and professionals<br>interested in and committed to science. In addition, it gave me the<br>opportunity to visualize the future towards graduate studies.\"],[33.21194,-111.73778,\"University of California, San Diego\",\"Phoenix, Arizona\",\"2023\",32.879955,-117.237122,\"Ana Dominguez\",\"I loved every minute of this program! Between lectures from experts in<br>their fields and guided data science tutorials, it was a fun<br>experience that has boosted my confidence in data science and<br>oceanography. The end of the day and weekend opportunities to explore<br>campus and Seattle were also amazing!\"],[43.0,-78.7,\"University at Buffalo, New York\",\"Buffalo, NY\",\"2023\",43.0011968087698,-78.7889776581776,\"Hope Johnson\",\"\"],[34.0605749072673,-118.242343667436,\"Boston University\",\"Los Angeles, CA\",\"2023\",42.3506027201439,-71.1054205536551,\"Dawn Kaufman\",\"\"],[39.906667,116.3975,\"University of California, Davis\",\"Beijing, China\",\"2023\",38.54,-121.75,\"Jinguang Chai\",\"This program is a chance for me to learn new things. The most precious<br>was its hybrid and systematic process to lead everyone exploring a new<br>world. Although I am not a natural science, environmental science, or<br>oceanography student, I still found plenty of possibility to develop<br>in oceanography after graduate school since I learned the connections<br>between my field and oceanography in the lectures. It gave me a clear<br>start by the assistant of a professional tutorial that could help me<br>get more useful skills to analyze this field by data science. I have<br>become more confident to find more potential directions in both my<br>current field and oceanography based on the final applicable project<br>accomplished with other wise students.\"],[47.305939,-122.444262,\"University of Washington, Tacoma\",\"Tacoma, Washington\",\"2023\",47.2455571758449,-122.438494636653,\"Ryan Wicklund\",\"This program was an excellent application of my technical skills and a<br>fantastic oppurtunity to not only learn about oceanography, but about<br>python coding, data science, and working as a team in the realm of the<br>sciences. I highly recommend this program to all others who seek<br>technical skills and cohesion therein with team and research skills.\"],[14.0768369678595,-87.2026202473028,\"Lake Washington Institute of Technology\",\"Tegucigalpa, Honduras\",\"2023\",47.7047141711346,-122.166990304149,\"Ana Herrera Cantarero\",\"The program was a great opportunity to learn how different data<br>science tools are used to facilitate research in Oceanography. The<br>daily lectures were engaging and the presentations showed us the<br>details of current research done by graduate students in the<br>oceanography program at UW. Also, we collaborated on projects that<br>gave us hands-on experience on the topics we learned the previous week<br>and applied the tools we used in the tutorials. This way we saw a bit<br>of what we would do in graduate school; they also had students and<br>professors go over the application process with us. I would highly<br>recommend this program to any student as it is a meaningful and highly<br>collaborative experience.\"],[34.66186,-77.06072,\"University of North Carolina, Chapel Hill\",\"Emerald Isle, North Carolina\",\"2023\",35.90509,-79.04521,\"Max Buglisi\",\"This opportunity was absolutely fantastic. Being able to meet all<br>sorts of driven and smart individuals from the faculty, to the<br>graduate student, and even the other undergraduates who are just as<br>interested in the intersection of data science oceanography as myself<br>was a great experience. I highly recommend it to anyone interested in<br>either of these topics as its a great opportunity to not only develop<br>your computer skills in a fascinating topic but to also explore the<br>Seattle area and beautiful campus of UW.\"],[47.6,-122.0,\"Skyline High School, Sammamish, WA\",\"Sammamish, WA\",\"2023\",47.6002364174591,-122.031860180412,\"Nilesh Sathyanarayanan\",\"\"]],\"geo\":\"geo\",\"hovertemplate\":\"<b>%{hovertext}</b><br><br>College=%{customdata[2]}<br>From=%{customdata[3]}<br>Testimonial=%{customdata[8]}<extra></extra>\",\"hovertext\":[\"Lacie Levy\",\"Connor Steinke\",\"Martina Berrutti\",\"Robin Chartrand\",\"Anna Fisher Lopez\",\"Gosha Geogdzhayeva\",\"Emma Nguyen\",\"Reynaldi Wijaya\",\"Ashley Gonzales\",\"Andrew Fagerheim\",\"Desiree Jones\",\"Samah Alam\",\"Lianne Leslie\",\"Janluis Rivera\",\"Ana Dominguez\",\"Hope Johnson\",\"Dawn Kaufman\",\"Jinguang Chai\",\"Ryan Wicklund\",\"Ana Herrera Cantarero\",\"Max Buglisi\",\"Nilesh Sathyanarayanan\"],\"lat\":[30.0,42.711769,-34.8714310659868,35.880448,38.97427,55.7586973313509,47.3074,3.33372225508045,41.88266,42.636944,29.228945,41.8,40.7201,18.2108367815413,33.21194,43.0,34.0605749072673,39.906667,47.305939,14.0768369678595,34.66186,47.6],\"legendgroup\":\"2023\",\"lon\":[-97.7,-88.826484,-56.1835591846032,-106.30235,-77.01025,37.625310132891,-122.23012,99.1599629871458,-87.623329,-76.178611,-81.0611432,-71.4,-73.8448,-67.1409117459915,-111.73778,-78.7,-118.242343667436,116.3975,-122.444262,-87.2026202473028,-77.06072,-122.0],\"marker\":{\"color\":\"#636efa\",\"symbol\":\"circle\",\"size\":10},\"mode\":\"markers\",\"name\":\"2023\",\"showlegend\":true,\"type\":\"scattergeo\"},{\"customdata\":[[40.3708256836883,45.3381094798311,\"University of Washington, Seattle, WA\",\"Lake Sevan, Armenia\",\"2022\",47.6511462501745,-122.31277301164,\"Georgy  Manucharyan \",\"I'm the creator and organizer of this program. It's been exciting and<br>incredibly rewarding to mentor such a diverse and talented group of<br>students. Looking forward to meeting the next cohort of students!\"],[22.3366304038441,77.0886485094228,\"University of California San Diego\",\"Harda, India\",\"2022\",32.8688789576349,-117.25032321347,\"Anuj Jain\",\"Being a rising sophomore, this program acted as a stepping stone to<br>making research projects for me. The tutorials were well-designed and<br>challenged my analytical skills. There was always someone from the<br>team to help me when I was stuck. The knowledge, experiences, and<br>skills shared by professors at UW are highly valuable to me.<br>OceanHackWeek gave me an opportunity to collaborate with other<br>students and work constructively. Not to mention, I enjoyed exploring<br>Seattle with my peers.\"],[38.5773034985388,-121.476539501912,\"University of California San Diego\",\"Sacramento, CA\",\"2022\",32.87,-117.2,\"Charlene Mae Ruiz\",\"\"],[33.9562,-83.988,\"Georgia Institute of Technology\",\"Lawrenceville, Georgia\",\"2022\",33.7756,-84.3963,\"Sara Delawalla\",\"\"],[32.7150218189326,-117.161864286428,\"UC San Diego\",\"San Diego, CA\",\"2022\",32.8797022286696,-117.235415852916,\"Noah Barton\",\"This program opened my mind to the myriad of possibilities at the<br>intersection of oceanography and data science. I got to work hands-on<br>with very knowledgeable mentors to solve complex and interdisciplinary<br>problems. Strongly recommended!\"],[51.0630762752369,-0.328306507351778,\"University of Cambridge\",\"Horsham, England\",\"2022\",52.2056715993379,0.113125084350578,\"Jennifer Dunstan\",\"The Data Science in Oceanography program was amazing. We learned about<br>all areas of oceanography and put it into practice using interesting<br>coding tutorials. We joined OceanHackWeek and had the chance to work<br>on real-life projects with people working in the field. It was also<br>such a great group of people, and I loved exploring Seattle and doing<br>all the fun activities organized with them. I would recommend it to<br>anyone interested in science and research!\"],[31.2293521648313,121.456902689366,\"University of Illinois Urbana Champaign\",\"Shanghai, China\",\"2022\",40.1020261021,-88.2271936839653,\"Xinjin Li\",\"I really like the second week that we learned new data analysis tools<br>and packages. I see the strong power behind creative methods to do<br>visualization. And various panel sessions to help me know how the real<br>industry works, in which case I know deeply how data science works in<br>fun parts for oceanography. Besides, getting in touch with how data<br>analysis in general oceanography fields through tutorials is extremely<br>helpful for me to validate my skills. The mentorship is also helpful<br>and nice! I collaborated with others on clustering projects<br>effectively, which brings me a feeling of achievement when doing<br>presentations.\"],[37.7619840514451,-122.435191627029,\"UC San Diego\",\"San Francisco, CA\",\"2022\",32.87,-117.2,\"Sandy Wu\",\"I spent an amazing three weeks in Seattle with some great people and<br>great oceanview! I have learned the different branches of oceanography<br>as well as data visualization and machine learning which are super<br>useful for my future career. I also love meeting and working with<br>people from all around the world who share the same passion. Professor<br>Georgy and his Ph.D. students are proficient, accessible, and patient,<br>and they are always there to support and help you. I would highly<br>recommend this program to people who have interests in oceanography or<br>data science. Thank you so much for the interesting and memorable<br>experience!\"],[46.029274,126.761835,\"University of Washington, Seattle\",\"Harbin, China\",\"2022\",47.654433,-122.308507,\"Jinting Liu\",\"The data science in oceanography program was a really meaningful<br>experience for me. I came in with an interest in data science and<br>oceanography, and the program helped develop those interests by giving<br>informative lectures on different aspects of oceanography (e.g.<br>physical, chemical, biological etc.) and tutorials where I can apply<br>my coding skills. I\\u2019m glad that the program had people from diverse<br>backgrounds so that everyone could teach others something and learn<br>something from others. The program supported and encouraged<br>collaboration, so I worked on a project in a group. I appreciate this<br>cooperative environment since we can bring together different<br>perspectives and expertise to achieve better results. I benefited a<br>lot by sharing codes, exchanging ideas with my group members, and<br>asking for their feedback and suggestions. Not only did I expand my<br>toolkit in terms of coding, but I also learned to think of problems in<br>a different and potentially more efficient way. Overall, it was a<br>meaningful experience for me and I really recommend attending this<br>program!\"],[39.9,116.0,\"University of Washington, Seattle\",\"Beijing, China\",\"2022\",47.6,-122.3,\"Lisa Li\",\"Thank you so much for giving us a fantastic experience!!!\"],[33.6841682548066,-117.780810262466,\"University of California, Irvine\",\"Irvine, CA\",\"2022\",33.6590064741119,-117.875378031532,\"Mariam Garcia\",\"The UW Data Science in Oceanography Summer Program gave the technical<br>skills to want to pursue a more tech-y career in environmental<br>science. It served a great purpose in helping me network with grad<br>students and postdocs and exploring other career options my degree<br>holds. Being in an uplifting environment like this program helped me<br>confidently flourish in learning and exploring Python. Every staff<br>member involved helped make this program an enjoyable learning<br>experience. From start to finish, every day I was given the<br>opportunity to learn something new. I came into this program with<br>little to no experience in Python, but the patience and faith Georgy<br>had really made me feel like my presence in the program did make the<br>difference. Because of this program, I am applying to a few schools in<br>their masters program in Environmental Data Science. A big thank you<br>to the staff and especially to Georgy for hosting such a great<br>program!\"]],\"geo\":\"geo\",\"hovertemplate\":\"<b>%{hovertext}</b><br><br>College=%{customdata[2]}<br>From=%{customdata[3]}<br>Testimonial=%{customdata[8]}<extra></extra>\",\"hovertext\":[\"Georgy  Manucharyan \",\"Anuj Jain\",\"Charlene Mae Ruiz\",\"Sara Delawalla\",\"Noah Barton\",\"Jennifer Dunstan\",\"Xinjin Li\",\"Sandy Wu\",\"Jinting Liu\",\"Lisa Li\",\"Mariam 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Year\"},\"tracegroupgap\":0},\"title\":{\"text\":\"DSO Participants\"},\"height\":800,\"width\":1200},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('a4994ee2-8521-4e7b-a69a-befb2974c469');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter_geo(data,\n",
    "                     lat='city_from_lat',lon='city_from_long', color='Program Year',\n",
    "                     hover_name='name', \n",
    "                     hover_data={'city_from_lat':False,'city_from_long':False, 'College':True,'From':True,'Program Year':False,'college_lat':False,'college_long':False,'name':False, 'Testimonial': True}, \n",
    "                     height=800, width=1200, title=\"DSO Participants\");\n",
    "\n",
    "#fig.update_geos(resolution=50, showrivers=False, showcountries=True, showlakes=True, scope='usa')\n",
    "fig.update_geos(projection_rotation_lon=-100, projection_rotation_lat=40)\n",
    "fig.update_geos(resolution=110, showrivers=False, showcountries=True, showlakes=True, projection_type='orthographic')\n",
    "fig.update_traces(marker=dict(size=10))\n",
    "#fig.update_geos(lataxis_showgrid=True, lonaxis_showgrid=True)\n",
    "\n",
    "#fig.update(layout_coloraxis_showscale=False)\n",
    "#fig.show(renderer=\"colab\")  #specify the renderer if you're using google colab\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scattergeo(\n",
    "        lat=[\n",
    "            v\n",
    "            for sub in [\n",
    "                np.array(f[\"geometry\"][\"coordinates\"])[:, 1].tolist() + [None]\n",
    "                for f in states_geojson[\"features\"]\n",
    "            ]\n",
    "            for v in sub\n",
    "        ],\n",
    "        lon=[\n",
    "            v\n",
    "            for sub in [\n",
    "                np.array(f[\"geometry\"][\"coordinates\"])[:, 0].tolist() + [None]\n",
    "                for f in states_geojson[\"features\"]\n",
    "            ]\n",
    "            for v in sub\n",
    "        ],\n",
    "        line=dict(\n",
    "                        color='rgba(204, 204, 204, 0.4)',\n",
    "                        width=1\n",
    "                    ),\n",
    "        mode=\"lines\",\n",
    "        hoverinfo='skip',\n",
    "        showlegend=False,\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.add_scattergeo(lat=data['college_lat'],lon=data['college_long']\n",
    "                      ,marker_size = 8\n",
    "                      ,name='Colleges'\n",
    "                      ,hoverinfo='location+text'\n",
    "                      ,text = data['College']\n",
    "                      ,marker_color = 'rgb(200, 105, 150)' # blue\n",
    "                      ,marker_symbol = 'star'\n",
    "                      ,showlegend = True\n",
    "                     )\n",
    "\n",
    "\n",
    "fig.write_html('participants.html')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "66d7565b-ccb9-4e78-830e-820d13aef340",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'set' type is unordered",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
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      "\u001b[0;32m~/.local/lib/python3.7/site-packages/plotly/express/_chart_types.py\u001b[0m in \u001b[0;36mscatter_geo\u001b[0;34m(data_frame, lat, lon, locations, locationmode, geojson, featureidkey, color, text, symbol, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, hover_name, hover_data, custom_data, size, animation_frame, animation_group, category_orders, labels, color_discrete_sequence, color_discrete_map, color_continuous_scale, range_color, color_continuous_midpoint, symbol_sequence, symbol_map, opacity, size_max, projection, scope, center, fitbounds, basemap_visible, title, template, width, height)\u001b[0m\n\u001b[1;32m   1146\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1147\u001b[0m         \u001b[0mconstructor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mScattergeo\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1148\u001b[0;31m         \u001b[0mtrace_patch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocationmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocationmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1149\u001b[0m     )\n\u001b[1;32m   1150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/plotly/express/_core.py\u001b[0m in \u001b[0;36mmake_figure\u001b[0;34m(args, constructor, trace_patch, layout_patch)\u001b[0m\n\u001b[1;32m   1946\u001b[0m     \u001b[0mapply_default_cascade\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1948\u001b[0;31m     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconstructor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1949\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mconstructor\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTreemap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSunburst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIcicle\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"path\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1950\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess_dataframe_hierarchy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/plotly/express/_core.py\u001b[0m in \u001b[0;36mbuild_dataframe\u001b[0;34m(args, constructor)\u001b[0m\n\u001b[1;32m   1404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1405\u001b[0m     df_output, wide_id_vars = process_args_into_dataframe(\n\u001b[0;32m-> 1406\u001b[0;31m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwide_mode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1407\u001b[0m     )\n\u001b[1;32m   1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/plotly/express/_core.py\u001b[0m in \u001b[0;36mprocess_args_into_dataframe\u001b[0;34m(args, wide_mode, var_name, value_name)\u001b[0m\n\u001b[1;32m   1249\u001b[0m                         \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfield\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margument\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlength\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1250\u001b[0m                     )\n\u001b[0;32m-> 1251\u001b[0;31m                 \u001b[0mdf_output\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_unindexed_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margument\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1253\u001b[0m             \u001b[0;31m# Finally, update argument with column name now that column exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/plotly/express/_core.py\u001b[0m in \u001b[0;36mto_unindexed_series\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1071\u001b[0m     \u001b[0mrequired\u001b[0m \u001b[0mto\u001b[0m \u001b[0mget\u001b[0m \u001b[0mthings\u001b[0m \u001b[0mto\u001b[0m \u001b[0mmatch\u001b[0m \u001b[0mup\u001b[0m \u001b[0mright\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mnew\u001b[0m \u001b[0mDataFrame\u001b[0m \u001b[0mwe\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0mre\u001b[0m \u001b[0mbuilding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1072\u001b[0m     \"\"\"\n\u001b[0;32m-> 1073\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1074\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1075\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m    437\u001b[0m                     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msanitize_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                 \u001b[0mmanager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"mode.data_manager\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.7/site-packages/pandas/core/construction.py\u001b[0m in \u001b[0;36msanitize_array\u001b[0;34m(data, index, dtype, copy, raise_cast_failure, allow_2d)\u001b[0m\n\u001b[1;32m    557\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrozenset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    558\u001b[0m             \u001b[0;31m# Raise only for unordered sets, e.g., not for dict_keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 559\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"'{type(data).__name__}' type is unordered\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    561\u001b[0m         \u001b[0;31m# materialize e.g. generators, convert e.g. tuples, abc.ValueView\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'set' type is unordered"
     ]
    }
   ],
   "source": [
    "fig = px.scatter_geo(data,\n",
    "                     lat={data['college_lat'][8]},lon={data['college_long'][8]})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f6aef738-2f2e-4188-bbff-3c911cbfd226",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.89845"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['college_lat'][8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e4d1ae9-c548-43d5-9ed3-204a6d3f14a0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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