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-
-
-# Welcome to the Barcelona Project!
-
-## Content
-- [Project Description](#project-description)
-- [Project Goals](#project-goals)
-- [Data Workflow](#data-workflow)
-- [Requirements & Deliverables](#requirements-&-deliverables)
-- [Mentoring](#mentoring)
-- [Schedule](#schedule)
-- [Datasets](#datasets)
-- [Presentation](#presentation)
-- [Tips & Tricks](#tips-&-tricks)
-- [Asking Questions](#asking-questions)
-
-## Project Description
-In this project you will present an analysis of a topic of your choosing related to the city of Barcelona.
-
-The topics are:
-* Transportation
-* Urban environment
-* Population
-* Administration
-* Economics & business
-
-In this project you will work in **teams**. Your team must come to an agreement with the other teams so that each of you work on a different topic.
-
-Possible datasets you can use are suggested in the ```Datasets``` section below.
-
-## Project Goals
-During this project you will:
-* Learn how to work in a team.
-* Fork and manage your own git repository and avoid conflicts with your teammate's code.
-* Ask yourself interesting questions related to your topic.
-* Search and explore data that will help you answer your questions.
-* Perform a simple analysis and identify interesting insights.
-* Learn something about Barcelona!
-
-## Data Workflow
-In this project you will focus on learning and developing Data Wrangling skills.
-
-## Requirements & Deliverables
-The **mandatory** requirements that this project needs to satisfy are:
-* The project must be planned. That is why creating a Kanban board is important. You can find a template for Trello [here](https://trello.com/b/usAykV9K/project-2-barcelona). Remember that you **CAN'T CODE** until your project is planned.
-* Your repository must be clean and organized; this means that it must include a *.gitignore* file and a README file and also have a functional file structure.
-* Your README file must clearly include which topic you chose and which are the questions you would like to find an answer to.
-* Also, mind that **NO PLOTTING IS ALLOWED IN THIS PROJECT**.
-* The project needs to be presented to your colleagues on the day of the presentation.
-
-The **mandatory** deliverables that you must turn in are:
-* Link to the repository you used while working on your project. The repository must include all the files you used to complete your analysis. Remember to commit often to avoid trouble in case you mess up: this means more than 1 commit!
-* Link to Trello or picture of your Kanban Board. Include the link or picture in the README file.
-
-
-The **deadline** to turn in the deliverables is right before the project presentations.
-
-## Mentoring
-One of the TAs will be your mentor!
-
-Your mentor will:
-* Keep track of your project in general terms. Your mentor will be the second person that knows more about the project, after you.
-* Check if you are following your plan: are you keeping up with your tasks and deadlines? Do you have any obstacles blocking you?
-* Help/support you with specific questions.
-
-Your mentor is **not** meant to:
-* Know everything.
-* Be your manager. You have to be responsible of your own tasks!
-
-## Schedule
-
-**Please note** that the following schedule is simply a guideline. Feel free to organize your work as you see fit.
-
-**Monday**
-* Choose your topic.
-* Find interesting questions related to your topic.
-
-**Tuesday**
-* Brainstorm to find out what kind of data you can use to answer those questions.
-* Research and look for the data you need. You can find some useful links to investigate in the ```Resources``` section.
-* Fork the project repository and edit the README Deoverview. You can find a [template](https://github.com/ta-data-bcn/Project-Week-2-Barcelona/blob/master/your-project/README.md) for your README file in this repository. Remember to keep the README up-to-date.
-
-**Wednesday**
-* Plan your project. Remember that we are providing you with a Trello [template](https://trello.com/b/usAykV9K/project-2-barcelona). Define tasks, specifying those to be done individually and those to be done together. Remember that you **CAN'T CODE** until your project is planned.
-* Once you finish, start coding!
-* Clean your data.
-
-**Thursday**
-* Analyze your data.
-* Find the most interesting and meaningful insights.
-* Start preparing the slides for Friday's presentation.
-
-**Friday**
-* Presentation time at **11:30 AM**! There will be a 15-minute break during the presentations.
-
-## Datasets
-### Transportation
-**accidents-2017**: List of accidents handled by the local police in the city of Barcelona.
-**bus-stops**: Bus stops, day bus stops, night bus stops, airport bus stops of the city of Barcelona.
-**public-transport**: Public transports (underground, Renfe, FGC, funicular, cable car, tramcar, etc) of the city of Barcelona.
-
-### Urban environment
-**air-quality-nov-2017**: Measures of O3 (tropospheric Ozone), NO2 (Nitrogen dioxide) and PM10 (Suspended particles).
-**air-stations-nov-2017**: Main characteristics of the air quality measure stations of the city of Barcelona.
-
-### Population
-**births**: Births by nationalities and by neighborhoods of the city of Barcelona (2013-2017).
-**deaths**: Deaths by quinquennial ages and by neighborhoods of the city of Barcelona (2015-2017).
-**immigrants_by_nationality**: Immigrants by nationality and by neighborhoods of the city of Barcelona (2015-2017).
-**immigrants_emigrants_by_age**: Immigrants and emigrants by quinquennial ages and by neighborhood of the city of Barcelona (2015-2017).
-**immigrants_emigrants_by_destination**: Immigrants and emigrants by place of origin and destination, respectively (2017).
-**immigrants_emigrants_by_sex**: Immigrants and emigrants by sex by neighborhoods of the city of Barcelona (2013-2017).
-**most_frequent_baby_names**: 25 Most common baby names in Barcelona, disaggregated by sex (1996-2016).
-**most_frequent_names**: 50 Most common names of the inhabitants of Barcelona, disaggregated by decade of birth and sex.
-**population**: Population by neighborhood, by quinquennial ages and by genre of the city of Barcelona (2013-2017). Reading registers of inhabitants.
-
-### Administration
-**general-election-results-2019**: Results of the general elections by census section in the city of Barcelona (2019).
-
-### Economics & business
-**unemployment**: Registered unemployment by neighborhood and genre in the city of Barcelona (2013-2017).
-
-## Presentation
-The presentation time limit is **5 minutes**! After all the presentations are over, there will be a 5-minutes break. After the break, we will hold a short debate about the projects.
-
-Our suggestion is to include at least the following slides in your presentation but feel free to add or remove slides:
-
-* Title of the project
-* Team presentation
-* Goals of the project
-* Data - sources, problems and limitations
-* Database - data wrangling/cleaning and database structure
-* Main insights
-* Questions you were not able to answer and why
-* Learnings
-
-**Remember to present your insights as understandably as possible without using plots!**
-
-## Tips & Tricks
-* Questions first. Data second.
-* Think about the workflow of your analysis BEFORE starting to code.
-* You will have more questions than answers. It's not a problem if you can't answer some or any of your questions. Just show us why you couldn't answer them, that itself will be interesting!
-
-## Asking Questions
-Imagine that our project is about the population of Barcelona.
-We could, for example, try to answer a *descriptive* question such as: How many people are living in my neighborhood? In this case you will simply need to find data that can directly answer your question.
-Let's instead assume that your question is an *analytic* one such as: Is my neighborhood the best? In this case you will need to think what the word *best* means in this context. For example it could mean:
-* Safer
-* More cultural events
-* More schools
-* ...
-
-Analytic questions require you to think about the *how* of answering a question.
-This is one of the most interesting parts of the process and you will be doing it for the rest of the bootcamp (and most likely for the rest of your career as a data analyst).
-
-### Resources
-* [Barcelona Open Data](https://opendata-ajuntament.barcelona.cat/en/)
-* [Catalonia Government Open Data](http://governobert.gencat.cat/ca/dades_obertes/)
-* [Spanish Government Open Data](https://datos.gob.es/)
diff --git a/README.md b/README.md
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+
+
+# Public Transport Systems in Barcelona
+*[Emanuele Pireddu, Aleksandar Nikolov, Kübra Bektas]*
+
+*[DA FT 2021, Remote & 2021-06-08]*
+
+## Content
+- [Project Description](#project-description)
+- [Questions & Hypotheses](#questions-hypotheses)
+- [Dataset](#dataset)
+- [Database](#database)
+- [Workflow](#workflow)
+- [Organization](#organization)
+- [Links](#links)
+
+
+## Project Description
+This project analyzes the accessibility of public transportation in Barcelona.
+Hereby the focus is on the number of bus, metro and/or taxi stops in certain areas e.g. around sightseeings.
+We chose this project because we believe we can draw conclusions on the density of the various elements offered by the public transportation entity.
+
+## Questions & Hypotheses
+-Nº of bus stops, metro, taxi stops per Region
+-Percentage of missing values in columns: District and Neighborhood
+-Nº of metro stations, bus stops, etc in a given district with an important sightseeing/cultural element, ex: Sagrada familia Cathedral is in ''Eixample'' District
+-What types of factors make public transport less/more accessible (frequent, ex: more schools, hospitals means more bus stops?)
+-Mean prices, time per distance covered, frequency of buses on time of day (how much less frequent are the buses if it's night time, holiday, etc)
+
+Hypothesis: We think that there is a positive correlation between the number of bus/metro/taxi stops and the location of sightseeings.
+
+
+## Dataset
+Transportation --> bus-stops.csv
+Transportation --> public-transport.csv
+own file with the locations of the most important sightseeings (source: Wikipedia)
+
+
+## Database
+
+We used the Transportation Dataset for Barcelona and added one more data set with top 10 sights in barcelona.
+
+accidents-2017: List of accidents handled by the local police in the city of Barcelona.
+bus-stops: Bus stops, day bus stops, night bus stops, airport bus stops of the city of Barcelona.
+public-transport: Public transports (underground, Renfe, FGC, funicular, cable car, tramcar, etc) of the city of Barcelona.
+sights: List of top 10 sightseeings of Barcelona
+
+
+## Workflow
+
+Brainstorming + Questions
+Collaboration Tools
+Trello Board + Work Division
+Data Cleaning + external Data
+Dataframe Filters
+Learnings and Problemsolving
+Presentation
+
+## Organization
+
+For the project we used following tools:
+
+-deepnote: for collaboration on the code
+-Prezi: for collaboration on the presentation
+-Zoom: for communication
+-Trello: work organization and dividing the work
+
+
+Repo:
+We have two folders named "datasets" and "your-project" as well as a README file. The "datasets" folder contains the sights csv file and another folder with the given datasets.
+The "your-project" folder contains the main project file.
+
+## Links