 Hi everyone. Thank you very much for being here. So, as he said, I'm going to talk about how Python can help monitor governments and I will bring some Brazil examples. So, I'm Patricia, as he said, this is our huge team in Data Office, in Rio de Janeiro, Cittal. And, Judici couldn't be here today, but she's remote, so she's seeing us now. And now, start the presentation, Government Transparency in Brazil. So, I think most of the countries has this problem on how can we access the information, our information that we have right to have this data. And, as you can see here, Brazil had created a law that name is Information Access Law, we call it LIFE. It was created in 2022, and after the creation of this law, we saw many of times when you request information, it was denied. So, this is that big picture. But after a while, we went in a plateau, so we were having 8.3%, 8.1% of denied information. But after when the former Government Bolsonaro entered on the presidency, we saw that the denial of this information was increasing. So, not only the denied information, but the incomplete responses too. But what can we do with that? So, how can we, how can you, how can this can get better? And is this getting better, since he's a former president? Ah, sorry for that. We have a big, big problem in Covid, because he was denying that the Covid was very... He's very bad thing, and the press was giving a lot of, a lot of, a lot of time to Covid, and he was very, very mad about that, and he said that the reports on the main journal was over, and it was not that. He tried to stop the communication from Covid information. So, what we have before was a big dashboard with accumulated data, regional distribution, case fatality rate, and a button to download data, so we have access for all the time series information. And after he said that, the health agency in Brazil stopped giving information to press to everyone, and they just gave to us the data from the last 24 hours and accumulated. So, the press get together and they start to talk to hospitals and talk to many places, and they created a group to share this information to the population. So, we have to, they have to create a group apart from the government to have the information in real time. But the problem continues, so even without Bolsonaro, we still have some difficulties to access this information. And I think we will have this problem like for many, many years from now. So, what can we do as a population, or what can governments can do, or what can private groups can do to be against this lack of data transparency? So, here we will talk about the Rio de Janeiro Town Hall, what we are doing to open our data, and what some NGOs are doing, and what some private groups are doing too. It's just to inspire you to enter a group or try something different. So, the government itself. And here's just a little, almost a little joke. Rio de Janeiro Town Hall had built the first data lake in the world. We say that it's the first data lake because we have, like, chat GPT saying that. It's in Portuguese, but I can assure you that he's saying that. But in this data lake, you can say, ah, Patricia, New York gives information to the population too. But this data lake is a little different. Because if you do some, if you try to communicate to the town hall, like, ah, there's a hole in my street. In one hour or two, you can access the data lake and see your problem there, and see when they are going to solve your problem. So, it's like, it's a real-time data lake. We can't put all the information. We know because we have data privacy. But the information that doesn't have any problem, we are putting in this data lake so anyone in the world can access it for free. And it's good because we had a lot of researchers and journalists reaching us to get this information, and now they can have the access of this data by themselves. But I don't know if everyone knows what is a data lake and why this difference from our data lake and, like, New York data information. It's because their data is not a data lake. It's more like a database. One of the main difference is that on a data lake, you can put, you can store any kind of format. So you can store a JSON, you can store an image, a video, an HDF5, that it's a scientific format. And your data don't need to be treated and don't need to be organized. This is the main difference between data lake and database. And how we are doing this and where we are using Python on this project. So we have some data source, some databases, like we have health database, we have education database, or any other agents inside the municipal government. And their databases are apart from each other. And with the data lake, we can put all this information together and try to connect citizens' information. So we can connect to this database, connect to FTP information, connect to API information. So, like, on API, we get some meteorological information to try to create an outcasting model. And all these connections we do with Python. So this extraction from the data sources and the treatment of this data are doing with Python. The persistence of this information on data lake is also doing, we use Python to do this. And all of our codes are open on the Internet. So not only the data is open, but the code. So we have an open GitHub, everyone can access there, can copy the code and do whatever it wants. So after we get this information, we save it on our raw level. Sometimes this raw level is treated, sometimes it's not treated. And then we get this information put in our production level. And then after the production level, we put some of this information on DataHeal. DataHeal is our official data lake that is open to everyone. Well, and while we do this, we also use Python. Python and prefect to who knows this pipeline orchestration. This is an example of a pipeline. So at the first type, we get data interval of the data we want to extract. After that, we download this information, we save it like in local machine, and then we upload it to the data lake. This is like just functions, the code is bigger than this. But this is the pipeline we wrote on prefect. So after treating this information and saving on the data lake, we can connect to many places, like dashboards, analysis, applications, and chatbots. I will try to pass it fast on an example of each one of these. So in dashboards, we have these nice stringlet dashboards. We have to create it because Brazil, it's a country that loves party. And we have a big party that, the name of it, it's Carnival. Probably already heard about it. And this carnival has a lot of parades. So you have inside parties, you have outside parties on the street, and sometimes these parades go along the city. And the town hall was having a big trouble trying to see if it was going to be two or more parades that was going to happen at the same time, at the same space, or if it was going to have a parade with another event like a soccer game, or a congress, or a show, or if it was near a hospital or something like that. And we built this dashboard so they could see if it was going to happen, if two events was going to happen at the same time in space using Python. We did this analysis using Python. And I will show you the video about that, the little video. It's not a big dashboard, but it helped them a lot. Some cities have the big difficulties that we can resolve easily. Okay, I think it's not going to work, but no problem. It was just a dashboard showing. And I can't get off this. So this is one of the examples about what we have done with Python inside there. The video note. The other example is we do a lot of analysis, and these analysis are open to the population, so the dashboards. And this one we brought here to you because we have a very famous song, that Tonjo being writing it, about the rain in March. So the music says that the rain stops in March and it has a lot of rain in March. And we built this analysis to show that the main part of the rain occurs in February, and sometimes, some years, it doesn't end in March. So it was just to make a joke with the music. Tonjo being was wrong, but I don't want to say that to him. And as you can see, this is a dashboard about rain again. Rain is very important to us because Rio de Janeiro has a mountain relief. So it starts raining from nothing. You are there, it's sun, and then from nothing, it starts raining a lot. So rain is a very big problem there. And we built this dashboard that shows the light green is where it's not raining. So this is the map of Rio de Janeiro, of course. The green is where it's not raining, the light green. The green is raining a little. Yellow, it's raining a lot. And the orange one is raining too, too much. And we have a same dashboard to show the population where the places that are flooded now. So this is very important there, too, because it rains a lot and the water can't go anywhere. So it's very, very important. And we are using Python in this raining problem. We have two projects, one with two universes in Brazil. One of them is with Google to try to create an outcasting model to predict when these rains are going to happen. And of course, we have a chatbot where the population can talk to the town hall and say, hey, this bus has a problem, this bus station has a problem. And if you put information there, you can see in the data lake where it's going or if they started seeing a problem when they are going to solve it. So now I will show you an example of an NGO and how he's helping us to open, to have data transparency. The name of this project is Darling Diary, that is querido diário in Portuguese. And it has this name because in Brazil we have... Every city has to publish their public acts in something that is named official diary. So this is why this name. But every city publish their diary in a different way. So it makes us crazy. It's hard to get this information together. And we have this NGO that had created 2040-20 robots to get this information daily on a daily basis. But we still have... You can see they have created robots for half of the cities. So we still need people to help us building that. And you can search the information in their platform, in their site. So it's a very, very nice project. We have the newspapers that are the private sector trying to open data. This is the Instituto Asmina. Asmina in Portuguese is a slug to grow. This map shows each point is a person in the Congress. And if this person is up, it shows that this person has created many laws. Many laws that it's good for women rights. And if this person is upside, it means that this person is creating laws that is against the women rights. So they are monitoring this information with Python. And treating this information with Python too. And publishing this in the site. I will not try to grab this. But I was going to show you how the site works. And the last one, this is InfoAmazonia. This is a journal that does this. As you know, Amazon is very important for the globe, for the world. And they collect data from the satellites and show us where we have fire now. Beside this, on the purple area. I don't know if you can see this. They show where are the indigenous areas that has some fires going on. And this you can follow, botch, if you want to see this map. Like every week. So thank you very much. You can see the many stuff that we already had. Like the visualizations and the dashboards in our site. You can follow us on YouTube and Twitter. We always put new information there. In YouTube, we have a thread teaching how can we build our pipelines so we show all the progress, the prefect, the flows, and our infrastructure. Our GitHub, as I said, it's open to everyone. And if you want to contact Judith or me, you can have your link in there. Thank you very much. Great talk. We have a bit of time for questions. So please reach out to the microphones. Hi there. The visualizations look really nice. Shame you couldn't show them working. I'm wondering what libraries you use to create the user interfaces. You mean the visualization with the map, like the rain map. Yeah, the rain map for the bottom of the plot points. Yeah, we use Webflow to create that. But all the information comes after the Python analyzing the data. So we treat this information with Python, put it on a Regis database, and then access with create an API for it, and then use the Webflow to access this information and create a map. So the map is not Python. Yeah, the map isn't Python. I was just curious what was generating the user interfaces. Yeah, and the other graphs, we usually use Data Rapper if I'm not wrong. Because Data Rapper makes the graphs very beautiful. And are they interactive? Yeah, not here, but on the site, yeah. Yeah, of course. Thank you for your talk and your work. Could you tell us a little bit about you? You work for the City Hall, you're from NGO. Where does the money come from? Yeah, I work for the City Hall, like two years for now. And that's it. So this is sponsored by the city itself? Not this time. I work there, and I think the work we do there is very nice. And then I wanted to come here and present what we are doing there in Brazil. Hi, thank you for your talk. Is the government trying to stop you or stop this project some way? No, the mayor, he loves what we do. We always get our visualizations and put in his Twitter. He loves what we do. And as we don't like, we don't propagate private information for him, it's very good. So you aren't sharing any information that the government doesn't want to be shared, basically? Or are you? Sometimes we share something. But we try to think. We are not trying to say bad things about the mayor, but we want to bring stuff that are good for the citizens, the civilians. So we think when we are going to think about some new visualization or new project, we try to prioritize things that are going to be good for them, like the chatbot, a way for them to talk faster with us or flood. It's a big problem there. Yeah, flood, okay. So we try to bring these problems inside and try to figure out how to solve them and how to show them to people in a format that they can understand. So what you basically do is very important journalism. Is there, like, sometimes do you think of, hey, we shouldn't share this because we will have issues or something like that? I'm more on the analysis part. If Judith was here, because Judith is the data journalist in the group. So she can have this kind of problem. But we have some analysis showing how the bus are doing on the city, what are the main problems there. So we try to do some kind of this kind of stuff. Yeah, thanks for the great talk. A little bit more on that. Your whole initiative, has it also changed the way that the government is looking at sharing data or improving their own infrastructure? Yeah. Does it set some of these initiatives from the government in motion? Like, we have other cities always trying to connect to us. So we can show them how we are doing this and how we are, because we have, like, a formation. We train people inside the town hall to know how to do Python, how to code, how to do visualization. And sometimes we go to the other cities to show them what we are doing and they are very interested in open their data too. It's not all cities, of course. Okay, thanks. So if the government changes its mind and decides to actively block you, we had some years ago in Spain problems with that, with the government taking down projects on GitHub and all the things. Do you have any plan, if something like that could happen? I didn't understand the beginning of the question. If the government changes its mind and tries to actively block your project or take down everything, do you have any plan to avoid that? I don't think we have any plan. Our plan is like to do a lot of stuff. The most stuff that we can do to show our value to the people. But I don't think that this major is going to put us down. Okay, thank you. Because the idea to create this group was from him. So he went to my chief and talked to him and said, hey, I want to create this data office and for him it's very important too. And if they measure changes? Yeah. Maybe I will be without work. Thanks, that was really inspiring. I worked on a couple of projects in San Francisco where the idea of a data lake would have been really useful. Where did that come from? I've never heard of it before. I don't know, maybe it's my boss. Because like my boss, he's the creator of... Is it the concept in data science? Yeah, yeah, yeah, yeah. Oh, is it? Yeah, yeah. Now understand your question. Yeah, data lakes. Yeah, it already exists. The word already exists. But the idea is like my chief and other guys, they created a Python library where people can put... Because of this lie, this law, information access, people go to the government and say, hey, I want this. These people didn't have anywhere to put this information. So my boss created a place where everyone could put their data and then to restore this information. And then he was invited to be the big boss of this project. Okay, thank you. More questions? Or you can reach me out after. Yeah, thank you. No more questions. Coffee break? Thank you very much.