Python bokeh have multiple plots9/22/2023 ![]() # a square figure, since we want all presidents next to each other First we are going to create a figure with a title and dimensions we want, then add the default line plot. To make the model plot, we are going to use the US data. After the whole configuration, we are going to define a function that compiles it all. We are going to start creating a single line plot, checking out the steps to change every important aspect of the plot to make it just like our model. president_br - the Brazilian president in that moment.president_us - the North American president in that moment.real - the rolling mean of Euro and BR Real exchange rates.dollar - the rolling mean of Euro and US dollar exchange rates.Time - the day referent to that currency.Our dataset is composed of Exchange Rates between Euro and two currencies: US Dollar and Brazilian Real. # reading the treated data from my github repoĮuro = pd.to_datetime(euro) After exporting it to csv, the time column was transformed into string again, but we can resolve it quickly. The original data was taken from the Kaggle dataset collected by Daria Chemkaeva, the data treatment process can be found in this jupyter notebook located in my GitHub repo. Warnings.simplefilter(action='ignore', category=FutureWarning)ī. If you don’t want to see the red bands of warning when you run the code, just run the code bellow. There’s also a functionality that is going to change in the next releases in pandas, so this will raise a warning in the process. For a detailed explanation of the installation process, access Bokeh’s documentation.īefore the installation, be sure you have the following dependencies installed: PyYAML>=3.10īeyond that, if you want to reproduce the code we are going to create, you’ll need to import the following: import pandas as pdįrom bokeh.models import DatetimeTickFormatter, Labelįrom import FixedTickerĪnd, to show the graphs inline, in the jupyter notebook, you need to run the code bellow. To download Bokeh you can use pip with the explicit version ( pip install Bokeh=2.2.0) or without the version, if you want to download the last release. In this tutorial, I’ll use an older version of Bokeh (2.2.0) because this one works better with Streamlit (the platform I’m using to create an app with the dashboard we are making). Before we start, let me give you some important information about how to make the code work and how the data looks like. With that in mind, we are going to create a function to reproduce the graphs with different data, this way we could create dashboards for any currencies just changing some details. ![]() I am Brazilian, so I have a special curiosity about exchange rates between Euro and BR Real, but instead of analyzing just that two, I would like to see how US Dollar behaved too. ![]() The main idea is to plot various single-line graphs in different colors and use it to create the dashboard above in the future (I may write another article about how to do it). ![]() This was one of the suggestions Dataquest gave, I liked it, so I chose to build it. In reality we are going to build a function to create line plots in FiveThirtyEight style, more specifically, we are going to create the first line of graphs suggested in the Guided Project of the Storytelling Data Visualization and Information Design course, from the Data Scientist in Python path here in Dataquest. Our approach is to create a simple one and add or change the configurations along the article, this way we are going to have a full styled complex graph at the end (actually, more than one). The great part of it is that you can add it all in batches, so you don’t need to create the full graph at once. It works similarly to matplotlib, the main difference is that it creates a html object that allows you to insert a lot of interesting features, like zooming in or out, inserting labels or buttons to do specific things. ![]() Hey everyone! In this article we are going to learn about a different data visualization library in Python called Bokeh and the objective will be to plot line graphs in FiveThirtyEight style.īokeh is a Python library with the purpose of simplifying the creation of common plots as well as handling custom and specialized use-cases, all of that in a powerful open-source and interactive environment. ![]()
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