The go.Scattergeo graph object will generate a color scale for the markers (square box)displayed on the map based on the level of confirmed cases in each country.Īt last, we set this subplot of the Scattergeo map positioned in row 1 and col 1 (Line 27). We set the data series of confirmed cases, df_final, to the “color” property (Line 19). The parameter I wish to highlight here is the “ color” property. Next, we proceed to set the parameter values for marker such as size, opacity, symbol, color, etc (Line 8–23). With this real-time data we identify the global regions with the greatest attack traffic, cities with the slowest Web connections (latency). This will enable the predefined annotation text (from Step 2) displayed on the map whenever a user hover the mouse over a country/region. Akamai monitors global Internet conditions around the clock. Next, we set the annotation text, df_final, to hovertext property. We set the “ Long_” and “ Lat” data from the previously generated dataframe, df_final to lon and lat property (Line 4–5). To create a Scattergeo map, we can use the Python Plotly go.Scattergeo graph object. bar plot - to show the top 10 countries with the highest number of death cases.bar plot - to show the top 10 countries with the highest number of recovered cases.bar plot - to show the top 10 countries with the highest number of confirmed cases.indicator plot - to show the daily grand total of confirmed, recovered and death cases around the world.scattergeo plot - to show the total number of confirmed, recovered, and death cases in each individual country on a map.The data can be visualized, processed, downloaded and prepared for. Real-time sensors can be connected to the INOWAS platform and time series data can be uploaded. In this case, we define 4 rows and 6 columns in our subplots layout. This tool encompasses a web-based monitoring system developed to integrate time series data into the INOWAS platform. We can set the number of rows and columns to position each of the subplots in our dashboard. Python Plotly library offers a make_subplots function to enable us to initialize the layout arrangement for the subplots. We will create several subplots (one for each part of the info) using Python Plotly Subplots and join them into a single dashboard. Top ten countries with the highest number of confirmed, recovered, and death cases, respectively.The total number of confirmed, recovered, and death cases in each individual country around the world.The daily grand total of confirmed, recovered and death cases around the world.The dashboard is expected to display the following info: Part 7: Building a dashboard using Python Plotly Subplots
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