{"id":46680,"date":"2022-01-13T00:00:00","date_gmt":"2022-01-13T08:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/visualizing-vaccination-data-with-streamlit\/"},"modified":"2025-11-13T12:55:45","modified_gmt":"2025-11-13T20:55:45","slug":"visualizing-vaccination-data-with-streamlit","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/en\/blog\/visualizing-vaccination-data-with-streamlit\/","title":{"rendered":"Visualizing Vaccination Data with Streamlit"},"content":{"rendered":"<h2>Introduction<\/h2>\n<p>Data is a source of insights. To extract these insights, the data should be analyzed and understood. However, it is difficult to analyze numerical. The easiest way to extract insights from data is through <em>data visualization<\/em>.<\/p>\n<p>Data visualization involves presenting data using charts and graphs. This way, it becomes easy for users to understand data, even non-technical users.<\/p>\n<p>In this article, we will be discussing how to visualize data using GridDB and Streamlit. The data to be visualized shows the percentage of population fully and partly vaccinated against Covid-19 in 12 different countries.<\/p>\n<h2>Requirements<\/h2>\n<p>For this article, ensure that you have installed Streamlit, GridDB, and its Python client.<\/p>\n<p>To install GridDB, visit its <a href=\"https:\/\/docs.griddb.net\">official documentation here<\/a> and follow the installation steps.<\/p>\n<p>To install Streamlit, you can use the Python&#8217;s pip package manager by running the following command:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">pip3 install streamlit --user<\/code><\/pre>\n<\/div>\n<h2>Ingest<\/h2>\n<p>We should now load the data into the GridDB server. Although it is possible to read the data directly from the CSV file, we can take advantage of GridDB&#8217;s hybrid in-memory architecture to increase the speed of the application. We will also be able to load the necessary data into dataframes, boosting the performance of our queries.<\/p>\n<p>First, let&#8217;s import the libraries to be used:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">import pandas as pd\nimport griddb_python as griddb<\/code><\/pre>\n<\/div>\n<p>Next, let&#8217;s initialize the GridDB container to be used:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">factory = griddb.StoreFactory.get_instance()\n\n# Initialize the GridDB container\ntry:\n    gridstore = factory.get_store(host=host_name, port=your_port, \n            cluster_name=cluster_name, username=your_username, \n            password=your_password)\n\n    info = griddb.ContainerInfo(\"Vaccination_Data\",\n                    [[\"country\", griddb.Type.STRING],[\"fully-vaccinated\",griddb.Type.FLOAT]], [\"partly-vaccinated\",griddb.Type.FLOAT]],\n                    griddb.ContainerType.COLLECTION, True)\n    cont = gridstore.put_container(info) <\/code><\/pre>\n<\/div>\n<p>Ensure that you substitute the above credentials with the specifics of your GridDB installation.<\/p>\n<p>The container will have three columns namely <code>country<\/code>, <code>fully-vaccinated<\/code> and <code>partly-vaccinated<\/code>.<\/p>\n<p>The data has been stored in a CSV file named <code>vaccination.csv<\/code>. Let us load it into the GridDB container:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">data = pd.read_csv(\"vaccination.csv\")\n    #Load data\n    for x in range(len(data)):\n        ret = cont.put(data.iloc[x, :])\n    print(\"Data loaded successfully\")\n\nexcept griddb.GSException as ex:\n    for x in range(e.get_error_stack_size()):\n        print(\"[\", x, \"]\")\n        print(ex.get_error_code(x))\n        print(ex.get_location(x))\n        print(ex.get_message(x))<\/code><\/pre>\n<\/div>\n<h2>Retrieve the Data<\/h2>\n<p>Let us now retrieve the data from GridDB. We will write an SQL query for that:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">sql_statement = ('SELECT * FROM Vaccination_Data')\noutput = pd.read_sql_query(sql_statement, cont)<\/code><\/pre>\n<\/div>\n<p>The <code>read_sql_query<\/code> function of Pandas will convert the retrieved data into a dataframe object.<\/p>\n<h2>Create a Dashboard<\/h2>\n<p>Now that the data is ready, we can use it to create visualizations. First, let&#8217;s import the Streamlit library:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">import streamlit as st<\/code><\/pre>\n<\/div>\n<p>We can give the dashboard a title and a description. We will use <code>st.title<\/code> and <code>st.write<\/code> for these respectively:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">st.title('Country vs Percentage Vaccinated')\n\nst.write('Percentage of Population Vaccinated Against Covid-19')<\/code><\/pre>\n<\/div>\n<p>Next, let&#8217;s add a select box to the dashboard. The select box will provide a drop-down button that allows users to choose a counrty. We will use the <code>st.selectbox()<\/code> method to create it as shown below:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">select = st.selectbox(\"Country\", [\"All\", \"Australia\", \"Belarus\", \"Brazil\", \"Cameroon\", \"Canada\", \"Egypt\", \"India\", \"Kenya\", \"Ukraine\", \"UK\", \"US\", \"Venezuela\" ])<\/code><\/pre>\n<\/div>\n<p>We can also create radio buttons to help users to choose between fully and partly vaccinated:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">radio = st.radio(\"Type\", [\"Fully-Vaccinated\", \"Partly-Vaccinated\" ])<\/code><\/pre>\n<\/div>\n<p>Let us add functionality to the select box and the radio buttons:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">if select != \"ALL\":\n    sql_statement = sql_statement + \" AND Country = '\"+select+\"'\"\n\nif radio != \"ALL\":\n    sql_statement = sql_statement + \" AND Type = '\"+select+\"'\"<\/code><\/pre>\n<\/div>\n<p>Let us generate a bar chart that shows the percentage of population vaccinated against Covid-19 in different countries. We can use the <code>st.bar_chart()<\/code> method for this as shown below:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-python\">st.bar_chart(data=output)<\/code><\/pre>\n<\/div>\n<h2>Run the Project<\/h2>\n<p>To see the dashboard, you should run the project. Use the following command:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">streamlit run myapp.py<\/code><\/pre>\n<\/div>\n<p>Open the generated URL on your web browser to view the dashboard. You can also play around with the select box and the radio buttons to see different results on the dashboard.<\/p>\n<p><a href=\"https:\/\/griddb.net\/wp-content\/uploads\/2021\/12\/image-6.png\"><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/griddb.net\/wp-content\/uploads\/2021\/12\/image-6.png\" alt=\"\" width=\"725\" height=\"583\" class=\"aligncenter size-full wp-image-27984\" srcset=\"\/wp-content\/uploads\/2021\/12\/image-6.png 725w, \/wp-content\/uploads\/2021\/12\/image-6-300x241.png 300w, \/wp-content\/uploads\/2021\/12\/image-6-600x482.png 600w\" sizes=\"(max-width: 725px) 100vw, 725px\" \/><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Introduction Data is a source of insights. To extract these insights, the data should be analyzed and understood. However, it is difficult to analyze numerical. The easiest way to extract insights from data is through data visualization. Data visualization involves presenting data using charts and graphs. This way, it becomes easy for users to understand [&hellip;]<\/p>\n","protected":false},"author":41,"featured_media":9497,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[121],"tags":[],"class_list":["post-46680","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Visualizing Vaccination Data with Streamlit | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"Introduction Data is a source of insights. To extract these insights, the data should be analyzed and understood. However, it is difficult to analyze\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.griddb.net\/en\/blog\/visualizing-vaccination-data-with-streamlit\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Visualizing Vaccination Data with Streamlit | GridDB: Open Source Time Series Database for IoT\" \/>\n<meta property=\"og:description\" content=\"Introduction Data is a source of insights. To extract these insights, the data should be analyzed and understood. 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