{"id":46750,"date":"2023-04-26T00:00:00","date_gmt":"2023-04-26T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/socio-economic-data-analysis-and-visualization-using-python-and-griddb\/"},"modified":"2025-11-13T12:56:29","modified_gmt":"2025-11-13T20:56:29","slug":"socio-economic-data-analysis-and-visualization-using-python-and-griddb","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/en\/blog\/socio-economic-data-analysis-and-visualization-using-python-and-griddb\/","title":{"rendered":"Socio-Economic Data Analysis and Visualization using Python and GridDB"},"content":{"rendered":"
\n
\n
\n
\n
\n<\/div>\n
\n<\/div>
\n

Today, we will explore the socio-economic dataset which contains various metrics and social indicators for 66 major countries. This dataset is originally inspired by UNData<\/a>. We will learn more about the dataset in the later section.<\/p>\n

The objective of this tutorial is to give a sneak peek into the socio-economic data preprocessing and analysis. We will begin with loading the libraries and our dataset followed by data preprocessing. Later on, we will visualize the statistics using interactive widgets.<\/p>\n

The outline of this tutorial looks like as follows:<\/p>\n

    \n
  1. Prerequisites and Environment Set up.<\/li>\n
  2. Introduction to the dataset<\/li>\n
  3. Importing Libraries<\/li>\n
  4. Loading the Dataset<\/li>\n
  5. Data Preprocessing<\/li>\n
  6. Data Filtering and Basic Data Visualization<\/li>\n
  7. Interactive Data Visualization<\/li>\n
  8. Conclusion<\/li>\n
  9. References<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
    \n
    \n<\/div>\n
    \n<\/div>
    \n

    1. Prerequisites and Environment Set up\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
    \n
    \n<\/div>\n
    \n<\/div>
    \n

    This tutorial is carried out in Anaconda's Jupyter Notebook with Python version - 3.8.3 on Windows 10 Operating System. Google Colab<\/a> also offers a similar environment and is cloud-based. Hence, it could be shared among users and devices.<\/p>\n

    The following libraries are used in this tutorial. Make sure to install them before moving to the code.<\/p>\n

      \n
    1. Pandas<\/a><\/li>\n
    2. Numpy<\/a><\/li>\n
    3. Matplotlib<\/a><\/li>\n
    4. Regex<\/a><\/li>\n
    5. ipywidgets<\/a><\/li>\n<\/ol>\n

      To install these packages using the command line, just type pip install package-name<\/code> or conda install package-name<\/code> if you are using Anaconda Navigator.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

      \n
      \n
      \n<\/div>\n
      \n<\/div>
      \n

      Moreover, if you are using GridDB, these additional libraries will be required:<\/p>\n

        \n
      1. GridDB C-client<\/a><\/li>\n
      2. SWIG (Simplified Wrapper and Interface Generator)<\/li>\n
      3. GridDB Python Client<\/a><\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
        \n
        \n
        \n<\/div>\n
        \n<\/div>
        \n

        After installing the prerequisites, we go ahead and introduce today's dataset.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

        \n
        \n
        \n<\/div>\n
        \n<\/div>
        \n

        2. Introduction to the Dataset\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
        \n
        \n
        \n<\/div>\n
        \n<\/div>
        \n

        The motivation behind this socio-economic dataset is to be able to analyze country-specific data. But why is it important?<\/p>\n

        Country-specific data can be proven useful to analyze statistics, successful approaches for policies, etc. This data has been made open-source on Kaggle<\/a>. It contains information about Population, GDP Growth, Agricultural Sector, etc. The dataset has 66 rows each row representing a country and 96 columns denoting a specific metric.<\/p>\n

        Now that we have a little background about our dataset, let's load it into our python environment. But before that, we need to import the necessary libraries!<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

        \n
        \n
        \n<\/div>\n
        \n<\/div>
        \n

        Please Note:\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
        \n
        \n
        \n<\/div>\n
        \n<\/div>
        \n

        We have made some changes to the column names in the original dataset by removing white spaces and special characters. The updated data can be downloaded from our repository.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

        \n
        \n
        \n<\/div>\n
        \n<\/div>
        \n

        3. Importing Libraries\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
        \n
        \n
        \n<\/div>\n
        \n
        In\u00a0[1]:<\/div>\n
        \n
        \n
        <\/span>import<\/span> <\/span>pandas<\/span> <\/span>as<\/span> <\/span>pd<\/span>\nimport<\/span> <\/span>numpy<\/span> <\/span>as<\/span> <\/span>np<\/span>\nimport<\/span> <\/span>matplotlib.pyplot<\/span> <\/span>as<\/span> <\/span>plt<\/span>\nimport<\/span> <\/span>regex<\/span> <\/span>as<\/span> <\/span>re<\/span>\nimport<\/span> <\/span>ipywidgets<\/span> <\/span>as<\/span> <\/span>widgets<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
        \n
        \n
        \n<\/div>\n
        \n<\/div>
        \n
        The cell will not return anything if executed successfully. In case of an error, kindly check the version of each package and see if it is compatible with your operating system.<\/p>\n
        Great! We have loaded the libraries successfully. Let's import our dataset.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
        \n
        \n
        \n<\/div>\n
        \n<\/div>
        \n
        4. Loading the Dataset\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
        \n
        \n
        \n<\/div>\n
        \n<\/div>
        \n
        Using Pandas\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
        \n
        \n
        \n<\/div>\n
        \n
        In\u00a0[2]:<\/div>\n
        \n
        \n
        <\/span>dataset<\/span> =<\/span> pd<\/span>.<\/span>read_csv<\/span>(<\/span>'soci_econ_country_profiles.csv'<\/span>)<\/span>\ndataset<\/span>.<\/span>head<\/span>()<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
        \n
        \n<\/div>\n
        \n
        \n
        Out[2]:<\/div>\n
        \n
        \n