8/17/2023 0 Comments Gui analysis![]() We start the code by importing Eikon Data API, Pandas, PandasGUI, and defining the required variables. Historical RCEP COVID-19 Death Cases Data (1 Year, interval daily).Historical RCEP COVID-19 New Cases Data (1 Year, interval daily).Historical Thailand COVID-19 Data (1 Year, interval daily).The data that we are going to requests are the following: The 15 member countries account for about 30% of the world's population (2.2 billion people) and 30% of global GDP ($26.2 trillion) as of 2020, making it the biggest trade bloc in history. ![]() The RCEP nations are Australia, Brunei, Cambodia, China, Indonesia, Japan, Laos, Malaysia, Myanmar, New Zealand, the Philippines, Singapore, South Korea, Thailand, and Vietnam. Seamless integration with the AMS2020 graphical user interface. We will use COVID-19 data (both today and historical data) of countries in RCEP (The Regional Comprehensive Economic Partnership) free trade agreement. Graphical User Interface: preparation, execution & analysis. Let's continue with more complex DataFrame objects. GUI testing is described as the testing of the application under the Test system graphical user interface. This example project is focusing on the console environment only. The tool is not compatible with Eikon Data API and Refinitiv Data Platform (RDP) Libraries - Python on IPython/Notebook environment yet. Note: The PandasGUI tool is still under development. The demo application uses Corona Virus Disease (COVID-19) data from Eikon Data API as an example of a dataset. This article shows how to use PandasGUI tool for basic data analysis with the simple GUI interface. The tool wraps Pandas functions into an easy to use data analytic tool for developers and data scientists to start with. PandasGUI is the Graphical User Interface tool that can solve this learning curve issue. This learning curve makes some developers and data scientists stuck with the "coding" time instead of "analysis" time. However, developers require a great skill of Python and the library to using Pandas efficiently. ![]() Pandas is powerful, flexible, has excellent community support, and it keeps improving. With the rise of Data Scientists, Financial coders, or Traders (aka Citizen Developers), the Pandas library has become the defacto tool for data analysis with Python programming language. ![]()
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