This way you will install only the libraries you need for your project in a dedicated workspace and not at the operating system level. You can download the dataset here.īefore installing the library we recommend that you create your own development environment. In particular, we will use the national trend data and focus only on the hospitalizations data. We will not do an analysis of that data, but we will only use it to show the functionality of the libraries. In this tutorial we will use the open data about the COVID-19 cases in Italy available here. There are also several repositories that provide so-called open data, i.e. For example, on Kaggle you can download free datasets covering a variety of areas, from financial data to weather data. There are many public datasets with which you can test these tools. The goal is to make the graphs interactive so that we can take full advantage of the information they present to us. In particular, we will limit ourselves to six basic graphs, namely line graphs, bar graphs, stacked bar graphs, histograms, scatter and pie graphs. We will analyze the syntax and the results obtained by comparing some available types of graphs. In this article, we will compare the pandas and pandas_boken library. However, if we want to include graph generation within our code, we need to use other libraries. We have seen in the article PandasGUI: Graphical user interface for analyzing data with Pandas how we can use a tool to interact with data through a graphical interface. However, there are open-source libraries that partially solve this problem. Others, however, allow interaction with graphs but the learning curve is steep. In fact, there are several libraries that are easy to use but limit interaction with the data itself. In addition, visualizing analysis results allows for immediate communication of the result of complex analyses.Ĭhoosing a library to display data and/or results is sometimes complicated. Exploring the nature of the data and its distribution allows the data analyst to understand how to analyze it. Data visualization is one of the fundamental aspects of data analysis.
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