Power BI에서 Python 시작하기

Now that you're up and running with Python in Power BI, let's move on to another important data processing step - identifying missing data and imputation. In this chapter, you will identify missing data in a dataset using Python, then Power BI. You will then work through addressing missing data by leveraging imputation techniques.

In this chapter, you will construct several Python-based visualizations, using the Seaborn package, in Power BI. Specifically, a line plot, pair plot, and joint plot. You will also learn how to interpret these visualizations to extract insights about the data. By this point, you will know some of the key differences between Python and Power BI in basic data processing steps. The next step is to visualize this data!

In this chapter, you will continue evaluating the relationship between variables. This time, you will be doing so quantitatively by calculating the correlation coefficient. You will learn how to do this in Power BI then Python. Finally, you will leverage the power of Seaborn visualizations to create a correlation heatmap! By the time you finish the course, you'll be skilled in Power BI, Python, and data visualization techniques. Nice work!