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Introduction to Python in Power BI

Learn how to use Python scripts in Power BI for data prep, visualizations, and calculating correlation coefficients.

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3 Horas9 Videos25 Exercises

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Descrição do Curso

Customize Power BI with Python

In this introduction to Python in Power BI course, you’ll use data from an overfishing study and an online retailer to learn how to use Python scripts in Power BI for data prep, visualizations, and calculating correlation coefficients.

Build Custom Visuals

Specifically for building custom Python-based visuals, you will be utilizing the Seaborn package. By the end, you should feel a little more comfortable using Python in (and outside of) Power BI.

Power up your Toolbox

Whether you were first a Pythonista or a Power BI power user, integrating Python into Power BI is a fantastic addition to the data toolbox. This course will demonstrate that, by using the two together, you can leverage the benefits of each, choosing the best one for the task at hand.

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1
Getting Started with Python in Power BI
Livre
In this first chapter, you will learn the advantages and limitations of Python in Power BI as well as how to enable this capability within a workbook. You will also perform the same task using both technologies separately to build familiarity with the strengths and weaknesses of both. Power BI is a powerful tool. Python can be leveraged to make it even more powerful!
Reproduzir Capítulo Agora
Learning the basics of Python in Power BI
50 xp
Pros and cons of Python in Power BI
100 xp
Set up, import, and transform data
50 xp
Set up Power BI for Python
100 xp
Power BI: import and join
100 xp
Python: import and join
100 xp
2
Missing Data and Imputation
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.
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Missing data and imputation
50 xp
Data missing not at random
50 xp
Finding missing data
50 xp
Finding missing data with Python
100 xp
Finding missing data in Power BI
100 xp
Addressing missing values with imputation
100 xp
3
Visualizations with Seaborn in Power BI
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!
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Building visualizations with Seaborn
50 xp
Ordering the steps for building a plot
100 xp
Building Python-based visualizations
50 xp
Starting with line plots
100 xp
Produce a pair plot
100 xp
Generate a joint plot
100 xp
4
Heatmaps and Correlation Coefficients
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!
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Calculating correlation coefficients
50 xp
Classifying correlation
100 xp
How to calculate in Power BI and Python
50 xp
Correlation coefficient in Power BI
100 xp
Correlation heatmap with seaborn
100 xp
A heatmap of fishing volume
100 xp
Wrap-up
50 xp