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! 

Learning the basics of Python in Power BI

Options

Benefit

Limitation

Pros and cons of Python in Power BI

Set up, import, and transform data

Set up Power BI for Python

Power BI: import and join

Python: import and join

Getting Started with Python in Power BI

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.

Missing data and imputation

Data missing not at random

Finding missing data

Finding missing data with Python

Finding missing data in Power BI

Addressing missing values with imputation

Missing Data and Imputation

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!

Building visualizations with Seaborn

Ordering the steps for building a plot

Building Python-based visualizations

Starting with line plots

Produce a pair plot

Generate a joint plot

Visualizations with Seaborn in Power BI

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!

Calculating correlation coefficients

Classifying correlation

How to calculate in Power BI and Python

Correlation coefficient in Power BI

Correlation heatmap with seaborn

A heatmap of fishing volume

Wrap-up

Heatmaps and Correlation Coefficients

DataCamp vs. Local Experience

Exercises and Datasets

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. 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.

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.

<h2>Customize Power BI with Python </h2>
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. 
<br><br>
<h2>Build Custom Visuals</h2>
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.
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<h2>Power up your Toolbox</h2>
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.


Introduction to DAX in Power BI

Introduction to Data Visualization with Seaborn

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

Introduction to Python in Power BI

Calculations are difficult to construct in DAX

Up to 150,000 rows can be used in a visualization

Pros and cons of Python in Power BI

Introduction to Python in Power BI

Ejercicio interactivo práctico