For instance, in pivoting the input table looks like this: CountryĪfter applying pivoting on the “Attribute” and “Value” columns, the dataset takes the following form: Country Pivoting, as the name suggests, refers to the process that is used to reverse an unpivoted dataset to its original form. Similarly, if you unpivot three columns, the number of rows in a dataset increases by three times. Notice that the number of rows has doubled. The “Value” column contains the corresponding values for the Attributes. In the above dataset, you can see that for each country name, the “Attribute” column now contains two unique values: GDP Per Capita and Literacy Rate. The dataset with two unpivoted columns looks like this. We will unpivot both the GDP Per Capita and Literacy Rate columns in the original dataset. Let’s now try to see what happens when we unpivot two columns. Also, the number of rows in the dataset with one unpivoted column will remain the same. Since only a single column is unpivoted, the values in the “Attribute” column will always be the same. The values in the “Attribute” column correspond to the column name that is unpivoted, whereas the “Value” column contains the values that previously existed in the unpivoted column. You can see that the column GDP Per Capita has been replaced by two columns, i.e., Attribute and Value. If you unpivot a single column, e.g., GDP Per Capita, the dataset with an unpivoted column will look like this. (Note: These are just dummy values, not the actual values) Country The rows in the following table correspond to countries, while the columns show information about the GDP Per Capita and Literacy Rate for corresponding countries in rows. UnpivotingĪssume you have a dataset that contains the following information. Let’s consider a very basic example of unpivoting. Pivoting and Unpivoting Theoryīefore we actually get down to pivoting and unpivoting columns with Power BI query editor. In the query editor, you can perform various preprocessing such as pivoting, unpivoting, and splitting columns. To load the data into the query editor, click the “Transform Data” button. The dataset contains information about the Population, Area, Birth Rate, Death Rate, Population Density, GDP Per capita, Phones per 1000, etc. Once the data is loaded, you should see the window below. Power BI will take some time to import the data. From the dropdown list, select “Text/CSV” as shown below. Next, open the Power BI Desktop, click on the “Get Data” button from the top menu. Download the CSV file into your local file system. The dataset used as an example in this article is in a CSV file format that can be downloaded using this kaggle link. Importing A Data Set into the Query Editor In this article, you will see how to pivot, unpivot, and split columns using Power BI Query editor. With query editor, you can perform various data transformation tasks, such as changing column types, handling missing values, deleting rows and columns, pivoting and unpivoting columns, splitting columns, etc. Before you can create actual visualizations with Power BI, you can perform data preprocessing using Power BI Query editor. Power BI can be used for static as well as interactive data visualization. For reference, Power BI is a data visualization and analytics software developed by Microsoft.
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