![]() The two keys generate two groups of data values: Before generating the key, each string is first transformed into lowercase letters and all special characters including whitespaces, punctuation, and control characters are removed.įor example, the values on the left are associated with the keys on the right. ![]() In Tableau Prep Builder, this method is case insensitive and only applies to numbers and letters. It tokenizes the value into a character set and sorts the characters to generate a key, known as a 1-gram. ![]() Ĭommon Characters: This method is useful to fix capitalization or formatting issues. The two keys produce two groups of data values: For example, the values on the left are associated with the keys on the right. It uses the Metaphone3 algorithm to generate keys based on the value’s English pronunciation. Pronunciation: This method is useful for fixing data entry errors where words sound similar. In key-based methods like Pronunciation and Common Characters, each value is transformed to a key, or token, and all values with the same key are grouped together. Wouldn't it be great if a data preparation tool could help automate this task? Updating this script is still tedious as he works backwards from errors in his analysis. After spending a lot of time manually fixing the city names, he converted that work to a Python script as he found he has to repeat the standardization with every campaign. He finds that users misspell several cities, which leads to errors in his analysis as data is not correctly reported. ![]() John, a Tableau customer, analyzes marketing call data where agents manually enter responses across the US. To correctly analyze this data, users must manually reconcile data values, which can be error-prone and time-consuming. For example, a city field with “Seattle” spelled as “Seattel” an address field with two variations of 5th street as "5th St" and "St, 5th" or a customer name represented as "First name Last name" and "Last name, First name". Text fields in data tables often have data with misspelled values or multiple alternatives of the same concept. Reference Materials Toggle sub-navigation.Teams and Organizations Toggle sub-navigation.Plans and Pricing Toggle sub-navigation.Please click the Subscribe button to get notified when we publish a new one. We’ll dive into these features and more in future episodes. Like the desktop version, it’s all done in a very visual, intuitive way that requires minimal coding.Every operation is captured in a change history that you can inspect and reorder.When joining flows, you can see and affect what data will be included or excluded using the graphic interface.You can split your data flow into branches then merge them back together.You can create steps that clean, filter, pivot, aggregate and include calculations.Within a step you can see preview exactly how that step is affecting your data either as a distribution, or as a subset of your data.Tableau Prep lets you create a step by step flow so each operation acts upon the results of the previous step.In this video series, we’ll explore its features and share some tips and techniques we’ve learned from using it on real client projects.Īs Tableau already includes powerful features for combining data from different sources and shaping for your visualizations, you may be wondering why you need Tableau Prep at all? Here are a few reasons: If you’re a Tableau Desktop user, you’ve probably heard that Tableau recently launched a new product called Tableau Prep that comes with your desktop license.
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