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This article introduces the relevant knowledge of "introduction and usage of dataWrangler". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

1. Introduction to tools

DataWrangler is an online data cleaning and data reorganization software developed by Stanford University. Mainly used to remove invalid data, organize the data into the format needed by users, and so on. Through the use of dataWrangler, users can save the time spent on data collation, so that they have more energy for data analysis.

two。 Main features

DataWrangler is extremely easy to operate, and a series of data collation can be completed with a simple click. Compared with the traditional data processing software, its unique intelligent analysis and suggestion function greatly facilitates the data processing operation of users. DataWrangler also lists a history of data changes, making it extremely easy for users to view past changes and undo a change.

At the same time, dataWrangler is an online tool, which saves users the tedious process of installing software and frees users from the restrictions imposed by the operating system on the use of software.

3. Tool interface (workspace, menus, terminology, etc.)

After entering the address of dataWrangler in the address bar of the browser, you will enter the interface where dataWrangler gets the input data, as shown in the following figure.

Enter the data input interface after dataWrangler.

Copy and paste the data in CSV format into the data input area and click the dataWrangle button to enter the data processing interface and begin to organize and repair the data. The data processing interface is shown in the following figure.

The main interface of data processing.

The panel on the left side of the data processing interface includes a list of data modification suggestions based on the currently selected data and a list of data operation history. Click on the bold section of the list of modification suggestions to implement the amendment proposal. On the right side of the interface is a data table containing specific data.

4. Operation flow (core function presentation)

The main functions of dataWrangler are described below.

-> remove invalid data

Click on the line number of the invalid data, and the line will turn red and highlighted, and the suggestion bar on the left will give a series of suggestions for changes. After clicking on the appropriate modification suggestion, the modification action will be carried out.

Delete a blank line operation.

As shown in the figure, after clicking the modification suggestion of "Delete empty rows", all blank lines will be deleted.

-> extract part of the data

When you need to extract part of the data as a separate column, first select the data you want to extract, and dataWrangler will automatically analyze the user's intention and extract the corresponding data. If the user makes a secondary selection, the selection intention will be modified to extract the data that the user really needs.

The following picture shows that when the user wants to extract the state name, he first selects "Alabama", but at this time dataWrangler thinks that the user wants to extract the corresponding length of characters, so the "Alaska" that does not meet the requirements is not selected, and only part of the longer characters such as "California" are intercepted.

Select the data you want to extract.

At this point, continue to select "Alaska", dataWrangler learned through the second selection that the user wants to extract the entire word in this position, and then successfully extracted the state name. This is shown in the following figure.

The extraction results are modified by secondary selection.

-> automatically populate data

After the state name is extracted, it needs to be populated into each row of data. At this point, just click on the title at the top of the state data column, and a suggestion for automatically populating the data will appear in the smart suggestion bar on the left. Click on the suggestion to automatically populate the data, as shown in the following figure.

Automatically populate the data.

-> Delete useless data

After the data is automatically populated, some of the data columns left behind are no longer meaningful and need to be deleted. Click on a row in China where you want to delete the data, and dataWrangler will automatically give you a suggestion for deletion. At the same time, the row to be deleted will be highlighted, as shown in the following figure.

Delete useless lines.

Click on the left side to delete. It is recommended to delete. The result is shown in the figure below.

The result after deleting useless rows.

-> data reconstruction

In some cases, you may need to reassemble the data into the desired format. After clicking on the green box at the top of the table, dataWrangler will give you a variety of data reconstruction suggestions. This is shown in the following figure.

Reconstruct the data.

Double-click the column name, you can edit the column name, the column name in the figure has been changed to "year", "state" and other meaningful text.

After clicking the refactoring suggestion on the left, the data results are shown in the following figure.

The result of data reconstruction.

At this point, each row is data for a state in a different year.

This is the end of the introduction and usage of dataWrangler. Thank you for your reading. If you want to know more about the industry, you can follow the website, the editor will output more high-quality practical articles for you!

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