Clean Data In Excel
Clean Data In Excel – Data is the foundation of any analysis performed in Excel. And when it comes to data, anything can go wrong – be it structure, placement, formatting, extra spaces, and so on.
The extra spaces are painfully hard to see. While you may somehow notice extra spaces between words or numbers, trailing spaces aren’t even visible. Here’s a handy way to get rid of those extra spaces: use the TRIM function.
Clean Data In Excel
The ExcelTRIM function takes a cell reference (or text) as input. Removes leading and trailing spaces and extra spaces between words (except single spaces).
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Empty cells can cause damage if left untreated. I often run into issues with blank cells in the dataset used to create reports/dashboards.
You can fill all empty “0” or “unavailable” cells or you just want to highlight them. If there is a huge dataset, it can take hours to do it manually. Fortunately, there is a way to select all blank cells at once.
Selects all blank cells in the data set. If you want to type 0 or Not Available in all of these cells, just type them in and press Control+Enter (remember, just pressing Enter will insert the value into the active cell only).
Sometimes when you import data from text files or external databases, numbers are stored as text. Some people also have a habit of using an apostrophe (‘) before a number to create lyrics. This can cause serious problems if you use these cells in your calculations. Here is a surefire way to convert those numbers stored as text back to numbers.
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So much more can be done with the Paste Special Operation options. Here are several other ways to multiply in Excel using Paste Special.
Removes duplicate values from the list. If you want the original list to remain intact, copy and paste the data elsewhere and then do so.
This highlights all cells containing the error. Now you can manually mark it, delete it or type something into it.
When you take over a workbook or import data from text files, the names or titles are often inconsistent. Sometimes all text can be in lowercase/uppercase or a combination of both. You can easily ensure consistency by using these three features:
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When you retrieve data from a database or import it from a text file, you may find that all the text is compressed into a single cell. You can parse this text into multiple cells by using the Text to Column function in Excel.
In my work, I have used multiple databases to get data into Excel. Each database had its own data format. Once you have all the data, here’s how to remove all formatting at once:
Find and replace is essential when it comes to cleaning data. For example, you can select and remove all zeros, change references in formulas, find and change formatting, and so on.
Here are my top 10 techniques for cleaning data in Excel. If you want to learn more techniques, here is the MS Excel Team Tutorial – Cleaning data in Excel.
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If there are any more techniques you use, share them with us in the comments! In this guest post from Filtered, we look at how to deal with a dirty dataset with tips for cleaning data with Excel.
Do you know what to expect from the data analysis? Data integrity. Well, it should. Because if your data is junk, so is your analytics.
Before you can perform an analysis with a dataset, it must be correct, consistent and complete. Otherwise, you might get a Kodak moment. Not the right kind, the $11 million kind of mistake.
One of the most common data challenges is duplicates. Dealing with duplicates can be easy. But before we start, it’s a good idea to grab the dataset and make a copy. Deleting data is permanent, so it’s always safer to keep a copy of what you started with. If the dataset is now full of duplicate rows, you can highlight the entire dataset and go to the Data tab on the Excel ribbon. There you can click on remove duplicates and voila – Excel removes the duplicate values so that only the first remains. Many of us are familiar with the process, but we believe that you should step back and do your due diligence before removing duplicates.
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In a dataset containing membership contact information, two lines can contain the same email address, but the names on each line are different.
If we’ve selected the entire dataset, Excel won’t see these rows as duplicates. So we could end up with an overabundance of data by giving a wrong impression of the number of members.
So how do you explain such things? Well, one way is with conditional formatting. We can highlight a column – let’s say the “Email” column – then go to the “Home” tab on the ribbon and click the “Conditional Formatting” drop-down menu. Hover over “Highlight Cell Lines” and then click “Duplicate Values”.
Now select Data > Filter on the Excel ribbon. Click the down arrow in the “email” column heading, then Filter by color, specifically the color used to highlight duplicate values. You are left with duplicate emails and can arbitrarily check the similarities and line differences before choosing which duplicate emails to delete.
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Sometimes, when we have a dataset, we want to look for something that connects cells. For example, you can view contacts with certain email domain extensions, for example “.co.uk” and “.org.uk”.
An easy and effective way to do this is by filtering. In the column heading, go to the Data tab on the Excel ribbon and click Filter. There is an option for text filters – hover over it and select “Contains…”.
We selected the content from the drop-down lists and those were the results. However, in addition to “contains”, there are other very useful options. For example, if you want to filter certain results from a payment column, you can use the “greater than or equal to” option to find payments above a certain threshold.
If you want to look up values in a cell and return values, Dave Bruns of ExcelJet has the perfect formula for that.
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Another extremely useful technique to use when cleaning data is to look for outliers in values with MIN / MAX. A classic Kodak mistake was adding too many zeros to a specific severance payment record, resulting in an overestimation of $11 million. It is nearly impossible to detect such errors with the naked eye in a massive database, but the MAX function allows you to return the largest value in the dataset, detecting errors that would otherwise go unnoticed.
On the other hand, you may want to make sure that no one has underpaid where the MIN appears. By acting in a similar manner, MIN simply returns the smallest value in the search area.
In the example below, we were able to detect both a significant overpayment and a similar severe underpayment by searching the payment column:
Another popular technique used in data cleaning is the Text to Columns wizard. If your spreadsheet has a column in which cells contain lists of items, you can place each item in a separate cell.
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Select the area you want to split – in this case the entire column D. Go back to the Data tab on the Excel ribbon and click the Text to Columns button (as shown above).
Since all our items in the list are separated by commas in the cell, we call it our separator. So, as below, select “Split” and click Next.
Now make sure the correct separator is selected. It’s a comma for us, so check the box. When you are satisfied with the preview, click Next again.
Normally it is fine to leave the column data format as General. Now just click Finish and you’re done.
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If you need the opposite, such as combining several cells, you need the CONCATENATE function. It’s quite simple – if you want to combine multiple cells in one row, such as from D2 to J2, use the formula =CONCATENATE(D2, E2, F2, G2, H2, I2, J2). For a large data set, you can use the auto-complete handle to fill the entire column:
Using one or a combination of these tips should help you ensure that your dataset is well prepared for the digging. There are many other tips that might be of interest, from freezing the top line of the worksheet to IFERROR, but that’s where you can start.
Cleaning data is an important skill. It is useful in many work situations and is a fundamental part of any data analysis project. But there is much more to it. To perform a perfect data analysis, you need to think
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