Identifying outliers in CSV data

An outlier is a data-point which differs significantly from the other data points in the set. This difference can be measured using statistical methods. The difference could be due to variability in the value itself or due to an observation error. In the latter case, the outlier data points can be removed from the data set to clean it up.

ArgonStudio implements one way of detecting outliers. This method uses the inter-quartile range as follows: Assuming Q1 and Q3 represent the upper and lower quartiles, the inter-quartile range IQR is defined as:

IQR = Q3 - Q1

With this definition of the IQR, an outlier is defined as any point outside the range:

(Q1 - 1.5 * IQR) and (Q3 + 1.5 * IQR)

Let us now look at how to easily identify outliers using ArgonStudio.

Load CSV data into the grid editor

The data we are looking at in this example is US census data which contains information about seniors (60+ adults).

We begin by loading the CSV data into the grid editor.

Identifying outliers of a single column

  • To identify outliers on a single column, click the drop-down button in the column header, and choose Show outliers.
  • Note that the Show outliers menu-item will be present and applicable for numeric-type columns only.
  • Some of the rows containing the outliers are now highlighted as shown.
  • Checking the boxplot shows the various parameters - upper and lower quartiles, the median, the left and right whiskers (which are the upper and lower limits) as well as the right and left outliers.

Showing all outliers in a dataset

  • Showing the outliers in a single column works when there are a few columns for which you want to check the outliers. But when you have a large number of numeric columns, it becomes tedious. For such situations, we have a different solution.
  • Once the data file is loaded into the grid editor, click the menu-bars at the right-side of the grid, and choose Highlight outliers as shown.
  • Below we can see that all outliers in all numeric columns are highlighted. The image shows states where population of various races (asian, black, etc.) are higher or lower than expected. However, it is useful to show the state too when browsing data. The state is in the first column and the first column should be frozen for a better view.
  • To freeze the first column, move the horizontal scroll-bar at the bottom so the first column is visible, and choose Freeze columns.
  • Now we can browse the data much better with the first (and second) columns frozen.

Outliers in US college rankings

Let us now check the US colleges ranking file for outliers.

  • Let us load the CSV file into the grid editor.
  • Some of the columns in this file contain numbers mixed with other characters such as % (Acceptance rate and Graduation rate) and $ (Fees-undergraduate). To be able to process these as numeric fields, we need to modify the values.
  • Click on the drop-down button on Acceptance rate column and choose Update Value.
  • In the Update Column Value dialog, make sure the Pre-configured tab is selected and choose Convert to number from the drop-down. Click Update to perform the update.
  • As we can now see, the % has been removed from the Acceptance rate column. Repeat this procedure for the Graduation rate and Fees-undergraduate fields.
  • Now to find the outliers. Click on the menu-bars icon at the top-right of the grid, and choose Highlight outliers. You get a notification and the single outlier is highlighted.
  • This means that New Jersey Institute of Technology has the least Graduation rate of 65% and is an outlier. For some reason, only 65% of students graduate from this college. Note that the median across the data set is 85% and the minimum (excluding this college) is 73% as can be seen from this boxplot.

Finding major outliers in data

Till now, we have talked about outliers which lie outside the inter-quartile range by a factor of 1.5. These outliers are considered minor outliers. A major outlier is defined as a data-point that lies outside the inter-quartile range by a factor of 3. In other words, a data point which lies outside the range of:

(Q1 - 3 * IQR) and (Q3 + 3 * IQR)

Let us now find out how to identify major outliers in your data with ArgonStudio.

For this example, we are looking at the United States per-capita income by county from Wikipedia. We have covered the procedure for loading the CSV data from a HTML table of a Wikipedia page here.

  • After the data is loaded into the grid editor, we need to convert the data from the Per-capita income column to numeric format. As it is loaded, it contains the $ sign and a ,. To remove these, click the drop-down button at the end of the column (1) and select Update Value (2).
  • In the Update Column Value dialog, ensure the Pre-configured tab is selected (1), click the Choose operation drop-down and select Convert to number (2). Click Update to apply the update (3).
  • The data is updated and the column is now a numeric column. Click the column drop-down again, and choose Show major outliers from the menu.
  • The software now highlights the 21 major outliers. The results are shown below.
County State Per-capita income
New York County New York 62498
Arlington Virginia 62018
Falls Church City Virginia 59088
Marin California 56791
Alexandria City Virginia 54608
Pitkin Colorado 51814
Los Alamos New Mexico 51044
Fairfax County Virginia 50532
Hunterdon New Jersey 50349
Borden Texas 50042
Montgomery Maryland 49038
Morris New Jersey 48814
Fairfield Connecticut 48721
San Francisco California 48486
Howard Maryland 48172
Westchester New York 47984
Somerset New Jersey 47803
Nantucket Massachusetts 47331
Loudoun Virginia 46565
North Slope Alaska 46457
San Mateo California 45732


Outliers are data points which differ significantly from the rest of the points in the dataset. Identifying them is important to decide whether the data is anomalous or represents a variability in the value.

  • ArgonStudio provides a simple way to identify such outliers.
  • Outliers in a single numerical column can be identified which highlights the entire row.
  • Alternatively, outliers in all numerical columns can be identified with a single click.