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Excerpt

Before you begin performing analytics on a dataset, it is important to identify and recognize outlier data patterns and values.

Unusual values or patterns in the data can be sources for the following:

• Missing data.
• Poorly formatted data
• Mismeasured data
• Data that skews statistics

This section provides guidance in how to locate these patterns of data in individual columns.

## Single-column outliers

For assessing anomalies in individual columns,

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provides visual features and statistical information to quickly locate them.

### Data Histogram

You can use the data quality bar and histogram to locate unusual values in your column data. The following example illustrates a dataset that contains two columns with outlier data. The first two rows are outliers with the subsequent rows to be consistently patterned data: D caption
type figure
`Numeric and string anomalies`

Numeric data

The `col-numbers` column contains 100 random values 0-10, and singleton values -100 and 100.

In the histogram, you can see the outliers at the extremes of the graph. Note the slight visual distinction between the two extreme values and the values next to them, which are not represented in the column data.

Tip

Tip: In a histogram for numeric data, the spread between the extreme values and the more frequent values is a visual cue for outliers.

For numeric data, the range of values is displayed as part of the histogram. In this dataset, the extreme values are singletons. If a dataset contains more instances of outlier values, you should investigate further.

Info

NOTE: In numeric datasets, a high count of outlier values may be statistically significant. You should review those values and related data in other columns before you perform operations to change or remove those rows.

Significant counts of unusual values

When your data contains a significant number of specific values, you should review them to see if the values have meaning. They may be placeholders for missing values.

For numeric data, you should be skeptical of occurrences of the following values:

Suspicious valueReason
`-1`In system generated data, `-1` is often an indicator of a failed result of some kind.
0Some systems will fill missing numeric values with the number 0. You should verify the meaning of the value of 0 in your dataset.
`555-####`In the United States, the phone number prefix `555` never corresponds to a person's phone number. These informational phone numbers and should not be considered as valid values for individuals' data.
`65535`

In older versions of Microsoft Excel, 65,535 was the maximum number of rows permitted in a single sheet.

Info

NOTE: 65,536 is 216, which is the maximum number of data bits in a 16-bit system.

`2147483647`This value is the largest positive integer that can be stored in an `int` datatype by 32-bit systems, which are still sources of data. If you see these values, the source system may have been unable to represent the true value and wrote this value instead.
`4294967295`This value is the largest raw value that can be stored in 32-bit systems. If you see these values, the source system may have been unable to represent the true value and wrote this value instead.
`January 1st, 1900`This value is the earliest date recognized by Microsoft Excel. The true date may not be accurately represented in your data.
`January 1st, 1904`
This value is the earliest date recognized by Microsoft Excel for Macintosh.
`00:00:00 UTC on January 1, 1970`This value is the earliest recognized date in UTC timestamp values. UTC timestamps are recorded as the number of milliseconds since this moment in time, stored as a signed 32-bit integer. Since datetime values may be represented in many different formats, you should identify these values for the date formats in your dataset.
`03:14:07 UTC on Tuesday, 19 January 2038`

This value is the latest recognized date in UTC timestamp values. Since datetime values may be represented in many different formats, you should identify these values for the date formats in your dataset.

• This limit is generally known as the "Year 2038" problem.

String data

The `col-strings` column contains approximately 25 values for `orange`, `red`, `green``yellow`, and two instances of `supercalifragilisticexpialidocious`.

Info

NOTE: For string-based data, outliers can be identified as strings with a low count of instances. These are the shorter stacks in the histogram.

### Column Details

In the Column Details panel, you can review detailed statistics on the values in the currently selected column, including data on outliers. In the Transformer page, select Column Details from a column's drop-down.

Tip

Tip: In the Column Details panel, you can select specific outlier values, prompting suggestions, which enables you to take action on values identified by the platform as outliers. D caption
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`Outliers in the Column Details`

#### Column Detail Statistics

The Column Details panel provides information on the following:

• Count of valid, mismatched, and missing values
• Count of value instances
• Min, max, and average
• Outlier values. See below.
• Lowest and highest quartiles
• Standard deviation
Info

NOTE: For string-based data types, these statistics pertain to string length.

Tip

Tip: Any green bar in the Column Details panel can be selected to prompt for suggestions on actions, including values in Outliers, Value Histogram, and Frequent Values graphs. Multi-select values as needed.

#### Outliers

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uses a special set of computations to identify values that it designates as outliers.

For more information on these computations and other calculations in the Column Details panel, see Column Statistics Reference.

### Tune standard deviation calculations

Although standard deviation information is available in the Column Details, you may want to generate your own standard deviation calculation. For example, the following transform generates a new column which computes the number of standard deviations that a column value is from the average value for the column:

D trans
Type step Formula type Single row formula Formula (col_numbers - AVERAGE(col_numbers)) / STDEV(col_numbers) New formula

You can then compute your own outlier function, using something like the following, which assumes that the above derived column has been renamed `col_numbers_stdev` and identifies outliers greater than 4 standard deviations from the average:

D trans
Type step Formula type Single row formula Formula ABS(col_numbers_stdev)> 4 New formula

The above function generates boolean values in a new column, setting the value to `true` if the absolute value of the standard deviation for the `col_numbers_stdev` is more than `4`. You can then perform operations based on the values written to this column or leave the column in place for downstream analytics tools.

The variance function is also supported.

## Methods for fixing single-column outliers

After you have identified the values that are outliers in your column, you must determine if those values are valid or invalid for your dataset. For example, a value of `0` may be a valid measurement, or it may be a value that was inserted for lack of a valid value.

For invalid values:

• Fix the values. The fix may require converting the values to be valid for the column's data type. For example, on import, values for `0` and `1` may be written as `false` or `true`. The following steps converts them back to numeric values:

D trans
Type step Columns col_numbers Formula IF((col_numbers == 'false'),'0',col_numbers) Edit column with formula

D trans
Type step Columns col_numbers Formula IF( (col_numbers == 'true'),'1',col_numbers) Edit column with formula

• Delete the rows. If the removal of these records does not skew your data, you can create a simple delete statement. For example, the following deletes rows where the value in the `col_numbers` column is less than `25`:

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p03Value col_numbers < 25 step Condition Custom formula Type of formula Custom single Condition Delete matching rows Action Filter rows

For valid values:

• Let them be. If the data is valid, do not remove it unless you have an explicit reason for doing so.

• Convert to more meaningful values. You can use the set transform to change outlier values to values that are valid for purposes of analysis.

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NOTE: Please be aware that changing of values may impact the validity of your statistical analysis.

Example of overwriting values where values in the `col_numbers` column that are below `25` are set to the average value for the column. Otherwise, use the current value:

D trans
Type step Columns col_numbers Formula IF((col_numbers < 25), AVERAGE(col_numbers), col_numbers) Edit column with formula

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inCQL true ((label = "discovery_tasks") OR (label = "transformation_ui") OR (label = "outlier"))