Functions
Last updated
Last updated
The Base64 is a method of representing data in a binary format over text protocols. Text values are converted to binary values 0-63. This transformation helps the user to convert an input Base64 value to text. The output type is String.
Check out the given walk-through on how to apply the Base64 Decode transform.
Steps to perform the Base 64 Decode transformation:
Select a String-based column with the data in the Base64 encrypted format.
Open the Transforms tab.
Select the Base64 Decode transform from the Functions category.
The Base64Decode dialog box opens.
Enable the Create New Column option.
Provide a name for the newly created column.
Click the Submit option.
The decoded data will be returned in a new column.
This transformation helps to convert an input value to base64 encoding with optional padding with an equal sign (=). Input can be of any type. The output type is String.
Check out the given walk-through on how to apply the Base64 Encode transform.
Steps to perform the Base 64 Encode transformation:
Select a String-based column.
Open the Transforms tab.
Select the Base64 Encode transform from the Functions category.
The Base64Encode dialog box opens.
Enable the Create New Column option.
Provide a name for the newly created column.
Click the Submit option.
The encoded data will be returned in a new column.
This transformation helps to generate the count of distinct values in a specified column, optionally counted by the group.
Check out the given illustration on how to use the Count Distinct transform.
Navigate to a dataset within the Data Preparation format.
Open the Transforms tab.
Select the Count Distinct transform from the Functions category.
The Count Distinct dialog box opens.
Provide a New Column name.
Select the Group by Columns.
Click the Submit option.
The result will appear in a new column for the selected group by column(s).
Count Occurrences is to check the count of occurrences of a word/letter/ digit in a sentence
For example, “In traditional grammar, it is typically defined as a string of words that expresses a complete thought, or as a unit consisting of a subject and predicate. “
The above sentence contains the count of occurrences of the word “as".
Check out the given walk-through on how to use Count Occurrences.
Select a string column from the dataset.
Open the Transform tab.
Click the Count Occurrences transform from the Functions section.
The Count Occurrences dialog box opens.
Provide a name for the new column.
Provide a value using the Value field.
Enable the Ignore case option using the given checkbox.
Click the Submit option.
A new column with the count of occurrences as per the set values will be added to the dataset.
The Create a Geopoint from Latitude and Longitude function helps create a geopoint column from two columns containing latitude and longitude.
A geo point (or geographical point) is a specific point on the Earth's surface identified by its latitude and longitude coordinates. Latitude measures how far north or south a location is from the equator, which is designated as 0 degrees latitude. Longitude measures how far east or west a location is from the Prime Meridian, which is defined as 0 degrees longitude.
Check out the given walk-through on the Geo points transform based on the Longitude and Latitude data.
Steps to perform the transformation:
Select a dataset that contains latitude and longitude data.
Open the Transforms tab.
Select the Create a Geopoint from Latitude and Longitude transform.
The Create a Geopoint from Latitude and Longitude dialog box appears.
Select the Latitude column from the dataset using the drop-down.
Select the Longitude column from the dataset using the drop-down.
Provide a name for the new column.
Click the Submit icon.
A new column with Geopoints will be added to the dataset.
The degree to radian data transformation is a simple mathematical conversion that allows you to convert angles from degrees to radians. Radians are another unit of measuring angles, and they are commonly used in advanced mathematics and physics because they have some convenient properties when working with circles and trigonometric functions.
The value can be a Decimal or an Integer.
Check out the illustration on how to use the Degree to Radians transform.
Steps to perform the transformation:
Navigate to the Dataset within the Data Preparation framework.
Open the Transforms tab.
Click the Degree to Radians transform from the Functions category.
The Degree to Radians dialog box appears.
Enable the Create a New Column option using the checkbox.
Provide a name for the newly added column.
Select one or multiple columns that need to be converted (Use Numerical Columns like int, float, decimal, etc.).
Click the Submit option.
The result will come as a different column with a prefix of radians for the selected column by converting the source column values into radians.
This transform computes the distance between a point column and another point. The computation outputs the distance (kilometers, miles) in another column.
Check out the given illustration on how to use Distance Between Geo Points.
Steps to perform the transformation:
Select a dataset that contains two latitude & two longitude points.
Open the Transforms tab.
Click the Distance between Geo Points transform from the Functions section.
The Distance between Geo Points dialog box opens.
Pass the respective fields with latitudes & longitudes.
Select the unit in which the result is required (km/miles) using the drop-down option.
Pass the New column name.
Click the Submit option.
As a result, new columns with the name passed and the selected unit will be added to the dataset.
The Exact data transform refers to a data transformation technique that involves exact matching of data records or values. In this context, the transformation could involve comparing data across multiple columns within the same dataset to find exact matches between specific fields. This could be useful for data deduplication or data integration tasks.
Check out the given walk-through on how to use the Exact transform.
Steps to perform the Exact transformation:
Navigate to the Data Preparation landing page.
Open the Transforms tab.
Click the Exact transform from the FUNCTIONS category.
A dialog box for the Exact transform appears to provide the required details.
Select Column 1 using the drop-down option.
Select another column using another drop-down option.
Provide a name for the new column that gets added with the result values.
Use the checkbox to ignore the case.
Click the Submit option.
The result column gets added to the dataset, reflecting either True or False values after matching data values from the selected columns.
This transform extracts parts from a column using GROK patterns and/or a set of regular expressions. The chunks to extract are delimited using named captures.
Check out the given video on how to use the GROK transform.
Steps to perform the transform:
Select the required column with the GROK pattern..
Open the Transforms tab.
Select the Extract Data with GROK transform.
The Extract Data with GROK dialog box appears.
Pass the New column name.
Pass the GROK pattern. Pattern Used: %{IPORHOST:clientip}--[%[HTTPDATE:timestamp}]
Click the Submit option.
The result will appear in a new column with the given name.
E.g., if a column has data like 192.168.1.1 [20/Jul/2023:10:00:00 +0000] "GET /index.html HTTP/1.1" 200
The Output after applying the Extract Data with GROK will come as: {'clientip': '192.168.1.1', 'timestamp': '20/Jul/2023:10:00:00 +0000', 'method': 'GET', 'request': '/index.html', 'httpversion': '1.1', 'response': '200'}
This transform extracts two latitude and longitude columns from a column in Geo point format. Geo point is a data type representing a point on Earth.
Check out the walk-through on extracting the Longitude and Latitude columns from the Geo point data.
Steps to perform the transform:
Select the geo point column from the dataset.
Open the Transforms tab.
Click the Extract Latitude and Longitude from a Geopoint transform.
The result will come in two new columns listing the Latitude and Longitude values from the selected Geo Points column:
N-grams are contiguous sequences of n items from a given sample of text or speech. This transform extracts sequences of words, called N-grams, from a text column.
Check out the given illustration on how to use the Extract Ngrams transform.
Steps to perform the transform:
Select one text column with string data. Preferably select a column with descriptive text.
Open the Transforms tab.
Click the Extract Ngrams transform from the Functions section.
The Extract Ngrams dialog box opens.
Provide a name for the new column for the output.
Set a number to indicate the Size for the Ngrams.
Click the Submit icon.
The result will be displayed in a new column based on the defined size. E.g., the following image displays Ngrams with size 2.
The Extract Number data transform is a data preparation technique used to extract numbers from a column that includes a combination of text and numbers as data points.
Steps to perform the Extract Number transform:
Please select a column that contains text and numbers together as data points.
Open the Transforms tab.
Click the Extract Number transform from the FUNCTIONS category.
Provide a new column name.
Click the Submit option.
A new column gets added to the dataset with the extracted numbers from the concerned column.
This transform parses and extracts information from a browser’s User-Agent string.
Check out the given illustration on how to use the Extract User Agent Information transform.
Steps to perform the Extract User Agent Information transform:
Select a column containing details of a browser user string data.
Open the Transforms tab.
Click the Extract User Agent Information transform.
The information about the user can be extracted in separate columns with the following suffix added to the column names:
device_family
browser_family
browser_version
os
os_version
The Extract with Regular Expression data transform, also known as Extract with regex, is a powerful technique used to extract specific patterns or substrings from textual data using regular expressions. Regular expressions (regex) are sequences of characters that define a search pattern. They are particularly useful for searching, matching, and extracting specific patterns from strings.
Check out the given walk-through on how to use Extract with Regular Expression.
Select a textual column that contains jumbled up information within the Data Preparation framework.
Open the Transforms tab.
Click the Extract with Regular Expression transform from the Functions category.
The Extract with Regular Expression dialog box opens.
Mention a name for the new column with the extracted data.
Provide a Pattern to extract particular information.
For example, suppose a text in a column is “Reach out at jane_smith123@mail.co.uk or call 987-654-3210”
Pattern used to extract email: [\w.-]+@[\w.-]+.\w+
Output as: ["jane_smith123@mail.co.uk"]
Click the Submit option.
As a result, a new column with the extracted values is added to the dataset.
A Formula-based transform typically refers to a data transformation or calculation using a formula describing the relationship between variables.
Check out the illustration on the Formula-based transform.
Select the Formula-based transform from the Functions transform section.
The Formula based Transformation window appears.
Select the required columns using the Select Columns drop-down option (This transform only supports mathematical operations, so please select numeric columns).
All the selected columns will be listed below.
Double-click on the required columns from the list. They will be listed under the Formula field. Pass the necessary operation along with the selected columns to create a formula.
Provide a name for the new column.
Click the Submit option.
As a result, a new column with the values of the applied formula on the selected columns will be added to the dataset.
The Generate Numerical Combination Transformation helps to find all the possible combinations of a set of numbers without repetition.
Check out the illustration on the Generate Numerical Combination Transformation.
Select a list data type column from the displayed dataset within the Data Preparation framework.
Open the Transforms tab.
Select the General Numerical Combination Transformation from the Functions section.
The Generate Numerical Combinations dialog box appears.
Provide the name of the column containing the result values.
Set a number to define the combination length.
Click the Submit option.
A new column is added as per the set combination length, displaying the numerical values.
This transform helps extract a URL's query parameters into an Object. The Object keys are the parameter names, and their values are the parameter values. Input values must be of URL or String type.
Check out the given illustration on how to use the Get URL Parameters data transform.
Steps to perform the transformation:
Select a URL-based column with query parameters.
Open the Transform tab.
Click the Get URL Parameters transform from the Functions category.
As a result, a new column with the extracted URL parameters will be added to the dataset.
Example: For a column with a URL like: http://example.com?color=blue;shape=square/; The result will be: {"color": "blue", "shape": "square"}
The If Missing data transform is a conditional data preprocessing technique to handle missing values in a dataset. It involves specifying a rule to detect missing entries in specific features and applying a predefined transformation to those entries. This method ensures consistent handling of incomplete data and supports improved model performance and integrity.
Check out the given walk-through on how to use the If Missing transform.
Steps to apply the If Missing transform:
Open a dataset within the Data Preparation framework.
Open the Transforms tab.
Click the If Missing transform from the Functions category.
The If Missing dialog box appears.
Provide the following information to apply the If Missing transform.
Enable the Create New Column option.
Select one or multiple columns with missing values.
Provide a value to be added in the missing cells.
Click the Submit option.
As a result, a new column will be added reflecting the set value in the empty cells or the same column will be modified with the new set values in the empty cells (In the following image, a new column has been added with the transformed values).
The If NULL function writes out a specified value if the source value is null. Otherwise, it writes the source value.
Check out the illustration on how to use the If NULL transform.
Steps to perform the If NULL transform:
Select a column that contains null cells.
Open the Transforms tab.
Click the If NULL transform from the Functions category.
The If NULL dialog box appears.
Configure the details:
Enable the Create New Column option to create a new column with the result values.
Select a column using the drop-down option.
Provide the value to be added if a null value is found in the source data.
Click the Submit option.
The selected value will be updated in those cells as a new column or in the same column based on the saved configuration.
This is used to return values according to the logical expression. If the test expression evaluates to true, the true_expression is executed otherwise, the false_expression is executed.
Check out the given walk-through on how to use the If Then Else transform.
Steps to perform the If Then Else transformation:
Select a column from the dataset that contains binary values.
Open the Transforms tab.
Select the If Then Else transform from the FUNCTIONS category.
The If Then Else dialog box appears.
Provide the following information:
Provide a name for the new column that appears with the transformed data.
Provide the Logical expression in the If Value.
Provide the Then Value.
Provide the Else Value.
Click the Submit option.
As a result, a new column with the configured name and values will be added to the dataset.
Imputation is a common technique used in data preprocessing to handle missing or incomplete values in a dataset. There are various methods to impute missing data, and one common approach is to use computed values such as mean, median, or mode to fill in the missing values.
Check out the illustration on how to use Impute with the Computed Values transform.
Navigate to a column with numerical values.
Open the Transforms tab.
Select the Impute with Computed Values transform.
The Impute with Computed Values dialog box opens.
Enable the Create a New Column checkbox.
Provide a name for the newly created column displaying the output.
Select a computing method using the Computed Value drop-down.
Click the Submit option.
A new column gets added to the dataset based on the set computing value. Based on the specified name and selected computed value option, the dataset can get new columns with Mean, Median, and Mode data.
The IN operation in data preparation is a filtering technique that includes only those records in a dataset where the value of a specific field matches any value in a given list. It is commonly applied when you need to isolate or extract data entries that meet one or more specific criteria.
Check out the given illustration on how to use the IN transform.
Steps to perform the IN transformation:
Navigate to the Transforms tab.
Select the IN transform from the Functions category.
The IN dialog box opens.
Provide the following information.
Select one or multiple columns using the drop-down menu.
Provide the values from the column (Use commas to separate multiple values).
Click the Submit option.
As a result, a new column will be created with the result values. The result will return True (for the values that are IN) or False.
The Is Even transform is a data manipulation operation that checks whether an integer is even or not. It evaluates the given integer and returns a Boolean value indicating whether it is an even number.
Check out the given illustration on how to use the Is Even transform.
Steps to perform the transformation:
Select an Integer column.
Open the Transforms tab.
Click the Is Even transform to apply it on the selected column.
The result gets displayed in a new column. The newly added column displays True if the integer is Even, else it will mention False.
The Is Mismatched function confirms whether a column of values does not contain the datatype is provided, then the function returns true or false.
Check out the given illustration on how to use the Is Mismatched transform.
Steps to perform the transformation:
Select a column from the dataset.
Open the Transforms tab.
Select the Is Mismatched transform from the FUNCTIONS category.
Provide the datatype name which is required to confirm that doesn’t belong to that datatype. E.g., Datetime is the selected data type in the given example.
Click the Submit option.
Result will come in a different column if the datatype is mismatched then returns True else False.
The Is Missing function tests whether a column of values is missing or null, then this function returns true or false.
Check out the given illustration on how to use Is Missing transform.
Steps to perform the Is Missing transformation:
Navigate to the dataset within the Data Preparation framework.
Open the Transforms tab.
Select the Is Missing transform from the FUNCTIONS category.
Select one or multiple columns that contain missing data.
Click the Submit option.
Result will come in a different column. If values are missing from a cell of the selected column, it returns True else False in the result column.
The Is NULL function tests whether a column of values contain null values. For input column references, this function returns either True or false.
Check out the given illustration on how to use the Is NULL transform.
Steps to perform the Is Null transformation:
Navigate to a Dataset in the Data Preparation framework.
Open the Transforms tab.
Select the Is NULL transform from the FUNCTIONS category.
Select one or multiple columns (It supports all datatypes).
Click the Submit option.
Result will come in a newly added column. If there is null value, it returns True else False.
The Is Odd transform is a data manipulation operation that checks whether an integer is odd or not. It evaluates the given integer and returns a Boolean value indicating whether it is an odd number.
Check out the given walk-through on how to use Is Odd transform.
Steps to perform the transformation:
Select an Integer column.
Open the Transforms tab.
Click the Is Odd transform to apply it to the selected column.
The result gets displayed in a new column. The newly added column displays True if the integer is Odd, else it will mention False.
The Is Valid data transform is a data validation technique used to check the validity of data in a specific column in a dataset. Its primary purpose is to identify and flag data that may be erroneous (invalid) or missing. This transform results in a binary output, where the transformed column contains Boolean values (e.g., True or False) indicating whether each data point is valid or not.
Check out the given illustration on how to use Is Valid transform.
Steps to perform the transformation:
Navigate to a Dataset within the Data Preparation framework.
Open the Transforms tab.
Select the Is Valid transform.
Provide the datatype name that is required to confirm.
Click the Submit option.
Result will come in a different column. If it is a valid data returns True else False.
The natural logarithm uses the base "e," which is approximately equal to 2.71828. The mathematical notation, the natural logarithm, is represented as "ln."
Check out the illustration on how to apply the Log of Column transform.
Steps to perform the Log of Column transformation:
Select a column from the dataset within the Data Preparation framework.
Open the Transforms tab.
Select the Log of column transform from the FUNCTIONS category.
Select the Create New Column option to create a new column with the result values.
Click the Submit option.
Result will come in a different column with the log result of the source column.
This processor merges values below a certain appearance threshold.
Check out the video on the usage of the Merge Long Tail Values transform.
Steps to perform the transform:
Select a column.
Open the Transforms tab.
Select the transform.
Update Count & Value.
Click the Submit option.
The result will get updated in the same column based on the count of occurrences. E.g., in the following image the given count is 2, so less than 2 only one value has been merged as the defined value (that is 200).
If the count is given 4 then less than 4, so any 3 values will turn into the given value digit.
Normalization is a process of scaling a measure or variable to a common range to facilitate comparisons or analysis. It is commonly used when dealing with measures that have different units or scales to bring them onto a consistent basis.
There are various methods to normalize measures, and the choice of method depends on the nature of the data and the specific requirements of the analysis. Here are a few commonly used normalization techniques:
Formula: (x - min) / (max - min)
Rescales the measure to a range between 0 and 1 by subtracting the minimum value (min) and dividing by the range (max-min). This method preserves the relative ordering of the data.
Check out the given walk-through on how to apply Min-Max Normalization method.
Formula: (x - mean) / standard deviation
Standardizes the measure by subtracting the mean and dividing it by the standard deviation. This method transforms the data to have a mean of 0 and a standard deviation of 1. It preserves the shape of the distribution but can result in negative values.
Check out the given walk-through on how to apply Z-Score Normalization method.
Steps to perform Normalization Transform with both the Method:
Navigate to a dataset within the Data Prep framework.
Open the Transforms tab.
Select the Normalization transform.
Select columns by using the check boxes given next to the column names.
Select a Normalization Method using the drop-down menu.
Click the Submit option.
The column data will be changed based on the selected Normalization method.
The PI function generates the value of pi to 15 decimal places: 3.1415926535897932.
Check out the given illustration on how to apply the PI transformation.
Steps to perform the PI transformation:
Select the column from the dataset using the Data Preparation framework.
Open the Transforms tab.
Select the PI transform from the FUNCTIONS category.
Provide the following information:
Provide a name for the New Column.
Provide value that behaves as the multiplicand.
Click the Submit option.
Result will come in a newly added column where it will display pi to 15 decimal places. E.g., Suppose values passed as 2 then the calculation will be as pi * 2.
The Power of Column data transform is a data manipulation technique used to raise a specific column of numerical data to a certain power. It is commonly employed in data preprocessing or data analysis tasks to modify the data in a meaningful way.
The general formula for the "Power of Column" transform is:
New Value=Original Value 〖^power〗
E.g., If value present in a column is 2, and the passed value as power is 3 then the result is 8.
where:
The New Value is the transformed value of the data after applying the power operation.
Original Value is the value in the original column.
Power is the exponent to which the "Original Value" is raised.
This transformation can be applied to any column in a dataset that contains numerical values. It is often used to address certain data distribution issues or to amplify or attenuate the values in the column, depending on the value of the power.
Steps to perform the Power of Column transformation:
Select a numerical column from the dataset within the Data Preparation framework.
Open the Transforms tab.
Select the Power of Column transform from the FUNCTIONS category.
Provide the following information:
Enable the Create New Column (optional) to create a new column with the result values.
Provide value that will act as power on the values of the selected column.
Click the Submit option.
Result will come in a different column or update on the same column (based on the configuration performed for this transform.
This transformation helps to compute the degrees of an input value measuring the radians of an angle. The value can be a Decimal or Integer literal or a reference to a column containing numeric values.
Check out the given illustration on how to use Radians To Degree transform.
Steps to perform the Radians To Degree transformation:
Select a dataset within the Data Preparation framework.
Select the Transforms tab.
Select the Radians To Degree transform from the FUNCTIONS category.
Do the required configuration for the Radians to Degree transform:
Enable the Create a New Column option to create a new column with the transformed data.
Select one or multiple columns that need to get converted (Use Numerical Columns like int, float, decimal, etc.).
Click the Submit option.
Result will come as a different column with a prefix of 'degree' for the selected column by converting it into degree (The calculation will be done by multiplying the value by 57.2957795).
This transformation rounds input value to the nearest integer and it supports multiple columns at a single time.
Steps to perform the transformation:
Select a dataset within the Data Preparation framework.
Select the Transforms tab.
Select one or multiple columns from the dataset where the round need to be performed.
Select required precision.
Click the Submit option.
Result will come as a new column where rounded based on the precision value and input value round to the nearest integer.
This transformation computes the positive or negative sign of a given numeric value. The value can be a Decimal or Integer. Please consider the following:
For positive values, this function returns 1.
For negative values, this function returns -1.
For the value 0, this function returns 0.
Check out the given illustration on how to use the Sign of Columns transform.
Steps to perform the transformation:
Open a dataset within the Data Preparation framework.
Open the Transforms tab.
Select the Sign of Columns transform from the FUNCTIONS.
Select one or multiple columns where the prefix is to be added (it can be any number-based columns).
Click the Submit option.
The result will appear as a new column where the function returns 1 for positive values, -1 for negative values, and 0 for value 0.
The Simplify Text transform allows the user to perform various simplifications on a text column.
The supported transforms under this Transformation are:
1. Normalize text: By selecting this option the text gets transformed to lowercase, remove punctuation and accents, and perform Unicode NFD normalization (Café -> cafe).
2. Stem words: Transform each word into its “stem”, i.e. its grammatical root. For example, the word grammatical is transformed into 'grammat'. This transformation is language-specific.
3. Stop words: Remove so-called “stop words” (the, I, a, of, …). This transformation is language-specific.
4. Sort words alphabetically: Sort all words of the text. For example, 'the small dog' is transformed into 'a dog small the', allowing strings containing the same words in a different order to be matched.
Check out the given walk-through on how to use the Simplify Text transform.
Steps to perform transformation:
Select a text containing a column (string datatype).
Open the Transforms tab.
Provide a name for the new column.
Select the required method using the given check box.
Click the Submit option.
The result column will be created with the given name by making the required changes based on the selected method.
The Split Email transformation splits an e-mail address into the local part (before the @) and the domain part (after the @).
Check out the given walk-through on how to use Split Email transform.
Steps to perform the transform:
Select a Column containing the Email values.
Navigate to the Transforms tab and click on the Split Email transform.
It will create two new columns as local_part and domain_part.
This transformation splits the elements of an HTTP query string. The query string is the part of the URL that comes after the `?` in the string.
Consider the following URL as an example in this context:
Query String: modal=detail&selectedIssue=BDPC-541
Output Column prefix: SplitURL_
Then the Output comes as: SplitURL_modal SplitURL_selectIssue
detail BDPC-541
Check out the given walk-through on how to use the Split HTTP Query String data transform.
Steps to perform the Split HTTP Query String transform:
Select a URL column with query string.
Open the Transforms tab.
Select the Split HTTP Query String transform from the Functions category.
Provide a Prefix for the Output Column.
Click the Submit option.
New columns will get created with the given prefix and the key of the HTTP Query string chunk.
Given Prefix for the output column: Split HTTP_
Then the Output comes as: Split HTTP_modal Split HTTP_selectIssue
The Split Invalid data transform refers to a data preprocessing technique where the input data is validated for correctness and completeness. During this step, the data is checked against predefined rules or constraints to identify and segregate the invalid or inconsistent records.
Check out the given walk-through on how to use the Split Invalid data transform.
Navigate to a dataset in the Data Preparation framework.
Open the Transforms tab.
Click the Split Invalid transform from the FUNCTIONS category.
Provide the following information:
Data Type
New Column Name
Click the Submit option.
As a result, the invalid values will be extracted in the newly added column from the concerned column.
This transform splits the elements of an URL into multiple columns. A valid URL contains the following format scheme ://hostname[:port][/path][?query string][#anchor]
Output -> It will split into multiple columns prefixed by the input column name.
Then Output as:
URL_scheme
URL_domain
URL_path
URL_querystring
http
www.google.com
/search
q=query
Check out the given walk-through on how to use Functions Split URL.
Steps to perform the Split URL transformation:
Select a URL column.
Open the Transforms tab.
Click the Split URL transform.
The source column with URL will get splitted into four columns displaying URL_scheme, URL_domain, URL_path, and URL_querystring.
Computes the square root of the input parameter.
Steps to perform the Sqrt of Column transformation:
Select a column from a dataset within the Data Preparation framework.
Select the Transforms tab.
Select the Sqrt of Column transform from the FUNCTIONS category.
Select the Create new column (optional) to get the result data in a new column.
Click the Submit option.
The result will come as a new column or update the same column with the sqrt of present value of the selected column.
This transformation can be defined as the function of an angle of a triangle. It means that the relationship between the angles and sides of a triangle is given by these trig functions. The basic trigonometric functions are SIN, COS, TAN, SINH, COSH, and TANH.
Check out the given walk-through on how to use Trigonometric Functions transform.
Steps to perform the Trigonometric transformation:
Navigate to a dataset within the Data Preparation framework.
Open the Transforms tab.
Select the Trigonometric Functions transform from the FUNCTIONS method.
Provide the required information to apply the Transform:
Select one or multiple columns where the trigonometric functions need to be applied.
Select a Method from the drop-down menu.
The Supported methods are: SIN, COS, TAN, SINH, COSH, TANH
Click the Submit option.
The result will come in a new column by converting to the respective (selected) trigonometric value.
Vectorization is a process of converting textual data into numerical vectors or arrays, which can then be used as input for machine learning models or other algorithms that require numerical data. Vectorizing text allows us to represent the textual information in a format that can be easily understood by the models.
Check out the given walk-through on how to use the Vectorization transform with the Count and TFIDF Vectorization methods.
Steps to use the Vectorization Transform:
Navigate to a dataset within the Data Preparation framework.
Open the Transforms tab.
Select the Vectorization transform from the FUNCTIONS method.
Select a Vectorization method using the drop-down option.
Click the Submit option.
The output gets displayed in a new column based on the selected Vectorization method.
E.g., If the selected Input data is The sun is shining.
Output gets displayed as below:
For Count Vectorizer: [0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,1,0,0,0,0]
For TFIDF Vectorizer: [0,0,0,0,0,0.42780918076667973,0,0,0,0,0,0,0.6126454381809064,0.5085510562171804,0,0.42780918076667973,0,0,0,0]
URL:
E.g., URL ->
