> For the complete documentation index, see [llms.txt](https://docs.bdb.ai/data-center-1/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bdb.ai/data-center-1/data-center/data-preparation/data-preparation-workspace/transforms/functions.md). # Functions ![](https://1292667781-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FTXhIkVsF2MUsgjZftfJq%2Fuploads%2FwtUEBMSElpGcx1UHA1ID%2Fimage.png?alt=media\&token=5c17c0e8-e797-455c-9b22-912b3ef8b850) ## Base 64 Decode 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 the text. The output type is String. {% hint style="success" %} *Check out the given walk-through on how to apply the Base64 Decode transform.* {% endhint %}

Using Base 64 Decode Transform

Steps to perform the Base 64 Decode transformation: * Select a String-based column in which the data is in the Base64 encrypted format. * Open the ***Transforms*** tab. * Select the ***Base64 Decode*** transform from the ***Functions*** category. * Enable the ***Create new Column*** option. * Click the ***Submit*** option.
* The decoded data will be returned in a new column.
## Base 64 Encode This transformation helps to convert an input value to base64 encoding with optional padding with an equals sign (=). Input can be of any type. The output type is String. {% hint style="success" %} *Check out the given walk-through on how to apply the Base64 Encode transform.* {% endhint %}

Using Base 64 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. * Enable the Create new Column option. * Click the ***Submit*** option.
* The encoded data will be returned in a new column.
{% hint style="info" %} *Please Note: The column with the encoded data gets added at the end of the dataset by default.* {% endhint %} ## Ceiling Columns This transformation helps to compute the **ceiling** of a value, which is the smallest integer that is greater than the input value. Input can be an Integer or a Decimal. Steps to perform the Ceiling Columns transformation * Select an ***Integer*** or ***Decimal*** column from the dataset. * Open the ***Transforms*** tab. * Select the ***Ceiling Columns*** transform from the ***Functions*** category. * Enable the ***Create New Column*** option to create a new column with the result value. * Select a column using the drop-down menu. * Click the ***Submit*** option.

Using the Ceiling columns

* As a result, a new column will get added to the dataset with the rounded values.
{% hint style="info" %} *Please Note: The Ceiling Columns transform supports measure values.* {% endhint %}

Using Ceiling Columns Transform

## Copy Column This transformation helps to duplicate the content of a column into another one. Steps to perform the transformation: * Select a source column from the dataset. * Open the ***Transforms*** tab. * Select the ***Copy Column*** transform from the ***FUNCTIONS*** tab. * Provide name for the new column. * Click the ***Submit*** option.
* Result will get displayed where the content is duplicated into a new one with a new column name.
{% hint style="success" %} *Check out the given walk-through on how to use the Copy Column transform.* {% endhint %}

Using Copy of Column Transform

## Count Distinct This transformation helps to generate the count of distinct values in a specified column, optionally counted by the group. Steps to perform the transformation: * Select a ***Source*** column from the dataset. * Select the ***Count Distinct*** transform from the ***FUNCTIONS*** category. * 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).
{% hint style="info" %} *Check out the given illustration on how to apply the **Count Distinct** transform.* {% endhint %}
{% hint style="info" %} *Please Note: The users can select multiple columns as Group By column while applying the Count Distinct transform.* {% endhint %} ## Degree To Radians 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 Integer. {% hint style="info" %} *Please Note: Input units are considered in degrees **dividing the value by 57.2957795*** {% endhint %} {% hint style="success" %} *Check out the given illustration on how to use the Degree to Radians transform.* {% endhint %}

Using Degree to Radians Transform

Steps to perform the transformation: * Select a column from the dataset within the Data Preparation framework. * Navigate to the ***Transforms*** tab. * Open the ***Degree to Radians*** transform from the ***Functions*** category. * Select ***Create a New Column*** to create a new column with the transformed values. * 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 radians for the selected column by converting the source column values into radians.
## Get URL Parameters This transform helps to extract the query parameters of a URL into an Object. The Object keys are the parameter's names, and their values are the parameter's values. Input values must be of URL or String type. {% hint style="success" %} *Check out the given illustration on how to use the Get URL Parameters data transform.* {% endhint %}

Using the Get URL Parameters

Steps to perform the transformation: * Select a URL-based column having query parameters in it. * Open the ***Transform*** tab. * Click the ***Get URL Parameters*** transform from the ***FUNCTIONS*** category.
* As a result, a new column gets added to the dataset with the extracted URL parameters. * Example: A Column having URL like: `http://example.com?color=blue;shape=square/;` then result will be: `{"color": "blue", "shape": "square"}`
## If NULL The ***If NULL*** function writes out a specified value if the source value is null. Otherwise, it writes the source value. {% hint style="success" %} *Check out the given illustration on how to use the If NULL transform.* {% endhint %}

Applying the If NULL transform

Steps to perform the If NULL transform: * Select a column that contains null cell(s). * Open the ***Transforms*** tab. * Click the ***If NULL*** transform from the ***FUNCTIONS*** category. * Configure the below-given 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 null value is found in the source transform. * Click the ***Submit*** option.
* ***Result:*** The selected value will get updated to that cell either as a new column or on the same column based on the saved configuration.
## If Then Else This is used to return values according to the logical expression. If the test expression evaluates to true, the ***true\_expression*** is executed else the ***false\_expression*** is executed.

Applying the If Then transform

Steps to perform the 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*.* * Provide the following information: * Provide New Column name for the column that appears with the transformed data. * Provide the Logical expression in the **I*****f Value.*** * Provide the ***true\_exp*** and ***false\_exp*** respectively in the ***Then Value*** & ***Else Value*** fields. * Click the ***Submit*** option.
* Result will come in a different column (with the configured name as given for the new column). * If the logical expression is true, then it will return the ***true\_exp*** else ***false\_exp***.
{% hint style="info" %} *Please Note:* * *While passing logical expression it should be in the below-given format as given -below* * Column Name =="Value of Column" (String columns) * Column Name == Value of Column (Numeric columns) * The user can use any supported arithmetic operators (e.g., = =, <, >, <=, >=, !=) {% endhint %} ## IN This is basically to check whether the combination of values exists in the selected column or not. If it is there then the function returns true else false. {% hint style="success" %} ***Check out the given illustration on how to use IN transform***. {% endhint %}

Applying the IN Transform

Steps to perform the transformation: * Navigate to the ***Transforms*** tab. * Select the ***IN*** transform from the ***FUNCTIONS*** category. * 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 gets created with the result values. The result will return as ***True*** (for the values which are IN) else ***False***.
{% hint style="info" %} *Please Note: When selecting one column, it will check the complete column where those values are present or not.* {% endhint %} ## Is Mismatched The ***Is Mismatched*** function confirms whether a column of values does not contain the datatype is provided, then the function returns ***true*** or ***false***. {% hint style="success" %} *Check out the given illustration on how to use the **Is Mismatched** transform.* {% endhint %}

Applying 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***.
## Is Missing The ***Is Missing*** function tests whether a column of values is missing or null, then this function returns ***true*** or ***false***. {% hint style="success" %} Check out the given illustration on how to use Is Missing transform. {% endhint %}
Steps to perform the 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 c~~o~~me in a different column. If values are missing from a cell, it returns ***True*** else ***False*** in the result column.
## Is NULL The ***Is NULL*** function tests whether a column of values contain null values. For input column references, this function returns either ***True*** or ***false.*** {% hint style="success" %} *Check out the given illustration on how to use the Is NULL transform.* {% endhint %}

Applying the Is NULL transform

Steps to perform the 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.***
## Is Valid 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. {% hint style="success" %} *Check out the given illustration on how to use Is Valid transform.* {% endhint %} {% embed url="" %} ***Using the Is Valid transform*** {% endembed %} 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 which 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.***
## Log of Column The natural logarithm uses the base "e," which is approximately equal to 2.71828. The mathematical notation, the natural logarithm is represented as "ln." {% hint style="success" %} *Check out the given illustration on how to apply the Log of Column transform.* {% endhint %}
Steps to perform the 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.
{% hint style="info" %} *Please Note: The calculation format to be followed for the Log of Column transform is **In(any value)** e.g., In(100).* {% endhint %} ## Nest Column To Object/ Array This is nesting one or more columns into an Array or Object column. Steps to perform the transformation *
Col1Col2Result as ArrayResult as Object
Val1Val2Val1,Val2{‘Col1’:’Val1’,’Col2’:’Val2’}

Using Nest Column to Object/Array transform

* Select the ***Nest Column to Object/ Array*** transform. * Provide a name for the new column. It will be the result column. * Select one or multiple columns (The given example displays multiple column selection). * Select the ***Nest Column*** ***to*** option as an ***Object*** or ***Array***. In the given image, the selected option is ***Object***. * Click the ***Submit***.
* Based on the selected ***Nest Column to*** option, the result will appear: * For ***Array*** with a square bracket * For ***Object*** with curly brackets (as displayed in the below-given example):
{% hint style="info" %} *Please Note: The result of the **Nest column to Array** transformation comes as list datatype & this datatype is not supported while loading the data in the datastore.* {% endhint %} ## PI The PI function generates the value of pi to 15 decimal places: 3.1415926535897932. {% hint style="success" %} *Check out the given illustration on how to apply the PI transformation.* {% endhint %}

Using PI transform

Steps to perform the 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.***
## Power of Column 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.

Using Power of Column transform

Steps to perform the transformation: * Select a numerical column from the dataset within the Data Preparation framework. * Open the ***Transforms*** tab. * Select the ***Power To 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.
## Radians To Degree 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. {% hint style="info" %} *Please Note: Input units are considered in radians, **Multiply the value by 57.2957795***
{% endhint %} {% hint style="success" %} *Check out the given illustration on how to use **Radians To Degree** transform.* {% endhint %}

Using the Radians to Degree

Steps to perform the 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).**
## Round Values for Columns 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.
## Sign of Columns 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: 1. For positive values, this function returns 1. 2. For negative values, this function returns -1. 3. For the value 0, this function returns 0. {% hint style="success" %} *Check out the given illustration on how to use the Sign of Columns transform.* {% endhint %}

Using Sign of Columns transform

Steps to perform the transformation: * Open a dataset within the Data Preparation framework. * Select the transform. * Select one or multiple columns where the prefix is to be added (it can be any number-based columns).
* Result will come as a new column where the function returns 1 for positive values, -1 for negative values, and 0 for value 0.
## Sqrt of Column Computes the square root of the input parameter.

Using the Sqrt of Column transform

Steps to perform the 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. * Follow the given steps to configure the transform: * Select the Create new column (optional) to get the result data in a new column. * Provide the value. * 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.
## Trigonometric Functions 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. {% hint style="success" %} *Check out the given walk-through on how to use Trigonometric Functions transform.* {% endhint %}

Using the Trigonometric Functions transform

Steps to perform the 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.