# Transformation Examples

This section contains examples that demonstrate how Wrangle transformations and functions can quickly transform your data.

**注意**

These examples appear in other pages, including language reference documentation. See Wrangle Language.

## Transformation Examples

Item | Description |
---|---|

This example illustrates how to convert the index value of an array for a specified value searching from left to right and right to left by using ARRAYINDEXOF and ARRAYRIGTHINDEXOF functions. | |

This example illustrates how to return n-based number of elements in an array. | |

This example illustrates how to generate an Array that is a slice of an another Array, based on index numbers. The elements of this Array can then be merged into a String value. | |

This example illustrates how to use the ARRAYSTOMAP and KEYS functions to convert values in Array or Object data type of key-value pairs. | |

This example demonstrates how to convert an input string to a base64-encoded value and back to ASCII text strings. | |

This example demonstrates functions that can be used to change the case of String values. | |

This example illustrates the comparison functions in Designer Cloud. | |

This example demonstrates functions for comparing the relative values of two functions. | |

This example demonstrates comparison functions. | |

This example illustrates how to use the conditional calculation functions. | |

This example demonstrates how to count the number of values and non-null values within a group. | |

This example demonstrates how to count the number of values within a group, based on a specified conditional test. | |

This example demonstrates how to count the number of occurrences of text patterns in a column. | |

This example provides an overview on various date and time functions. | |

This example demonstrates how to calculate the number of days between two input dates. | |

This example illustrates how to calculate the number of days that have elapsed between the order date and today. | |

This example illustrates to how to use date-related functions to derive specific values for a Datetime column type. | |

This example shows how you can apply statistical functions on Datetime columns. | |

This example illustrates how you can apply conditionals to calculate minimum, maximum, and most common date values. | |

This example illustrates how you can apply functions to derive day-of-week values out of a column of Datetime type. | |

This example illustrates to convert values from one unit of measure to the other. | |

This examples illustrates how you can keep and delete rows from your dataset. | |

This examples illustrates how you can extract component parts of a URL using specialized functions for the URL data type. | |

This example illustrates how to use double metaphone functions to generate phonetic spellings in Designer Cloud. | |

This example demonstrates the exponential functions. | |

This example shows how you can unpack data nested in an Object into separate columns. | |

This example illustrates how to extract values from a column. | |

In this example, you extract one or more values from a source column and assemble them in an Array column. | |

This section describes how to flatten the values in an Array into separate rows in your dataset. | |

This example illustrates you to use the flatten and unnest transforms. | |

This example shows how you can break out a column of nested values into separate rows and columns of data. | |

This example illustrates how to use the IF* functions for data type validation. | |

This examples illustrates how you can convert IP addresses to numeric values for purposes of comparison and sorting. | |

This example illustrates how you can apply conditionals to calculate minimum, maximum, and most common date values. | |

This example explores how you can use aggregation functions to calculate rank of values in a column. | |

This example illustrates how to use the conditional ranking functions. | |

This example demonstrates you to extract values from one column of an array into a new column. | |

This example illustrates you to identify and list all values within a group that meet a specified condition. | |

This example describes how to generate random array (list) data and then to apply statistical functions specifically created for arrays. | |

This example demonstrate the | |

This example illustrates how you can use unnesting and nesting transformations to reshape your JSON data. | |

This section provides simple examples of nesting columns into Arrays by extracting values from a column or nesting one or more columns into an Array column. | |

This section provides a simple example of nesting columns into a new column of Object data type. | |

This example illustrates how to use the nested functions. | |

This section illustrates a simple example of how to nest tabular data into JSON records. | |

This example covers how to use the NEXT function to create windows of data from the current row and subsequent (next) rows in the dataset. You can then apply rolling computations across these windows of data. | |

This example illustrates you to generate the date and time values for the current date and timestamp in the specified time zone. | |

This example demonstrates how to use numeric functions to perform computations in your recipe steps. | |

This example illustrates you to apply percentile functions. | |

In this example, you learn how to compute exponentials and square roots on your numeric data. | |

This example describes how you can use the PREV function to analyze data that is available in a window in rows before the current one. | |

This example demonstrates how to use | |

This example illustrates how you can apply functions to generate random numeric data in your dataset. | |

This example demonstrates you to generate a ranked order of values. | |

This example illustrates the different uses of the replacement transformations to replace or extract cell data. | |

This example describes how to use rolling functions for Datetime values. | |

This example describes how to use rolling computational functions. | |

This example describes how to use rolling statistical functions. | |

This example describes how to use rolling kthlargest functions for calculating ranking of values within a defined window of rows. | |

This example demonstrates how the rounding functions work together. | |

This example illustrates how to clean up data by changing its data type to String, manipulating it using String functions, and then retyping the data to its proper data type. | |

This example illustrates how you can rename columns based on the contents of specified rows. | |

This example shows how you can split data from a single column into multiple columns using delimiters. | |

This example demonstrates functions that can be used to evaluate the beginning and end of values of any type using patterns. | |

This example illustrates how you can apply statistical functions to your dataset. Calculations include average (mean), max, min, standard deviation, and variance. | |

This example shows some of the statistical functions that use the sample method of computation. | |

This example demonstrates functions that can be used to clean up strings. | |

This example demonstrates functions that can be used to compare two sets of strings. | |

This example illustrates how you can use conditional calculation functions. | |

This example can be used to sum the values in a column based on a condition and organized by group. | |

This example shows how you can use functions to convert Datetime values to different time zones. | |

This example illustrates how to apply the inverse trigonometric (Arc) functions to your transformations. | |

This example illustrates how to apply basic trigonometric functions to your transformations. | |

This example illustrates how to apply inverse (arc) hyperbolic functions to your transformations. | |

This example illustrates how to apply hyperbolic trigonometric functions to your transformations. All of the functions take inputs in radians. | |

This example illustrates statistical functions that can be applied across two columns of values. | |

This example illustrates how various type checking functions can be applied to your data. | |

This example shows how to use parsing functions for evaluating input values against the function-specific data type. | |

In this example, you can see how the | |

This example illustrates how you can use functions to manipulate Unix time values in a column of Datetime type. | |

This section describes how to unnest the values in an Array into separate columns in your dataset. | |

You can unnest a set of JSON records into new columns of tabular data for easier manipulation within the application. | |

You can extract the keys from an Object column into an Array of String values. | |

This simple example demonstrates how to extract nested values from Object elements into a separate column. | |

You can create nested objects by filtering strings. In this example, column headers and column values are nested into a single entity in a new column of Object data type. |