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• Introduction to Transformation

  Things to consider First record is header, then use column names to specify the target columns First record is not header, then use the column indexes to specify the target columns Data Transformation Average by group Change text casing Clean data Combine columns Combine tablesVery popular

• Average by group

  The Average by group step calculates the average of all values in one or more columns. This step is similar to the AVERAGEIF function in Excel. Average by group Input/output Our input data has number of sales orders showing how many quantities of a particular product was sold at a particular date. After connecting data to this step and setting it up, it gives us theVery popular

• Change text casing

  The Change text casing step converts the text in any column to a selected case type. Change text casing Input/output Our input data is customer information that displays first and last names in all normal case "Title Case". We can use the Change text casing step to turn our first and last names into upper case. As seen in our output data below, this step made our "firsPopular

• Clean data

  The Clean data step removes leading or trailing spaces and other unwanted characters (letters, numbers, or punctuation) from any rows of data you'd like. Clean data Input/output Our input data has a column of "Text" with various leading trailing spaces and punctuations. By using the Clean data step, we can easily remove spaces to clean up the "Text" column. ![](https://storPopular

• Combine columns

  The Combine columns step combines data from two or more columns into a single column separated by a specified character. Combine columns Input/output Let's say we need to combine the data from our columns "Product" and "Color" to create a new column called "Combined Order" for easy label creation. The Combine columns step is how we'll achieve this. Our input data has two separate columns "Product" and "Color". ![](https://storage.crisp.chat/users/helpdesk/website/2d0674738fbdPopular

• Combine tables

  The Combine Tables step joins multiple tables into one by matching rows between those tables. It is similar to a VLOOKUP in Excel or Google Sheets. In SQL, it's like a join clause. The principle is simple: if we have two tables of data that are related to each other, we can use the Combine Tables step to join them into one table by matching rows between those tables. This step can handle combining two tables at a time. Once we set it up, we can use it repeatedly. Even if the row amountPopular

• Compare dates

  The Compare dates step compares dates in a column to another column of dates, or compares dates in a column against the current time when your Easyflow flow runs. This step is similar to the DATEDIF function in Excel by creating a new column showing the measurement of time between two dates. Compare dates Input/output The data you connect to this step needs at least one column with date information. In this example, we pass a column called "OrderDate" and compare that date againstPopular

• Count by group

  The ** Count by group ** step counts the number of rows that exist for each unique value in one or more columns. This step is similar to the COUNTIF function in Excel. Count by group Input/output Our input data is a list of Sales. We want to know how many sales we have available at different product. After connecting the data to this step and setting it up, it gives us the outputSome readers

• Extract text from column

  The Extract text from column step extracts a portion of text based on a matching character or offset. You may use this to pull out company names from emails, remove part of an ID, or extract the timezone from any date/time stamp. Extract text from column Input/output In this case, we're looking to extract email domains from our customer email addresses. Our input data has three columns: "first name", "last name", and "email". ![](https://storage.crisp.chat/users/helpdesk/website/2Popular

• Fill in blanks

  The Fill in blanks step uses the nearest value above, below, to the left, or to the right of a cell in a column. You can also use a custom value or reference another column's value to fill in the blank cell. Fill in blanks Input/output We'll use the below input table for this step to process. It contains four columns including an empty one called Fill in Blanks. After usiSome readers

• Filter rows

  The ** Filter rows** step keeps rows that satisfy a set of rules. You can use this step to create simple filters with one rule, or complex filters with many rules. Filter rows Input/output The input data we'll connect to this step has 15 rows of webinar registrants. Let's say we want to filter this for people who registered after September 1, 2020. By setting a rule to include rows wSome readers

• Find and replace

  The Find and replace step works like you might imagine the name means: You can type in a value, specify the columns to look for it in, and type in a new value to replace it with if it is found. Find and replace Input/output Our input data below has orders information telling us how many units were sold today per product variant type. Let's say we need to replace the blank vaSome readers

• Find maximum by group

  The Find maximum by group step calculates the maximum value in a column or a group of columns. This step is similar to the MAX function in Excel. Find maximum by group Input/Output Our input data has 30 rows of orders information showing us how many quantities were sold, the food type, the customer name, and their payment type. After connecting the data into this stepSome readers

• Find min/max per row

  The Find min/max per row step calculates the minimum or maximum value across a range of columns. Find min/max per row Input/output Our input data shows how many items we sold on a given day. After we connect our data to this step, it produces an output with a new column to the right-side called "Most Sold in a Day". The values displayed in this column are the max valuesSome readers

• Find minimum by group

  The** Find minimum by group ** step calculates the minimum value in a column or a group of columns. This step is similar to the MIN function in Excel. Find minimum by group Input/output Our input data is order information showing us the quantity sold, the food type, the customer username, and their payment type. After connecting it into this step, our output data is a colSome readers

• Format dates

  The ** Format dates** step converts a column's date values to a new format. It's useful to use whenever you need to unify the date formats across various columns or change date structures into formats that make sorting by ascending or descending dates easier. Format dates Input/output Input data to be connected to this step needs at least one column with date data. In this example, we pass a column called "Sales Date" that it currently formatted as MM/DD/YY. ![](https://storage.crisp.Some readers

• Format numbers

  The Format numbers step can reformat numbers across as many columns as you choose in this step's settings. It can add or remove commas, currencies, and accounting formats to numbers, or round numbers and format them as percentages. Format numbers Input/output Let's use some of our store's sales information as the input data. In this example, we'll want to reformat the numbersSome readers

• Insert date & time column

  The Insert date & time column step adds a column with the current date and time, with customizable offset by days and timezones. The current date and time will be determined at the time the flow is run. This step can be useful for tasks like logging updated times. Insert date & time column Input/output Connect any data into this step. It'll give you the output data of a new "Current DateTime" column appended to your dataset where the current date and time is noted (based on when theSome readers

• Insert growth rate column

  The Insert growth rate column step calculates the growth rate from the previous row within a column of your choosing. Insert growth rate column Input/output The data we'll connect into this step shows us how many items of a certain product we've sold, on a specific day of the week. After connecting input data to this step, it gives us the output of a newly added coluSome readers

• Insert if/else column

  The Insert if/else column step enables you to insert a conditional column filter. A conditional statement is a core building block of any programming language. In Parabola, these are Insert if/else column steps. The goal of each Insert if/else column step is to create one column where the value of each cell in that column is dictated by the rules you create within the step. Insert if/else column Input/output Our input data for this step is a table with two columns. ![](hSome readers

• Insert if/else blank column

  Insert if/else blank column The Insert if/else blank column step enables you to insert a conditional column filter for blank values.Some readers

• Insert math column

  The Insert math column step executes a math equation on each row of data. This step functions similarly to writing a formula in a new Excel column. Insert math column Input/output Our input data has two rows of data: one for last week and one for this week, showing us how many Apples, Oranges, and Pears we sold. After connecting the data into this step, it producesSome readers

• Insert row numbers

  The Insert row number step creates a new column adding sequential numbers to each row. You can sort the numbers in ascending or descending order. You can add row numbers down the whole table, add numbers that repeat after a certain number of rows, repeat each number a set amount of times before advancing to the next number, and number based on another column. Numbering based on another column is a powerful way to add numbers to things like line items that share an order ID, or to rank yourSome readers

• Insert rows

  The Insert rows step adds any number of rows to a table. You can include default values for each column to make it easier to build a large table with duplicated values. Insert rows Input/output This table has 5 rows with the columns, "Row Number", "Organization", and "Product". To add more rows with default values, use the Insert rows step. The output table has 5 additionalSome readers

• Insert running total column

  The Insert running total column step calculates the running total sum of any column you choose. Insert running total column Input/output The input data we'll connect to this step has 30 rows of orders showing the quantity sold, food type, customer username, and payment type. The step will give us output data with a new column called "Running Total" that sums the "QuanSome readers

• Insert text column

  The Insert text column step adds a column filled with repeating row values. The rows can be filled with any integer/number, letter, special character, or value of your choosing. You can also reference other column values and combine them into this new column. Insert text column Input/output Our input data example has two columns: "Webinar ID" and "Registrant name". After uSome readers

• Limit rows

  The Limit rows step filters data by limiting the row amount. You can keep or remove a certain number of rows from the top, bottom, or at whichever position in a table that you choose. Limit rows Input/output In our input data below, we have 50 rows of data. By using the Limit rows step, we can easily filter the number of rows from 50 to 3, as seen in our below output data that thSome readers

• Merge duplicate rows

  The Merge duplicate rows step allows you to group rows in one or more columns and merge their values. Merge duplicate rows Input/output The data we'll input into this step has an identifier type to deduplicate and its corresponding columns of data. The step provides us with output of deduplicated data (based on a unique identifier value) with the merged values separated bSome readers

• Pivot columns

  The Pivot columns step takes a column and turns its values into multiple columns. It is particularly useful when needing to turn order line item information into a unique row per order. Pivot columns Input/Output In the example below, our input data has repeated "Order Ids" with columns containing "Variant Options" and "Variant Values". We'll want to create a unique row per "OrSome readers

• Remove duplicate rows

  The Remove duplicate rows step removes rows with a duplicate value in any column you choose. Remove duplicate rows Input/output Our input data has 50 rows with these 3 columns: "Webinar ID", "Registrant name", and "Registration time". After using the Remove duplicate rows step to remove duplicate rows in the "Webinar ID" column, we went from 50 rows to 41. This tellSome readers

• Rename columns

  The Rename columns step offers the ability to rename any and all columns that you'd like to create new titles for. Rename columns Input/output Our example input data has orders data that informs us of the units sold per product variant. We have a column called "ID", but we'd need this to be more exact about what "ID" this actually is for our colleagues. We can use the **RenameSome readers

• Select columns

  The Select columns step keeps or removes whichever columns you choose from your dataset. Select columns Input/output In our input data below, there are four columns: "Variant ID", "Product Name", "Variant Name", and "Sale Price". After connecting our data into this step, it produces an output of the two columns selected to keep: "Variant ID" and "Product Name". ![](https://stSome readers

• Reorder columns

  The Reorder columns step enables you to reorder any of your existing columns. Want to group your columns in with other related ones? Not a problem with this step. Reorder columns Input/output In this example, the input data we'll use has orders information that displays the units sold per product variant. However, our columns are in a randomized order that's difficult to understand quickly. ![](https://storage.crisp.chat/users/helpdesk/website/2d0674738fbd8000/reorder-columns-inpSome readers

• Sort rows

  The Sort rows step sorts the entire table in ascending or descending order based on values in the column(s) you choose. Sort rows Input/output The example data we'll connect to this step has a table of 50 webinar registrants. Let's say we're wanting to view this data with the top being the newest registrants by registration time. We can do this by setting a sorting rule where the vaSome readers

• Split column

  The Split column step splits a column into one or more based on a specified delimiter, such as a comma. Split column Input/output Our input data below is a single column called "Data". We'll use the Split column step to split this single column into three different columns using the delimiter of a dash mark "-". The output data will look like this: ![](https://storage.criSome readers

• Stack tables

  The Stack tables step provides the ability to vertically stack any table(s) on top of one other. Stack tables Input/Output The Stack tables step needs at least two data inputs. In the example below, we have two tables that both have the columns "Webinar ID" and "Registrant Name". ![](https://storage.crisp.chat/users/helpdesk/website/2d0674738fbd8000/stack-tables-output1_v6p031.pnSome readers

• Sum by Group

  The Sum by group step calculates the sum of all values in one or more columns. This functionality is similar to the SUMIF function in Excel. Sum by Group Input/output Our input data has 30 rows of order information showing the quantity sold, food type, customer username, and payment type per order. After we connect this data to the step, it'll produce an output of 10 rows thaSome readers

• Unpivot columns

  The Unpivot columns step will take every column that you choose and line them up into a single column called "Value". A new column will be made next to it called "Type" that holds the value of the column header that data points used to be in. Unpivot columns Input/output The input data we'll use for this step is a table of three columns: "Store Location", "Profit", and "Loss".Some readers

• Use regex

  The Use regex step matches patterns using Regular Expression to find or replace values. Regular Expressions, or RegEx for short, are useful for matching patterns in text or numbers (or anything, really). RegExr.com is an excellent resource to use when composing your regular expression. Before you jump in... We recommend exploring the "Extract text from column," "Find and replace," and "Clean data" steps. These steps are often able to accomplish the same result without wSome readers