Validations

This page describes the various validation options available inside the importer.

Column Data Types

While creating a sheet you can specify the column to be any of the data types mentioned below. If the incoming CSV data does not match the column data type (and its validation rules), then the user will see a relevant error message identifying what the problem is and how to fix it.

Text

This is the default column data type. It accepts any alphanumeric string. You can provide the Min Length and Max Length parameters to specify the acceptable length of the text data.

Number

This Number column data type accepts integer and float strings only. Additionally, you can provide the Min Value and Max Value parameters to specify the acceptable range of numbers.

Email

The column under validation must be formatted as an email address.

Date

The column under validation must be formatted as a date. You have to select a Date Format from the dropdown list to specify the acceptable format. The default format is 'MM/DD/YYYY'. If your desired format is not found in the list, you can simply provide your custom format using the formatting tokens below.

	Token	Output
Month	M	1 2 ... 11 12
	Mo	1st 2nd ... 11th 12th
	MM	01 02 ... 11 12
	MMM	Jan Feb ... Nov Dec
	MMMM	January February ... November December
Quarter	Q	1 2 3 4
	Qo	1st 2nd 3rd 4th
Day of Month	D	1 2 ... 30 31
	Do	1st 2nd ... 30th 31st
	DD	01 02 ... 30 31
Day of Year	DDD	1 2 ... 364 365
	DDDo	1st 2nd ... 364th 365th
	DDDD	001 002 ... 364 365
Day of Week	d	0 1 ... 5 6
	do	0th 1st ... 5th 6th
	dd	Su Mo ... Fr Sa
	ddd	Sun Mon ... Fri Sat
	dddd	Sunday Monday ... Friday Saturday
Day of Week (Locale)	e	0 1 ... 5 6
Day of Week (ISO)	E	1 2 ... 6 7
Week of Year	w	1 2 ... 52 53
	wo	1st 2nd ... 52nd 53rd
	ww	01 02 ... 52 53
Week of Year (ISO)	W	1 2 ... 52 53
	Wo	1st 2nd ... 52nd 53rd
	WW	01 02 ... 52 53
Year	YY	70 71 ... 29 30
	YYYY	1970 1971 ... 2029 2030
	YYYYYY	-001970 -001971 ... +001907 +001971 Note: Expanded Years (Covering the full time value range of approximately 273,790 years forward or backward from 01 January, 1970)
	Y	1970 1971 ... 9999 +10000 +10001 Note: This complies with the ISO 8601 standard for dates past the year 9999
Era Year	y	1 2 ... 2020 ...
Era	N, NN, NNN	BC AD Note: Abbr era name
	NNNN	Before Christ, Anno Domini Note: Full era name
	NNNNN	BC AD Note: Narrow era name
Week Year	gg	70 71 ... 29 30
	gggg	1970 1971 ... 2029 2030
Week Year (ISO)	GG	70 71 ... 29 30
	GGGG	1970 1971 ... 2029 2030
AM/PM	A	AM PM
	a	am pm
Hour	H	0 1 ... 22 23
	HH	00 01 ... 22 23
	h	1 2 ... 11 12
	hh	01 02 ... 11 12
	k	1 2 ... 23 24
	kk	01 02 ... 23 24
Minute	m	0 1 ... 58 59
	mm	00 01 ... 58 59
Second	s	0 1 ... 58 59
	ss	00 01 ... 58 59
Fractional Second	S	0 1 ... 8 9
	SS	00 01 ... 98 99
	SSS	000 001 ... 998 999
	SSSS ... SSSSSSSSS	000[0..] 001[0..] ... 998[0..] 999[0..]
Time Zone	z or zz	EST CST ... MST PST Note: as of 1.6.0, the z/zz format tokens have been deprecated from plain moment objects. Read more about it here. However, they *do* work if you are using a specific time zone with the moment-timezone addon.
	Z	-07:00 -06:00 ... +06:00 +07:00
	ZZ	-0700 -0600 ... +0600 +0700
Unix Timestamp	X	1360013296
Unix Millisecond Timestamp	x	1360013296123

For example, specifying a token string dddd, MMMM Do YYYY, h:mm:ss will require the date to be in the format Sunday, February 14th 2010, 3:25:50 pm.

Time

The column must be formatted in a time format. You have to select a Time Format from the dropdown list to specify the acceptable format. The default format is 'HH:mm:ss'. If your desired format is not found in the list, you can simply provide your custom format using the formatting tokens below.

	Token	Output
AM/PM	A	AM PM
	a	am pm
Hour	H	0 1 ... 22 23
	HH	00 01 ... 22 23
	h	1 2 ... 11 12
	hh	01 02 ... 11 12
	k	1 2 ... 23 24
	kk	01 02 ... 23 24
Minute	m	0 1 ... 58 59
	mm	00 01 ... 58 59
Second	s	0 1 ... 58 59
	ss	00 01 ... 58 59
Fractional Second	S	0 1 ... 8 9
	SS	00 01 ... 98 99
	SSS	000 001 ... 998 999
	SSSS ... SSSSSSSSS	000[0..] 001[0..] ... 998[0..] 999[0..]
Time Zone	z or zz	EST CST ... MST PST Note: as of 1.6.0, the z/zz format tokens have been deprecated from plain moment objects. Read more about it here. However, they *do* work if you are using a specific time zone with the moment-timezone addon.
	Z	-07:00 -06:00 ... +06:00 +07:00
	ZZ	-0700 -0600 ... +0600 +0700
Unix Timestamp	X	1360013296
Unix Millisecond Timestamp	x	360013296123

Boolean

The column under validation must be able to be cast as a boolean. Accepted inputs are true, false, TRUE, FALSE, 1, 0, yes, no, y, n, on, off, enabled, and disabled.

Regex

The column data must match the given regular expression. You need to specify the Regex and the importer will pattern match it with the incoming data.

IP

The column under validation must be an IP address. You have to select the IP Version.

URL

The column under validation must be a valid URL.

Credit Card

The column under validation must be formatted as a credit card number. Acceptable formats are 5555555555554444, 5555-5555-5555-4444 and 5555 5555 5555 4444.

Phone Number

The column will be validated for phone number formats based on the libphonenumber.js library. You have to select a default country code. If the incoming phone number does not have a country code then this default country code will be used for validation.

Currency

The column data should be in a currency amount format. You can specify currency formatting options based on the Validator.js library.

List

With the List data type, you can specify a list of acceptable values. The importer will compare the CSV column data with the list of acceptable values and throw a validation error if there is a mismatch. You can specify a list of acceptable Values and related Display Labels. The importer will match the column data with the list of Display Labels. If the data passes validation then the Values corresponding to the Display Labels will be pushed to the destination. For example say, you configure the List column as shown below:

List Data Type

In this case, the acceptable data in the CSV column will be Small, Medium, and Large. Based on the actual data found in the user CSV, the values s, m or l will be pushed to your data destination. This allows you to accept readable values from your users while pushing data to your system that is in a format it understands.

Dependent List

Sometimes, you may want to use more than one list such that the items available in a second drop-down list dependent on the selection made in the first drop-down list.

Let us call them dependent lists. Below is an example of a dependent list.

You can see that the options in the City column depend on the selection made in the Country column. If you select 'USA' in Drop Down 1, then you will cities from the USA, but if you select Canada in Drop Down 1, then you see the cities from Canada in Drop Down 2.

The same functionality can be achieved in CSVbox via a combination of List and Dependent List column types (or a combination of Dynamic List and Dependent Dynamic List column types).

Here are the steps to create conditional lists using the example above:

1. Create a column 'Country'. Configure column type as 'List'.

2. Create a column 'City'. Configure column type as 'Dependent List'. Select 'Country' as the Primary column.
3. Go back to the 'Country' column and add the list of valid values.

4. For each valid country value, you can add dependent (city) values as shown below.

If you change the name of the primary column or reorder the priority of any of the columns, then you need to reassign the primary column in the Dependent List column settings.

The Display Label and the Value should be unique across all the items. Primary items cannot share dependents having the same Display Label and/or same Value.

Dynamic List

This is similar to the List type column above where the importer will validate the column data against a list of values. However, instead of providing a static list of values, with the Dynamic List column type, you can now specify a list of acceptable values in real-time via an API.

The API should return the list of values in the JSON format as shown below.

[
   {"value": "uk", "display_label": "England"},
   {"value": "us", "display_label": "United States"},
   {"value": "au", "display_label": "Australia"}
]

For each list item, the value field is mandatory while display_label is optional.

You have the option to attach the custom user attributes as query parameters to the Dynamic List API request. csvbox_ prefix will be added to the custom user attribute query parameters. This will help you identify the users/environment and return back a relevant list of values.

For authenticating the requests you can pass the authorization headers via the dynamic_list_request_headers initialization option.

Dependent Dynamic List

Sometimes, you may want to use more than one list such that the items available in a second drop-down list depend on the selection made in the first drop-down list.

Let us call them dependent lists. Below is an example of a dependent list.

Conditional dependent list

You can see that the options in the City column depend on the selection made in the Country column. If you select 'USA' in Drop Down 1, then you will cities from the USA, but if you select Canada in Drop Down 1, then you see the cities from Canada in Drop Down 2.

The same functionality can be achieved in CSVbox via a combination of Dynamic List and Dependent Dynamic List column types (or a combination of List and Dependent List column types).

Here are the steps to create conditional lists using the example above:

1. Create a column 'Country'. Configure column type as 'Dynamic List'

2. Create a column 'City'. Configure column type as 'Dependent Dynamic List'. Select 'Country' as the Primary column.
3. Go back to the 'Country' column and configure the API that will fetch valid values from your application for 'Country' as well as its dependent column i.e. City column. Your API should return the list of values in the JSON format as shown below.

[
   {"value": "USA", "display_label": "USA", "dependents": [
      {"value": "ny", "display_label": "New York"},
      {"value": "ch", "display_label": "Chicago"},
      {"value": "se", "display_label": "Seatle"},
      {"value": "mi", "display_label": "Miami"}
   ]},
   {"value": "Canada", "display_label": "Canada", "dependents": [
      {"value": "to", "display_label": "Toronto"},
      {"value": "va", "display_label": "Vancouver"}    
   ]}
]

Note the dependents object above. It contains the list of valid values for the dependent column based on the primary column value.

If you change the name of the primary column or reorder the priority of any of the columns, then you need to reassign the primary column in the Dependent Dynamic List column settings.

The Display Label and the Value should be unique across all the items. Primary items cannot share dependents having the same Display Label and/or same Value.

Multi-select List

The Multi-select List data type is similar to the List data type, where you can specify a list of acceptable values. While the List data type accepts only one value per cell, the Multi-select List accepts multiple comma-separated values.

Multi-Select List

The importer will compare the values in the incoming data with the list of acceptable values and throw a validation error if there is a mismatch.

Dynamic Multi-select List

This is similar to the Multi-select List type column above where the importer accepts multiple comma-separated values. However, instead of providing a static list of values, here, you can now specify a list of acceptable values in real time via an API.

The API should return the list of values in the JSON format as shown below.

[
   {"value": "Red"},
   {"value": "Green"},
   {"value": "Blue"}
]

The importer will attach the custom user attributes as query parameters to the Dynamic List API request. csvbox_ prefix will be added to the custom user attribute query parameters. This will help you identify the users/environment and return back a relevant list of values.

For authenticating the requests you can pass the authorization headers via the dynamic_list_request_headers initialization option.

Other Validation Options

Column Required

You can check/uncheck the Required checkbox on the Column Settings window. The column data must be present, and not empty if the Required checkbox is ticked.