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  1. API reference
  2. Welcome
    1. Component overview
    2. Quickstart
    3. System requirements
    4. Troubleshooting
    5. Installation troubleshooting
    6. Managing license keys
  3. Connecting to Data Source
    1. JSON
      1. Connecting to JSON
      2. Data types in JSON
    2. CSV
      1. Connecting to CSV
      2. Data types in CSV
    3. SQL database
      1. Connecting to SQL database
      2. Connecting to database with .NET
      3. Connecting to database with .NET Core
      4. Connecting to database with Java
      5. Connecting to database with PHP
    4. Microsoft Analysis Services
      1. Connecting to Microsoft Analysis Services
      2. Getting started with Accelerator
      3. Installing Accelerator as a Windows Service
      4. Configuring username/password protection
      5. Configuring secure HTTPS connection
      6. Troubleshooting
    5. Pentaho Mondrian
      1. Connecting to Pentaho Mondrian
      2. Getting started with Accelerator
      3. Configuring Mondrian roles
      4. Сonfiguring username/password protection
      5. Сonfiguring secure HTTPS connection
      6. Troubleshooting
    6. icCube
  4. Configuring report
    1. What is a report
    2. Data source
    3. Slice
    4. Options
    5. Number formatting
    6. Conditional formatting
    7. Set report to the component
    8. Get report from the component
    9. Date and time formatting
    10. Configuring global options
    11. Export and print
    12. Calculated values
    13. Custom sorting
  5. Integration with frameworks
    1. Available tutorials
    2. Integration with jQuery
    3. Integration with AngularJS
    4. Integration with Angular 2
    5. Integration with Angular 4
    6. Integration with React
    7. Integration with RequireJS
    8. Integration with TypeScript
    9. Integration with ASP.NET
    10. Integration with JSP
    11. Integration with PhoneGap
  6. Integration with charts
    1. Integration with Highcharts
    2. Integration with FusionCharts
    3. Integration with Google Charts
    4. Integration with any charting library
  7. Customizing
    1. Customizing toolbar
    2. Customizing appearance
    3. Localizing component
  8. Updating to the latest version
    1. Updating to the latest version
    2. Release notes
    3. Migration guide from 2.3 to 2.4
    4. Migration guide from 2.2 to 2.3
  9. Older Versions
    1. Documentation 2.3
    2. Documentation 2.2
    3. API reference - Flex
Table of contents

Data types in CSV

Using CSV data source you can indicate how data will be interpreted by Pivot table component. For this, you can use special prefixes for columns names. Pivot table has the following prefixes for CSV files:

  • + means that field is a dimension.
  • - field is a value.
  • m+ this prefix will indicate that the field is a month.
  • w+ to indicate that the field is a day of the week.
  • d+ field is a date. Such field will be split into 3 different fields: Year, Month, Day. Date formats that are supported by Pivot table is described below.
  • D+ field is a date. You will see such date as a hierarchy: Year > Month > Day.
  • D4+ field is a date. You will see such date as a hierarchy: Year > Quarter > Month > Day.
  • ds+ field is a date. Such field will be formatted using date pattern (default is dd/MM/yyyy)
  • t+ field is a time (measure). Such field will be formatted using HH:mm pattern
  • dt+ field is a date (measure). Such field will be formatted using dd/MM/yyyy HH:mm:ss pattern
  • id+ field is an id of the fact.

Here is the minimal CSV file which will treat Year as dimension, rather than numeric measure:

Country, +Year, Sales 
US, 2010, 200 
UK, 2010, 100

Supported date formats

To make date column be interpreted as date, you should use prefixes d+, D+ and D4+ for CSV columns. Besides, data from these columns should have special date format to be understood properly.
The component supports ISO 8601 date (other formats may be used, but results can be unexpected). For example, "2016-03-20" (just date) or "2016-03-20T14:48:00" (date and time).

Here is an example of the CSV file with date columns – Date1 and Date2:

Size, Discount, d+Date1, D+Date2
214 oz, 14, 2009-11-01, 2009-11-09
214 oz, 12, 2010-12-09, 2009-12-09
212 oz, 36, 2009-09-01, 2009-12-01
212 oz, 27, 2009-09-01, 2010-12-02
212 oz, 18, 2010-11-09, 2009-12-11
212 oz, 16, 2009-09-01, 2009-12-20

The pivot table based on this CSV will look as follows:
date data types from CSV

As you can see, Date1 column with prefix d+ is split into three separate fields — Year, Month, Day. In Pivot component it will look as follows:
date data types from CSV in the fields list

Date2 column with D+ prefix is interpreted as a hierarchy that can be drilled down to months and days.