Akkio Docs
  • Akkio Documentation
    • Akkio FAQ
  • Account and Settings
    • Team Settings
    • Organization Settings
    • Account Settings
    • Role Based Access Control
  • Demo Models
    • Demo Models
      • Lead Scoring
      • Retail Sales Forecasting
      • Predict Credit Card Fraud
      • Identify Customer Churn
  • Setting up Integrations
    • Connecting Data
    • Airtable (Beta)
    • Google Ads (Beta)
    • Google Analytics 4 (Beta)
    • Google BigQuery
    • Google BigQuery (Service Account)
    • Google Sheets
    • HubSpot (Beta)
    • MariaDB (Beta)
    • MongoDB (Beta)
    • MySQL (Beta)
    • PostgreSQL (Beta)
    • Redshift (Beta)
    • Salesforce
    • Akkio Data Chat for Slack
    • Snowflake (Username / Password) (Beta)
    • Zapier
  • Prepare your Data
    • Prepare
      • Chat Data Prep
      • Clean
      • Merge & Fuzzy Merge
      • Table View
      • Pivot View
      • Deploying Chat Data Prep
  • Explore
    • Chat Explore
    • Chart Types
  • Building a Model
    • Predict
      • Insights Report - Classification
      • Insights Report - Regression
    • Forecasting
      • Insights Report - Forecasting
    • Model Types
  • Deploying a Model
    • Deploy
      • Google BigQuery
      • Google Sheets
      • HubSpot (Beta)
      • PostgreSQL (Beta)
      • Salesforce
      • Snowflake (Beta)
      • Web App
      • Zapier
  • REPORTING AND SHARING
    • Reports
    • Dashboards
  • REST API
    • API Introduction
      • Quickstart
    • API Options
      • cURL Commands
      • Python Library
      • Node.js Library
    • API FAQ
  • Rest API (v2)
    • Documentation
Powered by GitBook
On this page
  • Column Types
  • Column Details

Was this helpful?

  1. Prepare your Data
  2. Prepare

Table View

Features and options in the table view of your data

PreviousMerge & Fuzzy MergeNextPivot View

Last updated 1 year ago

Was this helpful?

Column Types

Akkio will automatically recognize the variable types in the dataset, which can be any of the following:

  • Text: This column type is used for textual data such as names, descriptions, or any other free-form text.

  • Number (Integer): It is used for columns containing whole numbers without any decimal points, e.g., 1, 20, -5.

  • Number: This column type is for numerical data that may contain decimal points, e.g., 3.14, 100.5, -0.75.

  • ID: The ID column type is used to uniquely identify each row in the dataset. It typically contains a unique identifier for each record, such as a customer ID or a transaction ID.

  • Date: This column type is used for date and time values, allowing for the analysis of temporal data.

  • Category: The Category column type is commonly used to represent categorical data, such as gender, product type, or any other distinct categories. It is also recommended to use the Category column type for binary data.

  • Disabled: Temporarily disable a category for specific analysis.

You can change a column’s variable type by clicking its existing variable type.

Column Details

Click on the column summary to see expanded details about the data in that column.

  • Rows - Number of rows in the column

  • Empty Rows - Rows in column with no value

  • Unique values - Number of different values in the column

  • Distribution - A visualization of the data distribution

  • Correlations - How values in this column correlate to values across the dataset