NodeData Explorer

Visualizer

The Data Explorer node offers a range of options for displaying properties of the input data in an interactive view.

The node supports custom CSS styling. You can simply put CSS rules into a single string and set it as a flow variable 'customCSS' in the node configuration dialog. You will find the list of available classes and their description on our documentation page.

Input Ports

  1. Port Type: Data
    Table from which to compute statistics.

Output Ports

  1. Port Type: Data
    A table with filtered out by user columns, chosen in the interactive view.

  1. ChEBML_REST_Services

    REST REST Service JavaScript ChEMBL File Download
    This workflow allows to connect to ChEMBL APi and extract the data from the latest version of ChEMBLdb. It sends /data/l…
    knime > Examples > 50_Applications > 30_RESTful_ChEMBL > 01_ChEMBL_REST_Services

  2. Guided Exploration

    guided analytics guided exploration machine learning automation JavaScript interactive view KNIME WebPortal CSS data scientist
    Guided exploration provides a data scientist with insights into data at hand. Any dataset can be used. The data are auto…
    knime > Examples > 50_Applications > 54_Guided_Visualization_and_Exploration > 01_Guided_Visualization_and_Exploration > 02_Guided_Exploration

  3. 01_Semi_Automated_ML

    Guided Analytics machine learning algorithm interactive WebPortal column expression domain expert string metrics
    This workflow is for semi-automated data blending from two different datasets. Each dataset consists of unique columns a…
    knime > Examples > 40_Partners > 04_GEMMACON > 01_Semi_Automated_ML

  4. Risk Scoring: Estimation of Probability of Default

    risk scoring risk assessment SMOTE random forest finance banking
    The probability of default is a metric that lenders use to assess risk on customers, group them into similar risk bands,…
    knime > Examples > 50_Applications > 38_Credit_Risk_Assessment > Credit_Scoring_Training

  5. First_Project_ML

    djibrina-barry > LOCAL > ML > First_Project_ML

  6. Univariate Visual Exploration with Data Explorer node

    Data Visualization dataviz univariate statistics histograms bar charts
    This is an example for univariate interactive data visualization using the Data Explorer node. - Read the dataset about …
    knime > Examples > 03_Visualization > 02_JavaScript > 11_Univariate_Visual_Exploration_with_Data_Explorer

  7. Open Source Visualizations and Modeling Integrations

    Open source R Python H2O.ai Flight delay
    This workflow uses airport and meteorlogical data to predict airline delays. It uses several open source integrations to…
    knime > Examples > 50_Applications > 28_Predicting_Departure_Delays > 03_OpenSourceVizAndModeling

  8. Identify PII and Special Category Data

    GDPR Customer Intelligence Legal Law Privacy
    This example is to show techniques for identifying personal data and special category data. It uses Quickforms and JavaS…
    knime > Examples > 50_Applications > 34_GDPR_examples > 01_Identify_PII_and_Special_Category_Data

  9. Guided Visualization and Exploration

    guided visualization guided analytics guided exploration machine learning automation JavaScript interactive view customization data type conversion nested wrapped metanode KNIME WebPortal CSS data scientist business analyst
    Guided exploration provides a data scientist with insights into data at hand. Any dataset can be used. The data are auto…
    knime > Examples > 50_Applications > 54_Guided_Visualization_and_Exploration > 01_Guided_Visualization_and_Exploration > 03_Guided_Visualization_and_Exploration

  10. Guided Automation

    autoML guided analytics automated machine learning guided automation feature engineering parameter optimization hyperparameter hyper-parameter optimization feature selection WebPortal application web app machine learning
    This workflow generates a fully automated web based application to select, train, test, and optimize a number of machine…
    knime > Examples > 50_Applications > 36_Guided_Analytics_for_ML_Automation > 01_Guided_Analytics_for_ML_Automation > 01_Guided_Analytics_for_ML_Automation