1. Data preparation

In the data section, you can perform all the classic data preparation processes, including cleaning, transforming, and organizing raw data into a format suitable for analysis or modeling.

Data Cleaning: Removing or correcting errors and inconsistencies in the data, such as missing values, duplicate records, and incorrect data entries.

Removing Duplicates: Identifying and eliminating duplicate records.
Split: Separating a single column into multiple columns based on a given string or character.
Flatten: When your data comes from an API, you may encounter values enclosed in curly brackets {} or square brackets []. Values in curly brackets are JSON objects, while those in square brackets are arrays. These data types contain a lot of information in a single cell, making analysis difficult. The flatten action addresses this issue by breaking down these complex structures into more manageable, analyzable components.
Correcting Errors: Fixing typos, misspellings, and incorrect data entries.
Handling Missing Values: Imputing missing data or removing records with missing values.
Standardizing Data: Converting data into a consistent format (e.g., date formats, units of measurement).
Validating Data: Ensuring data conforms to predefined rules or constraints (e.g., valid email addresses, phone numbers).

Data Transformation: Converting data into the required format, which might include normalizing values, aggregating data, creating new calculated fields or Pivot/Unpivot data

Unpivot is a powerful tool to convert table columns into rows. Specifically, it transforms multiple columns into two columns: unpivot_table_metric and unpitvot_table_value . Column names are placed in the former columns and their corresponding values are in the latter column.

Data Formatting: Structuring the data in a way that aligns with the requirements of the analytical tools or models to be used. This may include reshaping the data or converting it into specific file formats.

Data override

You can override data in any cell by double-clicking and modifying the value. Additionally, you can perform bulk changes by copying and pasting values across multiple rows. All changes are automatically reflected in the formulas.