Data Adjustment
Exclude specific rows of data. Populate or change the data of existing columns. Change data formats.
Last updated
Exclude specific rows of data. Populate or change the data of existing columns. Change data formats.
Last updated
Data adjustment will be done in the Prepare tab. All examples on this page utilize the Chat Data Prep function.
Remove specific rows of data.
Example Prompt: Filter for Retargeting.
AI Interpretation: The transform filters the dataset to only include rows where the 'Campaign Type' column is equal to 'Retargeting'.
Example Prompt: Remove rows where Views is less than 200.
AI Interpretation: The transform filters the dataset by keeping only the rows where the 'Views' column has a value greater than or equal to 200.
Example Prompt: Remove all empty values.
AI Interpretation: The transform function removes any rows from the dataset that contain missing values.
Tips:
Consider if it would be more effective to remove rows or columns from your dataset.
The Clean function, in the top right corner of Prepare, offers pre-configured options for removing redundant or unnecessary data.
Fill empty data points.
Example Prompt: Populate Click Through Rate by dividing Clicks by impressions and multiplying by 100.
AI Interpretation: Calculate the click through rate by dividing the number of clicks by the number of impressions, and then multiplying by 100.
Example Prompt: Replace all empty values with "other".
AI Interpretation: The transform function replaces missing values in the dataset with the string "other".
Tips:
Note the column type (number, category, ect.) before adding data. You can change the column type at any time.
Specify the column to populate. Otherwise, Akkio may fill all blank values throughout the dataset.
Change and edit specific data.
Example Prompt: Round ROAS values to the nearest whole number.
AI Interpretation: The transform function rounds the values in the 'ROAS' column to the nearest whole number and converts them to integers.
Example Prompt: Categorize the Views column, where <300 is low, 300-700 is medium, and >700 is high.
AI Interpretation: Transform the 'Views' column in the dataset by converting the values to numeric, categorizing them into three bins ('low', 'medium', 'high') based on their magnitude, and assigning the corresponding label to each value.
Example Prompt: Change Date columns to date time format.
AI Interpretation: The transform function converts specific columns in a dataset to datetime format. It ensures that the columns containing dates are correctly recognized as datetime objects, allowing for easier manipulation and analysis of the data.
Tips:
Categorize numerical columns/ variables by specifying the ends of each category.
