Numbers
Last updated
Last updated
It removes the fractional part from the numerical column. The float column is converted into the integer data type.
The Remove Fractional Part transform is applied on the expense column.
As a result, a new column gets added to the dataset after removing the fractional part from the selected column:
It replaces the number with a greater integer value if the number is between two integers values. The transformed value can replace the existing column value or can be added as a new column.
The Round value using ceil mode transform is applied to the ctc column,
A new column gets added with the set round off value using ceil mode:
It rounds the number down to a specified digit or gives the specified number of decimals without any change in value. The transformed value can replace the existing column value or can be added as a new column.
The Round Value using Down Mode transform has been applied to ctc column,
A new column gets added to the dataset with the round off values using the down mode on the ctc column data:
It replaces a number with the lesser integer value, if the number is between two integer value, or it rounds the number down to the nearest multiple of Specified significance. It does not consider whether the next digit is 5 or less than or greater than 5. The transformed value can replace the existing column value or can be added as a new column.
The Round Value using Floor Mode transform has been applied to the ctc column,
As a result, a new column gets added to the dataset with the round off values of the ctc column data by using the floor mode:
It replaces a number with the next integer value if its next digit is 5 or greater than 5. The transformed value can replace the existing column value or can be added as a new column.
The Round Value using Half-up mode transform is applied to the ctc column as displayed below:
As a result, a new column gets added to the dataset with the round off values of the ctc column data by using the half-up mode:
It gives the maximum value from the selected columns row-wise. The selected column should be numerical and more than one.
The Max transform is applied to the revenue column as displayed below:
As a result, a new column gets added to the dataset with the maximum values of the revenue column data:
It complements the sing of a numeric value. If the value is positive, then a negative value comes and vice-versa.
The Negate transform is applied to the revenue column as displayed below:
As a result, a new column gets added to the dataset with the negative values of the revenue column data:
It gives the minimum value from the selected columns row-wise. The selected column should be numerical and more than one.
The Min transform is applied to the revenue column as displayed below:
As a result, a new column gets added to the dataset with the minimum values of the revenue column data:
It gives the average value of the selected columns row-wise. The selected column should be numerical and more than one.
The Mean transform is applied to the revenue column as displayed below:
As a result, a new column gets added to the dataset with the mean values of the revenue column data:
It converts the value of the selected column into words. The column must be of integer type.
Use with: It gives the users an option to convert words into either western format or Indian format.
The Number Names transform is applied to the revenue column as displayed below (used with the Western option:
As a result, a new column gets added to the dataset with the Number Names of the revenue column data:
