Scale Columns
This function scales a column through standard or min/max scaling methods.
Name | Type | Description | Is Optional |
---|---|---|---|
columns_to_scale | column_list | A list of numeric columns that you want to scale | |
method | string | The method used to scale the column values ('standard' or 'min_max'). If 'standard' is chosen, this function scales a column by removing the mean and scaling by standard deviation. If 'min_max' is selected, this function scales a column by subtracting the min value in the column and dividing by the range between the max and min values. | |
overwrite_columns | boolean | Optional: if true, the scaled values will overwrite values in 'columns_to_scale'. If false, new columns with the scaled values will be generated. | True |
averages | value_list | Only applies when 'standard' method is chosen. This is an optional argument representing a list of the static averages to use for each column in columns_to_scale. If omitted, the averages are calculated directly off each column. | True |
standarddevs | int_list | Only applies when 'standard' method is chosen. This is an optional argument representing a list of the static standard deviations to use for each column in columns_to_scale. If omitted, the values are calculated directly off each column. | True |
minimums | value_list | Only applies when 'min_max' method is chosen. This is an optional argument representing a list of the static minimums to use for each column in columns_to_scale. If omitted, the minimums are calculated directly off each column. | True |
maximums | value_list | Only applies when 'min_max' method is chosen. This is an optional argument representing a list of the static maximums to use for each column in columns_to_scale. If omitted, the values are calculated directly off each column. | True |
ds = rasgo.get.dataset(id)
ds2 = ds.scale_columns(columns_to_scale=['DS_DAILY_HIGH_TEMP','DS_DAILY_LOW_TEMP'], method='standard')
ds2.preview()
ds2b = ds.scale_columns(columns_to_scale=['DS_DAILY_HIGH_TEMP','DS_DAILY_LOW_TEMP'],
averages=[68, 52],
standarddevs=[12, 8],
method='standard')
ds2b.preview()
ds2c = ds.scale_columns(columns_to_scale=['DS_DAILY_HIGH_TEMP','DS_DAILY_LOW_TEMP'],
minimums=[52, 4],
maximums=[101, 81],
method='min_max')
ds2c.preview()
Last modified 7mo ago