datamop.column_scaler

Functions

column_scaler(data, column[, method, new_min, ...])

Scales the values of a specified column in a DataFrame.

Module Contents

datamop.column_scaler.column_scaler(data, column, method='minmax', new_min=0, new_max=1, inplace=True)[source]

Scales the values of a specified column in a DataFrame.

Parameters:

  • data (pandas.DataFrame) – The DataFrame containing the column of interest for scaling.

  • column (str) – The name of the numeric column to scale.

  • method (str) –

    The method used for scaling. Options include:

    • minmax: Scales values between new_min and new_max, used as default method.

    • standard: Scales values with mean of 0 and standard deviation of 1.

  • new_min (float) – The lower boundary value for min-max scaling. Default value is 0.

  • new_max (float) – The upper boundary value for min-max scaling. Default value is 1.

  • inplace (bool) – If True the original column is replaced with new scaled values. If False the original column is retained and the new scaled column is added to the dataframe with title <column-name>-scaled. Default is True.

Returns:

A copy of the DataFrame with the scaled column replacing the original column if inplace is set to True. If inplace is set to False, the copy of DataFrame is returned with the new scaled column added, keeping the original column.

Return type:

pandas.DataFrame

Raises:

  • TypeError – If the input data is not a pandas DataFrame.

  • KeyError: – If the column passed for scaling does not exist in the DataFrame.

  • ValueError: – If the column passed for scaling is not numeric. If the method is not minmax or standard. If the new_min value is greater or equal to the new_max when using minmax method.

Examples

>>> import pandas as pd
>>> df = pd.DataFrame({"price": [25, 50, 75]})
>>> df_scaled = column_scaler(df, column = 'price', method='minmax', new_min=0, new_max=1)
>>> print(df_scaled)
        price
        0.0
        0.5
        1.0