preprocessing.checking.check_y

preprocessing.checking.check_y(y, series_id='`y`')

Validate that y is a pandas Series without missing values.

This function ensures that the input time series meets the basic requirements for forecasting: it must be a pandas Series and must not contain any NaN values.

Parameters

Name	Type	Description	Default
y	Any	Time series values to validate.	required
series_id	str	Identifier of the series used in error messages. Defaults to “y”.	'y'

Raises

Name	Type	Description
	TypeError	If y is not a pandas Series.
	ValueError	If y contains missing (NaN) values.

Examples

import numpy as np
import pandas as pd

from spotforecast2_safe.preprocessing.checking import check_y

# Valid series — no error raised
y = pd.Series([1, 2, 3, 4, 5])
check_y(y)

# Invalid: not a Series
try:
    check_y([1, 2, 3])
except TypeError as e:
    print(f"TypeError: {e}")

# Invalid: contains NaN
y_with_nan = pd.Series([1, 2, np.nan, 4])
try:
    check_y(y_with_nan)
except ValueError as e:
    print(f"ValueError: {e}")

TypeError: `y` must be a pandas Series with a DatetimeIndex or a RangeIndex. Found <class 'list'>.
ValueError: `y` has missing values.