ResampleTransformer

Resamples data to a specified frequency with interpolation to fill gaps.

Basic Usage

from epftoolbox2.data.transformers import ResampleTransformer

transformer = ResampleTransformer(freq="1h")
df = transformer.transform(df)

Parameters

Parameter	Type	Default	Description
`freq`	str	`"1h"`	Pandas frequency string
`method`	str	`"linear"`	Interpolation method
`columns`	list[str] \| str \| None	`None`	Columns to interpolate. If None, all columns are interpolated.

Frequency Strings

String	Meaning
`"1h"`	1 hour
`"30min"`	30 minutes
`"15min"`	15 minutes
`"1D"`	1 day

Interpolation Methods

Method	Description
`"linear"`	Linear interpolation between points
`"ffill"`	Forward fill (use last known value)
`"bfill"`	Backward fill (use next known value)

Selecting Columns

By default, interpolation is applied to all columns. You can restrict interpolation to specific columns using the columns parameter. Other columns will be resampled (introducing NaNs) but not interpolated.

# Interpolate only 'price', leave 'load' with NaNs where gaps occur
transformer = ResampleTransformer(freq="15min", columns=["price"])
df = transformer.transform(df)

Example: Handling DST Gaps

When converting to a local timezone with daylight saving time, a gap can occur (e.g., 2:00 AM doesn’t exist during spring-forward). Use ResampleTransformer to fill these gaps:

from epftoolbox2.pipelines import DataPipeline
from epftoolbox2.data.sources import EntsoeSource
from epftoolbox2.data.transformers import TimezoneTransformer, ResampleTransformer

pipeline = (
    DataPipeline()
    .add_source(EntsoeSource(country_code="PL", api_key="...", type=["load"]))
    # First convert to local timezone (creates DST gap)
    .add_transformer(TimezoneTransformer(target_tz="Europe/Warsaw"))
    # Then resample to fill the gap
    .add_transformer(ResampleTransformer(freq="1h", method="linear"))
)

df = pipeline.run(start="2024-03-30", end="2024-04-01")
# The missing 2:00 AM hour during DST transition is now filled

Notes

Input DataFrame must have a DatetimeIndex
Output values are rounded to 3 decimal places