energia.utils.data

Data management utilities

Functions

make_henry_price_df(file_name, year[, stretch])	Makes a DataFrame from data with missing values filled using previous day values.
remove_outliers(data[, sd_cuttoff, mean_range])	Removes outliers up to a chosen number of standard deviations.

make_henry_price_df(file_name: str, year: int, stretch: bool = False) → DataFrame

Makes a DataFrame from data with missing values filled using previous day values.

The costs are converted from $/MMBtu to $/kg using a factor of 1/22.4. Only works if there is a full year of data (365 days). If stretch is True, the timescale is repeated to expand from days (365) to hours (8760).

Parameters

file_namestr

Path to the CSV file containing the data.

yearint

Year to import data from.

stretchbool, optional

If True, stretches the timescale from days to hours. Defaults to False.

Returns

pandas.DataFrame

DataFrame containing varying natural gas prices with missing values filled.

remove_outliers(data: DataFrame, sd_cuttoff: int = 2, mean_range: int = 1) → DataFrame

Removes outliers up to a chosen number of standard deviations.

Outliers are replaced with the mean of data points on both sides of the point.

Parameters

datapandas.DataFrame

Input data.

sd_cutoffint, optional

Remove data points that are beyond this many standard deviations. Defaults to 2.

mean_rangeint, optional

Number of neighboring data points on each side to average over when replacing outliers. Defaults to 1.

Returns

pandas.DataFrame

DataFrame with outliers replaced by local means.