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use ffill and bfill

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This commit is contained in:
lisazeyen 2024-07-31 16:06:16 +02:00
parent c52737db72
commit d48500fc3b
1 changed files with 16 additions and 7 deletions
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23
scripts/build_energy_totals.py
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@ -587,7 +587,8 @@ def build_idees(countries: List[str]) -> pd.DataFrame:
def fill_missing_years(fill_values: pd.Series) -> pd.Series:
"""
Fill missing years for some countries by mean over the other years.
Fill missing years for some countries by first using forward fill (ffill)
and then backward fill (bfill).
Parameters
----------
@ -598,16 +599,23 @@ def fill_missing_years(fill_values: pd.Series) -> pd.Series:
Returns
-------
pd.Series
A pandas Series with zero values replaced by the mean value of the corresponding
country.
A pandas Series with zero values replaced by the forward-filled and
backward-filled values of the corresponding country.
Notes
-----
- The function groups the data by the 'country' level and computes the mean for each group.
- Zero values in the original Series are replaced by the mean value of their respective country group.
- The function groups the data by the 'country' level and performs forward fill
and backward fill to fill zero values.
- Zero values in the original Series are replaced by the ffilled and bfilled
value of their respective country group.
"""
means = fill_values.groupby(level="country").transform("mean")
return fill_values.where(fill_values != 0, means)
# Replace zero values with NaN for correct filling
fill_values = fill_values.replace(0, pd.NA)
# Forward fill and then backward fill within each country group
fill_values = fill_values.groupby(level="country").ffill().bfill()
return fill_values
def build_energy_totals(
@ -724,6 +732,7 @@ def build_energy_totals(
eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=[0, 1]).sum()
)
# fill missing years for some countries by mean over the other years
breakpoint()
fill_values = fill_missing_years(fill_values)
df.loc[to_fill, f"{fuel} {sector}"] = fill_values