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Address FutureWarnings in make_summary_perfect related to groupby

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This commit is contained in:
Koen van Greevenbroek 2024-02-02 10:41:05 +00:00
parent 6c7d79f524
commit 7fbb605134
1 changed files with 11 additions and 19 deletions
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30
scripts/make_summary_perfect.py
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@ -246,8 +246,9 @@ def calculate_energy(n, label, energy):
.groupby(level=0) .groupby(level=0)
.sum() .sum()
.multiply(c.df.sign) .multiply(c.df.sign)
.groupby(c.df.carrier, axis=1) .T.groupby(c.df.carrier)
.sum() .sum()
.T
) )
else: else:
c_energies = pd.DataFrame( c_energies = pd.DataFrame(
@ -394,16 +395,9 @@ def calculate_supply_energy(n, label, supply_energy):
if len(items) == 0: if len(items) == 0:
continue continue
s = ( s = (-1) * c.pnl["p" + end].reindex(items, axis=1).multiply(
(-1) n.snapshot_weightings.objective, axis=0
* c.pnl["p" + end] ).groupby(level=0).sum().T.groupby(c.df.loc[items, "carrier"]).sum()
.reindex(items, axis=1)
.multiply(n.snapshot_weightings.objective, axis=0)
.groupby(level=0)
.sum()
.groupby(c.df.loc[items, "carrier"], axis=1)
.sum()
).T
s.index = s.index + end s.index = s.index + end
s = pd.concat([s], keys=[c.list_name]) s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i]) s = pd.concat([s], keys=[i])
@ -514,9 +508,7 @@ def calculate_weighted_prices(n, label, weighted_prices):
if names.empty: if names.empty:
continue continue
load += ( load += n.links_t.p0[names].T.groupby(n.links.loc[names, "bus0"]).sum()
n.links_t.p0[names].groupby(n.links.loc[names, "bus0"], axis=1).sum()
)
# Add H2 Store when charging # Add H2 Store when charging
# if carrier == "H2": # if carrier == "H2":
@ -557,9 +549,9 @@ def calculate_market_values(n, label, market_values):
dispatch = ( dispatch = (
n.generators_t.p[gens] n.generators_t.p[gens]
.groupby(n.generators.loc[gens, "bus"], axis=1) .T.groupby(n.generators.loc[gens, "bus"])
.sum() .sum()
.reindex(columns=buses, fill_value=0.0) .T.reindex(columns=buses, fill_value=0.0)
) )
revenue = dispatch * n.buses_t.marginal_price[buses] revenue = dispatch * n.buses_t.marginal_price[buses]
@ -583,9 +575,9 @@ def calculate_market_values(n, label, market_values):
dispatch = ( dispatch = (
n.links_t["p" + i][links] n.links_t["p" + i][links]
.groupby(n.links.loc[links, "bus" + i], axis=1) .T.groupby(n.links.loc[links, "bus" + i])
.sum() .sum()
.reindex(columns=buses, fill_value=0.0) .T.reindex(columns=buses, fill_value=0.0)
) )
revenue = dispatch * n.buses_t.marginal_price[buses] revenue = dispatch * n.buses_t.marginal_price[buses]
@ -652,7 +644,7 @@ def calculate_co2_emissions(n, label, df):
emitted = n.generators_t.p[gens.index].mul(em_pu) emitted = n.generators_t.p[gens.index].mul(em_pu)
emitted_grouped = ( emitted_grouped = (
emitted.groupby(level=0).sum().groupby(n.generators.carrier, axis=1).sum().T emitted.groupby(level=0).sum().T.groupby(n.generators.carrier).sum().T
) )
df = df.reindex(emitted_grouped.index.union(df.index)) df = df.reindex(emitted_grouped.index.union(df.index))