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Fix pandas group by error
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@kcreekdev
kcreekdev committed Mar 1, 2024
1 parent 3aa7b17 commit c205fe2
Showing 1 changed file with 15 additions and 15 deletions.
30 changes: 15 additions & 15 deletions ccc/calculator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -249,21 +249,21 @@ def calc_by_asset(self, include_inventories=True, include_land=True):
"asset_name",
"tax_treat",
]
).apply(self.__f, include_groups=False)
).apply(lambda x: self.__f(x), include_groups=False)
).reset_index()
asset_df = self.calc_other(asset_df)
# Find values across minor asset groups
minor_asset_df = pd.DataFrame(
self.__assets.df.groupby(
["minor_asset_group", "major_asset_group", "tax_treat"]
).apply(self.__f, include_groups=False)
).apply(lambda x: self.__f(x), include_groups=False)
).reset_index()
minor_asset_df["asset_name"] = minor_asset_df["minor_asset_group"]
minor_asset_df = self.calc_other(minor_asset_df)
# Find values across major asset_groups
major_asset_df = pd.DataFrame(
self.__assets.df.groupby(["major_asset_group", "tax_treat"]).apply(
self.__f, include_groups=False
lambda x: self.__f(x), include_groups=False
)
).reset_index()
major_asset_df["minor_asset_group"] = major_asset_df[
Expand All @@ -278,7 +278,7 @@ def calc_by_asset(self, include_inventories=True, include_land=True):
if not include_inventories:
df1.drop(df1[df1.asset_name == "Inventories"].index, inplace=True)
overall_df = pd.DataFrame(
df1.groupby(["tax_treat"]).apply(self.__f, include_groups=False)
df1.groupby(["tax_treat"]).apply(lambda x: self.__f(x), include_groups=False)
).reset_index()
overall_df["major_asset_group"] = "Overall"
overall_df["minor_asset_group"] = "Overall"
Expand Down Expand Up @@ -329,19 +329,19 @@ def calc_by_industry(self, include_inventories=True, include_land=True):
ind_df = pd.DataFrame(
df1.groupby(
["major_industry", "bea_ind_code", "Industry", "tax_treat"]
).apply(self.__f, include_groups=False)
).apply(lambda x: self.__f(x), include_groups=False)
).reset_index()
ind_df = self.calc_other(ind_df)
major_ind_df = pd.DataFrame(
df1.groupby(["major_industry", "tax_treat"]).apply(
self.__f, include_groups=False
lambda x: self.__f(x), include_groups=False
)
).reset_index()
major_ind_df["Industry"] = major_ind_df["major_industry"]
major_ind_df = self.calc_other(major_ind_df)
# Can put some if statements here if want to exclude land/inventory/etc
overall_df = pd.DataFrame(
df1.groupby(["tax_treat"]).apply(self.__f, include_groups=False)
df1.groupby(["tax_treat"]).apply(lambda x: self.__f(x), include_groups=False)
).reset_index()
overall_df["major_industry"] = "Overall"
overall_df["Industry"] = "Overall"
Expand Down Expand Up @@ -409,7 +409,7 @@ def summary_table(
df.drop(df[df.asset_name == "Inventories"].index, inplace=True)
# Compute overall separately by tax treatment
treat_df = pd.DataFrame(
df.groupby(["tax_treat"]).apply(self.__f, include_groups=False)
df.groupby(["tax_treat"]).apply(lambda x: self.__f(x), include_groups=False)
).reset_index()
treat_df = self.calc_other(treat_df)
# Compute overall values, across corp and non-corp
Expand All @@ -418,7 +418,7 @@ def summary_table(
df["include"] = 1
all_df = pd.DataFrame.from_dict(
df.groupby(["include"])
.apply(self.__f, include_groups=False)
.apply(lambda x: self.__f(x), include_groups=False)
.to_dict()
)
# set tax_treat to corporate b/c only corp and non-corp
Expand Down Expand Up @@ -651,14 +651,14 @@ def asset_summary_table(
# Make dataframe with just results for major asset categories
major_asset_df = pd.DataFrame(
df.groupby(["major_asset_group", "tax_treat"]).apply(
self.__f, include_groups=False
lambda x: self.__f(x), include_groups=False
)
).reset_index()
major_asset_df["asset_name"] = major_asset_df["major_asset_group"]
major_asset_df = self.calc_other(major_asset_df)
# Compute overall separately by tax treatment
treat_df = pd.DataFrame(
df.groupby(["tax_treat"]).apply(self.__f, include_groups=False)
df.groupby(["tax_treat"]).apply(lambda x: self.__f(x), include_groups=False)
).reset_index()
treat_df = self.calc_other(treat_df)
treat_df["major_asset_group"] = "Overall"
Expand All @@ -668,7 +668,7 @@ def asset_summary_table(
df["include"] = 1
all_df = pd.DataFrame.from_dict(
df.groupby(["include"])
.apply(self.__f, include_groups=False)
.apply(lambda x: self.__f(x), include_groups=False)
.to_dict()
)
# set tax_treat to corporate b/c only corp and non-corp
Expand Down Expand Up @@ -854,14 +854,14 @@ def industry_summary_table(
# Make dataframe with just results for major industry
major_ind_df = pd.DataFrame(
df.groupby(["major_industry", "tax_treat"]).apply(
self.__f, include_groups=False
lambda x: self.__f(x), include_groups=False
)
).reset_index()
major_ind_df["Industry"] = major_ind_df["major_industry"]
major_ind_df = self.calc_other(major_ind_df)
# Compute overall separately by tax treatment
treat_df = pd.DataFrame(
df.groupby(["tax_treat"]).apply(self.__f, include_groups=False)
df.groupby(["tax_treat"]).apply(lambda x: self.__f(x), include_groups=False)
).reset_index()
treat_df = self.calc_other(treat_df)
treat_df["major_industry"] = "Overall"
Expand All @@ -871,7 +871,7 @@ def industry_summary_table(
df["include"] = 1
all_df = pd.DataFrame.from_dict(
df.groupby(["include"])
.apply(self.__f, include_groups=False)
.apply(lambda x: self.__f(x), include_groups=False)
.to_dict()
)
# set tax_treat to corporate b/c only corp and non-corp
Expand Down

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