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188 depreciated inplace keyword #189

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188 depreciated inplace keyword #189

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f9075a8
fix(inplace): remove bug
Nov 28, 2023
db0546b
feat(inplace): remove inplace keyword
Nov 28, 2023
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10 changes: 4 additions & 6 deletions cobra/evaluation/pigs_tables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -145,14 +145,12 @@ def plot_incidence(pig_tables: pd.DataFrame,
'the same set of variables.')

df_plot['label'] = df_plot['label'].astype('category')
df_plot['label'].cat.reorder_categories(column_order,
inplace=True)
df_plot['label'] = df_plot['label'].cat.reorder_categories(column_order)

df_plot.sort_values(by=['label'], ascending=True, inplace=True)
df_plot.reset_index(inplace=True)
df_plot = df_plot.sort_values(by=['label'], ascending=True).reset_index()
else:
df_plot.sort_values(by=['avg_target'], ascending=False, inplace=True)
df_plot.reset_index(inplace=True)
df_plot = df_plot.sort_values(by=['avg_target'], ascending=False)
df_plot = df_plot.reset_index()

with plt.style.context("seaborn-ticks"):
fig, ax = plt.subplots(figsize=dim)
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2 changes: 1 addition & 1 deletion cobra/preprocessing/kbins_discretizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -331,7 +331,7 @@ def _transform_column(self, data: pd.DataFrame,

# Replace NULL with "Missing"
# Otherwise these will be ignored in groupby
data[column_name_bin].fillna("Missing", inplace=True)
data[column_name_bin] = data[column_name_bin].fillna("Missing")

return data

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12 changes: 4 additions & 8 deletions cobra/preprocessing/target_encoder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -282,17 +282,13 @@ def _transform_column(self, data: pd.DataFrame,
# configured imputation strategy:
if _data[new_column].isnull().sum() > 0:
if self.imputation_strategy == "mean":
_data[new_column].fillna(self._global_mean,
inplace=True)
_data[new_column] = _data[new_column].fillna(self._global_mean)
elif self.imputation_strategy == "min":
_data[new_column].fillna(_data[new_column].min(),
inplace=True)
_data[new_column] = _data[new_column].fillna(_data[new_column].min())
elif self.imputation_strategy == "max":
_data[new_column].fillna(_data[new_column].max(),
inplace=True)
_data[new_column] = _data[new_column].fillna(_data[new_column].max())
elif self.imputation_strategy == "median":
_data[new_column].fillna(_data[new_column].median(),
inplace=True)
_data[new_column] = _data[new_column].fillna(_data[new_column].median())

return _data

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