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fix FutureWarning: ChainedAssignmentError #900

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fix FutureWarning: ChainedAssignmentError #900

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cc2d3d1
fix FutureWarning: ChainedAssignmentError
quant12345 Dec 18, 2024
e1bd945
fix FutureWarning: ChainedAssignmentError
quant12345 Dec 18, 2024
9657fc3
fix FutureWarning: ChainedAssignmentError, refactoring
quant12345 Dec 18, 2024
1cb9a65
Merge remote-tracking branch 'origin/fix/loc' into fix/loc
quant12345 Dec 18, 2024
3d7735f
fix FutureWarning: ChainedAssignmentError, refactoring
quant12345 Dec 18, 2024
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1 change: 1 addition & 0 deletions RELEASE_NOTES.md
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@

The next release must be bumped to v3.0.0.

- [#900](https://github.com/IAMconsortium/pyam/pull/900) fix ChainedAssignmentError, refactoring
- [#896](https://github.com/IAMconsortium/pyam/pull/896) Add `sort_data()` method
- [#896](https://github.com/IAMconsortium/pyam/pull/896) Sort columns of `timeseries()` with mixed time domain
- [#893](https://github.com/IAMconsortium/pyam/pull/893) No sorting of timeseries data on initialization or append
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11 changes: 7 additions & 4 deletions tests/test_plotting.py
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Expand Up @@ -139,7 +139,7 @@ def test_line_color_fill_between(plot_df):
@pytest.mark.mpl_image_compare(**MPL_KWARGS)
def test_line_color_fill_between_interpolate(plot_df):
# designed to create the sawtooth behavior at a midpoint with missing data
df = pyam.IamDataFrame(plot_df.data.copy())
df = plot_df.data.copy()
fig, ax = plt.subplots(figsize=(8, 8))
newdata = [
"test_model1",
Expand All @@ -150,7 +150,7 @@ def test_line_color_fill_between_interpolate(plot_df):
2010,
3.50,
]
df.data.loc[len(df.data) - 1] = newdata
df.loc[len(df) - 1] = newdata
newdata = [
"test_model1",
"test_scenario1",
Expand All @@ -160,7 +160,7 @@ def test_line_color_fill_between_interpolate(plot_df):
2012,
3.50,
]
df.data.loc[len(df.data)] = newdata
df.loc[len(df)] = newdata
newdata = [
"test_model1",
"test_scenario1",
Expand All @@ -170,7 +170,10 @@ def test_line_color_fill_between_interpolate(plot_df):
2015,
3.50,
]
df.data.loc[len(df.data) + 1] = newdata
df.loc[len(df) + 1] = newdata
columns_ = ['model', 'scenario', 'region', 'variable', 'unit', 'year']
df = df.drop_duplicates(subset=columns_).reset_index(drop=True)
df = pyam.IamDataFrame(df)
df.plot(ax=ax, color="model", fill_between=True, legend=True)
return fig

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