ruff ICN rule compliance

.ruff.toml

@@ -106,9 +106,9 @@ lint.ignore = [
     # https://docs.astral.sh/ruff/rules/#flake8-future-annotations-fa
     "FA",
 
-    # flake8-import-conventions (ICN)
-    # https://docs.astral.sh/ruff/rules/#flake8-import-conventions-icn
-    "ICN",
+    # flake8-implicit-str-concat (ISC)
+    # https://docs.astral.sh/ruff/rules/#flake8-implicit-str-concat-isc
+    "ISC",
 
     # flake8-logging-format (G)
     # https://docs.astral.sh/ruff/rules/#flake8-logging-format-g

benchmarks/benchmarks/plot.py

@@ -5,14 +5,14 @@
 """Plot benchmark tests.
 
 """
-import matplotlib
+import matplotlib as mpl
 import numpy as np
 
 from iris import coords, cube, plot
 
 from . import ARTIFICIAL_DIM_SIZE
 
-matplotlib.use("agg")
+mpl.use("agg")
 
 
 class AuxSort:

lib/iris/pandas.py

@@ -16,7 +16,7 @@
 import cftime
 import numpy as np
 import numpy.ma as ma
-import pandas
+import pandas as pd
 
 try:
     from pandas.core.indexes.datetimes import DatetimeIndex  # pandas >=0.20
@@ -44,7 +44,7 @@ def _get_dimensional_metadata(name, values, calendar=None, dm_class=None):
 
     # Convert out of NumPy's own datetime format.
     if np.issubdtype(values.dtype, np.datetime64):
-        values = pandas.to_datetime(values)
+        values = pd.to_datetime(values)
 
     # Convert pandas datetime objects to python datetime objects.
     if isinstance(values, DatetimeIndex):
@@ -87,7 +87,7 @@ def _add_iris_coord(cube, name, points, dim, calendar=None):
         cube.add_aux_coord(coord, dim)
 
 
-def _series_index_unique(pandas_series: pandas.Series):
+def _series_index_unique(pandas_series: pd.Series):
     """Find an index grouping of a :class:`pandas.Series` that has just one Series value per group.
 
     Iterates through grouping single index levels, then combinations of 2
@@ -357,7 +357,7 @@ def as_cubes(
     cell_measure_cols = cell_measure_cols or []
     ancillary_variable_cols = ancillary_variable_cols or []
 
-    is_series = isinstance(pandas_structure, pandas.Series)
+    is_series = isinstance(pandas_structure, pd.Series)
 
     if copy:
         pandas_structure = pandas_structure.copy()
@@ -604,7 +604,7 @@ def as_series(cube, copy=True):
     index = None
     if cube.dim_coords:
         index = _as_pandas_coord(cube.dim_coords[0])
-    series = pandas.Series(data, index)
+    series = pd.Series(data, index)
     if not copy:
         _assert_shared(data, series)
     return series
@@ -811,16 +811,16 @@ def merge_metadata(meta_var_list):
                 # dimension over the whole DataFrame
                 data_frame[meta_var_name] = meta_var.squeeze()
             else:
-                meta_df = pandas.DataFrame(
+                meta_df = pd.DataFrame(
                     meta_var.ravel(),
                     columns=[meta_var_name],
-                    index=pandas.MultiIndex.from_product(
+                    index=pd.MultiIndex.from_product(
                         [coords[i] for i in meta_var_index],
                         names=[coord_names[i] for i in meta_var_index],
                     ),
                 )
                 # Merge to main data frame
-                data_frame = pandas.merge(
+                data_frame = pd.merge(
                     data_frame,
                     meta_df,
                     left_index=True,
@@ -847,8 +847,8 @@ def merge_metadata(meta_var_list):
         # Extract dim coord information: separate lists for dim names and dim values
         coord_names, coords = _make_dim_coord_list(cube)
         # Make base DataFrame
-        index = pandas.MultiIndex.from_product(coords, names=coord_names)
-        data_frame = pandas.DataFrame(data.ravel(), columns=[cube.name()], index=index)
+        index = pd.MultiIndex.from_product(coords, names=coord_names)
+        data_frame = pd.DataFrame(data.ravel(), columns=[cube.name()], index=index)
 
         if add_aux_coords:
             data_frame = merge_metadata(_make_aux_coord_list(cube))
@@ -889,7 +889,7 @@ def merge_metadata(meta_var_list):
         if cube.coords(dimensions=[1]):
             columns = _as_pandas_coord(cube.coord(dimensions=[1]))
 
-        data_frame = pandas.DataFrame(data, index, columns)
+        data_frame = pd.DataFrame(data, index, columns)
         if not copy:
             _assert_shared(data, data_frame)
 

lib/iris/tests/graphics/__init__.py

@@ -27,15 +27,15 @@
 # Test for availability of matplotlib.
 # (And remove matplotlib as an iris.tests dependency.)
 try:
-    import matplotlib
+    import matplotlib as mpl
 
     # Override any user settings e.g. from matplotlibrc file.
-    matplotlib.rcdefaults()
+    mpl.rcdefaults()
     # Set backend *after* rcdefaults, as we don't want that overridden (#3846).
-    matplotlib.use("agg")
+    mpl.use("agg")
     # Standardise the figure size across matplotlib versions.
     # This permits matplotlib png image comparison.
-    matplotlib.rcParams["figure.figsize"] = [8.0, 6.0]
+    mpl.rcParams["figure.figsize"] = [8.0, 6.0]
     import matplotlib.pyplot as plt
 except ImportError:
     MPL_AVAILABLE = False

lib/iris/tests/unit/pandas/test_pandas.py

@@ -26,18 +26,18 @@
 # used by matplotlib for handling dates.
 default_units_registry = copy.copy(matplotlib.units.registry)
 try:
-    import pandas
+    import pandas as pd
 except ImportError:
     # Disable all these tests if pandas is not installed.
-    pandas = None
+    pd = None
 matplotlib.units.registry = default_units_registry
 
 skip_pandas = pytest.mark.skipif(
-    pandas is None,
+    pd is None,
     reason='Test(s) require "pandas", ' "which is not available.",
 )
 
-if pandas is not None:
+if pd is not None:
     from iris.coords import AncillaryVariable, AuxCoord, CellMeasure, DimCoord
     from iris.cube import Cube, CubeList
     import iris.pandas
@@ -234,7 +234,7 @@ def test_time_standard(self):
         days_to_2000 = 365 * 30 + 7
         # pandas Timestamp class cannot handle floats in pandas <v0.12
         timestamps = [
-            pandas.Timestamp(int(nanoseconds_per_day * (days_to_2000 + day_offset)))
+            pd.Timestamp(int(nanoseconds_per_day * (days_to_2000 + day_offset)))
             for day_offset in day_offsets
         ]
         assert all(data_frame.columns == timestamps)
@@ -588,7 +588,7 @@ def test_instance_error(self):
 )
 class TestSeriesAsCube(tests.IrisTest):
     def test_series_simple(self):
-        series = pandas.Series([0, 1, 2, 3, 4], index=[5, 6, 7, 8, 9])
+        series = pd.Series([0, 1, 2, 3, 4], index=[5, 6, 7, 8, 9])
         self.assertCML(
             iris.pandas.as_cube(series),
             tests.get_result_path(("pandas", "as_cube", "series_simple.cml")),
@@ -599,7 +599,7 @@ class Thing:
             def __repr__(self):
                 return "A Thing"
 
-        series = pandas.Series(
+        series = pd.Series(
             [0, 1, 2, 3, 4],
             index=[Thing(), Thing(), Thing(), Thing(), Thing()],
         )
@@ -609,14 +609,14 @@ def __repr__(self):
         )
 
     def test_series_masked(self):
-        series = pandas.Series([0, float("nan"), 2, np.nan, 4], index=[5, 6, 7, 8, 9])
+        series = pd.Series([0, float("nan"), 2, np.nan, 4], index=[5, 6, 7, 8, 9])
         self.assertCML(
             iris.pandas.as_cube(series),
             tests.get_result_path(("pandas", "as_cube", "series_masked.cml")),
         )
 
     def test_series_datetime_standard(self):
-        series = pandas.Series(
+        series = pd.Series(
             [0, 1, 2, 3, 4],
             index=[
                 datetime.datetime(2001, 1, 1, 1, 1, 1),
@@ -634,7 +634,7 @@ def test_series_datetime_standard(self):
         )
 
     def test_series_cftime_360(self):
-        series = pandas.Series(
+        series = pd.Series(
             [0, 1, 2, 3, 4],
             index=[
                 cftime.datetime(2001, 1, 1, 1, 1, 1),
@@ -650,13 +650,13 @@ def test_series_cftime_360(self):
         )
 
     def test_copy_true(self):
-        series = pandas.Series([0, 1, 2, 3, 4], index=[5, 6, 7, 8, 9])
+        series = pd.Series([0, 1, 2, 3, 4], index=[5, 6, 7, 8, 9])
         cube = iris.pandas.as_cube(series)
         cube.data[0] = 99
         assert series[5] == 0
 
     def test_copy_false(self):
-        series = pandas.Series([0, 1, 2, 3, 4], index=[5, 6, 7, 8, 9])
+        series = pd.Series([0, 1, 2, 3, 4], index=[5, 6, 7, 8, 9])
         cube = iris.pandas.as_cube(series, copy=False)
         cube.data[0] = 99
         assert series[5] == 99
@@ -668,7 +668,7 @@ def test_copy_false(self):
 )
 class TestDataFrameAsCube(tests.IrisTest):
     def test_data_frame_simple(self):
-        data_frame = pandas.DataFrame(
+        data_frame = pd.DataFrame(
             [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]],
             index=[10, 11],
             columns=[12, 13, 14, 15, 16],
@@ -679,7 +679,7 @@ def test_data_frame_simple(self):
         )
 
     def test_data_frame_nonotonic(self):
-        data_frame = pandas.DataFrame(
+        data_frame = pd.DataFrame(
             [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]],
             index=[10, 10],
             columns=[12, 12, 14, 15, 16],
@@ -690,7 +690,7 @@ def test_data_frame_nonotonic(self):
         )
 
     def test_data_frame_masked(self):
-        data_frame = pandas.DataFrame(
+        data_frame = pd.DataFrame(
             [[0, float("nan"), 2, 3, 4], [5, 6, 7, np.nan, 9]],
             index=[10, 11],
             columns=[12, 13, 14, 15, 16],
@@ -701,7 +701,7 @@ def test_data_frame_masked(self):
         )
 
     def test_data_frame_multidim(self):
-        data_frame = pandas.DataFrame(
+        data_frame = pd.DataFrame(
             [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]],
             index=[0, 1],
             columns=["col_1", "col_2", "col_3", "col_4", "col_5"],
@@ -712,7 +712,7 @@ def test_data_frame_multidim(self):
         )
 
     def test_data_frame_cftime_360(self):
-        data_frame = pandas.DataFrame(
+        data_frame = pd.DataFrame(
             [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]],
             index=[
                 cftime.datetime(2001, 1, 1, 1, 1, 1),
@@ -728,7 +728,7 @@ def test_data_frame_cftime_360(self):
         )
 
     def test_data_frame_datetime_standard(self):
-        data_frame = pandas.DataFrame(
+        data_frame = pd.DataFrame(
             [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]],
             index=[
                 datetime.datetime(2001, 1, 1, 1, 1, 1),
@@ -744,13 +744,13 @@ def test_data_frame_datetime_standard(self):
         )
 
     def test_copy_true(self):
-        data_frame = pandas.DataFrame([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])
+        data_frame = pd.DataFrame([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])
         cube = iris.pandas.as_cube(data_frame)
         cube.data[0, 0] = 99
         assert data_frame[0][0] == 0
 
     def test_copy_false(self):
-        data_frame = pandas.DataFrame([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])
+        data_frame = pd.DataFrame([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])
         cube = iris.pandas.as_cube(data_frame, copy=False)
         cube.data[0, 0] = 99
         assert data_frame[0][0] == 99
@@ -759,7 +759,7 @@ def test_copy_false(self):
 @skip_pandas
 class TestFutureAndDeprecation:
     def test_as_cube_deprecation_warning(self):
-        data_frame = pandas.DataFrame([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])
+        data_frame = pd.DataFrame([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]])
         with pytest.warns(IrisDeprecation, match="as_cube has been deprecated"):
             _ = iris.pandas.as_cube(data_frame)
 
@@ -794,10 +794,10 @@ def _create_pandas(index_levels=0, is_series=False):
         ]
 
         if index_levels == 1:
-            index = pandas.Index(index_values[0], name=index_names[0])
+            index = pd.Index(index_values[0], name=index_names[0])
             data_length = index_length
         elif index_levels > 1:
-            index = pandas.MultiIndex.from_product(index_values, names=index_names)
+            index = pd.MultiIndex.from_product(index_values, names=index_names)
             data_length = index.nunique()
         else:
             index = None
@@ -806,9 +806,9 @@ def _create_pandas(index_levels=0, is_series=False):
         data = np.arange(data_length) * 10
 
         if is_series:
-            class_ = pandas.Series
+            class_ = pd.Series
         else:
-            class_ = pandas.DataFrame
+            class_ = pd.DataFrame
 
         return class_(data, index=index)
 
@@ -1013,13 +1013,13 @@ def test_multi_phenom(self):
         assert result == [expected_cube_0, expected_cube_1]
 
     def test_empty_series(self):
-        series = pandas.Series(dtype=object)
+        series = pd.Series(dtype=object)
         result = iris.pandas.as_cubes(series)
 
         assert result == CubeList()
 
     def test_empty_dataframe(self):
-        df = pandas.DataFrame()
+        df = pd.DataFrame()
         result = iris.pandas.as_cubes(df)
 
         assert result == CubeList()
@@ -1165,7 +1165,7 @@ def _test_dates_common(self, mode=None, alt_calendar=False):
         datetime_args = [(1971, 1, 1, i, 0, 0) for i in df.index.values]
         if mode == "index":
             values = [datetime.datetime(*a) for a in datetime_args]
-            df.index = pandas.Index(values, name=coord_name)
+            df.index = pd.Index(values, name=coord_name)
         elif mode == "numpy":
             values = [datetime.datetime(*a) for a in datetime_args]
             df[coord_name] = values