Remove shorthand import pandas as pd

Author: mx-moth

src/emsarray/cli/commands/extract_points.py

@@ -2,7 +2,7 @@
 import logging
 from pathlib import Path
 
-import pandas as pd
+import pandas
 
 import emsarray
 from emsarray.cli import BaseCommand, CommandException
@@ -64,7 +64,7 @@ def add_arguments(self, parser: argparse.ArgumentParser) -> None:
     def handle(self, options: argparse.Namespace) -> None:
         logger.info("Extracting points from %r", str(options.input_path))
         dataset = emsarray.open_dataset(options.input_path)
-        dataframe = pd.read_csv(options.points)
+        dataframe = pandas.read_csv(options.points)
 
         try:
             point_data = point_extraction.extract_dataframe(

src/emsarray/operations/point_extraction.py

@@ -17,7 +17,7 @@
 from typing import Any, Hashable, List, Literal, Tuple
 
 import numpy
-import pandas as pd
+import pandas
 import shapely
 import xarray
 import xarray.core.dtypes as xrdtypes
@@ -42,7 +42,7 @@ def __post_init__(self) -> None:
 
 
 def _dataframe_to_dataset(
-    dataframe: pd.DataFrame,
+    dataframe: pandas.DataFrame,
     *,
     dimension_name: Hashable,
 ) -> xarray.Dataset:
@@ -109,7 +109,7 @@ def extract_points(
                 points=[points[i] for i in out_of_bounds])
 
     # Make a DataFrame out of all point indexers
-    selector_df = pd.DataFrame([
+    selector_df = pandas.DataFrame([
         convention.selector_for_index(index.index)
         for index in indexes
         if index is not None])
@@ -127,7 +127,7 @@ def extract_points(
 
 def extract_dataframe(
     dataset: xarray.Dataset,
-    dataframe: pd.DataFrame,
+    dataframe: pandas.DataFrame,
     coordinate_columns: Tuple[str, str],
     *,
     point_dimension: Hashable = 'point',
@@ -178,11 +178,11 @@ def extract_dataframe(
     .. code-block:: python
 
         import emsarray
-        import pandas as pd
+        import pandas
         from emsarray.operations import point_extraction
 
         ds = emsarray.tutorial.open_dataset('gbr4')
-        df = pd.DataFrame({
+        df = pandas.DataFrame({
             'lon': [152.807, 152.670, 153.543],
             'lat': [-24.9595, -24.589, -25.488],
             'name': ['a', 'b', 'c'],

src/emsarray/utils.py

@@ -25,7 +25,7 @@
 import cftime
 import netCDF4
 import numpy
-import pandas as pd
+import pandas
 import pytz
 import shapely
 import xarray
@@ -315,7 +315,7 @@ def fix_bad_time_units(dataset_or_array: Union[xarray.Dataset, xarray.DataArray]
                 continue  # Don't bother fixing this one - too broken
 
             try:
-                pd.Timestamp(timestamp)
+                pandas.Timestamp(timestamp)
                 continue  # These units are formatted fine and don't need fixing
             except ValueError:
                 pass

tests/cli/commands/test_extract_points.py

@@ -1,7 +1,7 @@
 import pathlib
 
 import numpy
-import pandas as pd
+import pandas
 import pytest
 import xarray
 from numpy.testing import assert_allclose, assert_equal
@@ -24,7 +24,7 @@ def test_extract_points(
     θs = numpy.random.sample(num_points) * 2 * numpy.pi
     xs = numpy.cos(θs) * rs
     ys = numpy.sin(θs) * rs
-    points_df = pd.DataFrame({'name': names, 'lon': xs, 'lat': ys})
+    points_df = pandas.DataFrame({'name': names, 'lon': xs, 'lat': ys})
     points_df.to_csv(csv_path, index=False)
 
     main(['extract-points', str(in_path), str(csv_path), str(out_path)])
@@ -63,7 +63,7 @@ def test_extract_points_out_of_bounds(
     csv_path = tmp_path / 'points.csv'
     out_path = tmp_path / 'out.nc'
 
-    points_df = pd.DataFrame({'lon': numpy.arange(10), 'lat': numpy.zeros(10)})
+    points_df = pandas.DataFrame({'lon': numpy.arange(10), 'lat': numpy.zeros(10)})
     points_df.to_csv(csv_path, index=False)
     print(points_df.iloc[[4, 5, 6, 7, 8, 9]])
 

tests/conventions/test_cfgrid1d.py

@@ -4,7 +4,7 @@
 import pathlib
 
 import numpy
-import pandas as pd
+import pandas
 import pytest
 import xarray
 from matplotlib.figure import Figure
@@ -69,11 +69,11 @@ def make_dataset(
     )
 
     time = xarray.DataArray(
-        # Note: Using pd.date_range() directly here will lead to strange
+        # Note: Using pandas.date_range() directly here will lead to strange
         # behaviours, where the `time` dimension becomes a data variable with
         # a datetime64 dtype. Using a list of datetimes instead seems to avoid
         # this, resulting in time simply being a dimension.
-        data=list(pd.date_range("2021-11-11", periods=time_size)),
+        data=list(pandas.date_range("2021-11-11", periods=time_size)),
         dims=[time_name],
         name=time_name,
         attrs={

tests/conventions/test_cfgrid2d.py

@@ -13,7 +13,7 @@
 from typing import Type
 
 import numpy
-import pandas as pd
+import pandas
 import pytest
 import xarray
 from matplotlib.figure import Figure
@@ -86,7 +86,7 @@ def make_dataset(
     layers = ShocLayerGenerator(k=k_size)
 
     time = xarray.DataArray(
-        data=pd.date_range("2021-11-11", periods=time_size),
+        data=pandas.date_range("2021-11-11", periods=time_size),
         dims=["time"],
         attrs={
             "long_name": "Time",

tests/conventions/test_shoc_standard.py

@@ -6,7 +6,7 @@
 from typing import Type
 
 import numpy
-import pandas as pd
+import pandas
 import pytest
 import xarray
 from matplotlib.figure import Figure
@@ -82,11 +82,11 @@ def make_dataset(
     layers = ShocLayerGenerator(k=k_size)
 
     t = xarray.DataArray(
-        # Note: Using pd.date_range() directly here will lead to strange
+        # Note: Using pandas.date_range() directly here will lead to strange
         # behaviours, where the `record` dimension becomes a data variable with
         # a datetime64 dtype. Using a list of datetimes instead seems to avoid
         # this, resulting in record simply being a dimension.
-        data=list(pd.date_range("2021-11-11", periods=time_size)),
+        data=list(pandas.date_range("2021-11-11", periods=time_size)),
         dims=["record"],
         attrs={
             "long_name": "Time",

tests/conventions/test_ugrid.py

@@ -7,7 +7,7 @@
 
 import geojson
 import numpy
-import pandas as pd
+import pandas
 import pytest
 import xarray
 from matplotlib.figure import Figure
@@ -178,11 +178,11 @@ def make_dataset(
     )
 
     t = xarray.DataArray(
-        # Note: Using pd.date_range() directly here will lead to strange
+        # Note: Using pandas.date_range() directly here will lead to strange
         # behaviours, where the `record` dimension becomes a data variable with
         # a datetime64 dtype. Using a list of datetimes instead seems to avoid
         # this, resulting in record simply being a dimension.
-        data=list(pd.date_range("2021-11-11", periods=time_size)),
+        data=list(pandas.date_range("2021-11-11", periods=time_size)),
         dims=[time_dimension],
         name='t',
         attrs={

tests/masking/test_mask_dataset.py

@@ -4,7 +4,7 @@
 
 import netCDF4
 import numpy
-import pandas as pd
+import pandas
 import pytest
 import shapely.geometry
 import xarray
@@ -127,7 +127,7 @@ def test_mask_dataset(tmp_path: pathlib.Path):
     )
 
     t = xarray.DataArray(
-        data=list(pd.date_range("2021-11-11", periods=records)),
+        data=list(pandas.date_range("2021-11-11", periods=records)),
         dims=["record"],
         attrs={
             "long_name": "Time",

tests/operations/depth/test_ocean_floor.py

@@ -1,5 +1,5 @@
 import numpy
-import pandas as pd
+import pandas
 import pytest
 import xarray
 from numpy.testing import assert_equal
@@ -27,7 +27,7 @@ def test_ocean_floor():
     dataset = xarray.Dataset(
         data_vars={"temp": temp},
         coords={
-            'time': (['t'], pd.date_range('2022-02-08', periods=5)),
+            'time': (['t'], pandas.date_range('2022-02-08', periods=5)),
             'lon': (['x'], -numpy.arange(5)),
             'lat': (['y'], numpy.arange(5)),
             'depth': (['z'], 4.25 - numpy.arange(5), {'positive': 'down'}),