DOC: Added docstrings to fixtures defined in array module (#47211)

Author: melissawm

pandas/tests/arrays/boolean/test_arithmetic.py

@@ -9,6 +9,7 @@
 
 @pytest.fixture
 def data():
+    """Fixture returning boolean array with valid and missing values."""
     return pd.array(
         [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False],
         dtype="boolean",
@@ -17,11 +18,13 @@ def data():
 
 @pytest.fixture
 def left_array():
+    """Fixture returning boolean array with valid and missing values."""
     return pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean")
 
 
 @pytest.fixture
 def right_array():
+    """Fixture returning boolean array with valid and missing values."""
     return pd.array([True, False, None] * 3, dtype="boolean")
 
 

pandas/tests/arrays/boolean/test_comparison.py

@@ -9,6 +9,7 @@
 
 @pytest.fixture
 def data():
+    """Fixture returning boolean array with valid and missing data"""
     return pd.array(
         [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False],
         dtype="boolean",
@@ -17,6 +18,7 @@ def data():
 
 @pytest.fixture
 def dtype():
+    """Fixture returning BooleanDtype"""
     return pd.BooleanDtype()
 
 

pandas/tests/arrays/boolean/test_reduction.py

@@ -6,6 +6,7 @@
 
 @pytest.fixture
 def data():
+    """Fixture returning boolean array, with valid and missing values."""
     return pd.array(
         [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False],
         dtype="boolean",

pandas/tests/arrays/categorical/conftest.py

@@ -11,4 +11,5 @@ def allow_fill(request):
 
 @pytest.fixture
 def factor():
+    """Fixture returning  a Categorical object"""
     return Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)

pandas/tests/arrays/datetimes/test_reductions.py

@@ -12,6 +12,7 @@
 class TestReductions:
     @pytest.fixture
     def arr1d(self, tz_naive_fixture):
+        """Fixture returning DatetimeArray with parametrized timezones"""
         tz = tz_naive_fixture
         dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
         arr = DatetimeArray._from_sequence(

pandas/tests/arrays/floating/conftest.py

@@ -10,11 +10,13 @@
 
 @pytest.fixture(params=[Float32Dtype, Float64Dtype])
 def dtype(request):
+    """Parametrized fixture returning a float 'dtype'"""
     return request.param()
 
 
 @pytest.fixture
 def data(dtype):
+    """Fixture returning 'data' array according to parametrized float 'dtype'"""
     return pd.array(
         list(np.arange(0.1, 0.9, 0.1))
         + [pd.NA]
@@ -27,12 +29,19 @@ def data(dtype):
 
 @pytest.fixture
 def data_missing(dtype):
+    """
+    Fixture returning array with missing data according to parametrized float
+    'dtype'.
+    """
     return pd.array([np.nan, 0.1], dtype=dtype)
 
 
 @pytest.fixture(params=["data", "data_missing"])
 def all_data(request, data, data_missing):
-    """Parametrized fixture giving 'data' and 'data_missing'"""
+    """Parametrized fixture returning 'data' or 'data_missing' float arrays.
+
+    Used to test dtype conversion with and without missing values.
+    """
     if request.param == "data":
         return data
     elif request.param == "data_missing":

pandas/tests/arrays/integer/conftest.py

@@ -27,11 +27,18 @@
     ]
 )
 def dtype(request):
+    """Parametrized fixture returning integer 'dtype'"""
     return request.param()
 
 
 @pytest.fixture
 def data(dtype):
+    """
+    Fixture returning 'data' array with valid and missing values according to
+    parametrized integer 'dtype'.
+
+    Used to test dtype conversion with and without missing values.
+    """
     return pd.array(
         list(range(8)) + [np.nan] + list(range(10, 98)) + [np.nan] + [99, 100],
         dtype=dtype,
@@ -40,12 +47,21 @@ def data(dtype):
 
 @pytest.fixture
 def data_missing(dtype):
+    """
+    Fixture returning array with exactly one NaN and one valid integer,
+    according to parametrized integer 'dtype'.
+
+    Used to test dtype conversion with and without missing values.
+    """
     return pd.array([np.nan, 1], dtype=dtype)
 
 
 @pytest.fixture(params=["data", "data_missing"])
 def all_data(request, data, data_missing):
-    """Parametrized fixture giving 'data' and 'data_missing'"""
+    """Parametrized fixture returning 'data' or 'data_missing' integer arrays.
+
+    Used to test dtype conversion with and without missing values.
+    """
     if request.param == "data":
         return data
     elif request.param == "data_missing":

pandas/tests/arrays/integer/test_construction.py

@@ -14,6 +14,10 @@
 
 @pytest.fixture(params=[pd.array, IntegerArray._from_sequence])
 def constructor(request):
+    """Fixture returning parametrized IntegerArray from given sequence.
+
+    Used to test dtype conversions.
+    """
     return request.param
 
 

pandas/tests/arrays/masked/test_arithmetic.py

@@ -21,6 +21,11 @@
 
 @pytest.fixture(params=zip(arrays, scalars), ids=[a.dtype.name for a in arrays])
 def data(request):
+    """Fixture returning parametrized (array, scalar) tuple.
+
+    Used to test equivalence of scalars, numpy arrays with array ops, and the
+    equivalence of DataFrame and Series ops.
+    """
     return request.param
 
 

pandas/tests/arrays/masked/test_arrow_compat.py

@@ -15,6 +15,10 @@
 
 @pytest.fixture(params=arrays, ids=[a.dtype.name for a in arrays])
 def data(request):
+    """
+    Fixture returning parametrized array from given dtype, including integer,
+    float and boolean
+    """
     return request.param
 
 
@@ -101,6 +105,10 @@ def test_arrow_sliced(data):
 
 @pytest.fixture
 def np_dtype_to_arrays(any_real_numpy_dtype):
+    """
+    Fixture returning actual and expected dtype, pandas and numpy arrays and
+    mask from a given numpy dtype
+    """
     np_dtype = np.dtype(any_real_numpy_dtype)
     pa_type = pa.from_numpy_dtype(np_dtype)
 

pandas/tests/arrays/masked/test_function.py

@@ -14,11 +14,18 @@
 
 @pytest.fixture(params=arrays, ids=[a.dtype.name for a in arrays])
 def data(request):
+    """
+    Fixture returning parametrized 'data' array with different integer and
+    floating point types
+    """
     return request.param
 
 
 @pytest.fixture()
 def numpy_dtype(data):
+    """
+    Fixture returning numpy dtype from 'data' input array.
+    """
     # For integer dtype, the numpy conversion must be done to float
     if is_integer_dtype(data):
         numpy_dtype = float

pandas/tests/arrays/sparse/test_arithmetics.py

@@ -22,7 +22,10 @@ def kind(request):
 
 @pytest.fixture(params=[True, False])
 def mix(request):
-    # whether to operate op(sparse, dense) instead of op(sparse, sparse)
+    """
+    Fixture returning True or False, determining whether to operate
+    op(sparse, dense) instead of op(sparse, sparse)
+    """
     return request.param
 
 

pandas/tests/arrays/sparse/test_array.py

@@ -18,16 +18,19 @@
 
 @pytest.fixture
 def arr_data():
+    """Fixture returning numpy array with valid and missing entries"""
     return np.array([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
 
 
 @pytest.fixture
 def arr(arr_data):
+    """Fixture returning SparseArray from 'arr_data'"""
     return SparseArray(arr_data)
 
 
 @pytest.fixture
 def zarr():
+    """Fixture returning SparseArray with integer entries and 'fill_value=0'"""
     return SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0)
 
 

pandas/tests/arrays/string_/test_string.py

@@ -18,11 +18,13 @@
 
 @pytest.fixture
 def dtype(string_storage):
+    """Fixture giving StringDtype from parametrized 'string_storage'"""
     return pd.StringDtype(storage=string_storage)
 
 
 @pytest.fixture
 def cls(dtype):
+    """Fixture giving array type from parametrized 'dtype'"""
     return dtype.construct_array_type()
 
 

pandas/tests/arrays/test_array.py

@@ -379,6 +379,7 @@ def test_array_unboxes(index_or_series):
 
 @pytest.fixture
 def registry_without_decimal():
+    """Fixture yielding 'registry' with no DecimalDtype entries"""
     idx = registry.dtypes.index(DecimalDtype)
     registry.dtypes.pop(idx)
     yield

pandas/tests/arrays/test_datetimelike.py

@@ -33,6 +33,7 @@
 # TODO: more freq variants
 @pytest.fixture(params=["D", "B", "W", "M", "Q", "Y"])
 def freqstr(request):
+    """Fixture returning parametrized frequency in string format."""
     return request.param
 
 
@@ -81,6 +82,7 @@ class SharedTests:
 
     @pytest.fixture
     def arr1d(self):
+        """Fixture returning DatetimeArray with daily frequency."""
         data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
         arr = self.array_cls(data, freq="D")
         return arr
@@ -635,6 +637,10 @@ class TestDatetimeArray(SharedTests):
 
     @pytest.fixture
     def arr1d(self, tz_naive_fixture, freqstr):
+        """
+        Fixture returning DatetimeArray with parametrized frequency and
+        timezones
+        """
         tz = tz_naive_fixture
         dti = pd.date_range("2016-01-01 01:01:00", periods=5, freq=freqstr, tz=tz)
         dta = dti._data
@@ -1068,6 +1074,9 @@ class TestPeriodArray(SharedTests):
 
     @pytest.fixture
     def arr1d(self, period_index):
+        """
+        Fixture returning DatetimeArray from parametrized PeriodIndex objects
+        """
         return period_index._data
 
     def test_from_pi(self, arr1d):
@@ -1389,6 +1398,10 @@ def test_from_pandas_array(dtype):
     ]
 )
 def array_likes(request):
+    """
+    Fixture giving a numpy array and a parametrized 'data' object, which can
+    be a memoryview, array, dask or xarray object created from the numpy array.
+    """
     # GH#24539 recognize e.g xarray, dask, ...
     arr = np.array([1, 2, 3], dtype=np.int64)
 

pandas/tests/arrays/test_datetimes.py

@@ -14,10 +14,12 @@
 class TestNonNano:
     @pytest.fixture(params=["s", "ms", "us"])
     def unit(self, request):
+        """Fixture returning parametrized time units"""
         return request.param
 
     @pytest.fixture
     def reso(self, unit):
+        """Fixture returning datetime resolution for a given time unit"""
         # TODO: avoid hard-coding
         return {"s": 7, "ms": 8, "us": 9}[unit]