CLN: C408 Unnecessary dict call - rewrite as a literal #38138 (#38383)

* last dict fixings * last dict fixings * last dict fixings * last dict fixings

Author: UrielMaD

pandas/tests/io/generate_legacy_storage_files.py

@@ -142,16 +142,16 @@ def create_data():
  "E": [0.0, 1, Timestamp("20100101"), "foo", 2.0],
  }
 
- scalars = dict(timestamp=Timestamp("20130101"), period=Period("2012", "M"))
-
- index = dict(
- int=Index(np.arange(10)),
- date=date_range("20130101", periods=10),
- period=period_range("2013-01-01", freq="M", periods=10),
- float=Index(np.arange(10, dtype=np.float64)),
- uint=Index(np.arange(10, dtype=np.uint64)),
- timedelta=timedelta_range("00:00:00", freq="30T", periods=10),
- )
+ scalars = {"timestamp": Timestamp("20130101"), "period": Period("2012", "M")}
+
+ index = {
+ "int": Index(np.arange(10)),
+ "date": date_range("20130101", periods=10),
+ "period": period_range("2013-01-01", freq="M", periods=10),
+ "float": Index(np.arange(10, dtype=np.float64)),
+ "uint": Index(np.arange(10, dtype=np.uint64)),
+ "timedelta": timedelta_range("00:00:00", freq="30T", periods=10),
+ }
 
  index["range"] = RangeIndex(10)
 
@@ -160,8 +160,8 @@ def create_data():
 
  index["interval"] = interval_range(0, periods=10)
 
- mi = dict(
- reg2=MultiIndex.from_tuples(
+ mi = {
+ "reg2": MultiIndex.from_tuples(
  tuple(
  zip(
  *[
@@ -172,35 +172,35 @@ def create_data():
  ),
  names=["first", "second"],
  )
- )
+ }
 
- series = dict(
- float=Series(data["A"]),
- int=Series(data["B"]),
- mixed=Series(data["E"]),
- ts=Series(
+ series = {
+ "float": Series(data["A"]),
+ "int": Series(data["B"]),
+ "mixed": Series(data["E"]),
+ "ts": Series(
  np.arange(10).astype(np.int64), index=date_range("20130101", periods=10)
  ),
- mi=Series(
+ "mi": Series(
  np.arange(5).astype(np.float64),
  index=MultiIndex.from_tuples(
  tuple(zip(*[[1, 1, 2, 2, 2], [3, 4, 3, 4, 5]])), names=["one", "two"]
  ),
  ),
- dup=Series(np.arange(5).astype(np.float64), index=["A", "B", "C", "D", "A"]),
- cat=Series(Categorical(["foo", "bar", "baz"])),
- dt=Series(date_range("20130101", periods=5)),
- dt_tz=Series(date_range("20130101", periods=5, tz="US/Eastern")),
- period=Series([Period("2000Q1")] * 5),
- )
+ "dup": Series(np.arange(5).astype(np.float64), index=["A", "B", "C", "D", "A"]),
+ "cat": Series(Categorical(["foo", "bar", "baz"])),
+ "dt": Series(date_range("20130101", periods=5)),
+ "dt_tz": Series(date_range("20130101", periods=5, tz="US/Eastern")),
+ "period": Series([Period("2000Q1")] * 5),
+ }
 
  mixed_dup_df = DataFrame(data)
  mixed_dup_df.columns = list("ABCDA")
- frame = dict(
- float=DataFrame({"A": series["float"], "B": series["float"] + 1}),
- int=DataFrame({"A": series["int"], "B": series["int"] + 1}),
- mixed=DataFrame({k: data[k] for k in ["A", "B", "C", "D"]}),
- mi=DataFrame(
+ frame = {
+ "float": DataFrame({"A": series["float"], "B": series["float"] + 1}),
+ "int": DataFrame({"A": series["int"], "B": series["int"] + 1}),
+ "mixed": DataFrame({k: data[k] for k in ["A", "B", "C", "D"]}),
+ "mi": DataFrame(
  {"A": np.arange(5).astype(np.float64), "B": np.arange(5).astype(np.int64)},
  index=MultiIndex.from_tuples(
  tuple(
@@ -214,45 +214,45 @@ def create_data():
  names=["first", "second"],
  ),
  ),
- dup=DataFrame(
+ "dup": DataFrame(
  np.arange(15).reshape(5, 3).astype(np.float64), columns=["A", "B", "A"]
  ),
- cat_onecol=DataFrame({"A": Categorical(["foo", "bar"])}),
- cat_and_float=DataFrame(
+ "cat_onecol": DataFrame({"A": Categorical(["foo", "bar"])}),
+ "cat_and_float": DataFrame(
  {
  "A": Categorical(["foo", "bar", "baz"]),
  "B": np.arange(3).astype(np.int64),
  }
  ),
- mixed_dup=mixed_dup_df,
- dt_mixed_tzs=DataFrame(
+ "mixed_dup": mixed_dup_df,
+ "dt_mixed_tzs": DataFrame(
  {
  "A": Timestamp("20130102", tz="US/Eastern"),
  "B": Timestamp("20130603", tz="CET"),
  },
  index=range(5),
  ),
- dt_mixed2_tzs=DataFrame(
+ "dt_mixed2_tzs": DataFrame(
  {
  "A": Timestamp("20130102", tz="US/Eastern"),
  "B": Timestamp("20130603", tz="CET"),
  "C": Timestamp("20130603", tz="UTC"),
  },
  index=range(5),
  ),
- )
+ }
 
- cat = dict(
- int8=Categorical(list("abcdefg")),
- int16=Categorical(np.arange(1000)),
- int32=Categorical(np.arange(10000)),
- )
+ cat = {
+ "int8": Categorical(list("abcdefg")),
+ "int16": Categorical(np.arange(1000)),
+ "int32": Categorical(np.arange(10000)),
+ }
 
- timestamp = dict(
- normal=Timestamp("2011-01-01"),
- nat=NaT,
- tz=Timestamp("2011-01-01", tz="US/Eastern"),
- )
+ timestamp = {
+ "normal": Timestamp("2011-01-01"),
+ "nat": NaT,
+ "tz": Timestamp("2011-01-01", tz="US/Eastern"),
+ }
 
  timestamp["freq"] = Timestamp("2011-01-01", freq="D")
  timestamp["both"] = Timestamp("2011-01-01", tz="Asia/Tokyo", freq="M")
@@ -282,18 +282,18 @@ def create_data():
  "Minute": Minute(1),
  }
 
- return dict(
- series=series,
- frame=frame,
- index=index,
- scalars=scalars,
- mi=mi,
- sp_series=dict(float=_create_sp_series(), ts=_create_sp_tsseries()),
- sp_frame=dict(float=_create_sp_frame()),
- cat=cat,
- timestamp=timestamp,
- offsets=off,
- )
+ return {
+ "series": series,
+ "frame": frame,
+ "index": index,
+ "scalars": scalars,
+ "mi": mi,
+ "sp_series": {"float": _create_sp_series(), "ts": _create_sp_tsseries()},
+ "sp_frame": {"float": _create_sp_frame()},
+ "cat": cat,
+ "timestamp": timestamp,
+ "offsets": off,
+ }
 
 
 def create_pickle_data():

pandas/tests/io/parser/test_comment.py

@@ -26,7 +26,7 @@ def test_comment(all_parsers, na_values):
 
 
 @pytest.mark.parametrize(
- "read_kwargs", [dict(), dict(lineterminator="*"), dict(delim_whitespace=True)]
+ "read_kwargs", [{}, {"lineterminator": "*"}, {"delim_whitespace": True}]
 )
 def test_line_comment(all_parsers, read_kwargs):
  parser = all_parsers

pandas/tests/io/parser/test_dialect.py

@@ -17,14 +17,14 @@
 @pytest.fixture
 def custom_dialect():
  dialect_name = "weird"
- dialect_kwargs = dict(
- doublequote=False,
- escapechar="~",
- delimiter=":",
- skipinitialspace=False,
- quotechar="~",
- quoting=3,
- )
+ dialect_kwargs = {
+ "doublequote": False,
+ "escapechar": "~",
+ "delimiter": ":",
+ "skipinitialspace": False,
+ "quotechar": "~",
+ "quoting": 3,
+ }
  return dialect_name, dialect_kwargs
 
 
@@ -91,7 +91,7 @@ def test_dialect_conflict_except_delimiter(all_parsers, custom_dialect, arg, val
  data = "a:b\n1:2"
 
  warning_klass = None
- kwds = dict()
+ kwds = {}
 
  # arg=None tests when we pass in the dialect without any other arguments.
  if arg is not None:
@@ -114,12 +114,12 @@ def test_dialect_conflict_except_delimiter(all_parsers, custom_dialect, arg, val
 @pytest.mark.parametrize(
  "kwargs,warning_klass",
  [
- (dict(sep=","), None), # sep is default --> sep_override=True
- (dict(sep="."), ParserWarning), # sep isn't default --> sep_override=False
- (dict(delimiter=":"), None), # No conflict
- (dict(delimiter=None), None), # Default arguments --> sep_override=True
- (dict(delimiter=","), ParserWarning), # Conflict
- (dict(delimiter="."), ParserWarning), # Conflict
+ ({"sep": ","}, None), # sep is default --> sep_override=True
+ ({"sep": "."}, ParserWarning), # sep isn't default --> sep_override=False
+ ({"delimiter": ":"}, None), # No conflict
+ ({"delimiter": None}, None), # Default arguments --> sep_override=True
+ ({"delimiter": ","}, ParserWarning), # Conflict
+ ({"delimiter": "."}, ParserWarning), # Conflict
  ],
  ids=[
  "sep-override-true",

pandas/tests/io/parser/test_encoding.py

@@ -47,7 +47,7 @@ def test_utf16_bom_skiprows(all_parsers, sep, encoding):
  ",", sep
  )
  path = f"__{tm.rands(10)}__.csv"
- kwargs = dict(sep=sep, skiprows=2)
+ kwargs = {"sep": sep, "skiprows": 2}
  utf8 = "utf-8"
 
  with tm.ensure_clean(path) as path:
@@ -91,17 +91,17 @@ def test_unicode_encoding(all_parsers, csv_dir_path):
  "data,kwargs,expected",
  [
  # Basic test
- ("a\n1", dict(), DataFrame({"a": [1]})),
+ ("a\n1", {}, DataFrame({"a": [1]})),
  # "Regular" quoting
- ('"a"\n1', dict(quotechar='"'), DataFrame({"a": [1]})),
+ ('"a"\n1', {"quotechar": '"'}, DataFrame({"a": [1]})),
  # Test in a data row instead of header
- ("b\n1", dict(names=["a"]), DataFrame({"a": ["b", "1"]})),
+ ("b\n1", {"names": ["a"]}, DataFrame({"a": ["b", "1"]})),
  # Test in empty data row with skipping
- ("\n1", dict(names=["a"], skip_blank_lines=True), DataFrame({"a": [1]})),
+ ("\n1", {"names": ["a"], "skip_blank_lines": True}, DataFrame({"a": [1]})),
  # Test in empty data row without skipping
  (
  "\n1",
- dict(names=["a"], skip_blank_lines=False),
+ {"names": ["a"], "skip_blank_lines": False},
  DataFrame({"a": [np.nan, 1]}),
  ),
  ],