fix frame, generic, series.py (#38169)

Author: liudj2008

pandas/core/frame.py

@@ -172,27 +172,27 @@
 # ---------------------------------------------------------------------
 # Docstring templates
 
-_shared_doc_kwargs = dict(
-    axes="index, columns",
-    klass="DataFrame",
-    axes_single_arg="{0 or 'index', 1 or 'columns'}",
-    axis="""axis : {0 or 'index', 1 or 'columns'}, default 0
+_shared_doc_kwargs = {
+    "axes": "index, columns",
+    "klass": "DataFrame",
+    "axes_single_arg": "{0 or 'index', 1 or 'columns'}",
+    "axis": """axis : {0 or 'index', 1 or 'columns'}, default 0
         If 0 or 'index': apply function to each column.
         If 1 or 'columns': apply function to each row.""",
-    optional_by="""
+    "optional_by": """
         by : str or list of str
             Name or list of names to sort by.
 
             - if `axis` is 0 or `'index'` then `by` may contain index
               levels and/or column labels.
             - if `axis` is 1 or `'columns'` then `by` may contain column
               levels and/or index labels.""",
-    optional_labels="""labels : array-like, optional
+    "optional_labels": """labels : array-like, optional
             New labels / index to conform the axis specified by 'axis' to.""",
-    optional_axis="""axis : int or str, optional
+    "optional_axis": """axis : int or str, optional
             Axis to target. Can be either the axis name ('index', 'columns')
             or number (0, 1).""",
-)
+}
 
 _numeric_only_doc = """numeric_only : boolean, default None
     Include only float, int, boolean data. If None, will attempt to use
@@ -524,7 +524,7 @@ def __init__(
                 return
 
             mgr = self._init_mgr(
-                data, axes=dict(index=index, columns=columns), dtype=dtype, copy=copy
+                data, axes={"index": index, "columns": columns}, dtype=dtype, copy=copy
             )
 
         elif isinstance(data, dict):
@@ -2902,7 +2902,7 @@ def transpose(self, *args, copy: bool = False) -> DataFrame:
         1    object
         dtype: object
         """
-        nv.validate_transpose(args, dict())
+        nv.validate_transpose(args, {})
         # construct the args
 
         dtypes = list(self.dtypes)
@@ -7358,7 +7358,7 @@ def unstack(self, level=-1, fill_value=None):
 
         return result.__finalize__(self, method="unstack")
 
-    @Appender(_shared_docs["melt"] % dict(caller="df.melt(", other="melt"))
+    @Appender(_shared_docs["melt"] % {"caller": "df.melt(", "other": "melt"})
     def melt(
         self,
         id_vars=None,

pandas/core/generic.py

@@ -128,15 +128,15 @@
 # goal is to be able to define the docs close to function, while still being
 # able to share
 _shared_docs = {**_shared_docs}
-_shared_doc_kwargs = dict(
-    axes="keywords for axes",
-    klass="Series/DataFrame",
-    axes_single_arg="int or labels for object",
-    args_transpose="axes to permute (int or label for object)",
-    optional_by="""
+_shared_doc_kwargs = {
+    "axes": "keywords for axes",
+    "klass": "Series/DataFrame",
+    "axes_single_arg": "int or labels for object",
+    "args_transpose": "axes to permute (int or label for object)",
+    "optional_by": """
         by : str or list of str
             Name or list of names to sort by""",
-)
+}
 
 
 bool_t = bool  # Need alias because NDFrame has def bool:
@@ -484,7 +484,7 @@ def _get_block_manager_axis(cls, axis: Axis) -> int:
     def _get_axis_resolvers(self, axis: str) -> Dict[str, Union[Series, MultiIndex]]:
         # index or columns
         axis_index = getattr(self, axis)
-        d = dict()
+        d = {}
         prefix = axis[0]
 
         for i, name in enumerate(axis_index.names):
@@ -1946,14 +1946,14 @@ def __array_ufunc__(
     @final
     def __getstate__(self) -> Dict[str, Any]:
         meta = {k: getattr(self, k, None) for k in self._metadata}
-        return dict(
-            _mgr=self._mgr,
-            _typ=self._typ,
-            _metadata=self._metadata,
-            attrs=self.attrs,
-            _flags={k: self.flags[k] for k in self.flags._keys},
+        return {
+            "_mgr": self._mgr,
+            "_typ": self._typ,
+            "_metadata": self._metadata,
+            "attrs": self.attrs,
+            "_flags": {k: self.flags[k] for k in self.flags._keys},
             **meta,
-        )
+        }
 
     @final
     def __setstate__(self, state):
@@ -1967,7 +1967,7 @@ def __setstate__(self, state):
             if typ is not None:
                 attrs = state.get("_attrs", {})
                 object.__setattr__(self, "_attrs", attrs)
-                flags = state.get("_flags", dict(allows_duplicate_labels=True))
+                flags = state.get("_flags", {"allows_duplicate_labels": True})
                 object.__setattr__(self, "_flags", Flags(self, **flags))
 
                 # set in the order of internal names
@@ -7422,7 +7422,7 @@ def isna(self: FrameOrSeries) -> FrameOrSeries:
 
         >>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
         ...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
-        ...                             pd.Timestamp('1940-04-25')],
+        ...                          pd.Timestamp('1940-04-25')],
         ...                    name=['Alfred', 'Batman', ''],
         ...                    toy=[None, 'Batmobile', 'Joker']))
         >>> df
@@ -7489,7 +7489,7 @@ def notna(self: FrameOrSeries) -> FrameOrSeries:
 
         >>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
         ...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
-        ...                             pd.Timestamp('1940-04-25')],
+        ...                          pd.Timestamp('1940-04-25')],
         ...                    name=['Alfred', 'Batman', ''],
         ...                    toy=[None, 'Batmobile', 'Joker']))
         >>> df
@@ -8209,8 +8209,8 @@ def resample(
         For DataFrame objects, the keyword `on` can be used to specify the
         column instead of the index for resampling.
 
-        >>> d = dict({'price': [10, 11, 9, 13, 14, 18, 17, 19],
-        ...           'volume': [50, 60, 40, 100, 50, 100, 40, 50]})
+        >>> d = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
+        ...      'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
         >>> df = pd.DataFrame(d)
         >>> df['week_starting'] = pd.date_range('01/01/2018',
         ...                                     periods=8,
@@ -8235,8 +8235,8 @@ def resample(
         specify on which level the resampling needs to take place.
 
         >>> days = pd.date_range('1/1/2000', periods=4, freq='D')
-        >>> d2 = dict({'price': [10, 11, 9, 13, 14, 18, 17, 19],
-        ...            'volume': [50, 60, 40, 100, 50, 100, 40, 50]})
+        >>> d2 = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
+        ...       'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
         >>> df2 = pd.DataFrame(d2,
         ...                    index=pd.MultiIndex.from_product([days,
         ...                                                     ['morning',
@@ -10483,10 +10483,10 @@ def pct_change(
         Percentage change in French franc, Deutsche Mark, and Italian lira from
         1980-01-01 to 1980-03-01.
 
-        >>> df = pd.DataFrame(dict(
-        ...     FR=[4.0405, 4.0963, 4.3149],
-        ...     GR=[1.7246, 1.7482, 1.8519],
-        ...     IT=[804.74, 810.01, 860.13]),
+        >>> df = pd.DataFrame({
+        ...     'FR': [4.0405, 4.0963, 4.3149],
+        ...     'GR': [1.7246, 1.7482, 1.8519],
+        ...     'IT': [804.74, 810.01, 860.13]},
         ...     index=['1980-01-01', '1980-02-01', '1980-03-01'])
         >>> df
                         FR      GR      IT
@@ -10503,10 +10503,10 @@ def pct_change(
         Percentage of change in GOOG and APPL stock volume. Shows computing
         the percentage change between columns.
 
-        >>> df = pd.DataFrame(dict([
-        ...     ('2016', [1769950, 30586265]),
-        ...     ('2015', [1500923, 40912316]),
-        ...     ('2014', [1371819, 41403351])]),
+        >>> df = pd.DataFrame({
+        ...     '2016': [1769950, 30586265],
+        ...     '2015': [1500923, 40912316],
+        ...     '2014': [1371819, 41403351]},
         ...     index=['GOOG', 'APPL'])
         >>> df
                   2016      2015      2014

pandas/core/series.py

@@ -111,21 +111,21 @@
 
 __all__ = ["Series"]
 
-_shared_doc_kwargs = dict(
-    axes="index",
-    klass="Series",
-    axes_single_arg="{0 or 'index'}",
-    axis="""axis : {0 or 'index'}
+_shared_doc_kwargs = {
+    "axes": "index",
+    "klass": "Series",
+    "axes_single_arg": "{0 or 'index'}",
+    "axis": """axis : {0 or 'index'}
         Parameter needed for compatibility with DataFrame.""",
-    inplace="""inplace : boolean, default False
+    "inplace": """inplace : boolean, default False
         If True, performs operation inplace and returns None.""",
-    unique="np.ndarray",
-    duplicated="Series",
-    optional_by="",
-    optional_mapper="",
-    optional_labels="",
-    optional_axis="",
-)
+    "unique": "np.ndarray",
+    "duplicated": "Series",
+    "optional_by": "",
+    "optional_mapper": "",
+    "optional_labels": "",
+    "optional_axis": "",
+}
 
 
 def _coerce_method(converter):
@@ -1113,7 +1113,7 @@ def repeat(self, repeats, axis=None) -> "Series":
         2    c
         dtype: object
         """
-        nv.validate_repeat(tuple(), dict(axis=axis))
+        nv.validate_repeat(tuple(), {"axis": axis})
         new_index = self.index.repeat(repeats)
         new_values = self._values.repeat(repeats)
         return self._constructor(new_values, index=new_index).__finalize__(