TYP: pandas/core/frame.py

pandas/core/frame.py

@@ -22,6 +22,7 @@
     TYPE_CHECKING,
     Any,
     AnyStr,
+    Callable,
     Dict,
     FrozenSet,
     Hashable,
@@ -71,6 +72,7 @@
     NpDtype,
     PythonFuncType,
     Renamer,
+    Scalar,
     StorageOptions,
     Suffixes,
     ValueKeyFunc,
@@ -1208,7 +1210,9 @@ def iterrows(self) -> Iterable[Tuple[Hashable, Series]]:
             s = klass(v, index=columns, name=k)
             yield k, s
 
-    def itertuples(self, index: bool = True, name: Optional[str] = "Pandas"):
+    def itertuples(
+        self, index: bool = True, name: Optional[str] = "Pandas"
+    ) -> Iterable[Tuple[Any, ...]]:
         """
         Iterate over DataFrame rows as namedtuples.
 
@@ -1452,7 +1456,11 @@ def __rmatmul__(self, other):
 
     @classmethod
     def from_dict(
-        cls, data, orient="columns", dtype: Optional[Dtype] = None, columns=None
+        cls,
+        data,
+        orient: str = "columns",
+        dtype: Optional[Dtype] = None,
+        columns=None,
     ) -> DataFrame:
         """
         Construct DataFrame from dict of array-like or dicts.
@@ -1890,7 +1898,7 @@ def from_records(
         exclude=None,
         columns=None,
         coerce_float: bool = False,
-        nrows=None,
+        nrows: Optional[int] = None,
     ) -> DataFrame:
         """
         Convert structured or record ndarray to DataFrame.
@@ -3080,7 +3088,7 @@ def info(
             show_counts=show_counts,
         )
 
-    def memory_usage(self, index=True, deep=False) -> Series:
+    def memory_usage(self, index: bool = True, deep: bool = False) -> Series:
         """
         Return the memory usage of each column in bytes.
 
@@ -3482,7 +3490,7 @@ def _getitem_multilevel(self, key):
             # loc is neither a slice nor ndarray, so must be an int
             return self._ixs(loc, axis=1)
 
-    def _get_value(self, index, col, takeable: bool = False):
+    def _get_value(self, index, col, takeable: bool = False) -> Scalar:
         """
         Quickly retrieve single value at passed column and index.
 
@@ -3650,7 +3658,7 @@ def _iset_item(self, loc: int, value):
         if len(self):
             self._check_setitem_copy()
 
-    def _set_item(self, key, value):
+    def _set_item(self, key, value) -> None:
         """
         Add series to DataFrame in specified column.
 
@@ -3675,16 +3683,21 @@ def _set_item(self, key, value):
 
         self._set_item_mgr(key, value)
 
-    def _set_value(self, index, col, value, takeable: bool = False):
+    def _set_value(
+        self, index: IndexLabel, col, value: Scalar, takeable: bool = False
+    ) -> None:
         """
         Put single value at passed column and index.
 
         Parameters
         ----------
-        index : row label
-        col : column label
+        index : Label
+            row label
+        col : Label
+            column label
         value : scalar
-        takeable : interpret the index/col as indexers, default False
+        takeable : bool, default False
+            Sets whether or not index/col interpreted as indexers
         """
         try:
             if takeable:
@@ -3708,7 +3721,7 @@ def _set_value(self, index, col, value, takeable: bool = False):
                 self.loc[index, col] = value
             self._item_cache.pop(col, None)
 
-    def _ensure_valid_index(self, value):
+    def _ensure_valid_index(self, value) -> None:
         """
         Ensure that if we don't have an index, that we can create one from the
         passed value.
@@ -3905,6 +3918,7 @@ def query(self, expr: str, inplace: bool = False, **kwargs):
 
         if inplace:
             self._update_inplace(result)
+            return None
         else:
             return result
 
@@ -4372,7 +4386,9 @@ def _series(self):
             for idx, item in enumerate(self.columns)
         }
 
-    def lookup(self, row_labels, col_labels) -> np.ndarray:
+    def lookup(
+        self, row_labels: Sequence[IndexLabel], col_labels: Sequence[IndexLabel]
+    ) -> np.ndarray:
         """
         Label-based "fancy indexing" function for DataFrame.
         Given equal-length arrays of row and column labels, return an
@@ -6547,7 +6563,7 @@ def _arith_method(self, other, op):
 
     _logical_method = _arith_method
 
-    def _dispatch_frame_op(self, right, func, axis: Optional[int] = None):
+    def _dispatch_frame_op(self, right, func: Callable, axis: Optional[int] = None):
         """
         Evaluate the frame operation func(left, right) by evaluating
         column-by-column, dispatching to the Series implementation.
@@ -7883,7 +7899,7 @@ def explode(
 
         return result
 
-    def unstack(self, level=-1, fill_value=None):
+    def unstack(self, level: Level = -1, fill_value=None):
         """
         Pivot a level of the (necessarily hierarchical) index labels.
 
@@ -7954,7 +7970,7 @@ def melt(
         var_name=None,
         value_name="value",
         col_level: Optional[Level] = None,
-        ignore_index=True,
+        ignore_index: bool = True,
     ) -> DataFrame:
 
         return melt(
@@ -8819,7 +8835,9 @@ def merge(
             validate=validate,
         )
 
-    def round(self, decimals=0, *args, **kwargs) -> DataFrame:
+    def round(
+        self, decimals: Union[int, Dict[IndexLabel, int], Series] = 0, *args, **kwargs
+    ) -> DataFrame:
         """
         Round a DataFrame to a variable number of decimal places.
 
@@ -8933,7 +8951,11 @@ def _series_round(s, decimals):
     # ----------------------------------------------------------------------
     # Statistical methods, etc.
 
-    def corr(self, method="pearson", min_periods=1) -> DataFrame:
+    def corr(
+        self,
+        method: Union[str, Callable[[np.ndarray, np.ndarray], float]] = "pearson",
+        min_periods: int = 1,
+    ) -> DataFrame:
         """
         Compute pairwise correlation of columns, excluding NA/null values.