DOC: Add missing docstrings

pandas/core/indexes/base.py

@@ -1163,6 +1163,9 @@ def to_frame(self, index=True, name=None):
 
  @property
  def name(self):
+ """
+ Return Index or MultiIndex name.
+ """
  return self._name
 
  @name.setter
@@ -1644,21 +1647,184 @@ def is_unique(self) -> bool:
 
  @property
  def has_duplicates(self) -> bool:
+ """
+ Check if the Index has duplicate values.
+
+ Returns
+ -------
+ boolean
+ Whether or not the Index has duplicate values.
+
+ Examples
+ --------
+ >>> idx = pd.Index([1, 5, 7, 7])
+ >>> idx.has_duplicates
+ True
+
+ >>> idx = pd.Index([1, 5, 7])
+ >>> idx.has_duplicates
+ False
+
+ >>> idx = pd.Index(["Watermelon", "Orange", "Apple",
+ ... "Watermelon"]).astype("category")
+ >>> idx.has_duplicates
+ True
+
+ >>> idx = pd.Index(["Orange", "Apple",
+ ... "Watermelon"]).astype("category")
+ >>> idx.has_duplicates
+ False
+ """
  return not self.is_unique
 
  def is_boolean(self) -> bool:
+ """
+ Check if the Index only consists of booleans.
+
+ Returns
+ -------
+ boolean
+ Whether or not the Index only consists of booleans.
+
+ Examples
+ --------
+ >>> idx = pd.Index([True, False, True])
+ >>> idx.is_boolean()
+ True
+
+ >>> idx = pd.Index(["True", "False", "True"])
+ >>> idx.is_boolean()
+ False
+
+ >>> idx = pd.Index([True, False, "True"])
+ >>> idx.is_boolean()
+ False
+ """
  return self.inferred_type in ["boolean"]
 
  def is_integer(self) -> bool:
+ """
+ Check if the Index only consists of integers.
+
+ Returns
+ -------
+ boolean
+ Whether or not the Index only consists of integers.
+
+ Examples
+ --------
+ >>> idx = pd.Index([1, 2, 3, 4])
+ >>> idx.is_integer()
+ True
+
+ >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
+ >>> idx.is_integer()
+ False
+
+ >>> idx = pd.Index([1, 2, 3, 4.0])
+ >>> idx.is_integer()
+ False
+ """
  return self.inferred_type in ["integer"]
 
  def is_floating(self) -> bool:
+ """
+ Check if the Index only consists of floats, NaNs, or
+ a mix of floats, integers, or NaNs.
+
+ Returns
+ -------
+ boolean
+ Whether or not the Index only consists of only consists of floats, NaNs, or
+ a mix of floats, integers, or NaNs.
+
+ Examples
+ --------
+ >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
+ >>> idx.is_floating()
+ True
+
+ >>> idx = pd.Index([1, 2, 3, 4.0])
+ >>> idx.is_floating()
+ True
+
+ >>> idx = pd.Index([1, 2, 3, 4.0, np.nan])
+ >>> idx.is_floating()
+ True
+
+ >>> idx = pd.Index([1, 2, 3, 4, np.nan])
+ >>> idx.is_floating()
+ True
+
+ >>> idx = pd.Index([1, 2, 3, 4])
+ >>> idx.is_integer()
+ False
+ """
  return self.inferred_type in ["floating", "mixed-integer-float", "integer-na"]
 
  def is_numeric(self) -> bool:
+ """
+ Check if the Index only consists of numeric
+ data.
+
+ Returns
+ -------
+ boolean
+ Whether or not the Index only only consists of numeric
+ data.
+
+ Examples
+ --------
+ >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
+ >>> idx.is_numeric()
+ True
+
+ >>> idx = pd.Index([1, 2, 3, 4.0])
+ >>> idx.is_numeric()
+ True
+
+ >>> idx = pd.Index([1, 2, 3, 4])
+ >>> idx.is_numeric()
+ True
+
+ >>> idx = pd.Index([1, 2, 3, 4.0, np.nan])
+ >>> idx.is_numeric()
+ True
+
+ >>> idx = pd.Index([1, 2, 3, 4.0, np.nan, "Apple"])
+ >>> idx.is_numeric()
+ False
+ """
  return self.inferred_type in ["integer", "floating"]
 
  def is_object(self) -> bool:
+ """
+ Check if the Index is of the object dtype.
+
+ Returns
+ -------
+ boolean
+ Whether or not the Index is of the object dtype.
+
+ Examples
+ --------
+ >>> idx = pd.Index(["Apple", "Mango", "Watermelon"])
+ >>> idx.is_object()
+ True
+
+ >>> idx = pd.Index(["Apple", "Mango", 2.0])
+ >>> idx.is_object()
+ True
+
+ >>> idx = pd.Index(["Watermelon", "Orange", "Apple",
+ ... "Watermelon"]).astype("category")
+ >>> idx.object()
+ False
+
+ >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
+ >>> idx.is_object()
+ False
+ """
  return is_object_dtype(self.dtype)
 
  def is_categorical(self) -> bool:
@@ -1698,9 +1864,50 @@ def is_categorical(self) -> bool:
  return self.inferred_type in ["categorical"]
 
  def is_interval(self) -> bool:
+ """
+ Check if the Index holds Interval objects.
+
+ Returns
+ -------
+ boolean
+ Whether or not the Index holds Interval objects.
+
+ See Also
+ --------
+ IntervalIndex : Index for Interval objects.
+
+ Examples
+ --------
+ >>> idx = pd.Index([pd.Interval(left=0, right=5),
+ ... pd.Interval(left=5, right=10)])
+ >>> idx.is_interval()
+ True
+
+ >>> idx = pd.Index([1, 3, 5, 7])
+ >>> idx.is_interval()
+ False
+ """
  return self.inferred_type in ["interval"]
 
  def is_mixed(self) -> bool:
+ """
+ Check if the Index holds data with mixed data types.
+
+ Returns
+ -------
+ boolean
+ Whether or not the Index holds data with mixed data types.
+
+ Examples
+ --------
+ >>> idx = pd.Index(['a', np.nan, 'b'])
+ >>> idx.is_mixed()
+ True
+
+ >>> idx = pd.Index([1.0, 2.0, 3.0, 5.0])
+ >>> idx.is_mixed()
+ False
+ """
  return self.inferred_type in ["mixed"]
 
  def holds_integer(self):
@@ -1718,6 +1925,9 @@ def inferred_type(self):
 
  @cache_readonly
  def is_all_dates(self) -> bool:
+ """
+ Whether or not the index values only consist of dates.
+ """
  return is_datetime_array(ensure_object(self.values))
 
  # --------------------------------------------------------------------