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galuhsahidjorisvandenbossche
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DOC: Add missing docstrings in pd.Index (#31047)
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‎pandas/core/indexes/base.py‎

Lines changed: 281 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1164,6 +1164,9 @@ def to_frame(self, index=True, name=None):
11641164

11651165
@property
11661166
def name(self):
1167+
"""
1168+
Return Index or MultiIndex name.
1169+
"""
11671170
return self._name
11681171

11691172
@name.setter
@@ -1645,21 +1648,230 @@ def is_unique(self) -> bool:
16451648

16461649
@property
16471650
def has_duplicates(self) -> bool:
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"""
1652+
Check if the Index has duplicate values.
1653+
1654+
Returns
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-------
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bool
1657+
Whether or not the Index has duplicate values.
1658+
1659+
Examples
1660+
--------
1661+
>>> idx = pd.Index([1, 5, 7, 7])
1662+
>>> idx.has_duplicates
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True
1664+
1665+
>>> idx = pd.Index([1, 5, 7])
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>>> idx.has_duplicates
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False
1668+
1669+
>>> idx = pd.Index(["Watermelon", "Orange", "Apple",
1670+
... "Watermelon"]).astype("category")
1671+
>>> idx.has_duplicates
1672+
True
1673+
1674+
>>> idx = pd.Index(["Orange", "Apple",
1675+
... "Watermelon"]).astype("category")
1676+
>>> idx.has_duplicates
1677+
False
1678+
"""
16481679
return not self.is_unique
16491680

16501681
def is_boolean(self) -> bool:
1682+
"""
1683+
Check if the Index only consists of booleans.
1684+
1685+
Returns
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-------
1687+
bool
1688+
Whether or not the Index only consists of booleans.
1689+
1690+
See Also
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--------
1692+
is_integer : Check if the Index only consists of integers.
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is_floating : Check if the Index is a floating type.
1694+
is_numeric : Check if the Index only consists of numeric data.
1695+
is_object : Check if the Index is of the object dtype.
1696+
is_categorical : Check if the Index holds categorical data.
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is_interval : Check if the Index holds Interval objects.
1698+
is_mixed : Check if the Index holds data with mixed data types.
1699+
1700+
Examples
1701+
--------
1702+
>>> idx = pd.Index([True, False, True])
1703+
>>> idx.is_boolean()
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True
1705+
1706+
>>> idx = pd.Index(["True", "False", "True"])
1707+
>>> idx.is_boolean()
1708+
False
1709+
1710+
>>> idx = pd.Index([True, False, "True"])
1711+
>>> idx.is_boolean()
1712+
False
1713+
"""
16511714
return self.inferred_type in ["boolean"]
16521715

16531716
def is_integer(self) -> bool:
1717+
"""
1718+
Check if the Index only consists of integers.
1719+
1720+
Returns
1721+
-------
1722+
bool
1723+
Whether or not the Index only consists of integers.
1724+
1725+
See Also
1726+
--------
1727+
is_boolean : Check if the Index only consists of booleans.
1728+
is_floating : Check if the Index is a floating type.
1729+
is_numeric : Check if the Index only consists of numeric data.
1730+
is_object : Check if the Index is of the object dtype.
1731+
is_categorical : Check if the Index holds categorical data.
1732+
is_interval : Check if the Index holds Interval objects.
1733+
is_mixed : Check if the Index holds data with mixed data types.
1734+
1735+
Examples
1736+
--------
1737+
>>> idx = pd.Index([1, 2, 3, 4])
1738+
>>> idx.is_integer()
1739+
True
1740+
1741+
>>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
1742+
>>> idx.is_integer()
1743+
False
1744+
1745+
>>> idx = pd.Index(["Apple", "Mango", "Watermelon"])
1746+
>>> idx.is_integer()
1747+
False
1748+
"""
16541749
return self.inferred_type in ["integer"]
16551750

16561751
def is_floating(self) -> bool:
1752+
"""
1753+
Check if the Index is a floating type.
1754+
1755+
The Index may consist of only floats, NaNs, or a mix of floats,
1756+
integers, or NaNs.
1757+
1758+
Returns
1759+
-------
1760+
bool
1761+
Whether or not the Index only consists of only consists of floats, NaNs, or
1762+
a mix of floats, integers, or NaNs.
1763+
1764+
See Also
1765+
--------
1766+
is_boolean : Check if the Index only consists of booleans.
1767+
is_integer : Check if the Index only consists of integers.
1768+
is_numeric : Check if the Index only consists of numeric data.
1769+
is_object : Check if the Index is of the object dtype.
1770+
is_categorical : Check if the Index holds categorical data.
1771+
is_interval : Check if the Index holds Interval objects.
1772+
is_mixed : Check if the Index holds data with mixed data types.
1773+
1774+
Examples
1775+
--------
1776+
>>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
1777+
>>> idx.is_floating()
1778+
True
1779+
1780+
>>> idx = pd.Index([1.0, 2.0, np.nan, 4.0])
1781+
>>> idx.is_floating()
1782+
True
1783+
1784+
>>> idx = pd.Index([1, 2, 3, 4, np.nan])
1785+
>>> idx.is_floating()
1786+
True
1787+
1788+
>>> idx = pd.Index([1, 2, 3, 4])
1789+
>>> idx.is_floating()
1790+
False
1791+
"""
16571792
return self.inferred_type in ["floating", "mixed-integer-float", "integer-na"]
16581793

16591794
def is_numeric(self) -> bool:
1795+
"""
1796+
Check if the Index only consists of numeric data.
1797+
1798+
Returns
1799+
-------
1800+
bool
1801+
Whether or not the Index only consists of numeric data.
1802+
1803+
See Also
1804+
--------
1805+
is_boolean : Check if the Index only consists of booleans.
1806+
is_integer : Check if the Index only consists of integers.
1807+
is_floating : Check if the Index is a floating type.
1808+
is_object : Check if the Index is of the object dtype.
1809+
is_categorical : Check if the Index holds categorical data.
1810+
is_interval : Check if the Index holds Interval objects.
1811+
is_mixed : Check if the Index holds data with mixed data types.
1812+
1813+
Examples
1814+
--------
1815+
>>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
1816+
>>> idx.is_numeric()
1817+
True
1818+
1819+
>>> idx = pd.Index([1, 2, 3, 4.0])
1820+
>>> idx.is_numeric()
1821+
True
1822+
1823+
>>> idx = pd.Index([1, 2, 3, 4])
1824+
>>> idx.is_numeric()
1825+
True
1826+
1827+
>>> idx = pd.Index([1, 2, 3, 4.0, np.nan])
1828+
>>> idx.is_numeric()
1829+
True
1830+
1831+
>>> idx = pd.Index([1, 2, 3, 4.0, np.nan, "Apple"])
1832+
>>> idx.is_numeric()
1833+
False
1834+
"""
16601835
return self.inferred_type in ["integer", "floating"]
16611836

16621837
def is_object(self) -> bool:
1838+
"""
1839+
Check if the Index is of the object dtype.
1840+
1841+
Returns
1842+
-------
1843+
bool
1844+
Whether or not the Index is of the object dtype.
1845+
1846+
See Also
1847+
--------
1848+
is_boolean : Check if the Index only consists of booleans.
1849+
is_integer : Check if the Index only consists of integers.
1850+
is_floating : Check if the Index is a floating type.
1851+
is_numeric : Check if the Index only consists of numeric data.
1852+
is_categorical : Check if the Index holds categorical data.
1853+
is_interval : Check if the Index holds Interval objects.
1854+
is_mixed : Check if the Index holds data with mixed data types.
1855+
1856+
Examples
1857+
--------
1858+
>>> idx = pd.Index(["Apple", "Mango", "Watermelon"])
1859+
>>> idx.is_object()
1860+
True
1861+
1862+
>>> idx = pd.Index(["Apple", "Mango", 2.0])
1863+
>>> idx.is_object()
1864+
True
1865+
1866+
>>> idx = pd.Index(["Watermelon", "Orange", "Apple",
1867+
... "Watermelon"]).astype("category")
1868+
>>> idx.object()
1869+
False
1870+
1871+
>>> idx = pd.Index([1.0, 2.0, 3.0, 4.0])
1872+
>>> idx.is_object()
1873+
False
1874+
"""
16631875
return is_object_dtype(self.dtype)
16641876

16651877
def is_categorical(self) -> bool:
@@ -1668,12 +1880,19 @@ def is_categorical(self) -> bool:
16681880
16691881
Returns
16701882
-------
1671-
boolean
1883+
bool
16721884
True if the Index is categorical.
16731885
16741886
See Also
16751887
--------
16761888
CategoricalIndex : Index for categorical data.
1889+
is_boolean : Check if the Index only consists of booleans.
1890+
is_integer : Check if the Index only consists of integers.
1891+
is_floating : Check if the Index is a floating type.
1892+
is_numeric : Check if the Index only consists of numeric data.
1893+
is_object : Check if the Index is of the object dtype.
1894+
is_interval : Check if the Index holds Interval objects.
1895+
is_mixed : Check if the Index holds data with mixed data types.
16771896
16781897
Examples
16791898
--------
@@ -1699,9 +1918,67 @@ def is_categorical(self) -> bool:
16991918
return self.inferred_type in ["categorical"]
17001919

17011920
def is_interval(self) -> bool:
1921+
"""
1922+
Check if the Index holds Interval objects.
1923+
1924+
Returns
1925+
-------
1926+
bool
1927+
Whether or not the Index holds Interval objects.
1928+
1929+
See Also
1930+
--------
1931+
IntervalIndex : Index for Interval objects.
1932+
is_boolean : Check if the Index only consists of booleans.
1933+
is_integer : Check if the Index only consists of integers.
1934+
is_floating : Check if the Index is a floating type.
1935+
is_numeric : Check if the Index only consists of numeric data.
1936+
is_object : Check if the Index is of the object dtype.
1937+
is_categorical : Check if the Index holds categorical data.
1938+
is_mixed : Check if the Index holds data with mixed data types.
1939+
1940+
Examples
1941+
--------
1942+
>>> idx = pd.Index([pd.Interval(left=0, right=5),
1943+
... pd.Interval(left=5, right=10)])
1944+
>>> idx.is_interval()
1945+
True
1946+
1947+
>>> idx = pd.Index([1, 3, 5, 7])
1948+
>>> idx.is_interval()
1949+
False
1950+
"""
17021951
return self.inferred_type in ["interval"]
17031952

17041953
def is_mixed(self) -> bool:
1954+
"""
1955+
Check if the Index holds data with mixed data types.
1956+
1957+
Returns
1958+
-------
1959+
bool
1960+
Whether or not the Index holds data with mixed data types.
1961+
1962+
See Also
1963+
--------
1964+
is_boolean : Check if the Index only consists of booleans.
1965+
is_integer : Check if the Index only consists of integers.
1966+
is_floating : Check if the Index is a floating type.
1967+
is_numeric : Check if the Index only consists of numeric data.
1968+
is_object : Check if the Index is of the object dtype.
1969+
is_categorical : Check if the Index holds categorical data.
1970+
is_interval : Check if the Index holds Interval objects.
1971+
1972+
Examples
1973+
--------
1974+
>>> idx = pd.Index(['a', np.nan, 'b'])
1975+
>>> idx.is_mixed()
1976+
True
1977+
1978+
>>> idx = pd.Index([1.0, 2.0, 3.0, 5.0])
1979+
>>> idx.is_mixed()
1980+
False
1981+
"""
17051982
return self.inferred_type in ["mixed"]
17061983

17071984
def holds_integer(self):
@@ -1719,6 +1996,9 @@ def inferred_type(self):
17191996

17201997
@cache_readonly
17211998
def is_all_dates(self) -> bool:
1999+
"""
2000+
Whether or not the index values only consist of dates.
2001+
"""
17222002
return is_datetime_array(ensure_object(self.values))
17232003

17242004
# --------------------------------------------------------------------

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