feat: added Column.summarize_statistics() (#715)

Closes #701

### Summary of Changes

Added `summarize_statistics` to the `Column` class to quickly get an
overview of relevant statistics.

The Column is converted into a Table with one Column and the results
from Table.summarize_statistics() are returned. This way, if someone
adds a new feature to Table.summarize_statistics(), it also appears in
Column.summarize_statistics().

---------

Co-authored-by: Lars Reimann <mail@larsreimann.com>

src/safeds/data/tabular/containers/_column.py

@@ -566,6 +566,39 @@ def transform(self, transformer: Callable[[T], R]) -> Column[R]:
     # Statistics
     # ------------------------------------------------------------------------------------------------------------------
 
+    def summarize_statistics(self) -> Table:
+        """
+        Return a table with a number of statistical key values.
+
+        The original Column is not modified.
+
+        Returns
+        -------
+        statistics:
+            The table with statistics.
+
+        Examples
+        --------
+        >>> from safeds.data.tabular.containers import Column
+        >>> column = Column("a", [1, 3])
+        >>> column.summarize_statistics()
+                         metric                   a
+        0               minimum                   1
+        1               maximum                   3
+        2                  mean                 2.0
+        3                  mode              [1, 3]
+        4                median                 2.0
+        5              variance                 2.0
+        6    standard deviation  1.4142135623730951
+        7   missing value count                   0
+        8   missing value ratio                 0.0
+        9                idness                 1.0
+        10            stability                 0.5
+        """
+        from safeds.data.tabular.containers import Table
+
+        return Table({self._name: self._data}).summarize_statistics()
+
     def correlation_with(self, other_column: Column) -> float:
         """
         Calculate Pearson correlation between this and another column. Both columns have to be numerical.

tests/safeds/data/tabular/containers/_column/test_summarize_statistics.py

@@ -0,0 +1,106 @@
+from statistics import stdev
+
+import pytest
+from safeds.data.tabular.containers import Column, Table
+
+
+@pytest.mark.parametrize(
+    ("column", "expected"),
+    [
+        (
+            Column("col1", [1, 2, 1]),
+            Table(
+                {
+                    "metric": [
+                        "minimum",
+                        "maximum",
+                        "mean",
+                        "mode",
+                        "median",
+                        "variance",
+                        "standard deviation",
+                        "missing value count",
+                        "missing value ratio",
+                        "idness",
+                        "stability",
+                    ],
+                    "col1": [
+                        "1",
+                        "2",
+                        str(4.0 / 3),
+                        "[1]",
+                        "1.0",
+                        str(1.0 / 3),
+                        str(stdev([1, 2, 1])),
+                        "0",
+                        "0.0",
+                        str(2.0 / 3),
+                        str(2.0 / 3),
+                    ],
+                },
+            ),
+        ),
+        (
+            Column("col1", ["a", "b", "c"]),
+            Table(
+                {
+                    "metric": [
+                        "minimum",
+                        "maximum",
+                        "mean",
+                        "mode",
+                        "median",
+                        "variance",
+                        "standard deviation",
+                        "missing value count",
+                        "missing value ratio",
+                        "idness",
+                        "stability",
+                    ],
+                    "col1": [
+                        "-",
+                        "-",
+                        "-",
+                        "['a', 'b', 'c']",
+                        "-",
+                        "-",
+                        "-",
+                        "0",
+                        "0.0",
+                        "1.0",
+                        str(1.0 / 3),
+                    ],
+                },
+            ),
+        ),
+        (
+            Column("col", [None, None]),
+            Table(
+                {
+                    "metric": [
+                        "minimum",
+                        "maximum",
+                        "mean",
+                        "mode",
+                        "median",
+                        "variance",
+                        "standard deviation",
+                        "missing value count",
+                        "missing value ratio",
+                        "idness",
+                        "stability",
+                    ],
+                    "col": ["-", "-", "-", "[]", "-", "-", "-", "2", "1.0", "0.0", "-"],
+                },
+            ),
+        ),
+    ],
+    ids=[
+        "Column of integers",
+        "Column of characters",
+        "Column of None",
+    ],
+)
+def test_should_summarize_statistics(column: Column, expected: Table) -> None:
+    assert column.summarize_statistics().schema == expected.schema
+    assert column.summarize_statistics() == expected