ENH: enable Series.info()

doc/source/whatsnew/v1.2.0.rst

@@ -230,6 +230,7 @@ Other enhancements
 - :class:`DatetimeIndex` and :class:`Series` with ``datetime64`` or ``datetime64tz`` dtypes now support ``std`` (:issue:`37436`)
 - :class:`Window` now supports all Scipy window types in ``win_type`` with flexible keyword argument support (:issue:`34556`)
 - :meth:`testing.assert_index_equal` now has a ``check_order`` parameter that allows indexes to be checked in an order-insensitive manner (:issue:`37478`)
+- :meth:`Series.info` has been added, for compatibility with :meth:`DataFrame.info` (:issue:`5167`)
 - :func:`read_csv` supports memory-mapping for compressed files (:issue:`37621`)
 - Improve error reporting for :meth:`DataFrame.merge()` when invalid merge column definitions were given (:issue:`16228`)
 - Improve numerical stability for :meth:`Rolling.skew()`, :meth:`Rolling.kurt()`, :meth:`Expanding.skew()` and :meth:`Expanding.kurt()` through implementation of Kahan summation (:issue:`6929`)

pandas/core/frame.py

@@ -159,7 +159,7 @@
 
 from pandas.io.common import get_filepath_or_buffer
 from pandas.io.formats import console, format as fmt
-from pandas.io.formats.info import DataFrameInfo
+from pandas.io.formats.info import BaseInfo, DataFrameInfo
 import pandas.plotting
 
 if TYPE_CHECKING:
@@ -2532,16 +2532,25 @@ def to_html(
     @Substitution(
         klass="DataFrame",
         type_sub=" and columns",
-        max_cols_sub=(
-            """max_cols : int, optional
+        max_cols_sub=dedent(
+            """\
+            max_cols : int, optional
                 When to switch from the verbose to the truncated output. If the
                 DataFrame has more than `max_cols` columns, the truncated output
                 is used. By default, the setting in
-                ``pandas.options.display.max_info_columns`` is used.
-            """
+                ``pandas.options.display.max_info_columns`` is used."""
         ),
-        examples_sub=(
-            """
+        null_counts_sub=dedent(
+            """\
+            null_counts : bool, optional
+                Whether to show the non-null counts. By default, this is shown
+                only if the DataFrame is smaller than
+                ``pandas.options.display.max_info_rows`` and
+                ``pandas.options.display.max_info_columns``. A value of True always
+                shows the counts, and False never shows the counts."""
+        ),
+        examples_sub=dedent(
+            """\
             >>> int_values = [1, 2, 3, 4, 5]
             >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
             >>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
@@ -2624,14 +2633,15 @@ def to_html(
             dtypes: object(3)
             memory usage: 165.9 MB"""
         ),
-        see_also_sub=(
-            """
+        see_also_sub=dedent(
+            """\
             DataFrame.describe: Generate descriptive statistics of DataFrame
                 columns.
             DataFrame.memory_usage: Memory usage of DataFrame columns."""
         ),
+        version_added_sub="",
     )
-    @doc(DataFrameInfo.to_buffer)
+    @doc(BaseInfo.render)
     def info(
         self,
         verbose: Optional[bool] = None,
@@ -2644,7 +2654,7 @@ def info(
             data=self,
             memory_usage=memory_usage,
         )
-        info.to_buffer(
+        info.render(
             buf=buf,
             max_cols=max_cols,
             verbose=verbose,

pandas/core/series.py

@@ -97,6 +97,7 @@
 from pandas.core.tools.datetimes import to_datetime
 
 import pandas.io.formats.format as fmt
+from pandas.io.formats.info import BaseInfo, SeriesInfo
 import pandas.plotting
 
 if TYPE_CHECKING:
@@ -4559,6 +4560,98 @@ def replace(
             method=method,
         )
 
+    @Substitution(
+        klass="Series",
+        type_sub="",
+        max_cols_sub="",
+        null_counts_sub=dedent(
+            """\
+            null_counts : bool, default True
+                Whether to show the non-null counts."""
+        ),
+        examples_sub=dedent(
+            """\
+            >>> int_values = [1, 2, 3, 4, 5]
+            >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
+            >>> s = pd.Series(text_values, index=int_values)
+            >>> s.info()
+            <class 'pandas.core.series.Series'>
+            Int64Index: 5 entries, 1 to 5
+            Series name: None
+            Non-Null Count  Dtype
+            --------------  -----
+            5 non-null      object
+            dtypes: object(1)
+            memory usage: 80.0+ bytes
+
+            Prints a summary excluding information about its values:
+
+            >>> s.info(verbose=False)
+            <class 'pandas.core.series.Series'>
+            Int64Index: 5 entries, 1 to 5
+            dtypes: object(1)
+            memory usage: 80.0+ bytes
+
+            Pipe output of Series.info to buffer instead of sys.stdout, get
+            buffer content and writes to a text file:
+
+            >>> import io
+            >>> buffer = io.StringIO()
+            >>> s.info(buf=buffer)
+            >>> s = buffer.getvalue()
+            >>> with open("df_info.txt", "w",
+            ...           encoding="utf-8") as f:  # doctest: +SKIP
+            ...     f.write(s)
+            260
+
+            The `memory_usage` parameter allows deep introspection mode, specially
+            useful for big Series and fine-tune memory optimization:
+
+            >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
+            >>> s = pd.Series(np.random.choice(['a', 'b', 'c'], 10 ** 6))
+            >>> s.info()
+            <class 'pandas.core.series.Series'>
+            RangeIndex: 1000000 entries, 0 to 999999
+            Series name: None
+            Non-Null Count    Dtype
+            --------------    -----
+            1000000 non-null  object
+            dtypes: object(1)
+            memory usage: 7.6+ MB
+
+            >>> s.info(memory_usage='deep')
+            <class 'pandas.core.series.Series'>
+            RangeIndex: 1000000 entries, 0 to 999999
+            Series name: None
+            Non-Null Count    Dtype
+            --------------    -----
+            1000000 non-null  object
+            dtypes: object(1)
+            memory usage: 55.3 MB"""
+        ),
+        see_also_sub=dedent(
+            """\
+            Series.describe: Generate descriptive statistics of Series.
+            Series.memory_usage: Memory usage of Series."""
+        ),
+        version_added_sub="\n.. versionadded:: 1.2.0\n",
+    )
+    @doc(BaseInfo.render)
+    def info(
+        self,
+        verbose: Optional[bool] = None,
+        buf: Optional[IO[str]] = None,
+        max_cols: Optional[int] = None,
+        memory_usage: Optional[Union[bool, str]] = None,
+        null_counts: bool = True,
+    ) -> None:
+        return SeriesInfo(self, memory_usage).render(
+            buf=buf,
+            max_cols=max_cols,
+            verbose=verbose,
+            show_counts=null_counts,
+        )
+
     def _replace_single(self, to_replace, method, inplace, limit):
         """
         Replaces values in a Series using the fill method specified when no