ENH: add Series.info

doc/source/whatsnew/v1.1.0.rst

@@ -43,7 +43,7 @@ Other enhancements
 
 - :class:`Styler` may now render CSS more efficiently where multiple cells have the same styling (:issue:`30876`)
 - When writing directly to a sqlite connection :func:`to_sql` now supports the ``multi`` method (:issue:`29921`)
--
+- :meth:`Series.info` has been added, for compatibility with :meth:`DataFrame.info` (:issue:`5167`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/core/frame.py

@@ -2224,9 +2224,111 @@ def to_html(
         )
 
     # ----------------------------------------------------------------------
+    @Substitution(
+        klass="DataFrame",
+        type_sub=" and columns",
+        max_cols_sub="""
+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.
+        """,
+        examples_sub="""
+>>> 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]
+>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
+...                   "float_col": float_values})
+>>> df
+    int_col text_col  float_col
+0        1    alpha       0.00
+1        2     beta       0.25
+2        3    gamma       0.50
+3        4    delta       0.75
+4        5  epsilon       1.00
+
+Prints information of all columns:
+
+>>> df.info(verbose=True)
+<class 'pandas.core.frame.DataFrame'>
+RangeIndex: 5 entries, 0 to 4
+Data columns (total 3 columns):
+    #   Column     Non-Null Count  Dtype
+---  ------     --------------  -----
+    0   int_col    5 non-null      int64
+    1   text_col   5 non-null      object
+    2   float_col  5 non-null      float64
+dtypes: float64(1), int64(1), object(1)
+memory usage: 248.0+ bytes
+
+Prints a summary of columns count and its dtypes but not per column
+information:
+
+>>> df.info(verbose=False)
+<class 'pandas.core.frame.DataFrame'>
+RangeIndex: 5 entries, 0 to 4
+Columns: 3 entries, int_col to float_col
+dtypes: float64(1), int64(1), object(1)
+memory usage: 248.0+ bytes
+
+Pipe output of DataFrame.info to buffer instead of sys.stdout, get
+buffer content and writes to a text file:
+
+>>> import io
+>>> buffer = io.StringIO()
+>>> df.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 DataFrames and fine-tune memory optimization:
+
+>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
+>>> df = pd.DataFrame({
+...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
+...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
+...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
+... })
+>>> df.info()
+<class 'pandas.core.frame.DataFrame'>
+RangeIndex: 1000000 entries, 0 to 999999
+Data columns (total 3 columns):
+    #   Column    Non-Null Count    Dtype
+---  ------    --------------    -----
+    0   column_1  1000000 non-null  object
+    1   column_2  1000000 non-null  object
+    2   column_3  1000000 non-null  object
+dtypes: object(3)
+memory usage: 22.9+ MB
+
+>>> df.info(memory_usage='deep')
+<class 'pandas.core.frame.DataFrame'>
+RangeIndex: 1000000 entries, 0 to 999999
+Data columns (total 3 columns):
+    #   Column    Non-Null Count    Dtype
+---  ------    --------------    -----
+    0   column_1  1000000 non-null  object
+    1   column_2  1000000 non-null  object
+    2   column_3  1000000 non-null  object
+dtypes: object(3)
+memory usage: 188.8 MB""",
+        see_also_sub="""
+DataFrame.describe: Generate descriptive statistics of DataFrame
+    columns.
+DataFrame.memory_usage: Memory usage of DataFrame columns.""",
+    )
     @Appender(info.__doc__)
     def info(
-        self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None
+        self,
+        verbose: Optional[bool] = None,
+        buf: Optional[IO[str]] = None,
+        max_cols: Optional[int] = None,
+        memory_usage: Optional[Union[bool, str]] = None,
+        null_counts: Optional[bool] = None,
     ) -> None:
         return info(self, verbose, buf, max_cols, memory_usage, null_counts)
 

pandas/core/series.py

@@ -15,6 +15,7 @@
     Optional,
     Tuple,
     Type,
+    Union,
 )
 import warnings
 
@@ -92,6 +93,7 @@
 from pandas.core.tools.datetimes import to_datetime
 
 import pandas.io.formats.format as fmt
+from pandas.io.formats.info import info
 import pandas.plotting
 
 if TYPE_CHECKING:
@@ -4137,6 +4139,83 @@ def replace(
             method=method,
         )
 
+    @Substitution(
+        klass="Series",
+        type_sub="",
+        max_cols_sub="",
+        examples_sub="""
+>>> 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
+---  --------------  -----
+ 0   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
+---  --------------    -----
+    0   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
+---  --------------    -----
+    0   1000000 non-null  object
+dtypes: object(1)
+memory usage: 62.9 MB""",
+        see_also_sub="""
+Series.describe: Generate descriptive statistics of Series.
+Series.memory_usage: Memory usage of Series.""",
+    )
+    @Appender(info.__doc__)
+    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: Optional[bool] = None,
+    ) -> None:
+        return info(self, verbose, buf, None, memory_usage, null_counts)
+
     @Appender(generic._shared_docs["shift"] % _shared_doc_kwargs)
     def shift(self, periods=1, freq=None, axis=0, fill_value=None) -> "Series":
         return super().shift(