add examples

Author: MarcoGorelli

pandas/core/frame.py

@@ -2232,6 +2232,89 @@ def to_html(
     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
+""",
     )
     @Appender(NDFrame.info.__doc__)
     def info(

pandas/core/generic.py

@@ -1792,87 +1792,7 @@ def info(
 
         Examples
         --------
-        >>> 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
+        %(examples_sub)s
         """
         if buf is None:  # pragma: no cover
             buf = sys.stdout

pandas/core/series.py

@@ -4182,7 +4182,25 @@ def replace(
             method=method,
         )
 
-    @Substitution(klass="Series", type_sub="", max_cols_sub="")
+    @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
+    """,
+    )
     @Appender(NDFrame.info.__doc__)
     def info(
         self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None