docs: add examples for dataframe.min, dataframe.max and dataframe.sum (#227)

* docs: add examples for dataframe.min, dataframe.max and dataframe.sum

* update spacing

Author: Genesis929

third_party/bigframes_vendored/pandas/core/frame.py

@@ -2597,7 +2597,7 @@ def any(self, *, axis=0, bool_only: bool = False):
             <BLANKLINE>
             [2 rows x 2 columns]
 
-        Checking if each column contains at least one True element(the default behavior without an explicit axis parameter).
+        Checking if each column contains at least one True element (the default behavior without an explicit axis parameter).
 
             >>> df.any()
             A     True
@@ -2644,7 +2644,7 @@ def all(self, axis=0, *, bool_only: bool = False):
             <BLANKLINE>
             [2 rows x 2 columns]
 
-        Checking if all values in each column are True(the default behavior without an explicit axis parameter).
+        Checking if all values in each column are True (the default behavior without an explicit axis parameter).
 
             >>> df.all()
             A     True
@@ -2688,7 +2688,7 @@ def prod(self, axis=0, *, numeric_only: bool = False):
             <BLANKLINE>
             [3 rows x 2 columns]
 
-        Calculating the product of each column(the default behavior without an explicit axis parameter).
+        Calculating the product of each column (the default behavior without an explicit axis parameter).
 
             >>> df.prod()
             A        6.0
@@ -2721,6 +2721,33 @@ def min(self, axis=0, *, numeric_only: bool = False):
         If you want the *index* of the minimum, use ``idxmin``. This is the
         equivalent of the ``numpy.ndarray`` method ``argmin``.
 
+        **Examples:**
+
+            >>> import bigframes.pandas as bpd
+            >>> bpd.options.display.progress_bar = None
+
+            >>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]})
+            >>> df
+                A	B
+            0	1	2
+            1	3	4
+            <BLANKLINE>
+            [2 rows x 2 columns]
+
+        Finding the minimum value in each column (the default behavior without an explicit axis parameter).
+
+            >>> df.min()
+            A    1.0
+            B    2.0
+            dtype: Float64
+
+        Finding the minimum value in each row.
+
+            >>> df.min(axis=1)
+            0    1.0
+            1    3.0
+            dtype: Float64
+
         Args:
             axis ({index (0), columns (1)}):
                 Axis for the function to be applied on.
@@ -2739,6 +2766,33 @@ def max(self, axis=0, *, numeric_only: bool = False):
         If you want the *index* of the maximum, use ``idxmax``. This is
         the equivalent of the ``numpy.ndarray`` method ``argmax``.
 
+        **Examples:**
+
+            >>> import bigframes.pandas as bpd
+            >>> bpd.options.display.progress_bar = None
+
+            >>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]})
+            >>> df
+                A	B
+            0	1	2
+            1	3	4
+            <BLANKLINE>
+            [2 rows x 2 columns]
+
+        Finding the maximum value in each column (the default behavior without an explicit axis parameter).
+
+            >>> df.max()
+            A    3.0
+            B    4.0
+            dtype: Float64
+
+        Finding the maximum value in each row.
+
+            >>> df.max(axis=1)
+            0    2.0
+            1    4.0
+            dtype: Float64
+
         Args:
             axis ({index (0), columns (1)}):
                 Axis for the function to be applied on.
@@ -2756,6 +2810,33 @@ def sum(self, axis=0, *, numeric_only: bool = False):
 
         This is equivalent to the method ``numpy.sum``.
 
+        **Examples:**
+
+            >>> import bigframes.pandas as bpd
+            >>> bpd.options.display.progress_bar = None
+
+            >>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]})
+            >>> df
+                A	B
+            0	1	2
+            1	3	4
+            <BLANKLINE>
+            [2 rows x 2 columns]
+
+        Calculating the sum of each column (the default behavior without an explicit axis parameter).
+
+            >>> df.sum()
+            A    4.0
+            B    6.0
+            dtype: Float64
+
+        Calculating the sum of each row.
+
+            >>> df.sum(axis=1)
+            0    3.0
+            1    7.0
+            dtype: Float64
+
         Args:
             axis ({index (0), columns (1)}):
                 Axis for the function to be applied on.