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

Author: Genesis929

third_party/bigframes_vendored/pandas/core/frame.py

@@ -2638,6 +2638,33 @@ 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.
@@ -2656,6 +2683,33 @@ 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.
@@ -2673,6 +2727,33 @@ 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.