feat: (Series | DataFrame).plot.bar (#1152)

* feat: (Series | DataFrame).plot.bar

* add warning message

* fix mypy

* 🦉 Updates from OwlBot post-processor

See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md

---------

Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>

bigframes/operations/_matplotlib/__init__.py

@@ -20,10 +20,11 @@
 PLOT_TYPES = typing.Union[type[core.SamplingPlot], type[hist.HistPlot]]
 
 PLOT_CLASSES: dict[str, PLOT_TYPES] = {
-    "hist": hist.HistPlot,
-    "line": core.LinePlot,
     "area": core.AreaPlot,
+    "bar": core.BarPlot,
+    "line": core.LinePlot,
     "scatter": core.ScatterPlot,
+    "hist": hist.HistPlot,
 }
 
 

bigframes/operations/_matplotlib/core.py

@@ -14,6 +14,7 @@
 
 import abc
 import typing
+import warnings
 
 import bigframes_vendored.constants as constants
 import pandas as pd
@@ -46,10 +47,15 @@ def result(self):
 
 
 class SamplingPlot(MPLPlot):
-    @abc.abstractproperty
+    @property
+    @abc.abstractmethod
     def _kind(self):
         pass
 
+    @property
+    def _sampling_warning_msg(self) -> typing.Optional[str]:
+        return None
+
     def __init__(self, data, **kwargs) -> None:
         self.kwargs = kwargs
         self.data = data
@@ -61,6 +67,15 @@ def generate(self) -> None:
     def _compute_sample_data(self, data):
         # TODO: Cache the sampling data in the PlotAccessor.
         sampling_n = self.kwargs.pop("sampling_n", DEFAULT_SAMPLING_N)
+        if self._sampling_warning_msg is not None:
+            total_n = data.shape[0]
+            if sampling_n < total_n:
+                warnings.warn(
+                    self._sampling_warning_msg.format(
+                        sampling_n=sampling_n, total_n=total_n
+                    )
+                )
+
         sampling_random_state = self.kwargs.pop(
             "sampling_random_state", DEFAULT_SAMPLING_STATE
         )
@@ -74,18 +89,33 @@ def _compute_plot_data(self):
         return self._compute_sample_data(self.data)
 
 
-class LinePlot(SamplingPlot):
-    @property
-    def _kind(self) -> typing.Literal["line"]:
-        return "line"
-
-
 class AreaPlot(SamplingPlot):
     @property
     def _kind(self) -> typing.Literal["area"]:
         return "area"
 
 
+class BarPlot(SamplingPlot):
+    @property
+    def _kind(self) -> typing.Literal["bar"]:
+        return "bar"
+
+    @property
+    def _sampling_warning_msg(self) -> typing.Optional[str]:
+        return (
+            "To optimize plotting performance, your data has been downsampled to {sampling_n} "
+            "rows from the original {total_n} rows. This may result in some data points "
+            "not being displayed. For a more comprehensive view, consider pre-processing "
+            "your data by aggregating it or selecting the top categories."
+        )
+
+
+class LinePlot(SamplingPlot):
+    @property
+    def _kind(self) -> typing.Literal["line"]:
+        return "line"
+
+
 class ScatterPlot(SamplingPlot):
     @property
     def _kind(self) -> typing.Literal["scatter"]:

bigframes/operations/plotting.py

@@ -23,7 +23,7 @@
 class PlotAccessor(vendordt.PlotAccessor):
     __doc__ = vendordt.PlotAccessor.__doc__
 
-    _common_kinds = ("line", "area", "hist")
+    _common_kinds = ("line", "area", "hist", "bar")
     _dataframe_kinds = ("scatter",)
     _all_kinds = _common_kinds + _dataframe_kinds
 
@@ -72,6 +72,14 @@ def area(
     ):
         return self(kind="area", x=x, y=y, stacked=stacked, **kwargs)
 
+    def bar(
+        self,
+        x: typing.Optional[typing.Hashable] = None,
+        y: typing.Optional[typing.Hashable] = None,
+        **kwargs,
+    ):
+        return self(kind="bar", x=x, y=y, **kwargs)
+
     def scatter(
         self,
         x: typing.Optional[typing.Hashable] = None,

tests/system/small/operations/test_plotting.py

@@ -195,6 +195,18 @@ def test_area(scalars_dfs):
         tm.assert_almost_equal(line.get_data()[1], pd_line.get_data()[1])
 
 
+def test_bar(scalars_dfs):
+    scalars_df, scalars_pandas_df = scalars_dfs
+    col_names = ["int64_col", "float64_col", "int64_too"]
+    ax = scalars_df[col_names].plot.bar()
+    pd_ax = scalars_pandas_df[col_names].plot.bar()
+    tm.assert_almost_equal(ax.get_xticks(), pd_ax.get_xticks())
+    tm.assert_almost_equal(ax.get_yticks(), pd_ax.get_yticks())
+    for line, pd_line in zip(ax.lines, pd_ax.lines):
+        # Compare y coordinates between the lines
+        tm.assert_almost_equal(line.get_data()[1], pd_line.get_data()[1])
+
+
 def test_scatter(scalars_dfs):
     scalars_df, scalars_pandas_df = scalars_dfs
     col_names = ["int64_col", "float64_col", "int64_too", "bool_col"]

third_party/bigframes_vendored/pandas/plotting/_core.py

@@ -215,6 +215,66 @@ def area(
         """
         raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
 
+    def bar(
+        self,
+        x: typing.Optional[typing.Hashable] = None,
+        y: typing.Optional[typing.Hashable] = None,
+        **kwargs,
+    ):
+        """
+        Draw a vertical bar plot.
+
+        This function calls `pandas.plot` to generate a plot with a random sample
+        of items. For consistent results, the random sampling is reproducible.
+        Use the `sampling_random_state` parameter to modify the sampling seed.
+
+        **Examples:**
+
+        Basic plot.
+
+            >>> import bigframes.pandas as bpd
+            >>> bpd.options.display.progress_bar = None
+            >>> df = bpd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})
+            >>> ax = df.plot.bar(x='lab', y='val', rot=0)
+
+        Plot a whole dataframe to a bar plot. Each column is assigned a distinct color,
+        and each row is nested in a group along the horizontal axis.
+
+            >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
+            >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
+            >>> index = ['snail', 'pig', 'elephant',
+            ...          'rabbit', 'giraffe', 'coyote', 'horse']
+            >>> df = bpd.DataFrame({'speed': speed, 'lifespan': lifespan}, index=index)
+            >>> ax = df.plot.bar(rot=0)
+
+        Plot stacked bar charts for the DataFrame.
+
+            >>> ax = df.plot.bar(stacked=True)
+
+        If you don’t like the default colours, you can specify how you’d like each column
+        to be colored.
+
+            >>> axes = df.plot.bar(
+            ...     rot=0, subplots=True, color={"speed": "red", "lifespan": "green"}
+            ... )
+
+        Args:
+            x (label or position, optional):
+                Allows plotting of one column versus another. If not specified, the index
+                of the DataFrame is used.
+            y (label or position, optional):
+                Allows plotting of one column versus another. If not specified, all numerical
+                columns are used.
+            **kwargs:
+                Additional keyword arguments are documented in
+                :meth:`DataFrame.plot`.
+
+        Returns:
+            matplotlib.axes.Axes or numpy.ndarray:
+                Area plot, or array of area plots if subplots is True.
+        """
+        raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
+
     def scatter(
         self,
         x: typing.Optional[typing.Hashable] = None,