plot.
bar
Vertical bar plot.
Allows plotting of one column versus another. If not specified, the index of the DataFrame is used.
Allows plotting of one column versus another. If not specified, all numerical columns are used.
Additional keyword arguments are documented in Koalas.Series.plot() or Koalas.DataFrame.plot().
Koalas.Series.plot()
Koalas.DataFrame.plot()
matplotlib.axes.Axes
numpy.ndarray
Return an ndarray when subplots=True. Return an custom object when backend!=matplotlib.
subplots=True
backend!=matplotlib
Examples
Basic plot.
For Series:
>>> s = ks.Series([1, 3, 2]) >>> ax = s.plot.bar()
For DataFrame:
>>> df = ks.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 = ks.DataFrame({'speed': speed, ... 'lifespan': lifespan}, index=index) >>> ax = df.plot.bar(rot=0)
Instead of nesting, the figure can be split by column with subplots=True. In this case, a numpy.ndarray of matplotlib.axes.Axes are returned.
>>> axes = df.plot.bar(rot=0, subplots=True) >>> axes[1].legend(loc=2)
Plot a single column.
>>> ax = df.plot.bar(y='speed', rot=0)
Plot only selected categories for the DataFrame.
>>> ax = df.plot.bar(x='lifespan', rot=0)