pyspark.pandas.DataFrame.plot.bar#

plot.bar(x=None, y=None, **kwds)#

Vertical bar plot.

A bar plot is a plot that presents categorical data with rectangular bars with lengths proportional to the values that they represent. A bar plot shows comparisons among discrete categories. One axis of the plot shows the specific categories being compared, and the other axis represents a measured value.

Parameters

xlabel or position, optional: Allows plotting of one column versus another. If not specified, the index of the DataFrame is used.
ylabel or position, optional: Allows plotting of one column versus another. If not specified, all numerical columns are used.
**kwdsoptional: Additional keyword arguments are documented in pyspark.pandas.Series.plot() or pyspark.pandas.DataFrame.plot().

Returns

plotly.graph_objs.Figure: Return an custom object when backend!=plotly. Return an ndarray when subplots=True (matplotlib-only).

Examples

Basic plot.

For Series:

>>> s = ps.Series([1, 3, 2])
>>> s.plot.bar()  

For DataFrame:

>>> df = ps.DataFrame({'lab': ['A', 'B', 'C'], 'val': [10, 30, 20]})
>>> df.plot.bar(x='lab', y='val')  

Plot a whole dataframe to a bar plot. Each column is stacked with a distinct color 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 = ps.DataFrame({'speed': speed,
...                    'lifespan': lifespan}, index=index)
>>> df.plot.bar()  

Instead of stacking, the figure can be split by column with plotly APIs.

>>> from plotly.subplots import make_subplots
>>> 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 = ps.DataFrame({'speed': speed,
...                    'lifespan': lifespan}, index=index)
>>> fig = (make_subplots(rows=2, cols=1)
...        .add_trace(df.plot.bar(y='speed').data[0], row=1, col=1)
...        .add_trace(df.plot.bar(y='speed').data[0], row=1, col=1)
...        .add_trace(df.plot.bar(y='lifespan').data[0], row=2, col=1))
>>> fig  

Plot a single column.

>>> 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 = ps.DataFrame({'speed': speed,
...                    'lifespan': lifespan}, index=index)
>>> df.plot.bar(y='speed')  

Plot only selected categories for the DataFrame.

>>> 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 = ps.DataFrame({'speed': speed,
...                    'lifespan': lifespan}, index=index)
>>> df.plot.bar(x='lifespan')  