pandas.Series.plot.bar

Series.plot.bar(x=None, y=None, **kwargs)[source]

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.

colorstr, array_like, or dict, optional

The color for each of the DataFrame’s columns. Possible values are:

  • A single color string referred to by name, RGB or RGBA code,

    for instance ‘red’ or ‘#a98d19’.

  • A sequence of color strings referred to by name, RGB or RGBA

    code, which will be used for each column recursively. For instance [‘green’,’yellow’] each column’s bar will be filled in green or yellow, alternatively. If there is only a single column to be plotted, then only the first color from the color list will be used.

  • A dict of the form {column namecolor}, so that each column will be

    colored accordingly. For example, if your columns are called a and b, then passing {‘a’: ‘green’, ‘b’: ‘red’} will color bars for column a in green and bars for column b in red.

New in version 1.1.0.

**kwargs

Additional keyword arguments are documented in DataFrame.plot().

Returns
matplotlib.axes.Axes or np.ndarray of them

An ndarray is returned with one matplotlib.axes.Axes per column when subplots=True.

See also

DataFrame.plot.barh

Horizontal bar plot.

DataFrame.plot

Make plots of a DataFrame.

matplotlib.pyplot.bar

Make a bar plot with matplotlib.

Examples

Basic plot.

>>> df = pd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})
>>> ax = df.plot.bar(x='lab', y='val', rot=0)
../../_images/pandas-Series-plot-bar-1.png

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 = pd.DataFrame({'speed': speed,
...                    'lifespan': lifespan}, index=index)
>>> ax = df.plot.bar(rot=0)
../../_images/pandas-Series-plot-bar-2.png

Plot stacked bar charts for the DataFrame

>>> ax = df.plot.bar(stacked=True)
../../_images/pandas-Series-plot-bar-3.png

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)  
../../_images/pandas-Series-plot-bar-4.png

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"}
... )
>>> axes[1].legend(loc=2)  
../../_images/pandas-Series-plot-bar-5.png

Plot a single column.

>>> ax = df.plot.bar(y='speed', rot=0)
../../_images/pandas-Series-plot-bar-6.png

Plot only selected categories for the DataFrame.

>>> ax = df.plot.bar(x='lifespan', rot=0)
../../_images/pandas-Series-plot-bar-7.png