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# pandas.Series.plot.bar

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

  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.

    **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)

  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)

  Plot stacked bar charts for the DataFrame

      >>> ax = df.plot.bar(stacked=True)

  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)

  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)

  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)