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# pandas.Series.plot.bar

Series.plot.bar(x=None, y=None, color=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:
    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.

    color str, 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 name : color}, 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)