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# pandas.Series.plot.line

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

  Plot Series or DataFrame as lines.

  This function is useful to plot lines using DataFrame’s values as coordinates.

  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 line 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 lines for column a in green and lines for column b in red.

    **kwargs

      Additional keyword arguments are documented in `DataFrame.plot()`. In addition, passing `x_compat=True` suppresses pandas’ automatic tick resolution adjustment for regular frequency time-series data in favor of the default matplotlib tick locators and formatters. See Suppressing tick resolution adjustment for more.

  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

  `matplotlib.pyplot.plot`

    Plot y versus x as lines and/or markers.

  Examples

      >>> s = pd.Series([1, 3, 2])
      >>> s.plot.line()

  The following example shows the populations for some animals over the years.

      >>> df = pd.DataFrame(
      ...     {
      ...         "pig": [20, 18, 489, 675, 1776],
      ...         "horse": [4, 25, 281, 600, 1900],
      ...     },
      ...     index=[1990, 1997, 2003, 2009, 2014],
      ... )
      >>> lines = df.plot.line()

  An example with subplots, so an array of axes is returned.

      >>> axes = df.plot.line(subplots=True)
      >>> type(axes)
      <class 'numpy.ndarray'>

  Let’s repeat the same example, but specifying colors for each column (in this case, for each animal).

      >>> axes = df.plot.line(
      ...     subplots=True, color={"pig": "pink", "horse": "#742802"}
      ... )

  The following example shows the relationship between both populations.

      >>> lines = df.plot.line(x="pig", y="horse")