bigframes.pandas.Series.line

Series.line(x: Hashable | None = None, y: Hashable | None = None, **kwargs)

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

This function calls pandas.plot to generate a plot with a random sample of items. For consistent results, the random sampling is reproducible. Use the sampling_random_state parameter to modify the sampling seed.

Examples:

>>> import bigframes.pandas as bpd
>>> df = bpd.DataFrame(
...     {
...         'one': [1, 2, 3, 4],
...         'three': [3, 6, 9, 12],
...         'reverse_ten': [40, 30, 20, 10],
...     }
... )
>>> ax = df.plot.line(x='one')

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 %(kind)s 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 %(kind)ss for column a in green and %(kind)ss for column b in red.

• sampling_n (int, default 100) – Number of random items for plotting.

• sampling_random_state (int, default 0) – Seed for random number generator.

• **kwargs – Additional keyword arguments are documented in DataFrame.plot().

Returns:

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

Return type:

matplotlib.axes.Axes or np.ndarray of them