Class PlotAccessor (1.9.0)

PlotAccessor(data)

Make plots of Series or DataFrame with the matplotlib backend.

Examples: For Series:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> ser = bpd.Series([1, 2, 3, 3])
>>> plot = ser.plot(kind='hist', title="My plot")

For DataFrame:

>>> df = bpd.DataFrame({'length': [1.5, 0.5, 1.2, 0.9, 3],
...                   'width': [0.7, 0.2, 0.15, 0.2, 1.1]},
...                   index=['pig', 'rabbit', 'duck', 'chicken', 'horse'])
>>> plot = df.plot(title="DataFrame Plot")

Parameters
Name	Description
`data`	`Series or DataFrame` The object for which the method is called.
`kind`	`str` The kind of plot to produce: - 'line' : line plot (default) - 'hist' : histogram - 'area' : area plot - 'scatter' : scatter plot (DataFrame only)

Methods

area

area(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    stacked: bool = True,
    **kwargs
)

Draw a stacked area plot. An area plot displays quantitative data visually.

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:

Draw an area plot based on basic business metrics:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame(
...     {
...         'sales': [3, 2, 3, 9, 10, 6],
...         'signups': [5, 5, 6, 12, 14, 13],
...         'visits': [20, 42, 28, 62, 81, 50],
...     },
...     index=["01-31", "02-28", "03-31", "04-30", "05-31", "06-30"]
... )
>>> ax = df.plot.area()

Area plots are stacked by default. To produce an unstacked plot, pass stacked=False:

>>> ax = df.plot.area(stacked=False)

Draw an area plot for a single column:

>>> ax = df.plot.area(y='sales')

Draw with a different x:

>>> df = bpd.DataFrame({
...     'sales': [3, 2, 3],
...     'visits': [20, 42, 28],
...     'day': [1, 2, 3],
... })
>>> ax = df.plot.area(x='day')

Parameters
Name	Description
`x`	`label or position, optional` Coordinates for the X axis. By default uses the index.
`y`	`label or position, optional` Column to plot. By default uses all columns.
`stacked`	`bool, default True` Area plots are stacked by default. Set to False to create a unstacked plot.
`sampling_n`	`int, default 100` Number of random items for plotting.
`sampling_random_state`	`int, default 0` Seed for random number generator.

Returns
Type	Description
`matplotlib.axes.Axes or numpy.ndarray`	Area plot, or array of area plots if subplots is True.

hist

hist(by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs)

Draw one histogram of the DataFrame’s columns.

A histogram is a representation of the distribution of data. This function groups the values of all given Series in the DataFrame into bins and draws all bins in one matplotlib.axes.Axes. This is useful when the DataFrame's Series are in a similar scale.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> import numpy as np
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame(np.random.randint(1, 7, 6000), columns=['one'])
>>> df['two'] = np.random.randint(1, 7, 6000) + np.random.randint(1, 7, 6000)
>>> ax = df.plot.hist(bins=12, alpha=0.5)

Parameters
Name	Description
`by`	`str or sequence, optional` Column in the DataFrame to group by. It is not supported yet.
`bins`	`int, default 10` Number of histogram bins to be used.

Returns
Type	Description
`class`	`matplotlib.AxesSubplot`: A histogram plot.

line

line(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    **kwargs
)

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

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> 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
Name	Description
`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 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 %(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.

Returns
Type	Description
`matplotlib.axes.Axes or np.ndarray of them`	An ndarray is returned with one `matplotlib.axes.Axes` per column when `subplots=True`.

scatter

scatter(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    s: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    c: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    **kwargs
)

Create a scatter plot with varying marker point size and color.

Examples:

Let's see how to draw a scatter plot using coordinates from the values in a DataFrame's columns.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],
...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],
...                   columns=['length', 'width', 'species'])
>>> ax1 = df.plot.scatter(x='length',
...                       y='width',
...                       c='DarkBlue')

And now with the color determined by a column as well.

>>> ax2 = df.plot.scatter(x='length',
...                       y='width',
...                       c='species',
...                       colormap='viridis')

Parameters
Name	Description
`x`	`int or str` The column name or column position to be used as horizontal coordinates for each point.
`y`	`int or str` The column name or column position to be used as vertical coordinates for each point.
`s`	`str, scalar or array-like, optional` The size of each point. Possible values are: - A string with the name of the column to be used for marker's size. - A single scalar so all points have the same size.
`c`	`str, int or array-like, optional` The color of each point. Possible values are: - A single color string referred to by name, RGB or RGBA code, for instance 'red' or '#a98d19'. - A column name or position whose values will be used to color the marker points according to a colormap.
`sampling_n`	`int, default 100` Number of random items for plotting.
`sampling_random_state`	`int, default 0` Seed for random number generator.

Returns
Type	Description
`matplotlib.axes.Axes or np.ndarray of them`	An ndarray is returned with one `matplotlib.axes.Axes` per column when `subplots=True`.

PlotAccessor

PlotAccessor(data)

Make plots of Series or DataFrame with the matplotlib backend.

Examples: For Series:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> ser = bpd.Series([1, 2, 3, 3])
>>> plot = ser.plot(kind='hist', title="My plot")

For DataFrame:

>>> df = bpd.DataFrame({'length': [1.5, 0.5, 1.2, 0.9, 3],
...                   'width': [0.7, 0.2, 0.15, 0.2, 1.1]},
...                   index=['pig', 'rabbit', 'duck', 'chicken', 'horse'])
>>> plot = df.plot(title="DataFrame Plot")

Parameters
Name	Description
`data`	`Series or DataFrame` The object for which the method is called.
`kind`	`str` The kind of plot to produce: - 'line' : line plot (default) - 'hist' : histogram - 'area' : area plot - 'scatter' : scatter plot (DataFrame only)

Returns
Type	Description
`matplotlib.axes.Axes or np.ndarray of them`	An ndarray is returned with one `matplotlib.axes.Axes` per column when `subplots=True`.

Class PlotAccessor (1.9.0)

Parameters

Methods

area

hist

line

scatter

PlotAccessor