Class PlotAccessor (2.29.0) 

PlotAccessor(data)

Make plots of Series or DataFrame with the matplotlib backend.

Examples: For Series:

>>> import bigframes.pandas as bpd
>>> 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")

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

bar

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

Draw a vertical bar plot.

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:

Basic plot.

>>> import bigframes.pandas as bpd
>>> df = bpd.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 = bpd.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)

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

barh

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

Draw a horizontal bar plot.

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:

Basic plot.

>>> import bigframes.pandas as bpd
>>> df = bpd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})
>>> ax = df.plot.barh(x='lab', y='val', rot=0)

Plot a whole dataframe to a barh 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 = bpd.DataFrame({'speed': speed, 'lifespan': lifespan}, index=index)
>>> ax = df.plot.barh(rot=0)

Plot stacked barh charts for the DataFrame.

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

If you don’t like the default colours, you can specify how you’d like each column to be colored.

>>> axes = df.plot.barh(
...     rot=0, subplots=True, color={"speed": "red", "lifespan": "green"}
... )
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
>>> 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)
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.

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')
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.

pie

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

Generate a pie plot.

A pie plot is a proportional representation of the numerical data in a column. This function wraps matplotlib.pyplot.pie for the specified column. If no column reference is passed and subplots=True a pie plot is drawn for each numerical column independently.

Examples:

In the example below we have a DataFrame with the information about planet's mass and radius. We pass the 'mass' column to the pie function to get a pie plot.

>>> import bigframes.pandas as bpd

>>> df = bpd.DataFrame({'mass': [0.330, 4.87 , 5.97],
...                    'radius': [2439.7, 6051.8, 6378.1]},
...                   index=['Mercury', 'Venus', 'Earth'])
>>> plot = df.plot.pie(y='mass', figsize=(5, 5))

>>> plot = df.plot.pie(subplots=True, figsize=(11, 6))
Returns
Type Description
matplotlib.axes.Axes or np.ndarray A NumPy array is returned when subplots is True.

scatter

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

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

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:

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

>>> import bigframes.pandas as bpd
>>> 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')
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
>>> 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")
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.