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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.
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
>>> 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 |
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.
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
>>> 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 . |