larray.Array.plot

Array.plot

Plots the data of the array into a graph (window pop-up).

The graph can be tweaked to achieve the desired formatting and can be saved to a .png file.

Parameters:
kind : str
  • ‘line’ : line plot (default)
  • ‘bar’ : vertical bar plot
  • ‘barh’ : horizontal bar plot
  • ‘hist’ : histogram
  • ‘box’ : boxplot
  • ‘kde’ : Kernel Density Estimation plot
  • ‘density’ : same as ‘kde’
  • ‘area’ : area plot
  • ‘pie’ : pie plot
  • ‘scatter’ : scatter plot (if array’s dimensions >= 2)
  • ‘hexbin’ : hexbin plot (if array’s dimensions >= 2)
ax : matplotlib axes object, default None
subplots : boolean, default False

Make separate subplots for each column

sharex : boolean, default True if ax is None else False

In case subplots=True, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in; Be aware, that passing in both an ax and sharex=True will alter all x axis labels for all axis in a figure!

sharey : boolean, default False

In case subplots=True, share y axis and set some y axis labels to invisible

layout : tuple (optional)

(rows, columns) for the layout of subplots

figsize : a tuple (width, height) in inches
use_index : boolean, default True

Use index as ticks for x axis

title : string

Title to use for the plot

grid : boolean, default None (matlab style default)

Axis grid lines

legend : False/True/’reverse’

Place legend on axis subplots

style : list or dict

matplotlib line style per column

logx : boolean, default False

Use log scaling on x axis

logy : boolean, default False

Use log scaling on y axis

loglog : boolean, default False

Use log scaling on both x and y axes

xticks : sequence

Values to use for the xticks

yticks : sequence

Values to use for the yticks

xlim : 2-tuple/list
ylim : 2-tuple/list
rot : int, default None

Rotation for ticks (xticks for vertical, yticks for horizontal plots)

fontsize : int, default None

Font size for xticks and yticks

colormap : str or matplotlib colormap object, default None

Colormap to select colors from. If string, load colormap with that name from matplotlib.

colorbar : boolean, optional

If True, plot colorbar (only relevant for ‘scatter’ and ‘hexbin’ plots)

position : float

Specify relative alignments for bar plot layout. From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)

layout : tuple (optional)

(rows, columns) for the layout of the plot

yerr : array-like

Error bars on y axis

xerr : array-like

Error bars on x axis

stacked : boolean, default False in line and bar plots, and True in area plot.

If True, create stacked plot.

**kwargs : keywords

Options to pass to matplotlib plotting method
Returns:
axes : matplotlib.AxesSubplot or np.array of them

Notes

See Pandas documentation of plot function for more details on this subject

Examples

>>> import matplotlib.pyplot as plt # doctest: +SKIP
>>> a = ndtest('gender=M,F;age=0..20')

Simple line plot

>>> a.plot() # doctest: +SKIP
>>> # shows figure (reset the current figure after showing it! Do not call it before savefig)
>>> plt.show() # doctest: +SKIP

Line plot with grid, title and both axes in logscale

>>> a.plot(grid=True, loglog=True, title='line plot') # doctest: +SKIP
>>> # saves figure in a file (see matplotlib.pyplot.savefig documentation for more details)
>>> plt.savefig('my_file.png') # doctest: +SKIP

2 bar plots sharing the same x axis (one for males and one for females)

>>> a.plot.bar(subplots=True, sharex=True) # doctest: +SKIP
>>> plt.show() # doctest: +SKIP

Create a figure containing 2 x 2 graphs

>>> # see matplotlib.pyplot.subplots documentation for more details
>>> fig, ax = plt.subplots(2, 2, figsize=(15, 15)) # doctest: +SKIP
>>> # 2 curves : Males and Females
>>> a.plot(ax=ax[0, 0], title='line plot') # doctest: +SKIP
>>> # bar plot with stacked values
>>> a.plot.bar(ax=ax[0, 1], stacked=True, title='stacked bar plot') # doctest: +SKIP
>>> # same as previously but with colored areas instead of bars
>>> a.plot.area(ax=ax[1, 0], title='area plot') # doctest: +SKIP
>>> # scatter plot
>>> a.plot.scatter(ax=ax[1, 1], x='M', y='F', title='scatter plot') # doctest: +SKIP
>>> plt.show() # doctest: +SKIP