plot(*args, scalex=True, scaley=True, data=None, **kwargs)
Call signatures:
plot([x], y, [fmt], *, data=None, **kwargs) plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)
The coordinates of the points or line nodes are given by x, y.
The optional parameter fmt is a convenient way for defining basic formatting like color, marker and linestyle. It's a shortcut string notation described in the Notes section below.
>>> plot(x, y) # plot x and y using default line style and color >>> plot(x, y, 'bo') # plot x and y using blue circle markers >>> plot(y) # plot y using x as index array 0..N-1 >>> plot(y, 'r+') # ditto, but with red plusses
You can use .Line2D
properties as keyword arguments for more control on the appearance. Line properties and fmt can be mixed. The following two calls yield identical results:
>>> plot(x, y, 'go--', linewidth=2, markersize=12) >>> plot(x, y, color='green', marker='o', linestyle='dashed', ... linewidth=2, markersize=12)
When conflicting with fmt, keyword arguments take precedence.
Plotting labelled data
There's a convenient way for plotting objects with labelled data (i.e. data that can be accessed by index obj['y']
). Instead of giving the data in x and y, you can provide the object in the data parameter and just give the labels for x and y:
>>> plot('xlabel', 'ylabel', data=obj)
All indexable objects are supported. This could e.g. be a :None:None:`dict`, a pandas.DataFrame
or a structured numpy array.
Plotting multiple sets of data
There are various ways to plot multiple sets of data.
The most straight forward way is just to call plot
multiple times. Example:
>>> plot(x1, y1, 'bo') >>> plot(x2, y2, 'go')
If x and/or y are 2D arrays a separate data set will be drawn for every column. If both x and y are 2D, they must have the same shape. If only one of them is 2D with shape (N, m) the other must have length N and will be used for every data set m.
Example:
>>> x = [1, 2, 3] >>> y = np.array([[1, 2], [3, 4], [5, 6]]) >>> plot(x, y)
is equivalent to:
>>> for col in range(y.shape[1]): ... plot(x, y[:, col])
The third way is to specify multiple sets of [x], y, [fmt] groups:
>>> plot(x1, y1, 'g^', x2, y2, 'g-')
In this case, any additional keyword argument applies to all datasets. Also this syntax cannot be combined with the data parameter.
By default, each line is assigned a different style specified by a 'style cycle'. The fmt and line property parameters are only necessary if you want explicit deviations from these defaults. Alternatively, you can also change the style cycle using axes.prop_cycle
.
Format Strings
A format string consists of a part for color, marker and line:
fmt = '[marker][line][color]'
Each of them is optional. If not provided, the value from the style cycle is used. Exception: If line
is given, but no marker
, the data will be a line without markers.
Other combinations such as [color][marker][line]
are also supported, but note that their parsing may be ambiguous.
Markers
============= =============================== character description ============= =============================== '.'
point marker ','
pixel marker 'o'
circle marker 'v'
triangle_down marker '^'
triangle_up marker '<'
triangle_left marker '>'
triangle_right marker '1'
tri_down marker '2'
tri_up marker '3'
tri_left marker '4'
tri_right marker '8'
octagon marker 's'
square marker 'p'
pentagon marker 'P'
plus (filled) marker '*'
star marker 'h'
hexagon1 marker 'H'
hexagon2 marker '+'
plus marker 'x'
x marker 'X'
x (filled) marker 'D'
diamond marker 'd'
thin_diamond marker '|'
vline marker '_'
hline marker ============= ===============================
Line Styles
============= =============================== character description ============= =============================== '-'
solid line style '--'
dashed line style '-.'
dash-dot line style ':'
dotted line style ============= ===============================
Example format strings:
'b' # blue markers with default shape 'or' # red circles '-g' # green solid line '--' # dashed line with default color '^k:' # black triangle_up markers connected by a dotted line
Colors
The supported color abbreviations are the single letter codes
============= =============================== character color ============= =============================== 'b'
blue 'g'
green 'r'
red 'c'
cyan 'm'
magenta 'y'
yellow 'k'
black 'w'
white ============= ===============================
and the 'CN'
colors that index into the default property cycle.
If the color is the only part of the format string, you can additionally use any matplotlib.colors
spec, e.g. full names ( 'green'
) or hex strings ( '#008000'
).
These parameters determine if the view limits are adapted to the data limits. The values are passed on to autoscale_view
.
kwargs are used to specify properties like a line label (for auto legends), linewidth, antialiasing, marker face color. Example:
>>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2) >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')
If you specify multiple lines with one plot call, the kwargs apply to all those lines. In case the label object is iterable, each element is used as labels for each set of data.
Here is a list of available .Line2D
properties:
Properties: agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array alpha: scalar or None animated: bool antialiased or aa: bool clip_box: .Bbox
clip_on: bool clip_path: Patch or (Path, Transform) or None color or c: color dash_capstyle: .CapStyle
or {'butt', 'projecting', 'round'} dash_joinstyle: .JoinStyle
or {'miter', 'round', 'bevel'} dashes: sequence of floats (on/off ink in points) or (None, None) data: (2, N) array or two 1D arrays drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default' figure: .Figure
fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'} gid: str in_layout: bool label: object linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...} linewidth or lw: float marker: marker style string, ~.path.Path
or ~.markers.MarkerStyle
markeredgecolor or mec: color markeredgewidth or mew: float markerfacecolor or mfc: color markerfacecoloralt or mfcalt: color markersize or ms: float markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool] path_effects: .AbstractPathEffect
picker: float or callable[[Artist, Event], tuple[bool, dict]] pickradius: float rasterized: bool sketch_params: (scale: float, length: float, randomness: float) snap: bool or None solid_capstyle: .CapStyle
or {'butt', 'projecting', 'round'} solid_joinstyle: .JoinStyle
or {'miter', 'round', 'bevel'} transform: unknown url: str visible: bool xdata: 1D array ydata: 1D array zorder: float
The horizontal / vertical coordinates of the data points. x values are optional and default to range(len(y))
.
Commonly, these parameters are 1D arrays.
They can also be scalars, or two-dimensional (in that case, the columns represent separate data sets).
These arguments cannot be passed as keywords.
A format string, e.g. 'ro' for red circles. See the Notes section for a full description of the format strings.
Format strings are just an abbreviation for quickly setting basic line properties. All of these and more can also be controlled by keyword arguments.
This argument cannot be passed as keyword.
An object with labelled data. If given, provide the label names to plot in x and y.
Technically there's a slight ambiguity in calls where the second label is a valid fmt. plot('n', 'o', data=obj)
could be plt(x, y)
or plt(y, fmt)
. In such cases, the former interpretation is chosen, but a warning is issued. You may suppress the warning by adding an empty format string plot('n', 'o', '', data=obj)
.
A list of lines representing the plotted data.
Plot y versus x as lines and/or markers.
scatter
XY scatter plot with markers of varying size and/or color ( sometimes also called bubble chart).
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal.exponentialscipy.signal.windows._windows.boxcarscipy.signal._wavelets.morlet2scipy.interpolate._polyint.krogh_interpolatescipy.signal._filter_design.gammatonescipy.spatial._qhull.ConvexHullscipy.signal.bartlettscipy.signal._bsplines.cspline1d_evalscipy.interpolate._bsplines.make_lsq_splinescipy.interpolate._bspl.evaluate_all_bsplscipy.signal.windows._windows.barthannscipy.signal.chebwinscipy.signal._czt.CZTscipy.spatial.transform._rotation_spline.RotationSplinescipy.signal.boxcarscipy.interpolate._polyint.barycentric_interpolatescipy.signal._arraytools.even_extscipy.signal._signaltools.lfilterscipy.interpolate._ndgriddata.griddatascipy.signal.windows._windows.hannscipy.signal._waveforms.squarescipy.signal._bsplines.cspline1dscipy.signal._peak_finding.peak_widthsscipy.signal._ltisys.lsim2scipy.signal.blackmanharrisscipy.interpolate._polyint.approximate_taylor_polynomialscipy.signal._filter_design.lp2lpscipy.fft._basic.fftscipy.signal._signaltools.resample_polyscipy.integrate._quadrature.cumulative_trapezoidscipy.integrate._quad_vec.quad_vecscipy.signal.windows._windows.chebwinscipy.signal.parzenscipy.interpolate._interpolate.interp2dscipy.signal._ltisys.dfreqrespscipy.signal._waveforms.sweep_polyscipy.interpolate._interpolate.interp1dscipy.signal.barthannscipy.interpolate._bsplines.make_interp_splinescipy.signal._filter_design.sosfreqzscipy.signal._max_len_seq.max_len_seqscipy.signal.windows._windows.general_cosinescipy.signal._czt.zoom_fftscipy.signal.flattopscipy.signal.windows._windows.triangscipy.signal.windows._windows.exponentialscipy.signal._filter_design.lp2bsscipy.signal._signaltools.filtfiltscipy.signal.windows._windows.blackmanscipy.fft._basic.ifftscipy.signal.windows._windows.cosinescipy.signal.gaussianscipy.signal._fir_filter_design.kaiserordscipy.signal._waveforms.sawtoothscipy.interpolate._fitpack2.UnivariateSplinescipy.spatial._kdtree.KDTree.query_ball_pointscipy.signal._ltisys.impulsescipy.signal._filter_design.group_delayscipy.signal.windows._windows.tukeyscipy.interpolate._fitpack_impl.splrepscipy.interpolate._interpolate.lagrangescipy.signal.hannscipy.spatial._kdtree.KDTree.query_pairsscipy.signal.tukeyscipy.signal._ltisys.stepscipy.signal.bohmanscipy.signal._spectral_py.istftscipy.signal.hammingscipy.interpolate._ndgriddata.NearestNDInterpolatorscipy.signal.windows._windows.kaiserscipy.signal.cosinescipy.misc._common.electrocardiogramscipy.signal._waveforms.chirpscipy.signal._czt.czt_pointsscipy.signal._signaltools.sosfilt_ziscipy.signal._bsplines.qspline1dscipy.signal._ltisys.lsimscipy.signal._signaltools.resamplescipy.linalg._basic.lstsqscipy.signal._wavelets.rickerscipy.signal._ltisys.step2scipy.interpolate._fitpack2.LSQUnivariateSplinescipy.signal._signaltools.sosfiltfiltscipy.signal._bsplines.qspline1d_evalscipy.signal._waveforms.unit_impulsescipy.signal._signaltools.decimatescipy.signal.windows._windows.taylorscipy.signal._signaltools.sosfiltscipy.spatial._qhull.Delaunayscipy.signal._ltisys.impulse2scipy.signal.windows._windows.bartlettscipy.signal._filter_design.lp2hpscipy.signal.triangscipy.optimize._optimize.bracketscipy.interpolate._fitpack2.InterpolatedUnivariateSplinescipy.special._orthogonal.hermitescipy.signal.windows._windows.bohmanscipy.signal._arraytools.odd_extscipy.signal._czt.ZoomFFTscipy.signal._ltisys.place_polesscipy.optimize._minpack_py.curve_fitscipy.interpolate._cubic.pchip_interpolatescipy.signal.blackmanscipy.signal.windows._windows.nuttallscipy.signal.windows._windows.general_gaussianscipy.spatial._kdtree.KDTree.query_ball_treescipy.signal.windows._windows.gaussianscipy.signal.windows._windows.flattopscipy.signal._waveforms.gausspulsescipy.signal._ltisys.freqrespscipy.signal._filter_design.lp2bpscipy.signal._peak_finding.find_peaksscipy.special._basic.diricscipy.signal.kaiserscipy.signal.general_gaussianscipy.signal.windows._windows.parzenscipy.signal._peak_finding.peak_prominencesscipy.signal.nuttallscipy.signal.windows._windows.blackmanharrisscipy.integrate._bvp.solve_bvpscipy.integrate._odepack_py.odeintscipy.signal._arraytools.const_extscipy.integrate._ivp.ivp.solve_ivpscipy.interpolate._fitpack_py.splrepscipy.interpolate.interpnd.LinearNDInterpolatorscipy.signal.windows._windows.hammingdask.array.ufunc.wrap_elemwise.<locals>.wrappeddask.array.random.RandomState.gammadask.array.random.RandomState.standard_gammadask.array.ufunc.coshdask.array.random.RandomState.noncentral_chisquaredask.array.ufunc.arccosdask.array.random.RandomState.weibulldask.array.random.RandomState.laplacedask.array.random.RandomState.paretodask.array.random.RandomState.powerdask.array.random.RandomState.logisticdask.array.random.RandomState.lognormaldask.array.ufunc.sindask.array.random.RandomState.zipfdask.array.random.RandomState.uniformdask.array.ufunc.absolutedask.array.random.RandomState.vonmisesdask.array.random.RandomState.noncentral_fdask.array.ufunc.arctandask.array.random.RandomState.logseriesdask.array.random.RandomState.normaldask.array.random.RandomState.gumbelmatplotlib.pyplot.scattermatplotlib.pyplot.plotmatplotlib.pyplot.plottingmatplotlib.widgets.ToolHandlesmatplotlib.pyplot.polarHover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them