matplotlib.pyplot.plot#

matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)[source]#

Plot y versus x as lines and/or markers.

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 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 rcParams["axes.prop_cycle"] (default: cycler('color', ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'])).

Parameters:

x, yarray-like or scalar

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.

fmtstr, optional

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.

dataindexable object, optional

An object with labelled data. If given, provide the label names to plot in x and y.

Note

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).

Returns:

list of Line2D: A list of lines representing the plotted data.

Other Parameters:

scalex, scaleybool, default: True

These parameters determine if the view limits are adapted to the data limits. The values are passed on to autoscale_view.

**kwargsLine2D properties, optional

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:

Property	Description
`agg_filter`	a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image
`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'}
`gapcolor`	color or None
`gid`	str
`in_layout`	bool
`label`	object
`linestyle` or ls	{'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
`linewidth` or lw	float
`marker`	marker style string, `Path` or `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]
`mouseover`	bool
`path_effects`	`AbstractPathEffect`
`picker`	float or callable[[Artist, Event], tuple[bool, dict]]
`pickradius`	unknown
`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

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

character	description
`'-'`	solid line style
`'--'`	dashed line style
`'-.'`	dash-dot line style
`':'`	dotted line style

character	color
`'b'`	blue
`'g'`	green
`'r'`	red
`'c'`	cyan
`'m'`	magenta
`'y'`	yellow
`'k'`	black
`'w'`	white

Examples using `matplotlib.pyplot.plot`#

Plotting masked and NaN values

Scatter Masked

Stairs Demo

Step Demo

Custom Figure subclasses

Managing multiple figures in pyplot

Shared Axis

Multiple subplots

Controlling style of text and labels using a dictionary

Solarized Light stylesheet

Set and get properties

transforms.offset_copy

Zorder Demo

Custom scale

Placing date ticks using recurrence rules

Rotating custom tick labels

Tool Manager

Pyplot tutorial

Customizing Matplotlib with style sheets and rcParams

Quick start guide

Path effects guide

matplotlib.pyplot.plot#

Examples using matplotlib.pyplot.plot#

Examples using `matplotlib.pyplot.plot`#