Python plot line types

Plot types#

Overview of many common plotting commands in Matplotlib.

Note that we have stripped all labels, but they are present by default. See the gallery for many more examples and the tutorials page for longer examples.

Basic#

Basic plot types, usually y versus x.

Plots of arrays and fields#

Plotting for arrays of data Z(x, y) and fields U(x, y), V(x, y) .

Statistics plots#

Plots for statistical analysis.

Unstructured coordinates#

Sometimes we collect data z at coordinates (x,y) and want to visualize as a contour. Instead of gridding the data and then using contour , we can use a triangulation algorithm and fill the triangles.

3D#

3D plots using the mpl_toolkits.mplot3d library.

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matplotlib.pyplot.plot#

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') 

>>> x = [1, 2, 3] >>> y = np.array([[1, 2], [3, 4], [5, 6]]) >>> plot(x, y) 

>>> for col in range(y.shape[1]): . plot(x, y[:, col]) 

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, y array-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.

fmt str, 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.

data indexable object, optional

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.

Other Parameters : scalex, scaley bool, default: True

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

**kwargs Line2D 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:

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

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Matplotlib Line

You can use the keyword argument linestyle , or shorter ls , to change the style of the plotted line:

Example

import matplotlib.pyplot as plt
import numpy as np

plt.plot(ypoints, linestyle = ‘dotted’)
plt.show()

Result:

Example

plt.plot(ypoints, linestyle = ‘dashed’)

Result:

Shorter Syntax

The line style can be written in a shorter syntax:

linestyle can be written as ls .

Example

Result:

Line Styles

You can choose any of these styles:

Style	Or
‘solid’ (default)	‘-‘	Try it »
‘dotted’	‘:’	Try it »
‘dashed’	‘—‘	Try it »
‘dashdot’	‘-.’	Try it »
‘None’	» or ‘ ‘	Try it »

Line Color

You can use the keyword argument color or the shorter c to set the color of the line:

Example

Set the line color to red:

import matplotlib.pyplot as plt
import numpy as np

plt.plot(ypoints, color = ‘r’)
plt.show()

Result:

Example

Plot with a beautiful green line:

Result:

Example

Plot with the color named «hotpink»:

Result:

Line Width

You can use the keyword argument linewidth or the shorter lw to change the width of the line.

The value is a floating number, in points:

Example

Plot with a 20.5pt wide line:

import matplotlib.pyplot as plt
import numpy as np

plt.plot(ypoints, linewidth = ‘20.5’)
plt.show()

Result:

Multiple Lines

You can plot as many lines as you like by simply adding more plt.plot() functions:

Example

Draw two lines by specifying a plt.plot() function for each line:

import matplotlib.pyplot as plt
import numpy as np

y1 = np.array([3, 8, 1, 10])
y2 = np.array([6, 2, 7, 11])

Result:

You can also plot many lines by adding the points for the x- and y-axis for each line in the same plt.plot() function.

(In the examples above we only specified the points on the y-axis, meaning that the points on the x-axis got the the default values (0, 1, 2, 3).)

The x- and y- values come in pairs:

Example

Draw two lines by specifiyng the x- and y-point values for both lines:

import matplotlib.pyplot as plt
import numpy as np

x1 = np.array([0, 1, 2, 3])
y1 = np.array([3, 8, 1, 10])
x2 = np.array([0, 1, 2, 3])
y2 = np.array([6, 2, 7, 11])

plt.plot(x1, y1, x2, y2)
plt.show()

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