Plotting

Questions

How do I plot my data?
How do I customise a plot’s appearance?
How do I save a plot to a file?

Objectives

Create line plots, scatter plots, error bar plots, bar charts, and histograms with Matplotlib.
Customise line styles, colours, and markers.
Add axis labels, titles, and legends.
Arrange multiple plots in a figure with subplots.
Save figures to PDF and PNG files.

Matplotlib is Python’s standard scientific plotting library

The most widely used scientific plotting library in Python is matplotlib.

import matplotlib.pyplot as plt

Basic line plots

plt.plot(x, y) draws a line connecting the data points. Always label your axes:

import numpy
import matplotlib.pyplot as plt

time = numpy.array([0, 1, 2, 3])
position = numpy.array([0, 100, 200, 300])

plt.plot(time, position)
plt.xlabel("Time (hr)")
plt.ylabel("Position (km)")
plt.show()

Figure 1: Line plot of position in km versus time in hours, showing a linear increase from 0 to 300 km over 3 hours

Controlling line and marker style

A compact format string as the third argument to plt.plot sets the colour and style together: 'b-' means a blue line, 'ro' means red circles, 'g+-' means green plus-markers connected by a line:

import numpy
import matplotlib.pyplot as plt
time = numpy.arange(10)
p1 = time
p2 = time * 2
p3 = time * 4

plt.plot(time, p1, 'b-')
plt.plot(time, p2, 'ro')
plt.plot(time, p3, 'g+-')
plt.xlabel("Time (hr)")
plt.ylabel("Position (km)")
plt.show()

Figure 2: Three position versus time plots using different formatting: a blue line (p1), red circles (p2), and a green line with plus markers (p3)

For finer control, use keyword arguments instead. The label keyword feeds into plt.legend():

plt.plot(time, p1, color='blue', linestyle='-', linewidth=5, label="blue line")
plt.plot(time, p2, 'ro', markersize=10, label="red dots")
plt.plot(time, p3, 'g-', marker='+')
plt.xlabel("Time (hr)")
plt.ylabel("Position (km)")
plt.legend()
plt.show()

Figure 3: Three position-versus-time series with a legend: a thick blue line, large red dots, and a green line with plus markers

Built-in styles

Matplotlib ships with a set of named styles that control colours, fonts, and backgrounds consistently. List available styles with plt.style.available and apply one with plt.style.use:

print("available style names: ", plt.style.available)

available style names:  ['Solarize_Light2', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'petroff10', 'petroff6', 'petroff8', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']

plt.style.use("seaborn-v0_8-whitegrid")
plt.plot(time, p1, linestyle='-', linewidth=5, label="blue line")
plt.plot(time, p2, 'o', markersize=10, label="dots")
plt.xlabel("Time (hr)")
plt.ylabel("Position (km)")
plt.legend()
plt.show()

Figure 4: The same three position-versus-time series rendered in the seaborn-v0_8-whitegrid style

Scatter plots

When your data points should not be connected by a line, use plt.scatter. It accepts the same colour and marker keywords as plt.plot:

numpy.random.seed(20)
x = numpy.cumsum(numpy.random.randint(0, 100, 100))
y = numpy.cumsum(numpy.random.randn(100))

plt.scatter(x, y)
plt.scatter(x, 10 - y**2, color='green', marker='<')
plt.xlabel("x")
plt.title("Scatter plot example")
plt.show()

Figure 5: Scatter plot with two series: blue dots and green left-pointing triangles

Error bars

In experimental physics you should always show measurement uncertainties. Use plt.errorbar with the yerr (and/or xerr) keyword. Set ls='' to suppress the connecting line and choose a marker explicitly:

numpy.random.seed(42)
x = numpy.cumsum(numpy.random.rand(10) * 10)
error = numpy.abs(numpy.random.randn(10) * 4)
y = x + numpy.random.randn(10) * 0.5

plt.errorbar(x, y, yerr=error, color='green', marker='o', ls='', lw=1, label="data")
plt.xlabel("x")
plt.title("Error bar example")
plt.legend()
plt.show()

Figure 6: Error bar plot of ten data points with green circle markers and vertical error bars

Bar charts and histograms

plt.bar draws a bar chart from pre-counted data. plt.hist bins raw data and draws the resulting histogram in one step — it also returns the counts and bin edges if you need them:

x = [0, 1, 2, 3, 4, 5]
y = [0, 4, 2, 6, 8, 2]
plt.bar(x, y)
plt.title("Bar chart")
plt.show()

Figure 7: Vertical bar chart with six bars (one of them is zero height)

x = numpy.random.randint(0, 100, 50)
bin_count, bin_edges, boxes = plt.hist(x, bins=10, rwidth=0.9)
plt.title("Histogram")
plt.show()

Figure 8: Histogram of 50 random integers

Controlling figure size

Call plt.figure(figsize=(width, height)) before plotting to set the output size in inches:

plt.figure(figsize=(8, 2))
x = [0, 1, 2, 3, 4, 5]
y = [0, 4, 2, 6, 8, 2]

plt.bar(x, y)
plt.title("Narrow bar chart")
plt.show()

Multiple panels with `subplot`

plt.subplot(rows, cols, index) selects one panel in a grid of panels. Subsequent plt.plot calls draw into the currently active panel:

plt.figure(figsize=(8, 3))
x = [0, 1, 2, 3, 4, 5]
y = [0, 4, 2, 6, 8, 2]
plt.subplot(1, 3, 1)
plt.bar(x, y)
plt.title("left")
plt.subplot(1, 3, 2)
plt.bar(y, x)
plt.title("centre")
plt.subplot(1, 3, 3)
plt.bar(x, y)
plt.title("right")
plt.show()

Figure 10: Three bar charts arranged side by side in a 1x3 subplot row

Saving figures

Call plt.savefig before the figure is displayed — once Matplotlib shows a figure it moves on to a new empty figure, and saving afterwards captures only a blank canvas. Supported formats include PDF, PNG, and SVG. For raster formats lke PNG use the dpi keyword to control resolution:

plt.figure(figsize=(8, 3))
plt.plot(x, y)
plt.savefig("data/fig1.pdf")
plt.savefig("data/fig1.png", dpi=150, transparent=True)
plt.show()

Figure 11: Line plot produced for the savefig example

Note

If you need to save after displaying, first capture a reference to the figure with plt.gcf() (get current figure), then call savefig on it:

fig = plt.gcf()
plt.plot(x, y)
fig.savefig('my_figure.png')
plt.show()

Figure 12: Example of saving via a captured current figure reference after plotting

Key Points

Import matplotlib.pyplot as plt
plt.plot draws lines; plt.scatter draws unconnected points; plt.errorbar adds uncertainty bars.
Format strings ('b-', 'ro') or keyword arguments control colour, linestyle, and markers.
Add plt.xlabel, plt.ylabel, plt.title, and plt.legend for readable figures.
plt.style.use applies a consistent visual style.
plt.figure(figsize=...) sets figure dimensions; plt.subplot creates multi-panel layouts.
Call plt.savefig before the figure is displayed to avoid saving a blank canvas.

Matplotlib is Python’s standard scientific plotting library

Basic line plots

Controlling line and marker style

Built-in styles

Scatter plots

Error bars

Bar charts and histograms

Controlling figure size

Multiple panels with subplot

Saving figures

Multiple panels with `subplot`