Click above image to access the interactive version of this notebook

Interactive Plotting with Matplotlib

One can use Jupyter notebook as a browser-based interactive data analysis tool to combine narrative, code, graphics, and much more into a single executable document.

Plotting interactively within a notebook can be done with the %matplotlib inline command and then importing pyplot from matplotlib

%matplotlib inline
import matplotlib.pyplot as plt

Before we get into learning some stuff, lets set up the style of our plots. Starting from Matplotlib v1.5, you have many different plotting styles to chose from. To get all the list of supported ones, run

plt.style.available

['seaborn-dark',
 'seaborn-darkgrid',
 'seaborn-ticks',
 'fivethirtyeight',
 'seaborn-whitegrid',
 'classic',
 '_classic_test',
 'fast',
 'seaborn-talk',
 'seaborn-dark-palette',
 'seaborn-bright',
 'seaborn-pastel',
 'grayscale',
 'seaborn-notebook',
 'ggplot',
 'seaborn-colorblind',
 'seaborn-muted',
 'seaborn',
 'Solarize_Light2',
 'seaborn-paper',
 'bmh',
 'tableau-colorblind10',
 'seaborn-white',
 'dark_background',
 'seaborn-poster',
 'seaborn-deep']

Personally, I prefer using seaborn-darkgrid but you are free to chose from whatever you like

plt.style.use('seaborn-darkgrid')

04 - 02 pyplot

The pyplot module is where everything in matplotlib comes together. It is the launching point for

• preparing your figures,
• making plots, and
• doing any modifications and decorations you want.

It all comes together here. Let us take a look at those three catagories of pyplot functions.

Plotting Preparation

Function	Description
autoscale	Autoscale the axis view to the data (toggle).
axes	Add an axes to the figure.
axis	Convenience method to get or set axis properties.
cla	Clear the current axes.
clf	Clear the current figure.
clim	Set the color limits of the current image.
delaxes	Remove an axes from the current figure.
locator_params	Control behavior of tick locators.
margins	Set or retrieve autoscaling margins.
figure	Creates a new figure.
gca	Return the current axis instance.
gcf	Return a reference to the current figure.
gci	Get the current colorable artist.
hold	Set the hold state.
ioff	Turn interactive mode off.
ion	Turn interactive mode on.
ishold	Return the hold status of the current axes.
isinteractive	Return status of interactive mode.
rc	Set the current rc params.
rc_context	Return a context manager for managing rc settings.
rcdefaults	Restore the default rc params.
savefig	Save the current figure.
sca	Set the current Axes instance.
sci	Set the current image.
set_cmap	Set the default colormap
setp	Set a property on an artist object
show	Display a figure
subplot	Return a subplot axes positioned by the given grid definition.
subplot2grid	Create a subplot in a grid.
subplot_tool	Launch a subplot tool window for a figure.
subplots	Create a figure with a set of subplots already made.
subplots_adjust	Tune the subplot layout.
switch_backend	Switch the default backend.
tick_params	Change the appearance of ticks and tick labels.
ticklabel_format	Change the ScalarFormatter used by default for linear axes.
tight_layout	Automatically adjust subplot parameters to give specified padding.
xkcd	Turns on XKCD sketch-style drawing mode.
xlabel	Set the x axis label of the current axis.
xlim	Get or set the x limits of the current axes.
xscale	Set the scaling of the x-axis.
xticks	Get or set the x-limits of the current tick locations and labels.
ylabel	Set the y axis label of the current axis.
ylim	Get or set the y-limits of the current axes.
yscale	Set the scaling of the y-axis.
yticks	Get or set the y-limits of the current tick locations and labels.

Plotting Functions

Function	Description
acorr	Plot the autocorrelation of x
bar	Make a bar plot
barbs	Plot a 2-D field of barbs
barh	Make a horizontal bar plot
boxplot	Make a box and whisker plot
broken_barh	Plot horizontal bars
cohere	Plot the coherence between x and y
contour	Plot contours
contourf	Plot filled contours
csd	Plot cross-spectral density
errorbar	Plot an errorbar graph
eventplot	Plot identical parallel lines at specific positions
fill	Plot filled polygons
fill_between	Make filled polygons between two curves
fill_betweenx	Make filled polygons between two horizontal curves
hexbin	Make a hexagonal binning plot
hist	Plot a histogram
hist2d	Make a 2D histogram plot
imshow	Display an image on the axes
loglog	Make a plot with log scaling on both the x and y axis
matshow	Display an array as a matrix in a new figure window
pcolor	Create a pseudocolor plot of a 2-D array
pcolormesh	Plot a quadrilateral mesh
pie	Plot a pie chart
plot	Plot lines and/or markers
plot_date	Plot with data with dates
polar	Make a polar plot
psd	Plot the power spectral density
quiver	Plot a 2-D field of arrows
scatter	Make a scatter plot of x vs y
semilogx	Make a plot with log scaling on the x axis
semilogy	Make a plot with log scaling on the y axis
specgram	Plot a spectrogram
spy	Plot the sparsity pattern on a 2-D array
stackplot	Draws a stacked area plot
stem	Create a stem plot
step	Make a step plot
streamplot	Draws streamlines of a vector flow
tricontour	Draw contours on an unstructured triangular grid
tricontourf	Draw filled contours on an unstructured triangular grid
tripcolor	Create a pseudocolor plot of an unstructured triangular grid
triplot	Draw a unstructured triangular grid as lines and/or markers
xcorr	Plot the cross-correlation between x and y

Plot modifiers

Function	Description
annotate	Create an annotation: a piece of text referring to a data point
arrow	Add an arrow to the axes
axhline	Add a horizontal line across the axis
axhspan	Add a horizontal span (rectangle) across the axis
axvline	Add a vertical line across the axes
axvspan	Add a vertical span (rectangle) across the axes
box	Turn the axes box on or off
clabel	Label a contour plot
colorbar	Add a colorbar to a plot
grid	Turn the axes grids on or off
hlines	Plot horizontal lines
legend	Place a legend on the current axes
minorticks_off	Remove minor ticks from the current plot
minorticks_on	Display minor ticks on the current plot
quiverkey	Add a key to a quiver plot
rgrids	Get or set the radial gridlines on a polar plot
suptitle	Add a centered title to the figure
table	Add a table to the current axes
text	Add text to the axes
title	Set a title of the current axes
vlines	Plot vertical lines
xlabel	Set the x axis label of the current axis
ylabel	Set the y axis label of the current axis

Don’t get bogged down by the enourmous list. We will look at some of them throughout the remaining UCSL coursework and slowly these options will start to click as you do more and more plotting.

Lets start with the very basics

Figure

All plotting is done through the Figure object. You can create as many figures as you need. Figures can’t do much by themselves, but no plotting can happen without them. They are, literally, the canvas of your plot.

Figure Properties

The figure properties include the figure size (figsize) and the resolution (dpi).

• figsize is a tuple of integers. It most often includes width and height in inches.
• dpi is resolution of the figure. It is given as an integer representing (predictably) dots per inch.

So to create an empty figure of size 10,4, we would type:

fig = plt.figure(figsize=(10, 4))
# Now plot
plt.plot()

[]

This lets you create a figure with specified aspect ratio. If arg is a number, use tIn the above code, it’s the line plt.plot() that actually generates the figure.

A really useful function is figaspect

Figaspect lets you create a figure with specified aspect ratio. If arg is a number, use that aspect ratio. If arg is an array, figaspect will determine the width and height for a figure that would fit the array while preserving aspect ratio.

Use it to create a plot that is twice as wide as it is high by adding the “plt.figaspect” line below: hat aspect ratio. If arg is an array, figaspect will determine the width and height for a figure that would fit array preserving aspect ratio.

# Twice as wide. Ratio of Height/ Width
fig = plt.figure(figsize=plt.figaspect(0.5))
plt.plot()

[]

If we want to save the plot, we can call the method plt.savefig(<filename>). The default format in which the image is saved is png. There are many other formats supported. To get a list of all the formats, you can use this function:

fig.canvas.get_supported_filetypes()

{'ps': 'Postscript',
 'eps': 'Encapsulated Postscript',
 'pdf': 'Portable Document Format',
 'pgf': 'PGF code for LaTeX',
 'png': 'Portable Network Graphics',
 'raw': 'Raw RGBA bitmap',
 'rgba': 'Raw RGBA bitmap',
 'svg': 'Scalable Vector Graphics',
 'svgz': 'Scalable Vector Graphics'}

Axes

Notice that in the previous plot, Matplotlib automatically created the axis for us. Since all plotting is done with respect to an Axes, we can use Matplotlib to change the axes.

An Axes is made up of Axis objects (and many other things). An Axes object must belong to a particular Figure (and only one Figure). Most commands you will ever issue will be with respect to this Axes object.

For example, let’s manually add an axis for X:

# Lets manually add axis for x axis
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111) 
theta = np.linspace(-np.pi, np.pi, 100)
plt.plot(theta, np.sin(theta))

[<matplotlib.lines.Line2D at 0x107689390>]

Here’s how to create a subplot:

In the line above: ax = fig.add_subplot(111)

In this case, Fig.add_subplot. This function accepts numrows numcols fignum as parameters. You already know what rows and columns are. Fignum represents the subplot number and ranges from 1 to the maximum number of rows and columns (numrows*numcols).

If you want to place an axes manually, i.e., not on a rectangular grid, use the axes() command, which allows you to specify the location as axes([left, bottom, width, height]) where all values are in fractional (0 to 1) coordinates.

Each figure can contain as many axes and subplots as your heart desires

Linestyles

Line styles are about as commonly used as colors. There are a few predefined linestyles available to use. Note that there are some advanced techniques to specify some custom line styles. Below you’ll see an example of a custom dash pattern. You can click here to see the code.

linestyle	description
‘-’	solid
‘–’	dashed
‘-.’	dashdot
‘:’	dotted
‘None’	draw nothing
' '	draw nothing
''	draw nothing

To generate a “dashdot” linestyle, add the proper command to your plt line:

fig = plt.figure()
ax = fig.add_subplot(111) 
theta = np.linspace(-np.pi, np.pi, 100)
# combining `-` and `.`
plt.plot(theta, np.sin(theta), ls='-.')

[<matplotlib.lines.Line2D at 0x1076f0f60>]

Limits and autoscaling

By default, matplotlib will attempt to determine limits for you that encompasses all the data you have plotted. This is the autoscale feature.

Continuing with the above example, now let’s set limits on the x-axis:

fig = plt.figure()
ax = fig.add_subplot(111) 
theta = np.linspace(-np.pi, np.pi, 100)
plt.plot(theta, np.sin(theta))
# Set limits on X-axis
ax.set_xlim(-np.pi, np.pi/2)

(-3.141592653589793, 1.5707963267948966)

As you’ll likely have noticed, the limit caused the plot to be truncated at value of x=np.pi/2 which is at x position of ~ 1.57

Labels and Legends

You can label just about anything in mpl. You can provide a label to your plot, which allows your legend to automatically build itself. A legend is like the legend on a map: a key to the symbols and colors the visualization is using to communicate. The X and Y axis can also be labeled, as well as the subplot itself via the title.

In the example below, we take the temperature “string” obtained from the nasa.gov for the years 1880 - 2018. We then load the temperature string as a numpy array using numpy’s fromstring method and create another array for the years between 1880 and 2018.

# Temperature data obtained from NASA: https://data.giss.nasa.gov/gistemp/
surface_temp_change = '-0.29,-0.1,0.11,-0.33,-0.18,-0.64,-0.42,-0.65,-0.42,-0.2,-0.47,-0.46,-0.25,-0.68,-0.55,-0.43,-0.22,-0.22,-0.06,-0.17,-0.39,-0.28,-0.18,-0.27,-0.63,-0.38,-0.3,-0.43,-0.45,-0.69,-0.43,-0.62,-0.26,-0.42,0.02,-0.18,-0.17,-0.46,-0.42,-0.2,-0.14,-0.03,-0.33,-0.25,-0.23,-0.32,0.21,-0.28,-0.02,-0.47,-0.29,-0.1,0.14,-0.34,-0.26,-0.36,-0.28,-0.1,0.0,-0.12,-0.15,0.13,0.26,0.0,0.41,0.13,0.15,-0.13,0.05,0.09,-0.3,-0.35,0.16,0.09,-0.28,0.11,-0.16,-0.14,0.39,0.06,-0.01,0.07,0.08,-0.03,-0.06,-0.09,-0.16,-0.06,-0.23,-0.11,0.09,-0.03,-0.24,0.28,-0.15,0.07,0.0,0.18,0.08,0.14,0.3,0.56,0.09,0.52,0.3,0.21,0.29,0.36,0.56,0.15,0.4,0.42,0.45,0.37,0.3,0.5,0.27,0.32,0.61,0.48,0.26,0.44,0.75,0.73,0.59,0.71,0.58,0.96,0.25,0.62,0.73,0.51,0.46,0.68,0.73,0.82,1.13,0.9'
surface_temp_change_arr = np.fromstring(surface_temp_change, sep=",")
surface_temp_year = np.array(range(1880, 2018))

Next, we plot the temperature array and pass the label for the temperature plot as “Global Surface Temp Change”

fig = plt.figure(figsize=plt.figaspect(0.5))
ax = fig.add_subplot(111)
ax.plot(surface_temp_year, surface_temp_change_arr, label='Global Surface Temp Change')
ax.set_ylabel('Temperature (deg C)')
ax.set_xlabel('Month of January')
ax.set_title("A tale of Global Warming")
ax.legend()

<matplotlib.legend.Legend at 0x1079a27f0>

“Twinning” axes

Sometimes one may want to overlay two plots on the same axes, but the scales may be entirely different. You can simply treat them as separate plots, but then twin them.

Please note: As we move forward in this unit, we’re going to shift more exclusively to commenting in the #hashtag in-code format that you will see in your CUSP courses and you will use in your own courses. It takes some getting used to, but it is critical to learn.

# CO2 data obtained from http://cdiac.ornl.gov/CO2_Emission/timeseries/global
co2_emission_str = '865.412,891.081,938.752,997.424,1008.425,1015.759,1030.427,1081.765,1199.109,1199.109,1305.452,1364.124,1371.458,1356.79,1404.461,1488.802,1536.473,1613.48,1705.155,1859.169,1958.178,2024.184,2075.522,2262.539,2288.208,2431.221,2592.569,2874.928,2750.25,2878.595,3003.273,3065.612,3223.293,3457.981,3116.95,3072.946,3303.967,3501.985,3432.312,2955.602,3417.644,2944.601,3098.615,3556.99,3531.321,3575.325,3604.661,3894.354,3905.355,4198.715,3861.351,3446.98,3105.949,3274.631,3567.991,3766.009,4143.71,4433.403,4187.714,4371.064,4763.433,4891.778,4921.114,5100.797,5071.461,4253.72,4539.746,5104.464,5386.823,5203.473,5977.21,6479.589,6582.265,6750.947,6838.955,7488.014,7983.059,8324.09,8544.11,8998.818,9420.523,9460.86,9849.562,10388.611,10982.665,11477.71,12057.096,12442.131,13076.522,13861.26,14862.351,15430.736,16046.792,16919.538,16952.541,16853.532,17836.288,18393.672,18606.358,19644.119,19438.767,18841.046,18679.698,18610.025,19281.086,19864.139,20472.861,20993.575,21767.312,22244.022,22354.032,22629.057,22405.37,22383.368,22764.736,23263.448,23802.497,24161.863,24095.857,24051.853,24667.909,25250.962,25470.982,27014.789,28364.245,29427.675,30461.769,31125.496,32042.246,31686.547,33505.379,34865.836,35463.557,35848.592'
co2_emissions_arr = np.fromstring(co2_emission_str, sep=",")

fig = plt.figure(figsize=plt.figaspect(0.5))
ax = fig.add_subplot(111)
ax.plot(surface_temp_year, surface_temp_change_arr, label='Global Surface Temp Change')
# Share x axis
ax2 = ax.twinx()
ax2.plot(surface_temp_year[:-4], co2_emissions_arr, label='CO2 emission', color='orange')
ax2.set_ylabel('Million Tons of $CO_2$')
ax.set_ylabel('Temperature (deg C)')
ax.set_xlabel('Month of January')
ax.set_title("A tale of Global Warming")
ax.legend(loc="upper left")
ax2.legend(loc="lower right")
# turn off grids for CO2
ax2.grid(False, which='both')

This is a peek into how easy it is to visualize the data. In the next few sub-modules, we will look at ways of using subplots, annotating plots, plotting a histogram and more.