Matplotlib is a library that can be used to visualize data that has been loaded with a library like Pandas, Numpy, or Scipy. For this tutorial, we'll use Pandas.
For a more detailed tutorial on loading data, see this lesson on .beginning with Pandas
For now, we'll just use a simple statement to load the surveys data.
import pandas as pd
df = pd.read_csv('data/surveys.csv', index_col='record_id')Matplotlib has a wide variety of plots that it can produce. First, we'll introduce the simplest of plots: the 2 dimensional line plot.
First, we'll import matplotlib.
import matplotlib.pyplot as pltMatplotlib can easily plot a set of data even larger than surveys.csv, but for this example, we'll take the first 50 of the ~35000 entries that are in surveys.csv. For a more detailed tutorial on slicing data, see this lesson on masking and grouping.
small_dataset = df[:50]There's a column in surveys.csv named "plot" which would make an excellent value to plot.
plot_data = small_dataset['plot']Now, we have an array of plot data indexed by the record_id value. Let's plot it and give it a label.
plt.plot(plot_data, label='My Data')The data has now been plotted, to see it we can do 2 things:
- We can interact with the plot by using
plt.showlike so:
plt.show()- Or we can save the plot to a file using
plt.savefiglike so:
plt.savefig('myplot.png')This would save the file as a rasterized PNG image. The format is deduced from the file name or can be given explicitly using format parameter, eg. format="png". For raster images, in order to enhance quality one can also request particulat resolution in dots per inch using dpi parameter. This may be useful when creating quality images for printing/publication. Vectorized images are supported as well, we just need to save the file as a SVG, EPS or PDF which is as simple as:
plt.savefig('myplot.pdf')What's a plot without a title, axis labels, and a legend? These can be easily set like so:
plt.xlabel('Index')
plt.ylabel('Plot Value')
plt.title('The Plot Value From surveys.csv')The plot is created using some default settings, eg. default line color. You may have noticed, the first line plotted is blue. We can change this by replotting the figure. But first, the blue line is still on the plot, so we must clear it like so:
plt.clf()It is important to note, that subsequent plots we may have created with plt.plot are (by default) superimposed on the same figure that is created implicitly upon first plt.plot call. Figures are numbered from 1 and one can switch between them by calling plt.figure(number). When creating a new figures one can give a number of options, for example one can fine tune the size and default resolution by using figsize and dpi parameters:
plt.figure(figsize=(10, 8), dpi=200)which will create a figure 8 inches high and 10 inches wide with resolution of 200 dots per inch.
It is also possible to control the margin between the edge of the axes box and the edge of the image:
plt. subplots_adjust(left=0.1, bottom=0.2, right=0.99, top=0.99)Values are fractions of the image size and denote the position of the respective edge of the axes bounding box. Lower left corner of the image is at (0,0). The invocation above leaves 10% margin on the left side, 20% margin on the bottom and 1% for top and right edges.
To use a different color, like red, we would plot our data like so:
plt.plot(plot_data, color='r')Note that creating more than one plot on the same figure will make them use different colors (like in gnuplot) unless specified otherwise. Here's a list of predefined colors:
| Code | Color |
|---|---|
| b | blue |
| g | green |
| r | red |
| c | cyan |
| m | magenta |
| y | yellow |
| k | black |
| w | white |
For more color flexibility, you can specify hexadecimal RGB values like so:
plt.plot(plot_data, color='#aa5599')or use RGB coefficients of range 0-1 (which makes it easy to create multiple color-encoded plots in a loop):
plt.plot(plot_data, color=(0.1, 0.9, 0.6))
The default line style is a solid line. We can make it thinner or thicker by specifying linewidth or lw:
plt.plot(plot_data, linewidth=3)The default linewidth is 1. A linewidth of 3 would be 3 times as thick as the default. Likewise, a linewidth of .75 would be 3/4 of the thickness of the default.
Matplotlib can do many types of plots. For example, a dot plot can be constructed like so:
plt.plot(plot_data, 'o')The o means a dot. There are a variety of markers you can use. Here's a complete list: http://matplotlib.org/api/markers_api.html#module-matplotlib.markers
A simple bar plot:
plt.plot(plot_data, linestyle='--')
| Value | Style |
|---|---|
| '-' | solid line (default) |
| '--' | dashed line |
| '-.' | dash-dot line |
| ':' | dotted line |
So far we have plotted only a simple line plot which is default. It is possible to specify also the data marker style which will create scatter plot or various connect-the-dots-like plot. For example, to use square data marker:
plt.plot(plot_data, marker='s')
| Marker | Meaning |
|---|---|
| '.' | point |
| 'o' | circle |
| 'v' | triangle down |
| '^' | triangle up |
| 's' | square |
| 'p' | pentagon |
| '*' | star |
| 'h' | hexagon |
| '+' | plus |
| 'D' | diamond |
So far we have used a simple, default, uniform axis. The user has, however, a complete control over the way axes are organized.
One can adjust the range of axes using set plt.xlim for horizontal and plt.ylim for vertical axis. For instence to set X limt to [-10; 15] one can use:
plt.xlim(-10, 15)
In many cases it is useful to use logarithmic scale on one or both axes. One can use dedicated plot methods:
| Method | Result |
|---|---|
plt.semilogx |
logarithmic scaling on X-axis |
plt.semilogy |
logarithmic scaling on Y-axis |
plt.loglog |
logarithmic scaling on both axes (log-log plot) |
plt.loglog(plot_data)To create a plot with two X or two Y axes having different scales, units, ranges one can use plt.twinx and plt.twiny:
plt.bar(plot_data.index, plot_data.values)
plt.twinx()
plt.plot(1/ plot_data, color='k')This will create a plot with two independent Y axes, one for barplot and one for line plot of inverse values. Both plots will share the same X-axis.
In the examples above the plot is not ready to be published. We would like to add titles, axes labels, tick markers, maybe some grid or legend.
All plots can be labelled upon creation:
plt.plot(..., label='some description')and a legend can be automatically generated in the automatically chosen best location:
plt.legend(loc='best')One can change the location and labels of the axes ticks using plt.xticks and plt.yticks methods:
`plt.xticks([1,2,3,4])` # put ticks in given locations of X-axis
`plt.yticks([1,2,3], ['A', 'B', 'C'])` # put ticks in given locations on Y-axis, denote them with lettersLabels can be rotated by adding parameter rotation=angle_in_degrees. To draw a grid with grid lines at the ticks use plt.grid().
Axes can be labelled using:
plt.xlabel('X-axis label')
plt.ylabel('Y-axis label')To set a title use
plt.title('Plot title')Matplotlib supports a number of different plot variations, eg. bar plot (plt.bar), contour plots (plt.contour), pie chart (plt.pie), error bars (plt.errorbar), polar plot (plt.polar), ...
To use a bar plot:
plt.bar(plot_data.index, plot_data.values)There are excellent examples on Matplotlib website, especially:
A box and whisker plot:
plt.boxplot(plot_data.values)You may have noticed there's some more data beyond just the plot value in surveys.csv. Let's plot the plot value and group them by the sex value. A dot plot would be ideal for this.
Pandas has some built-in tools that make it easy to group your data.
grouped_plot_data = small_dataset.groupby('sex')This returns our data in an iterable object. Each entry in grouped_plot_data is formatted like so:
('group_name', pandas data pertaining to the group)Keep in mind we need different colors and labels for each group. So we can plot the data like so:
colors = ['r', 'g'] #we'll be cycling through these colors
color_index = 0
for group in grouped_plot_data:
color = colors[color_index]
group_label = group[0]
group_data = group[1]
plt.plot(group_data['plot'], color=color, label=group_label)
color_index += 1
plt.legend()This is a basic tutorial to get you started using Python to make your graphs. For more information on Matplotlib, visit the official site: http://matplotlib.org/