Reading Data from a CSV File

You can use Matplotlib plots to visualize data created in another program and saved as a file, often as plain text in comma-separated values (CSV) format, such as csv-data.csv in Listing 4.

John,32
Mary,48
Ashok,25
Mario,41
Heather,38
Ashley,29

Figure 3 shows the data from the CSV file in Listing 4 in a histogram format. To create the histogram with Matplotlib, you can use the code in Listing 5 – after loading Matplotlib and another module (csv) to hande CSV data. Listing 5 creates two arrays, x and y (lines 4 and 5), and opens the data file (line 7). Lines 8-12 load the file's contents into a table called plots (using the comma as a column separator), copy the names from the first column into the x array and the ages (reformatted as integer numbers) into the y array. The rest of Listing 5 is self-explanatory, because each line corresponds to a specific, easy-to-spot visual feature in Figure 3.

01 import Matplotlib.pyplot as plt
02 import csv
03
04 x = []
05 y = []
06
07 with open('csv-data.csv','r') as csvfile:
08 plots = csv.reader(csvfile, delimiter = ',')
09
10 for row in plots:
11 x.append(row[0])
12 y.append(int(row[1]))
13
14 plt.bar(x, y, color = 'g', width = 0.72, label = "Age")
15 plt.xlabel('Names')
16 plt.ylabel('Ages')
17 plt.title('Customers ages')
18 plt.legend()
19 plt.savefig('03-csv.png')
20 plt.show()

Figure 3: You can also plot data from a CSV file generated by another program.

A Random Data, Scattered Plot

While an in-depth overview of NumPy's capabilities is beyond the scope of this tutorial, Figure 4 gives an idea of the possibilities of using Matplotlib and NumPy together. The scatter plot in Figure 4 is generated from the code in Listing 6, which is taken (with minimal changes) from the official Matplotlib documentation. For my purposes, I will divide the code into two sections and discuss them in reverse order. The last section (lines 11-15) draws and saves a series of distinct points, scattered in random positions across the Axes. The coordinates, size, and color of each point are loaded in line 12 by the powerful pyplot function scatter (see [6] for an explanation of scatter syntax). The interesting part of Listing 6 is how all the arrays of values passed to scatter are generated. It is easy to guess, by looking at lines 4-9, that all those arrays are generated randomly, on the spot, with simple calls to the NumPy functions that generate random numbers. For the settings of those functions, please see the documentation for these functions on the NumPy website [4].

01 import Matplotlib.pyplot as plt
02 import NumPy as np
03
04 np.random.seed(20010911)
05 data = {'a': np.arange(50),
06 'c': np.random.randint(0, 50, 50),
07 'd': np.random.randn(50)}
08 data['b'] = data['a'] + 10000 * np.random.randn(50)
09 data['d'] = np.abs(data['d']) * 100
10
11 fig, ax = plt.subplots(figsize=(5, 2.7))
12 ax.scatter('a', 'b', c='c', s='d', data=data)
13 ax.set_xlabel('Speed')
14 ax.set_ylabel('Distance');
15 plt.savefig('random-scatter.png')

Figure 4: A scatter plot, showing the distribution and weights of a generic dataset.

Subplots and Extra Axes

You can combine different plots into one Matplotlib Figure, add Axis elements, and link them to each other using the techniques in Listing 7.

01 import Matplotlib.pyplot as pls
02 import NumPy as np
03
04 t = np.arange(0.0, 40.0, 0.8)
05 s = t**2 #parabola
06
07 fig, (ax1, ax3) = plt.subplots(1, 2, figsize=(12, 5))
08 l1, = ax1.plot(t, s)
09 ax2 = ax1.twinx()
10 l2, = ax2.plot(t, np.cos(-1*t/2), 'C1')
11 ax2.legend([l1, l2], ['Parabola (left)', 'Sinusoid (right)'])
12
13 ax3.plot(t, 20 - t*np.sin(20 - t))
14 ax3.set_xlabel('Angle [∞]')
15
16 ax4 = ax3.secondary_xaxis('top', functions=(np.rad2deg, np.deg2rad))
17 ax4.set_xlabel('Angle [rad]')
18
19 plt.savefig('extra-axes.png')

Using more or less the same syntax shown in the previous examples, Listing 7 creates three distinct functions: a parabola (lines 5 and 8), a normal sinusoid (line 11), and a growing sinusoid (line 13). What is new is that these plots are grouped into two subplots (shown in Figure 5), whose Axis objects are indeed linked to each other. A full explanation of the code is beyond the scope of this article, but here are the main points you need to understand to create similar charts.

Figure 5: Plots can be combined in many ways with extra Axes that make them easier to read.

Line 7 splits the main Figure into two subplots, each with its own Axes, called ax1 and ax3. Then, the first subplot gets the ax2 of line 9, which is a "twin" of the ax1 of line 8, for a very precise purpose: to show the very different scales of both the parabola and the sinusoid separately, but as clearly as possible, on opposite sides of the subplot. Meanwhile, the growing sinusoid is plotted in the right subplot, with its own labels and titles, but also using another feature of Matplotlib (line 16): the secondary_xaxis method creates an extra horizontal Axis (ax4) at the top of that subplot that shows the same variable as the bottom Axis (ax3) but with a different unit (radiants instead of degrees).