Computational Programming. Part 5

55.3, 54.0, 56.7, 56.4, 57.3] >>> years = range(2000, 2013) >>> plot(years, nyc_temp, marker='o') [] >>> show()

We use the range() function we learned about in Chapter 1 to specify the years 2000 to 2012. Now you’ll see the years displayed on the x-axis
(see Figure 2-7).

at New York City, let’s see how the average monthly temperature has varied over the years. This will give us a chance to understand how to plot multiple lines on a single graph. We’ll choose three years: 2000, 2006, and 2012. For each of these years, we’ll plot the average temperature for all 12 months.

>>> nyc_temp_2000 = [31.3, 37.3, 47.2, 51.0, 63.5, 71.3, 72.3, 72.7, 66.0, 57.0, 45.3, 31.1] >>> nyc_temp_2006 = [40.9, 35.7, 43.1, 55.7, 63.1, 71.0, 77.9, 75.8, 66.6, 56.2, 51.9, 43.6] >>> nyc_temp_2012 = [37.3, 40.9, 50.9, 54.8, 65.1, 71.0, 78.8, 76.7, 68.8, 58.0, 43.9, 41.5]

two lists correspond to the years 2006 and 2012, respectively. We could plot the three sets of data on three different graphs, but that wouldn’t make it very easy to see how each year compares to the others. Try doing it! The clearest way to compare all of these temperatures is to plot all three data sets on a single graph, like this:

>>> months = range(1, 13) >>> plot(months, nyc_temp_2000, months, nyc_temp_2006, months, nyc_temp_2012) [, , ]

chooses a different color for each line to indicate that the lines have been plotted from different data sets. Instead of calling the plot function with all three pairs at once, we could also call the plot function three separate times, once for each pair:

>>> plot(months, nyc_temp_2000) [] >>> plot(months, nyc_temp_2006) [] >>> plot(months, nyc_temp_2012) [] >>> show()

as to which color corresponds to which year. To fix this, we can use the function legend(), which lets us add a legend to the graph. A legend is a small display box that identifies what different parts of the graph mean. Here, we’ll use a legend to indicate which year each colored line stands for. To add the legend, first call the plot() function as earlier:

>>> plot(months, nyc_temp_2000, months, nyc_temp_2006, months, nyc_temp_2012) [, , ]

it as follows:
>>> from pylab import legend >>> legend([2000, 2006, 2012])

we want to use to identify each plot on the graph. These labels are entered in this order to match the order of the pairs of lists that were entered in the plot() function. That is, 2000 will be the label for the plot of the first pair we entered in the plot() function; 2006, for the second pair; and 2012, for the third. You can also specify a second argument to the function that will specify the position of the legend. By default, it’s always positioned at the top right of the graph. However, you can specify a particular position, such as 'lower center', 'center left', and 'upper left'. Or you can set the position to 'best', and the legend will be positioned so as not to interfere with the graph. Finally, we call show() to display the graph: >>> show()

adding a legend. Now, we’ll learn about other ways to customize a graph and to make it clearer by adding labels to the x- and y-axes, adding a title to the graph, and controlling the range and steps of the axes. Adding a Title and Labels We can add a title to our graph using the title() function and add labels for the x- and y-axes using the xlabel() and ylabel() functions. Let’s re-create the last plot and add all this additional information:

>>> from pylab import plot, show, title, xlabel, ylabel, legend >>> plot(months, nyc_temp_2000, months, nyc_temp_2006, months, nyc_temp_2012) [, , ] >>> title('Average monthly temperature in NYC') >>> xlabel('Month') >>> ylabel('Temperature') >>> legend([2000, 2006, 2012])

to be automatically determined by Python based on the data supplied to the plot() function. This may be fine for most cases, but sometimes this automatic range isn’t the clearest way to present the data, as we saw in the graph where we plotted the average annual temperature of New York City (see Figure 2-7). There, even small changes in the temperature seemed large because the automatically chosen y-axis range was very narrow. We can adjust the range of the axes using the axis() function. This function can be used both to retrieve the current range and to set a new range for the axes. Consider, once again, the average annual temperature of New York City during the years 2000 to 2012 and create a plot as we did earlier.

>>> nyc_temp = [53.9, 56.3, 56.4, 53.4, 54.5, 55.8, 56.8, 55.0, 55.3, 54.0, 56.7, 56.4, 57.3] >>> plot(nyc_temp, marker='o') []

import axis >>> axis() (0.0, 12.0, 53.0, 57.5)

for the x-axis (0.0, 12.0) and the y-axis (53.0, 57.5). These are the same range values from the graph that we made earlier. Now, let’s change the y-axis to start from 0 instead of 53.0:

>>> axis(ymin=0) (0.0, 12.0, 0, 57.5)

do so using the savefig() function. This function saves the graph as an image file, which you can use in reports or presentations. You can choose among several image formats, including PNG, PDF, and SVG. Here’s an example:
>>> from pylab import plot, savefig >>> x = [1, 2, 3] >>> y = [2, 4, 6] >>> plot(x, y) >>> savefig('mygraph.png')