Resolving Dates on the X-Axis (Part 1)

Having previously discussed how to use np.linspace() to get evenly-spaced x-axis labels, my final challenge for this episode of “better understanding matplotlib’s plotting capability” is to do something similar with datetimes.

This will be a generalization of what I discussed in the last post and as mentioned in the fourth paragraph, articulation of exactly what I am trying to achieve is of the utmost importance.

I begin with the following code and a new method pd.date_range():

L5 generates a datetime index that I can convert to a list using the list() constructor (see output just above graph). Each element of the subsequent list is datatype pd.Timestamp, which is the pandas replacement for the Python datetime.datetime object. Observe that the first and second arguments are start date and end date, which are included in the Timestamp sequence. Also notice that the list has five elements, which is consistent with the third argument of pd.date_range().

Given a start date, end date, and n labels, this suggests I can generate (n – 1) evenly-spaced time intervals. Great start!

The enthusiasm fades when looking down at the graph, however. First, I get nine instead of five tick labels. Second, my desired format is yyyy-mm-dd as contained in L5. I do not know how/where the program makes either determination.

Another problem is that if I change the third argument (L5) to 15 to get more tick labels, a ValueError results: “x and y must have same first dimension, but have shapes (15,) and (5,).” That makes sense because I now have an unequal number of x- and y-coordinates. This date_range is really intended to be used only for tick labels and not as the source of x-coordinates. I may need to create a separate date_range (or make another list of x-coordinates) for plt.plot() and then create something customizable for evenly-spaced datetime tick labels.

I will continue next time.