There are several problems in your code. First the big ones: You are creating a new figure and a new axes in every iteration of your loop → put fig = plt.figure and ax = fig.add_subplot(1,1,1) outside of the loop. Don’t use the Locators. Call the functions ax.set_xticks() and ax.grid() with the correct keywords. With … Read more
Whatever reason it is you are getting 2 ticks only by default, you can fix it (customise it) by changing the ticker locator using a date locator. import matplotlib.pyplot as plt import matplotlib.dates as mdates x = [ datetime.datetime(1900,1,1,0,1,2), datetime.datetime(1900,1,1,0,1,3), … ] # ( more than 1000 elements ) y = [ 34, 33, 23, … Read more
If you prefer a non-object-oriented approach, move plt.xticks(rotation=70) to right before the two avail_plot calls, eg plt.xticks(rotation=70) avail_plot(axs[0], dates, s1, ‘testing’, ‘green’) avail_plot(axs[1], dates, s1, ‘testing2’, ‘red’) This sets the rotation property before setting up the labels. Since you have two axes here, plt.xticks gets confused after you’ve made the two plots. At the point … Read more
A quick dirty solution would be the following: ax.set_xticks(ax.get_xticks()[::2]) This would only display every second xtick. If you wanted to only display every n-th tick you would use ax.set_xticks(ax.get_xticks()[::n]) If you don’t have a handle on ax you can get one as ax = plt.gca(). Alternatively, you could specify the number of xticks to use … Read more
I had exactly the same problem, and these lines fixed the problem: from matplotlib.ticker import ScalarFormatter y_formatter = ScalarFormatter(useOffset=False) ax.yaxis.set_major_formatter(y_formatter)
This should be simpler: (from https://scivision.co/matplotlib-force-integer-labeling-of-axis/) import matplotlib.pyplot as plt from matplotlib.ticker import MaxNLocator #… ax = plt.figure().gca() #… ax.xaxis.set_major_locator(MaxNLocator(integer=True))
In short: import matplotlib.dates as mdates myFmt = mdates.DateFormatter(‘%d’) ax.xaxis.set_major_formatter(myFmt) Many examples on the matplotlib website. The one I most commonly use is here
You could explicitly set where you want to tick marks with plt.xticks: plt.xticks(np.arange(min(x), max(x)+1, 1.0)) For example, import numpy as np import matplotlib.pyplot as plt x = [0,5,9,10,15] y = [0,1,2,3,4] plt.plot(x,y) plt.xticks(np.arange(min(x), max(x)+1, 1.0)) plt.show() (np.arange was used rather than Python’s range function just in case min(x) and max(x) are floats instead of ints.) … Read more
You could explicitly set where you want to tick marks with plt.xticks: plt.xticks(np.arange(min(x), max(x)+1, 1.0)) For example, import numpy as np import matplotlib.pyplot as plt x = [0,5,9,10,15] y = [0,1,2,3,4] plt.plot(x,y) plt.xticks(np.arange(min(x), max(x)+1, 1.0)) plt.show() (np.arange was used rather than Python’s range function just in case min(x) and max(x) are floats instead of ints.) … Read more