Note
Go to the endto download the full example code.
Fixing too many ticks#
One common cause for unexpected tick behavior is passing a list of stringsinstead of numbers or datetime objects. This can easily happen without noticewhen reading in a comma-delimited text file. Matplotlib treats lists of stringsascategorical variables(Plotting categorical variables), and by defaultputs one tick per category, and plots them in the order in which they aresupplied. If this is not desired, the solution is to convert the strings toa numeric type as in the following examples.
Example 1: Strings can lead to an unexpected order of number ticks#
importmatplotlib.pyplotaspltimportnumpyasnpfig,ax=plt.subplots(1,2,layout='constrained',figsize=(6,2.5))x=['1','5','2','3']y=[1,4,2,3]ax[0].plot(x,y,'d')ax[0].tick_params(axis='x',color='r',labelcolor='r')ax[0].set_xlabel('Categories')ax[0].set_title('Ticks seem out of order / misplaced')# convert to numbers:x=np.asarray(x,dtype='float')ax[1].plot(x,y,'d')ax[1].set_xlabel('Floats')ax[1].set_title('Ticks as expected')
Example 2: Strings can lead to very many ticks#
Ifx has 100 elements, all strings, then we would have 100 (unreadable)ticks, and again the solution is to convert the strings to floats:
fig,ax=plt.subplots(1,2,figsize=(6,2.5))x=[f'{xx}'forxxinnp.arange(100)]y=np.arange(100)ax[0].plot(x,y)ax[0].tick_params(axis='x',color='r',labelcolor='r')ax[0].set_title('Too many ticks')ax[0].set_xlabel('Categories')ax[1].plot(np.asarray(x,float),y)ax[1].set_title('x converted to numbers')ax[1].set_xlabel('Floats')
Example 3: Strings can lead to an unexpected order of datetime ticks#
A common case is when dates are read from a CSV file, they need to beconverted from strings to datetime objects to get the proper date locatorsand formatters.
fig,ax=plt.subplots(1,2,layout='constrained',figsize=(6,2.75))x=['2021-10-01','2021-11-02','2021-12-03','2021-09-01']y=[0,2,3,1]ax[0].plot(x,y,'d')ax[0].tick_params(axis='x',labelrotation=90,color='r',labelcolor='r')ax[0].set_title('Dates out of order')# convert to datetime64x=np.asarray(x,dtype='datetime64[s]')ax[1].plot(x,y,'d')ax[1].tick_params(axis='x',labelrotation=90)ax[1].set_title('x converted to datetimes')plt.show()
Total running time of the script: (0 minutes 1.679 seconds)