Changing the "tick frequency" on x or y axis in matplotlib

Solution 1:

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.)

The plt.plot (or ax.plot) function will automatically set default x and y limits. If you wish to keep those limits, and just change the stepsize of the tick marks, then you could use ax.get_xlim() to discover what limits Matplotlib has already set.

start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, stepsize))

The default tick formatter should do a decent job rounding the tick values to a sensible number of significant digits. However, if you wish to have more control over the format, you can define your own formatter. For example,

ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))

Here's a runnable example:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, 0.712123))
ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
plt.show()

Solution 2:

Another approach is to set the axis locator:

import matplotlib.ticker as plticker

loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)

There are several different types of locator depending upon your needs.

Here is a full example:

import matplotlib.pyplot as plt
import matplotlib.ticker as plticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)
plt.show()

Solution 3:

I like this solution (from the Matplotlib Plotting Cookbook):

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]

tick_spacing = 1

fig, ax = plt.subplots(1,1)
ax.plot(x,y)
ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
plt.show()

This solution give you explicit control of the tick spacing via the number given to ticker.MultipleLocater(), allows automatic limit determination, and is easy to read later.