How to Change the Number of Ticks in Matplotlib?

Changing the number of ticks in Matplotlib improves the clarity of a plot by controlling how many tick marks appear along the axes. This can make the chart easier to read and interpret, especially when dealing with dense or sparse data. You can increase or decrease the number of ticks depending on the level of detail you want to show.

Using MaxBLocator

MaxNLocator from matplotlib.ticker automatically places "nice" tick locations and limits their number. It's highly efficient and ideal for dynamic plots where the range isn't fixed.

import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator

x = [1, 2, 3, 4]
y = [7, 13, 24, 22]

plt.plot(x, y)
plt.title("Plot")

plt.xlabel("X-Axis")
plt.ylabel("Y-Axis")

# Limit max ticks on x and y axis
plt.gca().xaxis.set_major_locator(MaxNLocator(nbins=4))
plt.gca().yaxis.set_major_locator(MaxNLocator(nbins=4))

plt.show()

Output

Explanation: MaxNLocator(nbins=4) limits the number of major ticks on the axes to a maximum of 4. plt.gca() gets the current axes, and set_major_locator() applies the locator to control tick placement.

Using locator_params()

locator_params() is a quick way to set the number of ticks across axes with minimal configuration.

import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator

x = [1, 2, 3, 4]
y = [7, 13, 24, 22]

plt.plot(x, y)
plt.title("Plot")

plt.xlabel("X-Axis")
plt.ylabel("Y-Axis")

plt.locator_params(axis='both', nbins=4)
plt.show()

Output

Explanation: plt.locator_params(axis='both', nbins=4) sets the maximum number of major ticks to 4 for both x and y axes.

Using xticks() and yticks()

xticks() and yticks() provide full manual control over tick positions and labels. Use this method when you know the exact locations.

import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator

x = [1, 2, 3, 4]
y = [7, 13, 24, 22]

plt.plot(x, y)
plt.title("Plot")

plt.xlabel("X-Axis")
plt.ylabel("Y-Axis")

plt.xticks([1, 2, 3, 4])
plt.yticks([7, 13, 24, 22])
plt.show()

Output

Explanation: plt.xticks([1, 2, 3, 4]) and plt.yticks([7, 13, 24, 22]) explicitly set the positions of the ticks on the x and y axes.

Using xlim() / ylim()

This method restricts the axis range using xlim() or ylim(), then applies locator_params() to control tick density. It's useful when you want to focus on a subset of the data.

import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator

x = [1, 2, 3, 4]
y = [7, 13, 24, 22]

plt.plot(x, y)
plt.title("Plot")

plt.xlabel("X-Axis")
plt.ylabel("Y-Axis")

plt.xlim(0, 3)  # restrict x-axis range
plt.locator_params(axis='x', nbins=3)
plt.show()

Output

Explanation: plt.xlim(0, 3) restricts the visible x-axis range from 0 to 3, trimming any data outside this interval. plt.locator_params(axis='x', nbins=3) then suggests a maximum of 3 ticks within this limited range.