Change grid interval and specify tick labels in Matplotlib

Question

I am trying to plot counts in gridded plots, but I am not being able to figure out how I go about it. I want to:

1. Have dotted grids at an interval of 5

Answer 1

A subtle alternative to MaxNoe's answer where you aren't explicitly setting the ticks but instead setting the cadence.

import matplotlib.pyplot as plt
from matplotlib.ticker import (AutoMinorLocator, MultipleLocator)

fig, ax = plt.subplots(figsize=(10, 8))

# Set axis ranges; by default this will put major ticks every 25.
ax.set_xlim(0, 200)
ax.set_ylim(0, 200)

# Change major ticks to show every 20.
ax.xaxis.set_major_locator(MultipleLocator(20))
ax.yaxis.set_major_locator(MultipleLocator(20))

# Change minor ticks to show every 5. (20/4 = 5)
ax.xaxis.set_minor_locator(AutoMinorLocator(4))
ax.yaxis.set_minor_locator(AutoMinorLocator(4))

# Turn grid on for both major and minor ticks and style minor slightly
# differently.
ax.grid(which='major', color='#CCCCCC', linestyle='--')
ax.grid(which='minor', color='#CCCCCC', linestyle=':')

Answer 2

There are several problems in your code.

First the big ones:

1. 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.

2. Don't use the Locators. Call the functions ax.set_xticks() and ax.grid() with the correct keywords.

3. With plt.axes() you are creating a new axes again. Use ax.set_aspect('equal').

The minor things: You should not mix the MATLAB-like syntax like plt.axis() with the objective syntax. Use ax.set_xlim(a,b) and ax.set_ylim(a,b)

This should be a working minimal example:

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)

# Major ticks every 20, minor ticks every 5
major_ticks = np.arange(0, 101, 20)
minor_ticks = np.arange(0, 101, 5)

ax.set_xticks(major_ticks)
ax.set_xticks(minor_ticks, minor=True)
ax.set_yticks(major_ticks)
ax.set_yticks(minor_ticks, minor=True)

# And a corresponding grid
ax.grid(which='both')

# Or if you want different settings for the grids:
ax.grid(which='minor', alpha=0.2)
ax.grid(which='major', alpha=0.5)

plt.show()

Output is this: