How to change the color of the axis, ticks and labels for a plot in matplotlib

I'd like to Change the color of the axis, as well as ticks and value-labels for a plot I did using matplotlib and PyQt.

Any ideas?

Solution 1:

As a quick example (using a slightly cleaner method than the potentially duplicate question):

import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(111)

ax.plot(range(10))
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')

ax.spines['bottom'].set_color('red')
ax.spines['top'].set_color('red')
ax.xaxis.label.set_color('red')
ax.tick_params(axis='x', colors='red')

plt.show()

alt text

Alternatively

[t.set_color('red') for t in ax.xaxis.get_ticklines()]
[t.set_color('red') for t in ax.xaxis.get_ticklabels()]

Solution 2:

If you have several figures or subplots that you want to modify, it can be helpful to use the matplotlib context manager to change the color, instead of changing each one individually. The context manager allows you to temporarily change the rc parameters only for the immediately following indented code, but does not affect the global rc parameters.

This snippet yields two figures, the first one with modified colors for the axis, ticks and ticklabels, and the second one with the default rc parameters.

import matplotlib.pyplot as plt
with plt.rc_context({'axes.edgecolor':'orange', 'xtick.color':'red', 'ytick.color':'green', 'figure.facecolor':'white'}):
    # Temporary rc parameters in effect
    fig, (ax1, ax2) = plt.subplots(1,2)
    ax1.plot(range(10))
    ax2.plot(range(10))
# Back to default rc parameters
fig, ax = plt.subplots()
ax.plot(range(10))

enter image description here

You can type plt.rcParams to view all available rc parameters, and use list comprehension to search for keywords:

# Search for all parameters containing the word 'color'
[(param, value) for param, value in plt.rcParams.items() if 'color' in param]

Solution 3:

For those using pandas.DataFrame.plot(), matplotlib.axes.Axes is returned when creating a plot from a dataframe. Therefore, the dataframe plot can be assigned to a variable, ax, which enables the usage of the associated formatting methods.
The default plotting backend for pandas, is matplotlib.
See matplotlib.spines
Tested in python 3.8.12, pandas 1.3.3, matplotlib 3.4.3

import pandas as pd

# test dataframe
data = {'a': range(20), 'date': pd.bdate_range('2021-01-09', freq='D', periods=20)}
df = pd.DataFrame(data)

# plot the dataframe and assign the returned axes
ax = df.plot(x='date', color='green', ylabel='values', xlabel='date', figsize=(8, 6))

# set various colors
ax.spines['bottom'].set_color('blue')
ax.spines['top'].set_color('red') 
ax.spines['right'].set_color('magenta')
ax.spines['right'].set_linewidth(3)
ax.spines['left'].set_color('orange')
ax.spines['left'].set_lw(3)
ax.xaxis.label.set_color('purple')
ax.yaxis.label.set_color('silver')
ax.tick_params(colors='red', which='both')  # 'both' refers to minor and major axes