Tick Control¶

This document covers the XAxis/YAxis objects, tick locators, and tick formatters for fine-grained control over axis behavior.

XAxis and YAxis Objects¶

Access axis objects via the axes:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-2*np.pi, 2*np.pi, 100)
y = np.sin(x)

fig, ax = plt.subplots()
ax.plot(x, y)

# Access axis objects
xaxis = ax.xaxis
yaxis = ax.yaxis

print(type(xaxis))  # <class 'matplotlib.axis.XAxis'>
print(type(yaxis))  # <class 'matplotlib.axis.YAxis'>

set_ticks_position¶

Control where ticks appear:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-10, 10, 500)
y = np.sin(x)

fig, ax = plt.subplots(figsize=(12, 3))
ax.plot(x, y)
ax.xaxis.set_ticks_position('top')
ax.yaxis.set_ticks_position('right')
plt.show()

Options: 'top', 'bottom', 'left', 'right', 'both', 'none', 'default'

Axis Visibility¶

Hide an entire axis:

ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)

get_ticklocs and get_ticklabels¶

Get current tick positions and labels:

print(ax.xaxis.get_ticklocs())
print(ax.yaxis.get_ticklocs())
print(ax.xaxis.get_ticklabels())  # List of Text objects

Tick Locators¶

Tick locators determine where tick marks appear on an axis.

Using Locators¶

import matplotlib as mpl

ax.xaxis.set_major_locator(locator)
ax.yaxis.set_major_locator(locator)
ax.xaxis.set_minor_locator(locator)
ax.yaxis.set_minor_locator(locator)

MultipleLocator¶

Place ticks at multiples of a base value:

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

x = np.linspace(-2*np.pi, 2*np.pi, 500)
y = np.sin(x)

fig, ax = plt.subplots(figsize=(12, 3))
ax.plot(x, y)

ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(5))
ax.yaxis.set_major_locator(mpl.ticker.MultipleLocator(0.5))

ax.xaxis.set_minor_locator(mpl.ticker.MultipleLocator(1))
ax.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.1))

plt.show()

FixedLocator¶

Place ticks at specific locations:

ax.xaxis.set_major_locator(mpl.ticker.FixedLocator([-1, 0, 1]))
ax.yaxis.set_major_locator(mpl.ticker.FixedLocator([-0.2, 0, 0.2]))

MaxNLocator¶

Automatically choose up to N tick locations:

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

fig, axes = plt.subplots(2, 3, sharex=True, sharey=True, figsize=(12, 3))

for ax in axes.flatten():
    ax.xaxis.set_major_locator(mpl.ticker.MaxNLocator(3))
    ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(3))
    ax.plot(np.random.randn(10))

plt.show()

NullLocator¶

Remove all ticks:

ax.xaxis.set_major_locator(mpl.ticker.NullLocator())
ax.yaxis.set_major_locator(mpl.ticker.NullLocator())

Useful for image displays:

import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn.datasets import fetch_olivetti_faces

faces = fetch_olivetti_faces().images

fig, ax = plt.subplots(5, 5, figsize=(5, 5))
fig.subplots_adjust(hspace=0, wspace=0)

for i in range(5):
    for j in range(5):
        ax[i, j].xaxis.set_major_locator(mpl.ticker.NullLocator())
        ax[i, j].yaxis.set_major_locator(mpl.ticker.NullLocator())
        ax[i, j].imshow(faces[10 * i + j], cmap="bone")

plt.show()

Locators Summary¶

Locator	Description
`MultipleLocator(base)`	Ticks at multiples of base
`FixedLocator(locs)`	Ticks at specified locations
`MaxNLocator(n)`	At most n ticks
`NullLocator()`	No ticks
`AutoLocator()`	Automatic (default)
`LogLocator()`	For log scales
`LinearLocator(n)`	Exactly n evenly spaced
`IndexLocator(base, offset)`	Ticks at base*i + offset

Tick Formatters¶

Tick formatters control how tick labels are displayed.

Using Formatters¶

import matplotlib as mpl

ax.xaxis.set_major_formatter(formatter)
ax.yaxis.set_major_formatter(formatter)
ax.xaxis.set_minor_formatter(formatter)
ax.yaxis.set_minor_formatter(formatter)

NullFormatter¶

Remove tick labels while keeping ticks:

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

x = np.linspace(-2*np.pi, 2*np.pi, 500)
y = np.sin(x) * np.exp(-x**2/20)

fig, ax = plt.subplots()
ax.plot(x, y)

ax.xaxis.set_major_formatter(mpl.ticker.NullFormatter())

plt.show()

FuncFormatter¶

Custom formatting with a function:

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

def format_func(value, tick_number):
    # Convert to multiples of pi/2
    N = int(np.round(2 * value / np.pi))
    if N == 0:
        return "0"
    elif N == 1:
        return "$\\pi/2$"
    elif N == -1:
        return "$-\\pi/2$"
    elif N == 2:
        return "$\\pi$"
    elif N == -2:
        return "$-\\pi$"
    elif N % 2 > 0:
        return f"${N}\\pi/2$"
    else:
        return f"${N // 2}\\pi$"

x = np.linspace(-2*np.pi, 2*np.pi, 500)
y = np.sin(x)

fig, ax = plt.subplots(figsize=(12, 3))
ax.plot(x, y)

ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(np.pi / 2))
ax.xaxis.set_minor_locator(mpl.ticker.MultipleLocator(np.pi / 4))
ax.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(format_func))

plt.show()

DateFormatter¶

Format datetime tick labels:

import matplotlib.pyplot as plt
import matplotlib.dates as mpl_dates
import yfinance as yf

df = yf.Ticker('AAPL').history(start='2020-07-01', end='2020-12-31')

fig, ax = plt.subplots(figsize=(15, 3))
ax.plot(df.index, df['Close'])

# Date formatting
date_format = mpl_dates.DateFormatter('%b, %d %Y')
ax.xaxis.set_major_formatter(date_format)

fig.autofmt_xdate()
plt.show()

Date Format Codes¶

Code	Meaning	Example
`%Y`	Year (4 digit)	2024
`%y`	Year (2 digit)	24
`%m`	Month (number)	01-12
`%b`	Month (abbrev)	Jan
`%B`	Month (full)	January
`%d`	Day	01-31
`%H`	Hour (24h)	00-23
`%M`	Minute	00-59
`%S`	Second	00-59

StrMethodFormatter¶

Format using string format specification:

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

x = np.linspace(0, 1, 100)
y = x ** 2 * 1000

fig, ax = plt.subplots()
ax.plot(x, y)

# Format with commas and 0 decimals
ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))

plt.show()

PercentFormatter¶

Format as percentages:

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

x = np.linspace(0, 1, 100)
y = x ** 2

fig, ax = plt.subplots()
ax.plot(x, y)

# xmax=1.0 means 1.0 = 100%
ax.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1.0))

plt.show()

ScalarFormatter¶

Default formatter with scientific notation control:

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

x = np.linspace(0, 1, 100)
y = x * 1e6

fig, ax = plt.subplots()
ax.plot(x, y)

formatter = mpl.ticker.ScalarFormatter(useMathText=True)
formatter.set_scientific(True)
formatter.set_powerlimits((-2, 2))
ax.yaxis.set_major_formatter(formatter)

plt.show()

Formatters Summary¶

Formatter	Description
`NullFormatter()`	No labels
`FuncFormatter(func)`	Custom function
`StrMethodFormatter(fmt)`	String format
`PercentFormatter(xmax)`	Percentage
`ScalarFormatter()`	Default with options
`LogFormatter()`	For log scales
`DateFormatter(fmt)`	Date/time

Complete Example: Stock Chart¶

import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.dates as mpl_dates
import numpy as np
import pandas as pd

# Create sample time series
dates = pd.date_range('2024-01-01', periods=100, freq='D')
values = np.cumsum(np.random.randn(100)) * 1000 + 50000

fig, ax = plt.subplots(figsize=(12, 4))
ax.plot(dates, values)

# Date formatter for x-axis
ax.xaxis.set_major_formatter(mpl_dates.DateFormatter('%b %Y'))
ax.xaxis.set_major_locator(mpl_dates.MonthLocator())

# Currency formatter for y-axis
ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('${x:,.0f}'))

ax.set_title('Portfolio Value')
fig.autofmt_xdate()
plt.show()

Complete Example: Math Plot with Grid¶

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

x = np.linspace(-2*np.pi, 2*np.pi, 500)
y = np.sin(x) * np.exp(-x**2/20)

fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(x, y)

# Configure x-axis
ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(np.pi))
ax.xaxis.set_minor_locator(mpl.ticker.MultipleLocator(np.pi/4))
ax.xaxis.set_ticks_position('bottom')

# Configure y-axis
ax.yaxis.set_major_locator(mpl.ticker.MultipleLocator(0.2))
ax.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.05))
ax.yaxis.set_ticks_position('left')

# Add grid for both major and minor ticks
ax.grid(which='major', linestyle='-', linewidth=0.5)
ax.grid(which='minor', linestyle=':', linewidth=0.5, alpha=0.5)

ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Damped Sine Wave')

plt.show()

Key Takeaways¶

Access axis objects via ax.xaxis and ax.yaxis
Locators control where ticks appear
Formatters control how tick labels are displayed
set_ticks_position() controls tick placement
Apply locators/formatters to major and minor ticks separately
Use MultipleLocator for regular intervals
Use FuncFormatter for custom label formatting
Use DateFormatter for time series data