Python基础指南之matplotlib刻度与格式控制设置详解

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

fig, axes = plt.subplots(3, 2, figsize=(12, 10))
x = np.linspace(0, 10, 100)

locators = [
    ('autolocator', ticker.autolocator()),
    ('maxnlocator', ticker.maxnlocator(nbins=5)),
    ('linearlocator', ticker.linearlocator(numticks=10)),
    ('multiplelocator', ticker.multiplelocator(base=0.5)),
    ('fixedlocator', ticker.fixedlocator([0.5, 2, 4.7, 8.3])),
    ('loglocator', ticker.loglocator(base=10)),
]

for (name, loc), ax in zip(locators, axes.flat):
    ax.plot(x, np.sin(x))
    ax.xaxis.set_major_locator(loc)
    ax.set_title(name)

# 常用 locator 示例
ax.xaxis.set_major_locator(ticker.maxnlocator(nbins=6, integer=true, steps=[1, 2, 5, 10]))
ax.yaxis.set_major_locator(ticker.multiplelocator(0.2))     # 每 0.2 一个刻度
ax.xaxis.set_major_locator(ticker.loglocator(base=10, subs='all'))  # 对数
ax.xaxis.set_major_locator(ticker.nulllocator())  # 隐藏刻度

fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(x, np.sin(x))

# 主刻度（major ticks）
ax.xaxis.set_major_locator(ticker.multiplelocator(2))

# 次刻度（minor ticks）
ax.xaxis.set_minor_locator(ticker.multiplelocator(0.5))

# 开启次刻度网格
ax.grid(which='major', color='gray', linestyle='-', linewidth=0.8)
ax.grid(which='minor', color='lightgray', linestyle='--', linewidth=0.4)

# autominorlocator 自动设置次刻度
ax.xaxis.set_minor_locator(ticker.autominorlocator(n=4))  # 每个主刻度间 4 个次刻度

formatters = [
    ('scalarformatter', ticker.scalarformatter()),
    ('formatstrformatter', ticker.formatstrformatter('%.2f')),
    ('percentformatter', ticker.percentformatter(xmax=100, decimals=1)),
    ('funcformatter', ticker.funcformatter(lambda x, p: f'{x:.1f}°c')),
    ('fixedformatter', ticker.fixedformatter(['a', 'b', 'c', 'd', 'e'])),
    ('strmethodformatter', ticker.strmethodformatter('{x:.3f}')),
    ('engformatter', ticker.engformatter(unit='v')),
    ('logformatter', ticker.logformatter(base=10)),
    ('nullformatter', ticker.nullformatter()),
]

ax.yaxis.set_major_formatter(ticker.formatstrformatter('%.2f'))
# 常用格式: '%.2f'(两位小数), '%.0f'(整数), '%d'(整数), '%e'(科学计数)

# xmax=100: 0-100 范围显示为 0%-100%
# xmax=1: 0-1 范围显示为 0%-100%
ax.yaxis.set_major_formatter(ticker.percentformatter(xmax=1, decimals=0))

# 自定义格式化函数
# 接收两个参数: x(刻度值), pos(位置，通常不用)
def currency_fmt(x, pos):
    if x >= 1e6:
        return f'¥{x/1e6:.1f}m'
    elif x >= 1e3:
        return f'¥{x/1e3:.0f}k'
    else:
        return f'¥{x:.0f}'

ax.yaxis.set_major_formatter(ticker.funcformatter(currency_fmt))

# lambda 版本
ax.yaxis.set_major_formatter(
    ticker.funcformatter(lambda x, p: f'{x:,.0f}')
)

# 日期格式化
from datetime import datetime
ax.xaxis.set_major_formatter(
    ticker.funcformatter(lambda x, p: datetime.fromtimestamp(x).strftime('%y-%m'))
)

ax.yaxis.set_major_formatter(ticker.engformatter(unit='hz'))
# 自动使用 k, m, g, m, μ 等单位前缀

formatter = ticker.scalarformatter()
formatter.set_powerlimits((-3, 4))   # 超出范围才用科学计数法
formatter.set_useoffset(true)         # 使用偏移量
formatter.set_usemathtext(true)       # latex 风格
ax.yaxis.set_major_formatter(formatter)

# 刻度线参数
ax.tick_params(
    axis='both',           # 'x', 'y', 'both'
    which='major',         # 'major', 'minor', 'both'
    direction='in',        # 'in', 'out', 'inout'
    length=8,              # 刻度线长度
    width=1.5,             # 刻度线宽度
    color='red',           # 刻度线颜色
    pad=8,                 # 刻度与标签的间距
    labelsize=12,          # 标签字体大小
    labelcolor='black',    # 标签颜色
    labelrotation=45,      # 标签旋转角度
    top=true,              # 是否显示顶部刻度
    right=true,            # 是否显示右侧刻度
    bottom=true,           # 是否显示底部刻度
    left=true              # 是否显示左侧刻度
)

# 单独设置
ax.tick_params(axis='x', labelrotation=45, labelsize=10)
ax.tick_params(axis='y', which='minor', length=4, color='gray')

fig, ax = plt.subplots(figsize=(8, 5))
ax.plot(x, np.sin(x))

# 隐藏上方和右侧边框
ax.spines['top'].set_visible(false)
ax.spines['right'].set_visible(false)

# 移动边框位置
ax.spines['left'].set_position(('data', 0))     # 左框移到 x=0
ax.spines['bottom'].set_position(('data', 0))   # 下框移到 y=0
ax.spines['left'].set_position(('axes', 0.05))  # 左框在 5% 处
ax.spines['left'].set_position('center')         # 左框在中间

# 边框样式
ax.spines['bottom'].set_color('red')
ax.spines['bottom'].set_linewidth(2)
ax.spines['bottom'].set_linestyle('--')

import matplotlib.dates as mdates
from datetime import datetime, timedelta

# 生成日期数据
dates = [datetime(2024, 1, 1) + timedelta(days=i) for i in range(365)]
values = np.random.randn(365).cumsum()

fig, ax = plt.subplots(figsize=(14, 5))
ax.plot(dates, values)

# 日期 locator
ax.xaxis.set_major_locator(mdates.monthlocator(interval=1))   # 每月
ax.xaxis.set_minor_locator(mdates.weekdaylocator(byweekday=mdates.mo))  # 每周一

# 日期 formatter
ax.xaxis.set_major_formatter(mdates.dateformatter('%y-%m'))
ax.xaxis.set_major_formatter(mdates.dateformatter('%b %d'))      # "jan 01"
ax.xaxis.set_major_formatter(mdates.concisedateformatter(
    ax.xaxis.get_major_locator()
))  # 简洁自适应格式（推荐）

# 自动格式化
fig.autofmt_xdate(rotation=45, ha='right')  # 自动旋转日期标签

# 日期 locator 速查
# daylocator, hourlocator, minutelocator, secondlocator
# monthlocator, yearlocator
# weekdaylocator, autodatelocator

fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x, y)

# 左侧用原始值
ax.yaxis.set_major_formatter(ticker.formatstrformatter('%.1f'))

# 右侧辅助轴用百分比
secax = ax.secondary_yaxis('right', functions=(
    lambda x: x / y_total * 100,       # forward
    lambda x: x / 100 * y_total        # inverse
))
secax.yaxis.set_major_formatter(ticker.percentformatter())
secax.set_ylabel('percentage')

fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(x, np.sin(x))

# 只保留左/下边框
for spine in ['top', 'right']:
    ax.spines[spine].set_visible(false)

# 刻度线朝外
ax.tick_params(axis='both', direction='out', length=5, width=1)

# 网格
ax.grid(true, which='major', axis='y',
        color='lightgray', linestyle='-', linewidth=0.5)

# 偏移边框
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('outward', 10))