Matplotlib 简明教程
Matplotlib - Axis Ticks
What are Axis Ticks?
Matplotlib 中的轴刻度标记是指沿轴的标记，它们表示特定的数据值。它们有助于理解绘图的范围，并为数据可视化提供参考点。让我们深入了解轴刻度标记的详细信息 −
Axis ticks in Matplotlib refer to the markers along the axes that denote specific data values. They aid in understanding the scale of the plot and provide reference points for data visualization. Let’s delve into the details of axis ticks −
Key Concepts in Axis ticks
以下是由轴刻度标记提供的关键概念。
The below are the key concepts available in axis ticks.
Major Ticks − 这些是沿轴的突出刻度，表示显著的数据值。
Major Ticks − These are the prominent ticks along the axis that represent significant data values.
Minor Ticks − 这些是主要刻度之间的较小刻度，它们在刻度上提供了更精细的粒度，但通常不太突出。
Minor Ticks − These are the smaller ticks between major ticks which provides more granularity in the scale but usually less prominent.
Customizing Axis Ticks
根据需要和需求，可以自定义图上轴刻度线。自定义需要执行以下步骤。
We can customize the axis ticks on the plot as per the requirement and need. There are few steps to be followed to perform customization.
Setting Ticks
可以用两种方法设置轴刻度线，一种是手动设置，另一种是自动调整。
We can set the axis ticks in two ways, one is by manual setting and the other is by automatic adjustment.
Manual Setting
可以使用 plt.xticks() 或 plt.yticks() 函数为轴设置特定刻度线位置和标签。
We can set specific tick locations and labels for the axis using plt.xticks() or plt.yticks() functions.
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Plot with Custom Axis Ticks')
# Customize x-axis ticks
plt.xticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K'])
# Customize y-axis ticks and labels
plt.yticks([-1, 0, 1], ['Min', 'Zero', 'Max'])
plt.show()
Automatic Adjustment
在 Matplotlib 中，轴刻度线的自动调整涉及让程序库根据数据范围确定刻度线的标注和位置。当我们创建图形时，此进程默认处理，但我们可以使用各种格式化选项或调整定位器和格式设置来微调自动调整。以下是一些与轴刻度线自动调整相关的要点。
In Matplotlib the automatic adjustment of axis ticks involves letting the library determine the positions and labels of ticks based on the data range. This process is handled by default when we create a plot but we can fine-tune the automatic adjustment using various formatting options or by adjusting the locator and formatter settings. Here are some aspects related to automatic adjustment of axis ticks.
在此示例中，Matplotlib 根据数据范围自动调整刻度线的标注和位置。
In this example Matplotlib automatically adjusts the positions and labels of ticks based on the data range.
import matplotlib.pyplot as plt
# Example data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
# Creating a plot (automatic adjustment of ticks)
plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Automatic Adjustment of Axis Ticks')
plt.show()
Customizing Automatic Adjustment
我们可以使用 pyplot 中的一些函数自动自定义标注。可以针对我们数据的特性进行调整，以增强图形的可读性。
We can customize the ticks automatically by using few functions available in pyplot. These adjustments can be made to suit the nature of our data and enhance the readability of the plot.
理解和利用轴刻度线的自动调整对于在 Matplotlib 中创建清晰且具有信息性的可视化至关重要。
Understanding and leveraging automatic adjustment of axis ticks is crucial for creating clear and informative visualizations in Matplotlib.
Adjusting the Number of Ticks
我们可以使用 plt.locator_params(axis='x', nbins=5) 控件 x 轴上的刻度线数。调整设置为所需数字的参数 nbins 。
We can use plt.locator_params(axis='x', nbins=5) to control the number of ticks on the x-axis. Adjust the parameter nbins set to the desired number.
Scientific Notation
要以科学计数法显示刻度线标签，我们可以使用 plt.ticklabel_format(style='sci', axis='both', scilimits=(min, max)) 。
To display tick labels in scientific notation we can use the plt.ticklabel_format(style='sci', axis='both', scilimits=(min, max)).
Date Ticks (For Time Series Data)
如果我们正在处理日期/时间数据，那么 Matplotlib 可以自动格式化日期刻度线。
If we are dealing with date/time data then Matplotlib can automatically format date ticks.
在此示例中，我们将应用自定义自动调整到图形的轴刻度线。
In this example we are applying the customizing automatic adjustment to the axis ticks of the plot.
import matplotlib.dates as mdates
from datetime import datetime
# Example date data
dates = [datetime(2022, 1, 1), datetime(2022, 2, 1), datetime(2022, 3, 1)]
# Creating a plot with automatic adjustment of date ticks
plt.plot(dates, [2, 4, 6])
# Formatting x-axis as date
plt.gca().xaxis.set_major_locator(mdates.MonthLocator())
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
plt.xlabel('Date')
plt.ylabel('Y-axis')
plt.title('Automatic Adjustment of Date Ticks')
plt.show()
Tick Formatting
我们可以使用 fontsize, color 和 rotation 参数基于字体大小、颜色和旋转自定义刻度线标签的外观。
We can customize the appearance of tick labels based on font size, color and rotation using fontsize, color and rotation parameters.
Example
import matplotlib.pyplot as plt
# Example data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
# Creating a plot (automatic adjustment of ticks)
plt.plot(x, y)
plt.xticks(fontsize=10, color='red', rotation=45)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Automatic Adjustment of Axis Ticks')
plt.show()
Tick Frequency and Appearance
Setting Tick Frequency
我们可以使用 plt.locator_params(axis='x', nbins=5) 调整刻度线的频率，以控制显示的刻度线数。
We can adjust the frequency of ticks using plt.locator_params(axis='x', nbins=5) to control the number of ticks displayed.
import matplotlib.pyplot as plt
# Example data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
# Creating a plot (automatic adjustment of ticks)
plt.plot(x, y)
plt.locator_params(axis='x', nbins=10)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Automatic Adjustment of Axis Ticks')
plt.show()
Minor Ticks
我们可以使用 plt.minorticks_on() 启用次要刻度线。我们可以使用 plt.minorticks_on(), plt.minorticks_off() 自行定制它们的外观或指定它们的位置。
We can enable the minor ticks using plt.minorticks_on(). We can customize their appearance with plt.minorticks_on(), plt.minorticks_off() or by specifying their positions.
Use Cases
Precision Control - 调整刻度线以提供有关数据值更精确的信息。
Precision Control − Adjust ticks to provide more precise information about data values.
Enhanced Readability - 自行定制刻度线标签和外观，增强可读性。
Enhanced Readability − Customize tick labels and appearance for better readability.
Fine-tuning - 手动设置刻度线以强调特定数据点或区间。
Fine-tuning − Manually set ticks to emphasize specific data points or intervals.
了解和自定义轴刻度线对于在图形中有效传递信息至关重要，因为它允许我们根据可视化需要调整数据展示。
Understanding and customizing axis ticks is crucial for effectively communicating information in plots by allowing us to tailor the presentation of data according to our visualization needs.
Adding extra axis ticks
在此示例中，为了添加其他刻度线，我们使用 xticks() 函数，并将刻度线的范围从 1 到 10 增加到 1 到 20。
In this example to add extra ticks we use xticks() function and increase the range of ticks to 1 to 20 from 1 to 10.
Customize X-axis ticks
在此示例中，为了添加其他刻度线，我们使用 xticks() 函数，并将刻度线的范围从 1 到 10 增加到 1 到 20。
In this example to add extra ticks we use xticks() function and increase the range of ticks to 1 to 20 from 1 to 10.
Example
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True
height = [3, 12, 5, 18, 45]
bars = ('A', 'B', 'C', 'D', 'E')
y_pos = np.arange(len(bars))
plt.bar(y_pos, height, color='yellow')
plt.tick_params(axis='x', colors='red', direction='out', length=7, width=2)
plt.show()
Remove the X-axis ticks while keeping the grids
要删除 X 刻度线同时保留网格，我们可以使用以下代码作为参考。
To remove the X-ticks while keeping the grids we can use the below code as the reference.
Example
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams["figure.figsize"] = [7.00, 3.50]
plt.rcParams["figure.autolayout"] = True
x = np.linspace(0, 2*np.pi, 100)
ax = plt.gca()
ax.plot(x, np.sin(x), c='r', lw=5, label='y=sin(x)')
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.grid(True)
plt.legend(loc="upper right")
plt.show()
Turn off the ticks and marks
在此示例中，我们关闭 matplotlib 坐标轴的刻度线和标记，我们可以使用 set_tick_params() 隐藏 X 和 Y 坐标轴标记，set_xticks() 和 set_yticks() 隐藏 X 和 Y 坐标轴刻度线标记。
In this example we turn off the ticks and marks of a matplotlib axes we can use set_tick_params() to hide X and Y axes label marks, set_xticks() and set_yticks() to hide X and Y axes tick marks.
Example
import numpy as np
from matplotlib import pyplot as plt
# Set the figure size
plt.rcParams["figure.figsize"] = [7.00, 3.50]
plt.rcParams["figure.autolayout"] = True
# Create x and y data points
x = np.linspace(-10, 10, 100)
y = np.sin(x)
plt.plot(x, y)
ax = plt.gca()
# Hide X and Y axes label marks
ax.xaxis.set_tick_params(labelbottom=False)
ax.yaxis.set_tick_params(labelleft=False)
# Hide X and Y axes tick marks
ax.set_xticks([])
ax.set_yticks([])
plt.show()
Remove the digits after the decimal point in axis ticks
在此示例中，我们使用 x.astype(int) 函数仅将刻度线标记设置为数字。
In this example we use use x.astype(int) function to set the xtick labels only in digits.
Example
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True
x = np.array([1.110, 2.110, 4.110, 5.901, 6.00, 7.90, 8.90])
y = np.array([2.110, 1.110, 3.110, 9.00, 4.001, 2.095, 5.890])
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xticks(x.astype(int))
plt.show()
Add Matplotlib Colorbar Ticks
在此示例中，我们为一个 ScalarMappable 实例 mappable 创建一个颜色轴，其中参数 ticks=ticks 用于添加颜色轴刻度线。
Here in this example we create a colorbar for a ScalarMappable instance, mappable with ticks=ticks to add colorbar ticks.
Example
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True
x, y = np.mgrid[-1:1:100j, -1:1:100j]
z = (x + y) * np.exp(-5.0 * (x ** 2 + y ** 2))
plt.imshow(z, extent=[-1, 1, -1, 1])
ticks = np.linspace(z.min(), z.max(), 5, endpoint=True)
cb = plt.colorbar(ticks=ticks)
plt.show()
Adjusting gridlines and ticks in Matplotlib imshow
在此示例中，我们使用 set_xticklabels 和 set_yticklabels 方法设置 x 刻度线标签和 y 刻度线标签。
In this example we set the xticklabels and yticklabels by using the set_xticklabels and set_yticklabels method.
Example
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams["figure.figsize"] = [7.00, 3.50]
plt.rcParams["figure.autolayout"] = True
data = np.random.rand(9, 9)
plt.imshow(data, interpolation="nearest")
ax = plt.gca()
ax.set_xticks(np.arange(-.5, 9, 1))
ax.set_yticks(np.arange(-.5, 9, 1))
ax.set_xticklabels(np.arange(0, 10, 1))
ax.set_yticklabels(np.arange(0, 10, 1))
ax.grid(color='red', linestyle='-.', linewidth=1)
plt.show()
Change the spacing between ticks
在此示例中，我们在固定的位置上设置刻度线或改变 matplotlib 刻度线之间的间距。
In this example we set ticks on a fixed position or change the spacing between ticks in matplotlib
Move the Y-axis ticks from the left side of the plot to the right side
在此示例中，要将 Y 坐标轴刻度线从左向右移动，我们使用 ax.yaxis.tick_right()。
In this example to shift the Y-axis ticks from left to right we use ax.yaxis.tick_right()
Add third level of ticks
此示例是添加第三级刻度线的参考。
This example is a reference for adding the third level of ticks.
Example
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True
t = np.arange(0.0, 100.0, 0.1)
s = np.sin(0.1 * np.pi * t) * np.exp(-t * 0.01)
fig, ax = plt.subplots()
plt.plot(t, s)
ax1 = ax.twiny()
ax1.plot(t, s)
ax1.xaxis.set_ticks_position('bottom')
majors = np.linspace(0, 100, 6)
minors = np.linspace(0, 100, 11)
thirds = np.linspace(0, 100, 101)
ax.xaxis.set_major_locator(matplotlib.ticker.FixedLocator(majors))
ax.xaxis.set_minor_locator(matplotlib.ticker.FixedLocator(minors))
ax1.xaxis.set_major_locator(matplotlib.ticker.FixedLocator([]))
ax1.xaxis.set_minor_locator(matplotlib.ticker.FixedLocator(thirds))
ax1.tick_params(which='minor', length=2)
ax.tick_params(which='minor', length=4)
ax.tick_params(which='major', length=6)
ax.grid(which='both', axis='x', linestyle='--')
plt.axhline(color='gray')
plt.show()
Create minor ticks for a polar plot
此示例是添加第三级刻度线的参考。
This example is a reference for adding the third level of ticks.
Example
import numpy as np
import matplotlib.pyplot as plt
# Set the figure size
plt.rcParams["figure.figsize"] = [7.00, 3.50]
plt.rcParams["figure.autolayout"] = True
# radius and theta for the polar plot
r = np.arange(0, 5, 0.1)
theta = 2 * np.pi * r
# Add a subplot
ax = plt.subplot(111, projection='polar')
tick = [ax.get_rmax(), ax.get_rmax() * 0.97]
# Iterate the points between 0 to 360 with step=10
for t in np.deg2rad(np.arange(0, 360, 10)):
ax.plot([t, t], tick, lw=1, color="red")
# Display the plot
plt.show()
Overlapping Y-axis tick label and X-axis tick label
在此示例中，我们减少了 matplotlib 中 x 和 y 刻度线标签重叠的机会。
In this example we reduce the chances of overlapping between x and y tick labels in matplotlib.
Example
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import numpy as np
plt.rcParams["figure.figsize"] = [7.00, 3.50]
plt.rcParams["figure.autolayout"] = True
xs = np.linspace(0, 5, 10)
ys = np.linspace(0, 5, 10)
plt.subplot(121)
plt.margins(x=0, y=0)
plt.plot(xs, ys)
plt.title("Overlapping")
plt.subplot(122)
plt.margins(x=0, y=0)
plt.plot(xs, ys)
plt.title("Non overlapping")
plt.gca().xaxis.set_major_locator(MaxNLocator(prune='lower'))
plt.gca().yaxis.set_major_locator(MaxNLocator(prune='lower'))
plt.show()
Disable the minor ticks of a log-plot
此示例通过使用 minorticks_off() 函数禁用 matplotlib 中对数图的次要刻度线。
This example disable the minor ticks of a log plot in matplotlib by using minorticks_off() function.
Example
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True
x = np.random.randint(-3, 3, 10)
y = np.exp(x)
plt.subplot(121)
plt.plot(y, x, c='red')
plt.xscale('log')
plt.title("With minor ticks")
plt.subplot(122)
plt.plot(y, x, c='green')
plt.xscale('log')
plt.minorticks_off()
plt.title("Without minor ticks")
plt.show()