Matplotlib 简明教程
Matplotlib - Plotting with Keywords
通过关键字作图通常是指使用特定的单词或命令定制和控制如何在绘图或图表中显示数据。
Plotting with keywords generally refers to using specific words or commands to customize and control how data is displayed in a plot or graph.
想象一下您有一些数据，例如一周内不同城市的温度。您希望创建一个图形来显示此数据，但您还希望使其外观精良且内容丰富。通过关键字作图可以让您做到这一点。
Imagine you have some data, like the temperatures of different cities over a week. You want to create a graph to show this data, but you also want to make it look nice and informative. Plotting with keywords allows you to do just that.
您可以使用关键字或命令告诉作图软件您想要什么，而不是手动指定绘图的每一个小细节，比如线条颜色、轴标签或点的大小。
Instead of manually specifying every little detail of the plot, like the color of the lines, the labels on the axes, or the size of the points, you can use keywords or commands to tell the plotting software what you want.
例如，您可能会使用“color”这样的关键字，后跟一个特定的颜色名称来更改绘图中线条的颜色。或者，您可以使用“xlabel”和“ylabel”分别给 x 轴和 y 轴添加标签。
For example, you might use a keyword like "color" followed by a specific color name to change the color of a line in your plot. Or you might use "xlabel" and "ylabel" to add labels to the x-axis and y-axis respectively.
Plotting with Keywords in Matplotlib
使用 Matplotlib 创建绘图时，您可以使用关键字来控制绘图的各个方面，如颜色、线条样式、标记样式、标签、标题以及许多其他属性。您应该使用描述性关键字指定这些属性，而不是直接提供数值或配置。
When you create a plot using Matplotlib, you can use keywords to control various aspects of the plot, such as the color, line style, marker style, labels, titles, and many other attributes. Instead of providing numerical values or configurations directly, you specify these attributes using descriptive keywords.
例如，给 x 轴和 y 轴添加标签，您可以分别使用“xlabel”和“ylabel”关键字。
For example, to add labels to the x-axis and y-axis, you can use the keywords "xlabel" and "ylabel", respectively.
Plotting with Keyword "Color"
在 Matplotlib 中绘图时，可以使用“color”关键字参数指定要绘制的元素的颜色。可以使用多种方式指定颜色−
When plotting in Matplotlib, you can use the "color" keyword argument to specify the color of the elements you are drawing. You can specify colors in several ways −
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Named Colors − You can use common color names such as "red", "blue", "green", etc., to specify colors.
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Hexadecimal Colors − You can use hexadecimal color codes (e.g., "#FF5733") to specify precise colors.
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RGB or RGBA Colors − You can specify colors using RGB or RGBA values, where R stands for red, G for green, B for blue, and A for alpha (opacity).
Example
在以下示例中，我们将创建一个线条图，其中线条颜色使用“color”关键字更改为红色 −
In the following example, we are creating a line plot where the line color is changed to red using the "color" keyword −
import matplotlib.pyplot as plt
# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
# Changing line color to red
plt.plot(x, y, color='red')
# Customizing Plot
plt.title('Line Plot with Red Color')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
# Displaying Plot
plt.show()以下是上面代码的输出： -
Following is the output of the above code −
Plotting with Keyword "marker"
在 Matplotlib 中，通过关键字“marker”绘图用于指定符号或标记，这些符号或标记用于表示绘图上各个数据点。
In Matplotlib, plotting with the keyword "marker" is used to specify the symbols or markers used to denote individual data points on a plot.
在 Matplotlib 中创建散点图或折线图时，每个数据点都可以由标记表示，这是一个小符号或形状。“marker”关键字允许选择这些标记的形状、大小和颜色。常见的标记选项包括圆圈、正方形、三角形和圆点。
When you create a scatter plot or line plot in Matplotlib, each data point can be represented by a marker, which is a small symbol or shape. The "marker" keyword allows you to choose the shape, size, and color of these markers. Common marker options include circles, squares, triangles, and dots.
Example
在这里，我们使用“marker”关键字在折线图上的数据点处添加圆形标记 −
In here, we are using the "marker" keyword to add circle markers to the data points on the line plot −
import matplotlib.pyplot as plt
# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
# Adding circle markers
plt.plot(x, y, marker='o')
# Customizing Plot
plt.title('Line Plot with Circle Markers')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
# Displaying Plot
plt.show()执行上述代码，我们将得到以下输出 −
On executing the above code we will get the following output −
Plotting with Keyword "linestyle"
在 Matplotlib 中，通过关键字“linestyle”绘图用于指定连接绘图中数据点的线条的样式。
In Matplotlib, plotting with the keyword "linestyle" is used to specify the style of the lines connecting data points in a plot.
在 Matplotlib 中创建折线图时，每个数据点都由一条线连接。“linestyle”关键字允许选择这些线的样式。常见的线条样式选项包括实线、虚线、点线和点划线。您可以分别使用字符串“-”、"--"、":"和"-."指定这些样式。
When you create a line plot in Matplotlib, each data point is connected by a line. The "linestyle" keyword allows you to choose the style of these lines. Common linestyle options include solid lines, dashed lines, dotted lines, and dash-dot lines. You can specify these styles using strings such as "-", "--", ":", and "-." respectively.
Example
现在，我们使用“linestyle”关键字将线型更改为“dashed”−
Now, we are using the "linestyle" keyword to change the line style to "dashed" −
import matplotlib.pyplot as plt
# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
# Changing line style to dashed
plt.plot(x, y, linestyle='--')
# Customizing Plot
plt.title('Line Plot with Dashed Line Style')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
# Displaying Plot
plt.show()执行上面的代码后，我们得到以下输出： -
After executing the above code, we get the following output −
Plotting with Keyword "grid"
在 Matplotlib 中，使用关键字“grid”绘图可将网格线添加到您的绘图中。网格线是水平和垂直线，有助于在绘图中直观地对齐数据点。
In Matplotlib, plotting with the keyword "grid" is used to add gridlines to your plot. Gridlines are horizontal and vertical lines that help in visually aligning data points on the plot.
“grid”关键字允许您控制网格线是否显示在绘图中。您可以指定是否需要沿着 x 轴、y 轴或两个轴绘制网格线。启用网格线时，它们将显示为跨越绘图区域并形成网格状图案的虚线。
The "grid" keyword allows you to control whether gridlines are displayed on the plot. You can specify whether you want gridlines along the x-axis, y-axis, or both axes. When you enable the gridlines, they appear as faint lines spanning the plot area, forming a grid-like pattern.
Example
在以下示例中，我们使用“grid”关键字向绘图中添加一个网格 −
In the following example, we are using the "grid" keyword to add a grid to the plot −
import matplotlib.pyplot as plt
# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
# Plotting
plt.plot(x, y)
# Customizing Plot
plt.title('Line Plot with Grid')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
# Adding grid
plt.grid(True)
# Displaying Plot
plt.show()执行上述代码，我们将得到以下输出 −
On executing the above code we will get the following output −
