如何在Python中使用 Matplotlib 注释 Barplot 中的条？

注释意味着在图表中添加注释，说明它代表什么值。当图形缩小或过度填充时，用户从图形中读取值通常会让人厌烦。在本文中，我们将讨论如何使用matplotlib库对在Python中创建的条形图进行注释。

以下是带注释和未带注释的条形图示例：

非注释与带注释的条形图

循序渐进的方法：

• 让我们首先从 Pandas 数据帧绘制简单的图形，现在我们准备好了以下数据帧：

Python3

# Importing libraries for dataframe creation
# and graph plotting
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
 
# Creating our own dataframe
data = {"Name": ["Alex", "Bob", "Clarein", "Dexter"],
        "Marks": [45, 23, 78, 65]}
 
# Now convert this dictionary type data into a pandas dataframe
# specifying what are the column names
df = pd.DataFrame(data, columns=['Name', 'Marks'])

输出：

用户生成的熊猫数据帧

• 现在让我们开始使用seaborn库绘制数据框。我们得到以下结果。但是，条形图中的实际值是多少并不是很明显。当相邻地块的值彼此非常接近时，也会出现这种情况。

蟒蛇3

# Defining the plotsize
plt.figure(figsize=(8, 6))
 
# Defining the x-axis, the y-axis and the data
# from where the values are to be taken
plots = sns.barplot(x="Name", y="Marks", data=df)
 
# Setting the x-acis label and its size
plt.xlabel("Students", size=15)
 
# Setting the y-axis label and its size
plt.ylabel("Marks Secured", size=15)
 
# Finallt plotting the graph
plt.show()

输出：

数据框的原始条形图

• 添加注释。我们在这里的策略是遍历所有条形并在所有条形上放置一个文本，以指出该特定条形的值。这里我们将使用 Matplpotlib 的annotate()函数。我们可以在各种场景中找到此函数的各种用途，目前，我们只会在其顶部显示相应条形的值。

我们的步骤将是：

1. 遍历条形
2. 获取条形的 x 轴位置（x）和宽度（w），这将帮助我们获取文本的 x 坐标，即get_x()+get_width()/2 。
3. 文本的 y 坐标（y）可以使用条形的高度找到，即get_height()
4. 所以我们有了注释值的坐标即get_x()+get_width()/2, get_height()
5. 但这将准确地在条的边界上打印注释，因此为了获得更令人愉悦的注释图，我们使用参数xyplot=(0, 8) 。这里 8 表示将从条形顶部留下的像素。因此，要低于条形线，我们可以使用xy=(0,-8) 。
6. 所以我们执行下面的代码来得到带注释的图：

蟒蛇3

# Defining the plot size
plt.figure(figsize=(8, 8))
 
# Defining the values for x-axis, y-axis
# and from which dataframe the values are to be picked
plots = sns.barplot(x="Name", y="Marks", data=df)
 
# Iterrating over the bars one-by-one
for bar in plots.patches:
   
  # Using Matplotlib's annotate function and
  # passing the coordinates where the annotation shall be done
  # x-coordinate: bar.get_x() + bar.get_width() / 2
  # y-coordinate: bar.get_height()
  # free space to be left to make graph pleasing: (0, 8)
  # ha and va stand for the horizontal and vertical alignment
    plots.annotate(format(bar.get_height(), '.2f'),
                   (bar.get_x() + bar.get_width() / 2,
                    bar.get_height()), ha='center', va='center',
                   size=15, xytext=(0, 8),
                   textcoords='offset points')
 
# Setting the label for x-axis
plt.xlabel("Students", size=14)
 
# Setting the label for y-axis
plt.ylabel("Marks Secured", size=14)
 
# Setting the title for the graph
plt.title("This is an annotated barplot")
 
# Finally showing the plot
plt.show()

输出：

用条形值注释的条形图

以下是基于上述方法的完整程序：

蟒蛇3

# Importing libraries for dataframe creation
# and graph plotting
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
 
# Creating our own dataframe
data = {"Name": ["Alex", "Bob", "Clarein", "Dexter"],
        "Marks": [45, 23, 78, 65]}
 
# Now convert this dictionary type data into a pandas dataframe
# specifying what are the column names
df = pd.DataFrame(data, columns=['Name', 'Marks'])
 
 
# Defining the plot size
plt.figure(figsize=(8, 8))
 
# Defining the values for x-axis, y-axis
# and from which dataframe the values are to be picked
plots = sns.barplot(x="Name", y="Marks", data=df)
 
# Iterrating over the bars one-by-one
for bar in plots.patches:
   
  # Using Matplotlib's annotate function and
  # passing the coordinates where the annotation shall be done
  # x-coordinate: bar.get_x() + bar.get_width() / 2
  # y-coordinate: bar.get_height()
  # free space to be left to make graph pleasing: (0, 8)
  # ha and va stand for the horizontal and vertical alignment
  plots.annotate(format(bar.get_height(), '.2f'),
                   (bar.get_x() + bar.get_width() / 2,
                    bar.get_height()), ha='center', va='center',
                   size=15, xytext=(0, 8),
                   textcoords='offset points')
 
# Setting the label for x-axis
plt.xlabel("Students", size=14)
 
# Setting the label for y-axis
plt.ylabel("Marks Secured", size=14)
 
# Setting the title for the graph
plt.title("This is an annotated barplot")
 
# Finally showing the plot
plt.show()

输出：

用条形值注释的条形图

下面是一些使用matplotlib 库描述条形图中注释条的示例：

示例 1：

蟒蛇3

# Importing libraries for dataframe creation
# and graph plotting
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
 
# Creating our own dataframe
data = {"Language": ["Python", "C++", "Java"],
        "Students": [75, 50, 25]}
 
# Now convert this dictionary type data into a pandas dataframe
# specifying what are the column names
df = pd.DataFrame(data, columns=['Language', 'Students'])
 
 
# Defining the plot size
plt.figure(figsize=(5, 5))
 
# Defining the values for x-axis, y-axis
# and from which dataframe the values are to be picked
plots = sns.barplot(x="Language", y="Students", data=df)
 
# Iterrating over the bars one-by-one
for bar in plots.patches:
   
    # Using Matplotlib's annotate function and
    # passing the coordinates where the annotation shall be done
    plots.annotate(format(bar.get_height(), '.2f'),
                   (bar.get_x() + bar.get_width() / 2,
                    bar.get_height()), ha='center', va='center',
                   size=15, xytext=(0, 5),
                   textcoords='offset points')
 
 
# Setting the title for the graph
plt.title("Example 1")
 
# Finally showing the plot
plt.show()

输出：

示例 2：

蟒蛇3

# Importing libraries for dataframe creation
# and graph plotting
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
 
# Creating our own dataframe
data = {"Section": ["A", "B", "C", 'D', 'E'],
        "Students": [0, 10, 20, 30, 40]}
 
# Now convert this dictionary type data into a pandas dataframe
# specifying what are the column names
df = pd.DataFrame(data, columns=['Section', 'Students'])
 
 
# Defining the plot size
plt.figure(figsize=(5, 5))
 
# Defining the values for x-axis, y-axis
# and from which dataframe the values are to be picked
plots = sns.barplot(x="Section", y="Students", data=df)
 
# Iterrating over the bars one-by-one
for bar in plots.patches:
 
    # Using Matplotlib's annotate function and
    # passing the coordinates where the annotation shall be done
    plots.annotate(format(bar.get_height(), '.2f'),
                   (bar.get_x() + bar.get_width() / 2,
                    bar.get_height()), ha='center', va='center',
                   size=15, xytext=(0, 5),
                   textcoords='offset points')
 
 
# Setting the title for the graph
plt.title("Example 2")
 
# Finally showing the plot
plt.show()

蟒蛇3

# Importing libraries for dataframe creation
# and graph plotting
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
 
# Creating our own dataframe
data = {"Section": ["A", "B", "C", 'D', 'E'],
        "Students": [0, 10, 20, 30, 40]}
 
# Now convert this dictionary type data into a pandas dataframe
# specifying what are the column names
df = pd.DataFrame(data, columns=['Section', 'Students'])
 
 
# Defining the plot size
plt.figure(figsize=(5, 5))
 
# Defining the values for x-axis, y-axis
# and from which dataframe the values are to be picked
plots = sns.barplot(x="Section", y="Students", data=df)
 
# Iterrating over the bars one-by-one
for bar in plots.patches:
 
    # Using Matplotlib's annotate function and
    # passing the coordinates where the annotation shall be done
    plots.annotate(format(bar.get_height(), '.2f'),
                   (bar.get_x() + bar.get_width() / 2,
                    bar.get_height()), ha='center', va='center',
                   size=15, xytext=(0, 5),
                   textcoords='offset points')
 
 
# Setting the title for the graph
plt.title("Example 2")
 
# Finally showing the plot
plt.show()

输出：