柱状图 bar

Posted 2022-11-24 smile-yan

2.1 快速开始——第一个例子

import matplotlib.pyplot as plt


if __name__ == '__main__':
    fruits = ['apple', 'blueberry', 'cherry', 'orange']
    counts = [40, 100, 30, 55]
    bar_colors = ['tab:red', 'tab:blue', 'tab:gray', 'tab:orange']
    plt.bar(fruits, counts, color=bar_colors)
    plt.ylabel('fruit supply')
    plt.title('Fruit supply by kind and color')
    plt.show()

效果如图所示：

2.2 添加网格

import matplotlib.pyplot as plt


if __name__ == '__main__':
    fruits = ['apple', 'blueberry', 'cherry', 'orange']
    counts = [40, 100, 30, 55]
    bar_colors = ['tab:red', 'tab:blue', 'tab:gray', 'tab:orange']
    plt.bar(fruits, counts, color=bar_colors, width=1, edgecolor="white", linewidth=0.7)
    plt.ylabel('fruit supply')
    plt.title('Fruit supply by kind and color')
    plt.show()

效果如下：

2.3 添加标准差 std

假设这些数据的标准差分别为 20，40，10，20，设置的参数主要包括

• yerr： 数组，一般用来表示 偏差或者数据分布的标准差；
• capsize：绘制横线的宽度。

import matplotlib.pyplot as plt


if __name__ == '__main__':
    fruits = ['apple', 'blueberry', 'cherry', 'orange']
    counts = [40, 100, 30, 55]
    stds = [20, 30, 10, 20]
    bar_colors = ['tab:red', 'tab:blue', 'tab:gray', 'tab:orange']
    plt.bar(fruits, counts, yerr=stds, capsize=10,
            color=bar_colors, width=1, edgecolor="white", linewidth=0.7)
    plt.ylabel('fruit supply')
    plt.title('Fruit supply by kind and color')
    plt.show()

结果如下所示：

2.4 柱上显示数值

import matplotlib.pyplot as plt


if __name__ == '__main__':
    fruits = ['apple', 'blueberry', 'cherry', 'orange']
    counts = [40, 100, 30, 55]
    stds = [20, 30, 10, 20]
    bar_colors = ['tab:red', 'tab:blue', 'tab:gray', 'tab:orange']
    rect1 = plt.bar(fruits, counts,
                    color=bar_colors, width=1, edgecolor="white", linewidth=0.7)
    plt.bar_label(rect1)
    plt.ylabel('fruit supply')
    plt.title('Fruit supply by kind and color')
    plt.show()

效果如下：

2.5 分组对比

import matplotlib.pyplot as plt
import numpy as np


if __name__ == '__main__':
    labels = ['G1', 'G2', 'G3', 'G4', 'G5']
    men_means = [20, 34, 30, 35, 27]
    women_means = [25, 32, 34, 20, 25]

    x = np.arange(len(labels))  # the label locations
    width = 0.35  # the width of the bars

    rects1 = plt.bar(x - width/2, men_means, width, label='Men')
    rects2 = plt.bar(x + width/2, women_means, width, label='Women')

    # Add some text for labels, title and custom x-axis tick labels, etc.
    plt.ylabel('Scores')
    plt.title('Scores by group and gender')
    plt.xticks(x, labels)
    plt.legend()

    plt.bar_label(rects1, padding=3)
    plt.bar_label(rects2, padding=3)

    plt.tight_layout()

    plt.show()

效果如下：