Matplotlib可视化教程

た入场券 2023-02-11 12:28 24阅读 0赞

转载自微信公众号：大邓和他的Python

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

#### 基础图表 ####

**基本示例**

# 生成数据
    x = np.linspace(0, 10, 100)
    y = x ** 2
    
    plt.figure()
    plt.plot(x, y)
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70]

**另一种实现方式**

# 生成数据
    x = np.linspace(0, 10, 100)
    y = x ** 3
    
    fig, ax = plt.subplots()  # 创建画布(包含axes)
    ax.plot(x, y)
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70 1]

#### 折线图 ####

*  设置标题
 *  画布大小设置
 *  线型设置

# 生成数据
    x = np.linspace(-10, 10, 1000)
    
    plt.figure(figsize=(8, 5))  # 新建一个空画布，设置尺寸
    plt.plot(x, np.sin(x), ':r', label='sin(x)')  # ':r'表示线型为:，颜色为红色
    plt.plot(x, np.cos(x), '-.g', label='cso(x)')
    plt.legend()  # 显示图例
    plt.title("I'm Title")
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70 2]

#### 中文显示（默认中文会显示方框） ####

**坐标轴文本设置**

# 指定字体，默认不支持中文显示
    # 显示中文
    plt.rcParams['font.sans-serif'] = ['SimHei']  # Windows
    # plt.rcParams['font.sans-serif'] = ['Arial Unicode WS'] # Mac
    plt.rcParams['axes.unicode_minus'] = False
    
    # 生成数据
    x = np.linspace(-10, 10, 1000)
    
    plt.figure(figsize=(8, 5))  # 新建一个空画布，设置尺寸
    plt.plot(x, np.sin(x), ':r', label='sin(x)')  # ':r'表示线型为:，颜色为红色
    plt.plot(x, np.cos(x), '-.g', label='cso(x)')
    plt.legend()  # 显示图例
    plt.title("我是中文标题")
    plt.ylabel("我是Y轴")
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70 3]

#### 柱状图/直方图 ####

*  标题/标题位置设置
 *  图例显示

# 示例数据
    # 指定字体，默认不支持中文显示
    # 显示中文
    plt.rcParams['font.sans-serif'] = ['SimHei']  # Windows
    # plt.rcParams['font.sans-serif'] = ['Arial Unicode WS'] # Mac
    plt.rcParams['axes.unicode_minus'] = False
    
    cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
    data1 = [123, 153, 157, 107, 98, 142]
    data2 = [56, 77, 93, 68, 45, 67]
    
    plt.figure(figsize=(8, 5))  # 新建一个空画布，设置尺寸
    plt.bar(cate, data1, label='总计')
    plt.bar(cate, data2, label='线上')
    plt.legend()
    plt.title('各手机品牌销售额对比',
               # 设置标题位置
               loc="left")
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70 4]

*  图像不重叠
 *  设置字体/字体大小
 *  刻度值设置

# 指定字体，默认不支持中文显示
    # 显示中文
    plt.rcParams['font.sans-serif'] = ['SimHei']  # Windows
    # plt.rcParams['font.sans-serif'] = ['Arial Unicode WS'] # Mac
    plt.rcParams['axes.unicode_minus'] = False
    
    # 示例数据
    cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
    data1 = [89, 45, 56, 74, 23, 21]
    data2 = [56, 77, 93, 68, 45, 67]
    
    x_location = np.arange(len(cate))
    
    plt.figure(figsize=(8, 5))  # 新建一个空画布，设置尺寸
    plt.bar(x_location, data1, label='线下', width=0.3)
    plt.bar(x_location+0.3, data2, label='线上', width=0.3)
    
    # 为x轴设置刻度值
    plt.xticks(x_location+0.15, cate)
    plt.legend()
    plt.title('各手机品牌销售额对比',
               # 设置标题位置
               loc="left",
              # 设置标题字体
               fontdict={ 'fontsize':18, 'color':'red'})
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70 5]

#### 纵向显示 ####

如遇标签过长，可选择纵向显示图像；

# 指定字体，默认不支持中文显示
    # 显示中文
    plt.rcParams['font.sans-serif'] = ['SimHei']  # Windows
    # plt.rcParams['font.sans-serif'] = ['Arial Unicode WS'] # Mac
    plt.rcParams['axes.unicode_minus'] = False
    
    # 示例数据
    cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
    data1 = [89, 45, 56, 74, 23, 21]
    data2 = [56, 77, 93, 68, 45, 67]
    
    x_location = np.arange(len(cate))
    
    plt.figure(figsize=(8, 5))  # 新建一个空画布，设置尺寸
    plt.barh(x_location, data1, label='线下', height=0.3)
    plt.barh(x_location+0.3, data2, label='线上', height=0.3)
    
    # 为y轴设置刻度值
    plt.yticks(x_location+0.15, cate)
    plt.legend()
    plt.title('各手机品牌销售额对比',
               # 设置标题位置
               loc="left",
              # 设置标题字体
               fontdict={ 'fontsize':18, 'color':'red'})
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70 6]

#### 饼图 ####

*  基础示例
 *  显示百分比

cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
    data = [153, 124, 107, 99, 89, 46]
    
    plt.figure(figsize=(5, 5))
    plt.pie(data, labels=cate,
            # 显示百分比，支持调用方法
            autopct='%.2f%%')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70 7]

*  突出显示类别
 *  显示阴影

# 示例数据
    cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
    data = [153, 124, 107, 99, 89, 46]
    
    plt.figure(figsize=(5, 5))
    plt.pie(data, labels=cate,
            # 对应类目偏移，0为不偏移
            explode=[0, 0.1, 0, 0, 0.2, 0],
            autopct='%.2f%%',
            shadow=True)
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70 8]

*  标签文本设置
 *  颜色设置

# 示例数据
    cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
    data = [153, 124, 107, 99, 89, 46]
    
    plt.figure(figsize=(8, 8))
    plt.pie(data, labels=cate,
            # 对应类目偏移，0为不偏移
            explode=[0, 0.1, 0, 0, 0.2, 0],
            autopct='%.2f%%',
            shadow=True,
            # 自定义颜色，不需要与类目数目相等，循环调用
            colors=['#FFC0CB', '#FF00FF', '#FFD700', '#FFA500', '#00FF7F', 'blue'],
            # 文本设置，包括字体，大小，颜色等属性
            textprops={ 'fontsize':12, 'color':'red'})
    plt.legend()
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70 9]

#### 散点图 ####

*  基础示例

data_x = [random.randint(1, 100) for _ in range(1000)]
    data_y = [random.randint(1, 100) for _ in range(1000)]
    
    plt.figure(figsize=(8, 8))
    plt.scatter(data_x, data_y)
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70 10]

*  设置散点颜色
 *  设置散点大小
 *  透明度设置

N = 100
    x = np.random.rand(N)
    y = np.random.rand(N)
    colors = np.random.rand(N)
    area = np.pi * (15 * np.random.rand(N)) ** 2
    
    plt.figure(figsize=(8, 8))
    plt.scatter(x, y,
                # 图形大小
                s = area,
                # 颜色
                c = colors,
                # 透明度
                alpha=0.3)
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70 11]

#### 图标动态刷新 ####

**交互模式**

*  我们在训练模型的时候，可能需要去不停的去更新图表数据

# jupyter中动态现实需要
    from IPython import display
    
    x, y1, y2 = [], [], []
    plt.ion()  # 交互模式打开
    for i in np.linspace(0,20,50):
        x.append(i)
        y1.append(np.cos(i))
        y2.append(np.sin(i))
        plt.figure(figsize=(12, 2))
        plt.clf()
        plt.ylim(-1, 1)  # 设置y轴刻度值最大最小值
        plt.plot(x, y1, '--r')
        plt.plot(x, y2, 'g')
        plt.pause(0.1)
        display.clear_output(wait=True)  # 清除输出
    plt.ioff()  # 关闭交互模式

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70 12]  
这个图是动态效果的，大家可以自己尝试一下。

#### 子图/图表风格 ####

*  多子图
 *  图表风格设置

# 可用风格
    style_list = plt.style.available
    
    # 数据
    cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
    data1 = [89, 45, 56, 74, 23, 21]
    data2 = [56, 77, 93, 68, 45, 67]
    x_location = np.arange(len(cate))
    
    plt.figure(figsize=(20, 40))
    
    for i, style in enumerate(style_list):
    
        with plt.style.context(style):  # 设置图表风格
            plt.subplot(7, 4, i+1)
            plt.bar(x_location, data1, label='线下', width=0.3)
            plt.bar(x_location+0.3, data2, label='线上', width=0.3)
    
            # 为x轴设置刻度值
            plt.xticks(x_location+0.15, cate)
            plt.title(style,
                      fontdict={ 'fontsize':20, 'color':'red'})
    plt.show()

![在这里插入图片描述][20200524123911463.png]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200523193908249.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5ODYyNTA1,size_16,color_FFFFFF,t_70
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[20200524123911463.png]: https://img-blog.csdnimg.cn/20200524123911463.png