四张图弄懂matplotlib的一些基本用法

Posted 2022-04-20 Icy Hunter

其实matplotlib画的图也没那么丑吧，尤其是第一张，感觉还是可以接受的（因为东西加的多所以看起来丑了点吧

代码如下：

# coding:utf-8
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import cm


mpl.rcParams['font.sans-serif'] = ['FangSong']  # 指定中文字体
mpl.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False  # 正常显示负号


fig = plt.figure(figsize=(10, 20), dpi=100)  # 调整画布大小，清晰度
fig1 = fig.add_subplot(221)  # 画布1
fig2 = fig.add_subplot(222)  # 画布2
fig3 = fig.add_subplot(223)  # 画布3
fig4 = fig.add_subplot(224)  # 画布4


def draw_fig1():
    # fig1
    x = np.linspace(0, 5 * np.pi, 50)
    y1 = np.sin(x)
    y2 = np.cos(x)
    y3 = x
    # x轴到y1的区域填充
    fig1.fill(x, y1, 'b', alpha=0.5, label="sin(x)")
    fig1.plot(x, y2, 'r', alpha=0.5, label="cos(x)")
    fig1.scatter(x, y3, alpha=0.8, label="scatter")
    # y1 +1 1 的区域， 且y1 > 0的区域填充
    fig1.fill_between(x, y1 + 1, y1 - 1, where=y1 > 0, facecolor='b', alpha=0.2)
    fig1.grid(True)
    # fig1.set_ylim(bottom=-10, top=10)  # 设置x刻度
    # fig1.set_xlim(left=0, right=10, emit=1)
    fig1.set_title("填充图")
    fig1.set_xlabel("x轴", fontsize=10, labelpad=10)  # 字的大小和里轴的距离
    fig1.set_ylabel("y轴", fontsize=10, labelpad=10)
    fig1.set_xticks([1, 2, 3])
    fig1.set_xticklabels(["A", "B", "C"],  rotation=30, fontsize=10)  # 设置x轴坐标
    # fig1.set_yticks([-1, 1, 0, 0.1])
    fig1.legend(fontsize=10, loc="center")  # 显示图例
    # loc 可取(‘best’, ‘upper right’, ‘upper left’, ‘lower left’, ‘lower right’, ‘right’,
    # ‘center left’, ‘center , right’, ‘lower center’, ‘upper center’, ‘center’)


def draw_fig2():
    import numpy as np
    import matplotlib.pyplot as plt
    # 饼型图共分五份，每份的值
    z = [10, 20, 30, 40, 50]
    # 对应的块偏离圆心的比例
    explode = [0, 0.2, 0, 0, 0]
    # 每块对应的内容
    labels = ('A', 'B', 'C', 'D', 'E')
    colors = ["r", "g", "b", "y", "pink"]
    # pie(x, explode=None, labels=None, colors=None, autopct=None,
    # pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None,
    # radius=None, hold=None)
    # x:饼状图的块数，及每块的值
    # explode:对应的块偏离圆心的比例
    # labels:每块的标签
    # colors:也是数组，表示每块内容的颜色
    # autopct:表示显示每块所占的比例
    # pctdistance=0.8：百分比标号距中心的距离比例
    # shadow=True：是否有阴影效果
    # labeldistance:表示每块对应内容文字距离中心点是饼图半径的倍数
    # startangle=90 起始绘制角度，并且是逆时针绘制
    # radius:饼状图的半径，默认是1  hold:是否覆盖其他图形，如果在一个figure中绘制，选择True会覆盖，False会共存
    fig2.pie(z, explode=explode, labels=labels, startangle=90, autopct='%1.1f%%',
             colors=colors, shadow=True, pctdistance=0.8, labeldistance=1.2)
    fig2.legend()


def draw_fig3():
    data = np.random.randint(100, size=(1, 100))[0]
    data[99] = 300
    fig3.boxplot(data)
    fig3.set_xlabel("特征")


def draw_fig4():
    key_name = ["A", "B", "C", "D", "E"]
    key_values = [0.1, 0.9, 1, 1, -0.5]

    def auto_label(rects):
        for rect in rects:
            height = rect.get_height()
            if height >= 0:
                # 标上对应柱上的数字
                fig4.text(rect.get_x() + rect.get_width() / 2.0 - 0.3, height + 0.02, '%.3f' % height)
            else:
                fig4.text(rect.get_x() + rect.get_width() / 2.0 - 0.3, height - 0.06, '%.3f' % height)
                # 如果存在小于0的数值，则画0刻度横向直线
                fig4.axhline(y=0, color='black')

    # 归一化,让颜色分布均匀些
    norm = plt.Normalize(-1, 1)
    norm_values = norm(key_values)
    map_vir = cm.get_cmap(name='viridis')  # 获得对应的颜色映射条的风格
    colors = map_vir(norm_values)  # 对映射条应到数据
    ax = fig4.bar(key_name, key_values, width=0.5, color=colors, edgecolor='white')  # 设置长方体边框的颜色
    sm = cm.ScalarMappable(cmap=map_vir, norm=norm)  # norm设置最大最小值
    plt.colorbar(sm)
    auto_label(ax)

    bwith = 4  # 边框宽度设置为2
    TK = plt.gca()  # 获取边框
    TK.spines['bottom'].set_linewidth(bwith)  # 图框下边
    TK.spines['left'].set_linewidth(bwith)  # 图框左边
    TK.spines['top'].set_linewidth(bwith)  # 图框上边
    TK.spines['right'].set_linewidth(bwith)  # 图框右边


draw_fig1()
draw_fig2()
draw_fig3()
draw_fig4()
plt.savefig("四张图.png", dpi=100)
plt.show()

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