利用matplotlib对数据进行可视化

利用matplotlib对数据进行可视化

  • 简单曲线图
  • 复杂曲线图
  • 子图
  • 柱状图
  • 横向柱状图
  • 高级柱状图
  • 层次柱状图
  • 直方图
  • 饼图
  • 散点图
  • 雷达图

导包

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import numpy as np
import matplotlib
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm

1、简单曲线图

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def simple_plot():
    """
    simple plot
    """
    # 生成测试数据
    x = np.linspace(-np.pi, np.pi, 256, endpoint=True)
    y_cos, y_sin = np.cos(x), np.sin(x)

    # 生成画布，并设定标题
    plt.figure(figsize=(8, 6), dpi=80)
    plt.title("简单曲线图", fontproperties=myfont)
    plt.grid(True)

    # 设置X轴
    plt.xlabel("X轴", fontproperties=myfont)
    plt.xlim(-4.0, 4.0)
    plt.xticks(np.linspace(-4, 4, 9, endpoint=True))

    # 设置Y轴
    plt.ylabel("Y轴", fontproperties=myfont)
    plt.ylim(-1.0, 1.0)
    plt.yticks(np.linspace(-1, 1, 9, endpoint=True))

    # 画两条曲线
    plt.plot(x, y_cos, "b--", linewidth=2.0, label="cos示例")
    plt.plot(x, y_sin, "g-", linewidth=2.0, label="sin示例")

    # 设置图例位置,loc可以为[upper, lower, left, right, center]
    plt.legend(loc="upper left", prop=myfont, shadow=True)

    # 图形显示
    plt.show()
    return

2、复杂曲线图

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def simple_advanced_plot():
    """
    simple advanced plot
    """
    # 生成测试数据
    x = np.linspace(-np.pi, np.pi, 256, endpoint=True)
    y_cos, y_sin = np.cos(x), np.sin(x)

    # 生成画布, 并设定标题
    plt.figure(figsize=(8, 6), dpi=80)
    plt.title("复杂曲线图", fontproperties=myfont)
    plt.grid(True)

    # 画图的另外一种方式
    ax_1 = plt.subplot(111)
    ax_1.plot(x, y_cos, color="blue", linewidth=2.0, linestyle="--", label="左cos")
    ax_1.legend(loc="upper left", prop=myfont, shadow=True)

    # 设置Y轴(左边)
    ax_1.set_ylabel("左cos的y轴", fontproperties=myfont)
    ax_1.set_ylim(-1.0, 1.0)
    ax_1.set_yticks(np.linspace(-1, 1, 9, endpoint=True))

    # 画图的另外一种方式
    ax_2 = ax_1.twinx()
    ax_2.plot(x, y_sin, color="green", linewidth=2.0, linestyle="-", label="右sin")
    ax_2.legend(loc="upper right", prop=myfont, shadow=True)

    # 设置Y轴(右边)
    ax_2.set_ylabel("右sin的y轴", fontproperties=myfont)
    ax_2.set_ylim(-2.0, 2.0)
    ax_2.set_yticks(np.linspace(-2, 2, 9, endpoint=True))

    # 设置X轴(共同)
    ax_1.set_xlabel("x轴", fontproperties=myfont)
    ax_1.set_xlim(-4.0, 4.0)
    ax_1.set_xticks(np.linspace(-4, 4, 9, endpoint=True))

    # 图形显示
    plt.show()
    return

3、子图

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def subplot_plot():
    """
    subplot plot
    """
    # 子图的style列表
    style_list = ["g+-", "r*-", "b.-", "yo-"]

    # 依次画图
    for num in range(4):
        # 生成测试数据
        x = np.linspace(0.0, 2+num, num=10*(num+1))
        y = np.sin((5-num) * np.pi * x)

        # 子图的生成方式
        plt.subplot(2, 2, num+1)
        plt.title("子图 %d" % (num+1), fontproperties=myfont)
        plt.plot(x, y, style_list[num])

    # 图形显示
    plt.show()
    return

4、柱状图

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def bar_plot():
    """
    bar plot
    """
    # 生成测试数据
    means_men = (20, 35, 30, 35, 27)
    means_women = (25, 32, 34, 20, 25)

    # 设置标题
    plt.title("柱状图", fontproperties=myfont)

    # 设置相关参数
    index = np.arange(len(means_men))
    bar_width = 0.35

    # 画柱状图
    plt.bar(index, means_men, width=bar_width, alpha=0.2, color="b", label="男生")
    plt.bar(index+bar_width, means_women, width=bar_width, alpha=0.8, color="r", label="女生")
    plt.legend(loc="upper right", prop=myfont, shadow=True)

    # 设置柱状图标示
    for x, y in zip(index, means_men):
        plt.text(x, y+0.3, y, ha="center", va="bottom")
    for x, y in zip(index, means_women):
        plt.text(x+bar_width, y+0.3, y, ha="center", va="bottom")

    # 设置刻度范围/坐标轴名称等
    plt.ylim(0, 45)
    plt.xlabel("分组Group", fontproperties=myfont)
    plt.ylabel("得分Scores", fontproperties=myfont)
    plt.xticks(index+(bar_width/2), ("A组", "B组", "C组", "D组", "E组"), fontproperties=myfont)

    # 图形显示
    plt.show()
    return

5、横向柱状图

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def barh_plot():
    """
    barh plot
    """
    # 生成测试数据
    means_men = (20, 35, 30, 35, 27)
    means_women = (25, 32, 34, 20, 25)

    # 设置标题
    plt.title("横向柱状图", fontproperties=myfont)

    # 设置相关参数
    index = np.arange(len(means_men))
    bar_height = 0.35

    # 画柱状图(水平方向)
    plt.barh(index, means_men, height=bar_height, alpha=0.2, color="b", label="Men")
    plt.barh(index+bar_height, means_women, height=bar_height, alpha=0.8, color="r", label="Women")
    plt.legend(loc="upper right", shadow=True)

    # 设置柱状图标示
    for x, y in zip(index, means_men):
        plt.text(y+0.3, x, y, ha="left", va="center")
    for x, y in zip(index, means_women):
        plt.text(y+0.3, x+bar_height, y, ha="left", va="center")

    # 设置刻度范围/坐标轴名称等
    plt.xlim(0, 45)
    plt.xlabel("Scores")
    plt.ylabel("Group")
    plt.yticks(index+(bar_height/2), ("A", "B", "C", "D", "E"))

    # 图形显示
    plt.show()
    return

6、高级柱状图

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def bar_advanced_plot():
    """
    bar advanced plot
    """
    # 生成测试数据
    means_men = np.array((20, 35, 30, 35, 27, 25, 32, 34, 20, 25))
    means_women = np.array((25, 32, 34, 20, 25, 20, 35, 30, 35, 27))

    # 设置标题
    plt.title("高级柱状图", fontproperties=myfont)

    # 设置相关参数
    index = np.arange(len(means_men))
    bar_width = 0.8

    # 画柱状图(两种:X轴以上/X轴以下)
    plt.bar(index, means_men, width=bar_width, alpha=0.4, color="b", label="Men")
    plt.bar(index, -means_women, width=bar_width, alpha=0.4, color="r", label="Women")

    # 画折线图(两种,和柱状图对应)
    plt.plot(index, means_men, marker="o", linestyle="-", color="r", label="Men line")
    plt.plot(index, -means_women, marker=".", linestyle="--", color="b", label="Women line")

    # 设置图形标示(两种,和柱状图对应)
    for x, y in zip(index, means_men):
        plt.text(x, y+1, y, ha="center", va="bottom")
    for x, y in zip(index, means_women):
        plt.text(x, -y-1, y, ha="center", va="top")

    # 设置Y轴和图例位置
    plt.ylim(-45, 80)
    plt.legend(loc="upper left", shadow=True)

    # 图形显示
    plt.show()
    return

7、层次柱状图

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def table_plot():
    """
    table plot
    """
    # 生成测试数据
    data = np.array([
        [1, 4, 2, 5, 2],
        [2, 1, 1, 3, 6],
        [5, 3, 6, 4, 1]
    ])

    # 设置标题
    plt.title("层次柱状图", fontproperties=myfont)

    # 设置相关参数
    index = np.arange(len(data[0]))
    color_index = ["r", "g", "b"]

    # 声明底部位置
    bottom = np.array([0, 0, 0, 0, 0])

    # 依次画图,并更新底部位置
    for i in range(len(data)):
        plt.bar(index, data[i], width=0.5, color=color_index[i], bottom=bottom, alpha=0.7, label="标签 %d" % i)
        bottom += data[i]

    # 设置图例位置
    plt.legend(loc="upper left", prop=myfont, shadow=True)

    # 图形显示
    plt.show()
    return

8、直方图

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def histograms_plot():
    """
    histograms plot
    """
    # 生成测试数据
    mu, sigma = 100, 15
    x = mu + sigma * np.random.randn(10000)

    # 设置标题
    plt.title("直方图", fontproperties=myfont)

    # 画直方图, 并返回相关结果
    n, bins, patches = plt.hist(x, bins=50, normed=1, cumulative=False, color="green", alpha=0.6, label="直方图")

    # 根据直方图返回的结果, 画折线图
    y = mlab.normpdf(bins, mu, sigma)
    plt.plot(bins, y, "r--", label="线条")

    # 设置图例位置
    plt.legend(loc="upper left", prop=myfont, shadow=True)

    # 图形显示
    plt.show()
    return

9、饼图

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def pie_plot():
    """
    pie plot
    """
    # 生成测试数据
    sizes = [15, 30, 45, 10]
    labels = ["Frogs", "中文", "Dogs", "Logs"]
    colors = ["yellowgreen", "gold", "lightskyblue", "lightcoral"]

    # 设置标题
    plt.title("饼图", fontproperties=myfont)

    # 设置突出参数
    explode = [0, 0.05, 0, 0]

    # 画饼状图
    patches, l_text, p_text = plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct="%1.1f%%", shadow=True, startangle=90)
    for text in l_text:
        text.set_fontproperties(myfont)
    plt.axis("equal")

    # 图形显示
    plt.show()
    return

10、散点图

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def scatter_plot():
    """
    scatter plot
    """
    # 生成测试数据
    point_count = 1000
    x_index = np.random.random(point_count)
    y_index = np.random.random(point_count)

    # 设置标题
    plt.title("散点图", fontproperties=myfont)

    # 设置相关参数
    color_list = np.random.random(point_count)
    scale_list = np.random.random(point_count) * 100

    # 画散点图
    plt.scatter(x_index, y_index, s=scale_list, c=color_list, marker="o")

    # 图形显示
    plt.show()
    return

11、雷达图

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def radar_plot():
    """
    radar plot
    """
    # 生成测试数据
    labels = np.array(["A组", "B组", "C组", "D组", "E组", "F组"])
    data = np.array([68, 83, 90, 77, 89, 73])
    theta = np.linspace(0, 2*np.pi, len(data), endpoint=False)

    # 数据预处理
    data = np.concatenate((data, [data[0]]))
    theta = np.concatenate((theta, [theta[0]]))

    # 画图方式
    plt.subplot(111, polar=True)
    plt.title("雷达图", fontproperties=myfont)

    # 设置"theta grid"/"radar grid"
    plt.thetagrids(theta*(180/np.pi), labels=labels, fontproperties=myfont)
    plt.rgrids(np.arange(20, 100, 20), labels=np.arange(20, 100, 20), angle=0)
    plt.ylim(0, 100)

    # 画雷达图,并填充雷达图内部区域
    plt.plot(theta, data, "bo-", linewidth=2)
    plt.fill(theta, data, color="red", alpha=0.25)

    # 图形显示
    plt.show()
    return

数据可视化的库推荐

这里推荐几个比较常用的：

#教程 #技术
Python可视化之matplotlib
http://example.com/2018/11/11/2018-11-11-Python可视化之matplotlib/
作者
NSX
发布于
2018年11月11日
许可协议