【数据分析】基础技术篇 三——matplotlib

matplotlib

https://matplotlib.org/

基本线图绘制

1.

import matplotlib.pyplot as plt
import numpy as np

def main():
    x = np.linspace(-np.pi, np.pi, 256, endpoint=True)
    c, s = np.cos(x), np.sin(x)
    plt.figure(1)
    plt.plot(x, c)
    plt.plot(x, s)
    plt.savefig('1.png')
    plt.show()


if __name__ == '__main__':
    main()

2.添加属性： 

def main2():
    x = np.linspace(-np.pi, np.pi, 256, endpoint=True)
    c, s = np.cos(x), np.sin(x)
    plt.figure(1)
    plt.plot(x, c, color='blue', linewidth=1.0, linestyle='-', label='COS', alpha=0.5)
    plt.plot(x, s, 'r*', label='SIN')
    plt.savefig('2.png')
    plt.show()

注意：‘r*’，表示的是red，和线形状是*，于是有了图中看起来不平滑。

3. 

def main3():
    x = np.linspace(-np.pi, np.pi, 256, endpoint=True)
    c, s = np.cos(x), np.sin(x)
    plt.figure(1)
    plt.plot(x, c, color='blue', linewidth=1.0, linestyle='-', label='COS', alpha=0.5)
    plt.plot(x, s, 'r*', label='SIN')
    plt.savefig('3.png')
    plt.title('COS & SIN')
    ax = plt.gca()
    ax.spines['right'].set_color('none')
    ax.spines['top'].set_color('none')
    ax.spines['left'].set_position(('data', 0))
    ax.spines['bottom'].set_position(('data', 0))
    plt.show()

 

4.

def main4():
    x = np.linspace(-np.pi, np.pi, 256, endpoint=True)
    c, s = np.cos(x), np.sin(x)
    plt.figure(1)
    plt.plot(x, c, color='blue', linewidth=1.0, linestyle='-', label='COS', alpha=0.5)
    plt.plot(x, s, 'r*', label='SIN')
    plt.savefig('3.png')
    plt.title('COS & SIN')
    ax = plt.gca()
    ax.spines['right'].set_color('none')
    ax.spines['top'].set_color('none')
    ax.spines['left'].set_position(('data', 0))
    ax.spines['bottom'].set_position(('data', 0))
    ax.xaxis.set_ticks_position('bottom')
    ax.yaxis.set_ticks_position('left')
    plt.xticks([-np.pi, -np.pi / 2.0, np.pi / 2, np.pi],
               [r'$-\pi$', r'$-\pi/2$', r'$-\pi/2$', r'$+\pi$'])
    plt.yticks(np.linspace(-1, 1, 5, endpoint=True))
    for label in ax.get_xticklabels()+ax.get_yticklabels():
        label.set_fontsize(16)
        label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.2))
    plt.show()

 

5.

def main5():
    x = np.linspace(-np.pi, np.pi, 256, endpoint=True)
    c, s = np.cos(x), np.sin(x)
    plt.figure(1)
    plt.plot(x, c, color='blue', linewidth=1.0, linestyle='-', label='COS', alpha=0.5)
    plt.plot(x, s, 'r*', label='SIN')
    plt.savefig('5.png')
    plt.title('COS & SIN')
    ax = plt.gca()
    ax.spines['right'].set_color('none')
    ax.spines['top'].set_color('none')
    ax.spines['left'].set_position(('data', 0))
    ax.spines['bottom'].set_position(('data', 0))
    ax.xaxis.set_ticks_position('bottom')
    ax.yaxis.set_ticks_position('left')
    plt.xticks([-np.pi, -np.pi / 2.0, np.pi / 2, np.pi],
               [r'$-\pi$', r'$-\pi/2$', r'$-\pi/2$', r'$+\pi$'])
    plt.yticks(np.linspace(-1, 1, 5, endpoint=True))
    for label in ax.get_xticklabels()+ax.get_yticklabels():
        label.set_fontsize(16)
        label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.2))
    plt.legend(loc='upper left')#设置左上角图例
    # plt.axis([-1, 1, 0.5, 1])#设置显示范围
    plt.fill_between(x, np.abs(x) < 0.5, c, c > 0.5, color='green', alpha=0.25)#填充颜色
    plt.grid()#网格线可视
    plt.show()

6.

def main6():
    x = np.linspace(-np.pi, np.pi, 256, endpoint=True)
    c, s = np.cos(x), np.sin(x)
    plt.figure(1)
    plt.plot(x, c, color='blue', linewidth=1.0, linestyle='-', label='COS', alpha=0.5)
    plt.plot(x, s, 'r*', label='SIN')
    plt.savefig('5.png')
    plt.title('COS & SIN')
    ax = plt.gca()
    ax.spines['right'].set_color('none')
    ax.spines['top'].set_color('none')
    ax.spines['left'].set_position(('data', 0))
    ax.spines['bottom'].set_position(('data', 0))
    ax.xaxis.set_ticks_position('bottom')
    ax.yaxis.set_ticks_position('left')
    plt.xticks([-np.pi, -np.pi / 2.0, np.pi / 2, np.pi],
               [r'$-\pi$', r'$-\pi/2$', r'$-\pi/2$', r'$+\pi$'])
    plt.yticks(np.linspace(-1, 1, 5, endpoint=True))
    for label in ax.get_xticklabels()+ax.get_yticklabels():
        label.set_fontsize(16)
        label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.2))
    plt.legend(loc='upper left')#设置左上角图例
    # plt.axis([-1, 1, 0.5, 1])#设置显示范围
    plt.fill_between(x, np.abs(x) < 0.5, c, c > 0.5, color='green', alpha=0.25)#填充颜色
    t = 1
    plt.plot([t, t], [0, np.cos(t)], 'y', linewidth=3, linestyle='--')
    #添加注释
    plt.annotate('cos(1)', xy=(t, np.cos(1)), xycoords='data', xytext=(+10, +30), textcoords="offset points", arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=.2'))
    plt.grid()#网格线可视
    plt.show()

 

子图与多种图形绘制

1.

#scatter 散点图
def main7():
    fig = plt.figure()
    fig.add_subplot(3, 3, 1)
    n = 128
    X = np.random.normal(0, 1, n)
    Y = np.random.normal(0, 1, n)
    T = np.arctan2(Y, X)
    plt.axes([0.025, 0.025, 0.95, 0.95])
    plt.scatter(X, Y, s=75, c=T, alpha=0.5)
    plt.xlim(-1.5, 1.5)
    plt.xticks([])
    plt.ylim(-1.5, 1.5)
    plt.yticks([])
    plt.axis()
    plt.title('scatter')
    plt.xlabel('x')
    plt.ylabel('y')
    plt.show()

 2.柱状图

#bar 柱状图
def main8():
    fig = plt.figure()
    # ax = fig.add_subplot(332)
    n = 10
    X = np.arange(n)
    Y1 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)
    Y2 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)

    plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')
    plt.bar(X, -Y1, facecolor='#ff9999', edgecolor='white')
    for x, y in zip(X, Y1):
        plt.text(x + 0.4, y + 0.05, '%.2f' % y, ha='center', va='bottom')
    for x, y, in zip(X, Y2):
        plt.text(x + 0.4, -y - 0.05, '%.2f' % y, ha='center', va='top')
    plt.show()

3.饼状图

#pie 饼图
def main9():
    fig = plt.figure()
    # fig.add_subplot(333)
    n = 20
    Z = np.ones(n)
    Z[-1] *= 2
    plt.pie(Z, explode=Z * .05, colors=['%f' % (i / float(n)) for i in range(n)],
            labels=['%.2f' % (i / float(n)) for i in range(n)])
    plt.gca().set_aspect('equal')
    plt.xticks([])
    plt.yticks([])
    plt.show()

set_aspect('equal')设置成正的圆形

4.极值图

#polar 极值图
def main10():
    fig = plt.figure()
    # fig.add_subplot(polar=True)
    n = 20
    theta = np.arange(0.0, 2 * np.pi, 2 * np.pi / n)
    radii = 10 * np.random.rand(n)
    plt.plot(theta, radii)
    plt.show()

稍微调整一下 ！！！

#polar 极值图
def main10():
    fig = plt.figure()
    fig.add_subplot(polar=True)
    n = 20
    theta = np.arange(0.0, 2 * np.pi, 2 * np.pi / n)
    radii = 10 * np.random.rand(n)
    plt.plot(theta, radii)
    plt.show()

还可以这样

#polar 极值图
def main10():
    fig = plt.figure()
    # fig.add_subplot(polar=True)
    n = 20
    theta = np.arange(0.0, 2 * np.pi, 2 * np.pi / n)
    radii = 10 * np.random.rand(n)
    # plt.plot(theta, radii)
    plt.polar(theta, radii)
    plt.show()

 5.热图

#heatMap 热图
from matplotlib import cm
def main11():
    fig = plt.figure()
    data = np.random.rand(3, 3)
    cmap = cm.Blues
    map = plt.imshow(data, interpolation='nearest',
            cmap=cmap, aspect='auto', vmin=0, vmax=1)
    plt.show()

6.3D图

#3D图
from mpl_toolkits.mplot3d import Axes3D
#引入3D坐标系
def main12():
    fig = plt.figure()
    ax = fig.add_subplot(projection='3d')
    ax.scatter(1, 1, 3, s=100)
    plt.show()

7. hot map 热力图

#hot map 热力图
def main13():
    fig = plt.figure()
    def f(x, y):
        return (1 - x / 2 + x ** 5 + y ** 3) \
               * np.exp(-x ** 2 - y ** 2)
    n = 256
    x = np.linspace(-3, 3, n)
    y = np.linspace(-3, 3, n)
    X, Y = np.meshgrid(x, y)
    plt.contourf(X, Y, f(X, Y), 8, alpha=.75,
                 cmap=plt.cm.hot)

    plt.show()

 

 

 

 

 

 

 

 

 

本文地址：https://blog.csdn.net/weixin_44566432/article/details/107183212