Four pictures to understand some basic usage of Matplotlib

Actually matplotlib The picture is not so ugly , Especially the first one , It feels acceptable （ It looks a little ugly because there are too many things

The code is as follows ：

# 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']  #  Specify Chinese font 
mpl.rcParams['axes.unicode_minus'] = False  #  Resolve save image is negative '-' Questions displayed as squares 
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False  #  The minus sign is displayed normally 


fig = plt.figure(figsize=(10, 20), dpi=100)  #  Resize canvas , clarity 
fig1 = fig.add_subplot(221)  #  canvas 1
fig2 = fig.add_subplot(222)  #  canvas 2
fig3 = fig.add_subplot(223)  #  canvas 3
fig4 = fig.add_subplot(224)  #  canvas 4


def draw_fig1():
    # fig1
    x = np.linspace(0, 5 * np.pi, 50)
    y1 = np.sin(x)
    y2 = np.cos(x)
    y3 = x
    # x Axis to y1 Fill the area with 
    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  Region ,  And y1 > 0 Fill the area with 
    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) #  Set up x scale 
    # fig1.set_xlim(left=0, right=10, emit=1)
    fig1.set_title(" Fill in the picture ")
    fig1.set_xlabel("x Axis ", fontsize=10, labelpad=10)  #  The size of the word and the distance from the inner axis 
    fig1.set_ylabel("y Axis ", fontsize=10, labelpad=10)
    fig1.set_xticks([1, 2, 3])
    fig1.set_xticklabels(["A", "B", "C"],  rotation=30, fontsize=10)  #  Set up x Axis coordinates 
    # fig1.set_yticks([-1, 1, 0, 0.1])
    fig1.legend(fontsize=10, loc="center")  #  Show Legend 
    # loc  Take (‘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
    #  The pie chart is divided into five parts , Value of each 
    z = [10, 20, 30, 40, 50]
    #  The proportion that the corresponding block deviates from the center of the circle 
    explode = [0, 0.2, 0, 0, 0]
    #  The corresponding content of each piece 
    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: The number of pie charts , And the value of each piece 
    # explode: The proportion that the corresponding block deviates from the center of the circle 
    # labels: The label of each piece 
    # colors: It's also an array , Represents the color of each piece of content 
    # autopct: It shows the proportion of each block 
    # pctdistance=0.8： The percentage of the distance between the label and the center 
    # shadow=True： Whether there is a shadow effect 
    # labeldistance: Indicates that the distance from the center point of each corresponding content text is a multiple of the pie chart radius 
    # startangle=90  Start drawing angle , And it's drawn counterclockwise 
    # radius: Radius of pie chart , The default is 1 hold: Whether to overwrite other graphics , If in a figure Draw in , choice True Will be covered ,False Will coexist 
    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(" features ")


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:
                #  Mark the number on the corresponding column 
                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)
                #  If there is less than 0 The numerical , Then draw 0 Scale horizontal straight line 
                fig4.axhline(y=0, color='black')

    #  normalization , Let the colors be evenly distributed 
    norm = plt.Normalize(-1, 1)
    norm_values = norm(key_values)
    map_vir = cm.get_cmap(name='viridis')  #  Get the style of the corresponding color mapping bar 
    colors = map_vir(norm_values)  #  The mapping bar should be mapped to the data 
    ax = fig4.bar(key_name, key_values, width=0.5, color=colors, edgecolor='white')  #  Set the color of the box border 
    sm = cm.ScalarMappable(cmap=map_vir, norm=norm)  # norm Set max min 
    plt.colorbar(sm)
    auto_label(ax)

    bwith = 4  #  The border width is set to 2
    TK = plt.gca()  #  Get border 
    TK.spines['bottom'].set_linewidth(bwith)  #  Lower edge of frame 
    TK.spines['left'].set_linewidth(bwith)  #  Frame left 
    TK.spines['top'].set_linewidth(bwith)  #  On the frame 
    TK.spines['right'].set_linewidth(bwith)  #  On the right side of the frame 


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