教你利用python的matplotlib(pyplot)绘制折线图和柱状图
目录
前言一、折线图二、柱状图总结前言
今天帮师兄赶在deadline之前画论文的图,现学现卖很是刺激,现把使用matplotlib的子库pyplot画折线图和柱状图的代码记录分享一下,方便大家参考,个人感觉pyplot真的蛮方便的,非常值得使用。
先看下官方对pyplot的描述:“Provides a MATLAB-like plotting framework.”。对,就是一个类似matlab的画图框架。就不多多说了,直接上代码吧:
一、折线图
代码:
import matplotlib.pyplot as plt #折线图 x = [5,7,11,17,19,25]#点的横坐标 k1 = [0.8222,0.918,0.9344,0.9262,0.9371,0.9353]#线1的纵坐标 k2 = [0.8988,0.9334,0.9435,0.9407,0.9453,0.9453]#线2的纵坐标 plt.plot(x,k1,"s-",color = "r",label="ATT-RLSTM")#s-:方形 plt.plot(x,k2,"o-",color = "g",label="CNN-RLSTM")#o-:圆形 plt.xlabel("region length")#横坐标名字 plt.ylabel("accuracy")#纵坐标名字 plt.legend(loc = "best")#图例 plt.show()
运行结果:
关于上述代码参数的一点解释:
plt.legend(loc=”best”) 中loc=”best”意思是自动选择放图例的合适位置,上图就是放在了右下角,这个参数还有其他的设置如下:
二、柱状图
多个柱状图用颜色区分
代码如下:
#柱状图 import numpy as np import matplotlib.pyplot as plt ATT_LSTM = [0.8892,0.861,0.9243] MATT_CNN = [0.8966,0.8556,0.9316] ATT_RLSTM = [0.8867,0.8543,0.9344] CNN_RLSTM = [0.9016,0.8636,0.9435] #x = ["REST","LAPT","AUTO"] x = np.arange(3) #总共有几组,就设置成几,我们这里有三组,所以设置为3 total_width, n = 0.8, 4 # 有多少个类型,只需更改n即可,比如这里我们对比了四个,那么就把n设成4 width = total_width / n x = x - (total_width - width) / 2 plt.bar(x, ATT_LSTM, color = "r",width=width,label="ATT-LSTM ") plt.bar(x + width, MATT_CNN, color = "y",width=width,label="MATT-CNN") plt.bar(x + 2 * width, ATT_RLSTM , color = "c",width=width,label="ATT-RLSTM") plt.bar(x + 3 * width, CNN_RLSTM , color = "g",width=width,label="CNN-RLSTM") plt.xlabel("dataset") plt.ylabel("accuracy") plt.legend(loc = "best") plt.xticks([0,1,2],["REST","LAPT","AUTO"]) my_y_ticks = np.arange(0.8, 0.95, 0.02) plt.ylim((0.8, 0.95)) plt.yticks(my_y_ticks) plt.show()
结果如下:
2. 多个柱状图用填充图形(\,/,*,o等)
代码如下:
#柱状图 import numpy as np import matplotlib.pyplot as plt ATT_LSTM = [0.8892,0.861,0.9243] MATT_CNN = [0.8966,0.8556,0.9316] ATT_RLSTM = [0.8867,0.8543,0.9344] CNN_RLSTM = [0.9016,0.8636,0.9435] #x = ["REST","LAPT","AUTO"] x = np.arange(3) total_width, n = 0.8, 4 # 有多少个类型,只需更改n即可 width = total_width / n x = x - (total_width - width) / 2 plt.bar(x, ATT_LSTM, color = "w",edgecolor = "k",width=width,hatch = "/",label="ATT-LSTM ")#edgecolor柱状边框颜色,hatch填充的内容 plt.bar(x + width, MATT_CNN, color = "w",edgecolor = "k",width=width, hatch = "+",label="MATT-CNN") plt.bar(x + 2 * width, ATT_RLSTM , color = "w",edgecolor = "k",width=width, hatch = "*",label="ATT-RLSTM") plt.bar(x + 3 * width, CNN_RLSTM , color = "w",edgecolor = "k",width=width, hatch = "\\",label="CNN-RLSTM") plt.xlabel("dataset") plt.ylabel("accuracy") plt.legend(loc = "best") plt.xticks([0,1,2],["REST","LAPT","AUTO"]) my_y_ticks = np.arange(0.8, 0.95, 0.02) plt.ylim((0.8, 0.95)) plt.yticks(my_y_ticks) plt.show()
运行结果为:
这就是画折线图和柱状图的示例。
总结
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