1200字范文 > 机器学习-数据科学库：matplotlib绘图

机器学习-数据科学库：matplotlib绘图

时间：2023-01-23 00:34:13

matplotlib绘图matplotlib折线图matplotlib散点图matplotlib条形图matplotlib直方图对比常用统计图

matplotlib绘图

matplotlib折线图

折线图绘制要求：

绘制折线图(plt.plot)设置图片的大小和分辨率(plt.figure)实现图片的保存(plt.savefig)设置xy轴上的刻度和字符串(xticks)解决刻度稀疏和密集的问题(xticks)设置标题,xy轴的lable(title,xlable,ylable)设置字体(font_manager. fontProperties,matplotlib.rc)在一个图上绘制多个图形(plt多次plot)为不同的图形添加图例(plt.legend)

任务1：

假设大家在30岁的时候,根据自己的实际情况,统计出来了从11岁到30岁每年交的女(男)朋友的数量如列表a,请绘制出该数据的折线图,以便分析自己每年交女(男)朋友的数量走势

a = [1,0,1,1,2,4,3,2,3,4,4,5,6,5,4,3,3,1,1,1]

要求:

y轴表示个数

x轴表示岁数,比如11岁,12岁等

python代码：

import matplotlib.pyplot as plta = [1,0,1,1,2,4,3,2,3,4,4,5,6,5,4,3,3,1,1,1]b = range(len(a))#设置图像分辨率plt.figure(figsize=(10,8),dpi=200)#设置字体和负号的代码plt.rcParams['font.sans-serif']=['Microsoft YaHei']plt.rcParams['axes.unicode_minus'] = False#绘制曲线图plt.plot(b,a)#x坐标轴的刻度标签xlabels = ["{}岁".format(i) for i in range(len(a))]#设置x轴上的刻度稀疏程度和刻度字符串plt.xticks(b[::2],xlabels[::2],rotation=90)#设置标题,xy轴的lableplt.xlabel("年龄")plt.ylabel("女朋友数量")plt.title("交女(男)折线图")#设置网格plt.grid(alpha=0.4)#保存图片plt.savefig("./line_plot1.png")plt.show()

任务二：

假设大家在30岁的时候,根据自己的实际情况,统计出来了你和你同桌各自从11岁到30岁每年交的女(男)朋友的数量如列表a和b,请在一个图中绘制出该数据的折线图,以便比较自己和同桌间的差异,同时分析每年交女(男)朋友的数量走势

a = [1,0,1,1,2,4,3,2,3,4,4,5,6,5,4,3,3,1,1,1]

b = [1,0,3,1,2,2,3,3,2,1,2,1,1,1,1,1,1,1,1,1]

要求:

y轴表示个数

x轴表示岁数,比如11岁,12岁等

import matplotlib.pyplot as plty_1 = [1,0,1,1,2,4,3,2,3,4,4,5,6,5,4,3,3,1,1,1]y_2 = [1,0,3,1,2,2,3,3,2,1,2,1,1,1,1,1,1,1,1,1]x = range(len(y_1))#设置图像分辨率plt.figure(figsize=(10,8),dpi=100)#设置字体和负号的代码plt.rcParams['font.sans-serif']=['Microsoft YaHei']plt.rcParams['axes.unicode_minus'] = False#绘制曲线图plt.plot(x,y_1, label="小张")plt.plot(x,y_2, label="小李")#x坐标轴的刻度标签xlabels = ["{}岁".format(i) for i in range(len(y_1))]#设置x轴上的刻度稀疏程度和刻度字符串plt.xticks(x[::2],xlabels[::2],rotation=90)#设置网格plt.grid(linestyle=':')#设置标题,xy轴的lableplt.xlabel("年龄")plt.ylabel("女朋友数量")plt.title("交女(男)折线图")#画图例plt.legend(loc='best')#保存图片plt.savefig("./line_plot1.png")plt.show()

matplotlib散点图

任务：

网站统计了客户收货天数和满意度结果，满意度最高为5分。分析收货天数和满意度直接的相关关系。

a = [6, 2, 8, 6, 8, 7, 5, 2, 3, 2, 1, 7, 3, 2, 9, 2, 3, 5, 8, 10]

b = [1.5, 5, 0.5, 2, 1, 1.3, 1.2, 4.1, 3.5, 4.5, 3.0, 3.5, 5, 0.3, 4.3, 3.8, 0.8, 1.5, 0.1, 0.3]

代码

import matplotlib.pyplot as plta = [6, 2, 8, 6, 8, 7, 5, 2, 3, 2, 1, 7, 3, 2, 9, 2, 3, 5, 8, 10]b = [1.5, 5, 0.5, 2, 1, 1.3, 1.2, 4.1, 3.5, 4.5, 5.0, 3.5, 5, 4.3, 2.3, 3.8, 0.8, 1.5, 0.1, 0.3]#设置字体和负号的代码plt.rcParams['font.sans-serif']=['Microsoft YaHei']plt.rcParams['axes.unicode_minus'] = False#绘图风格plt.style.use('ggplot')#图片大小plt.figure(figsize=(10,8), dpi = 80)#绘图plt.scatter(a,b)#坐标轴plt.xlabel("收货天数")plt.ylabel("满意评分")plt.title("满意评分关于收货天数的散点图",fontsize=26)plt.show()

matplotlib条形图

任务1:

假设你获取到了内地电影票房前20的电影(列表a)和电影票房数据(列表b),那么如何更加直观的展示该数据?

a = [“战狼2”,“速度与激情8”,“功夫瑜伽”,“西游伏妖篇”,“变形金刚5：最后的骑士”,“摔跤吧！爸爸”,“加勒比海盗5：死无对证”,“金刚：骷髅岛”,“极限特工：终极回归”,“生化危机6：终章”,“乘风破浪”,“神偷奶爸3”,“智取威虎山”,“大闹天竺”,“金刚狼3：殊死一战”,“蜘蛛侠：英雄归来”,“悟空传”,“银河护卫队2”,“情圣”,“新木乃伊”,]

b=[56.01,26.94,17.53,16.49,15.45,12.96,11.8,11.61,11.28,11.12,10.49,10.3,8.75,7.55,7.32,6.99,6.88,6.86,6.58,6.23] 单位:亿

纵向条形图：

import matplotlib.pyplot as plta = ["战狼2","速度与激情8","功夫瑜伽","西游伏妖篇","变形金刚5：\n最后的骑士","摔跤吧！爸爸","加勒比海盗5：\n死无对证","金刚：骷髅岛","极限特工：\n终极回归","生化危机6：\n终章","乘风破浪","神偷奶爸3","智取威虎山","大闹天竺","金刚狼3：\n殊死一战","蜘蛛侠：\n英雄归来","悟空传","银河护卫队2","情圣","新木乃伊",]b=[56.01,26.94,17.53,16.49,15.45,12.96,11.8,11.61,11.28,11.12,10.49,10.3,8.75,7.55,7.32,6.99,6.88,6.86,6.58,6.23]#设置字体和负号的代码plt.rcParams['font.sans-serif']=['Microsoft YaHei']plt.rcParams['axes.unicode_minus'] = False#绘图风格plt.style.use('ggplot')#图片大小plt.figure(figsize=(10,8), dpi = 80)#横坐标标签x = range(len(a))plt.xticks(x, a, rotation=90)#绘图plt.bar(range(len(a)),b)plt.show()

横向条形图：

import matplotlib.pyplot as plta = ["战狼2","速度与激情8","功夫瑜伽","西游伏妖篇","变形金刚5：\n最后的骑士","摔跤吧！爸爸","加勒比海盗5：\n死无对证","金刚：骷髅岛","极限特工：\n终极回归","生化危机6：\n终章","乘风破浪","神偷奶爸3","智取威虎山","大闹天竺","金刚狼3：\n殊死一战","蜘蛛侠：\n英雄归来","悟空传","银河护卫队2","情圣","新木乃伊",]b=[56.01,26.94,17.53,16.49,15.45,12.96,11.8,11.61,11.28,11.12,10.49,10.3,8.75,7.55,7.32,6.99,6.88,6.86,6.58,6.23]#设置字体和负号的代码plt.rcParams['font.sans-serif']=['Microsoft YaHei']plt.rcParams['axes.unicode_minus'] = False#绘图风格plt.style.use('ggplot')#图片大小plt.figure(figsize=(10,8), dpi = 80)#横坐标标签y = range(len(a))plt.yticks(y, a)#绘图plt.barh(range(len(a)),b)plt.show()

任务二：

假设你知道了列表a中电影分别在-09-14(b_14), -09-15(b_15), -09-16(b_16)三天的票房,为了展示列表中电影本身的票房以及同其他电影的数据对比情况,应该如何更加直观的呈现该数据?

a = [“猩球崛起3：终极之战”,“敦刻尔克”,“蜘蛛侠：英雄归来”,“战狼2”]

b_16 = [15746,312,4497,319]

b_15 = [12357,156,2045,168]

b_14 = [2358,399,2358,362]

import matplotlib.pyplot as plta = ["猩球崛起3：终极之战","敦刻尔克","蜘蛛侠：英雄归来","战狼2"]b_16 = [15746,312,4497,319]b_15 = [12357,156,2045,168]b_14 = [2358,399,2358,362]#图形参数barwidth = 0.2#设置字体和负号的代码plt.rcParams['font.sans-serif']=['Microsoft YaHei']plt.rcParams['axes.unicode_minus'] = False#绘图风格plt.style.use('ggplot')#图片大小plt.figure(figsize=(10,8), dpi = 80)#横坐标标签x_1 = list(range(len(a)))x_2 = [i + barwidth for i in range(len(a))]x_3 = [i + barwidth*2 for i in range(len(a))]plt.xticks(x_2, a)plt.bar(x_1, b_14, width=barwidth, label="9月14日")plt.bar(x_2, b_15, width=barwidth, label="9月15日")plt.bar(x_3, b_16, width=barwidth, label="9月16日")plt.show()

matplotlib直方图

任务：

假设你获取了250部电影的时长(列表a中),希望统计出这些电影时长的分布状态(比如时长为100分钟到120分钟电影的数量,出现的频率)等信息,你应该如何呈现这些数据?

a=[131, 98, 125, 131, 124, 139, 131, 117, 128, 108, 135, 138, 131, 102, 107, 114, 119, 128, 121, 142, 127, 130, 124, 101, 110, 116, 117, 110, 128, 128, 115, 99, 136, 126, 134, 95, 138, 117, 111,78, 132, 124, 113, 150, 110, 117, 86, 95, 144, 105, 126, 130,126, 130, 126, 116, 123, 106, 112, 138, 123, 86, 101, 99, 136,123, 117, 119, 105, 137, 123, 128, 125, 104, 109, 134, 125, 127,105, 120, 107, 129, 116, 108, 132, 103, 136, 118, 102, 120, 114,105, 115, 132, 145, 119, 121, 112, 139, 125, 138, 109, 132, 134,156, 106, 117, 127, 144, 139, 139, 119, 140, 83, 110, 102,123,107, 143, 115, 136, 118, 139, 123, 112, 118, 125, 109, 119, 133,112, 114, 122, 109, 106, 123, 116, 131, 127, 115, 118, 112, 135,115, 146, 137, 116, 103, 144, 83, 123, 111, 110, 111, 100, 154,136, 100, 118, 119, 133, 134, 106, 129, 126, 110, 111, 109, 141,120, 117, 106, 149, 122, 122, 110, 118, 127, 121, 114, 125, 126,114, 140, 103, 130, 141, 117, 106, 114, 121, 114, 133, 137, 92,121, 112, 146, 97, 137, 105, 98, 117, 112, 81, 97, 139, 113,134, 106, 144, 110, 137, 137, 111, 104, 117, 100, 111, 101, 110,105, 129, 137, 112, 120, 113, 133, 112, 83, 94, 146, 133, 101,131, 116, 111, 84, 137, 115, 122, 106, 144, 109, 123, 116, 111,111, 133, 150]

import matplotlib.pyplot as plta=[131, 98, 125, 131, 124, 139, 131, 117, 128, 108, 135, 138, 131, 102, 107, 114, 119, 128, 121, 142,127, 130, 124, 101, 110, 116, 117, 110, 128, 128, 115, 99, 136, 126, 134, 95, 138, 117, 111,78, 132,124, 113, 150, 110, 117, 86, 95, 144, 105, 126, 130,126, 130, 126, 116, 123, 106, 112, 138, 123, 86,101, 99, 136,123, 117, 119, 105, 137, 123, 128, 125, 104, 109, 134, 125, 127,105, 120, 107, 129, 116,108, 132, 103, 136, 118, 102, 120, 114,105, 115, 132, 145, 119, 121, 112, 139, 125, 138, 109, 132, 134,156, 106, 117, 127, 144, 139, 139, 119, 140, 83, 110, 102,123,107, 143, 115, 136, 118, 139, 123, 112,118, 125, 109, 119, 133,112, 114, 122, 109, 106, 123, 116, 131, 127, 115, 118, 112, 135,115, 146, 137,116, 103, 144, 83, 123, 111, 110, 111, 100, 154,136, 100, 118, 119, 133, 134, 106, 129, 126, 110, 111,109, 141,120, 117, 106, 149, 122, 122, 110, 118, 127, 121, 114, 125, 126,114, 140, 103, 130, 141, 117,106, 114, 121, 114, 133, 137, 92,121, 112, 146, 97, 137, 105, 98, 117, 112, 81, 97, 139, 113,134,106, 144, 110, 137, 137, 111, 104, 117, 100, 111, 101, 110,105, 129, 137, 112, 120, 113, 133, 112, 83,94, 146, 133, 101,131, 116, 111, 84, 137, 115, 122, 106, 144, 109, 123, 116, 111,111, 133, 150]#计算组数d = 6 #组距num_bins = (max(a)-min(a)) // d#设置字体和负号的代码plt.rcParams['font.sans-serif']=['Microsoft YaHei']plt.rcParams['axes.unicode_minus'] = False#绘图风格plt.style.use('ggplot')#设置x轴的刻度plt.xticks(range(min(a),max(a)+d,d))#绘图plt.hist(a,num_bins)plt.show()