机器学习-数据科学库（HM）第二课总结
matplotlib折线图
# coding=utf-8
from matplotlib import pyplot as plt
import matplotlib
matplotlib.rc("font", family="Microsoft YaHei")
x = range(11, 31)
y_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]
# 设置图形大小
plt.figure(figsize=(20, 8), dpi=80)
# 绘图
plt.plot(x, y_1, label="自己", color="orange", linestyle="-")
plt.plot(x, y_2, label="同桌", color="cyan", linestyle="-.")
# 设置X轴刻度
_xtick_labels = ["{}岁".format(i) for i in x]
plt.xticks(x, _xtick_labels)
plt.yticks(range(0,9))
# 绘制网格
plt.grid(linestyle=":")
# 添加图例
plt.legend(loc="upper left")
# 展示图像
plt.show()
#保存
plt.savefig("./t3.png")
绘制散点图
from matplotlib import pyplot as plt
import matplotlib
my_font = matplotlib.rc("font", family="Microsoft YaHei")
y_3 = [11, 17, 16, 11, 12,
11, 12, 6, 6, 7,
8, 9, 12, 15, 14,
17, 18, 21, 16, 17,
20, 14, 15, 15, 15,
19, 21, 22, 22, 22,
23]
y_10 =[26, 26, 28, 19, 21,
17, 16, 19, 18, 20,
20, 19, 22, 23, 17,
20, 21, 20, 22, 15,
11, 15, 5, 13, 17,
10, 11, 13, 12, 13,
6]
x_3 = range(1, 32)
x_10 = range(51, 82)
# 设置图形大小
plt.figure(figsize=(20, 8), dpi=80)
# 使用scatter方法绘制散点图与之前的唯一区别
plt.scatter(x_3, y_3, label="3月份")
plt.scatter(x_10, y_10, label="10月份")
# 调整x轴的刻度
_x = list(x_3)+list(x_10)
_xtick_labels = ["3月{}日".format(i) for i in x_3]
_xtick_labels += ["10月{}日".format(i-50) for i in x_10]
plt.xticks(_x[::3], _xtick_labels[::3], rotation=45)
# 添加图例
plt.legend(loc="upper left")
# 添加描述信息
plt.xlabel("时间")
plt.ylabel("温度")
plt.title("3月与10月的温度散点图")
# 展示图片
plt.show()
# 保存图片
plt.savefig("./t4.png")
绘制条形图
# coding=utf-8
# 绘制横着的条形图
from matplotlib import pyplot as plt
import matplotlib
matplotlib.rc("font", family="Microsoft YaHei")
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]
# 设置图像大小
plt.figure(figsize=(20, 10), dpi=80)
# 绘图
plt.barh(range(len(a)), b, height=0.3, color="orange")
# 添加图例
# plt.legend(loc="upper left")
# 设置y轴
plt.yticks(range(len(a)), a)
# 设置标题
plt.title("票房排行")
# 绘制网格
plt.grid(alpha=0.3)
# 保存图像
plt.savefig("./t6.png")
# 展示图像
plt.show()
绘制直方图
# coding = utf-8
from matplotlib import pyplot as plt
import matplotlib
matplotlib.rc("font", family="Microsoft YaHei")
# 设置图像大小
plt.figure(figsize=(20, 8), dpi=80)
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]
# 计算组数
d = 6 # 组距
num_bins = (max(a) - min(a)) // d
# 绘制图像
plt.hist(a, num_bins, density=True)
# 设置x轴刻度
plt.xticks(range(min(a), max(a)+d, d))
plt.grid()
# 保存图像
plt.savefig("./t7.png")
# 显示图像
plt.show()
plt.hist ()方法只能用于原始数据绘制直方频率图
更多工具
百度echart
plotly
seaborn