numpy画图

 1 # coding=utf-8
 2 
 3 # np.loadtxt(fname,dtype=np.float,delimiter=None,skiprows=0,usecols=None,unpack=False)
 4 # frame 文件/字符串或产生器,可以是.gz或者bz2压缩文件
 5 # dtype 数据类型,可选,CSV的字符串以什么数据类型读入数组中,默认np.float
 6 # delimiter 分隔字符串,默认是任何空格,改为 逗号
 7 # skiprows 跳过前x行, 一般跳过第一行表头
 8 # usecols 读取指定的列,索引,元组类型
 9 # unpack 如果True, 读入属性将分别写入不同数组变量,False 读入数据只写入一个数组变量,默认False
10 
11 
12 '''
13 import numpy as np
14 from matplotlib import pyplot as plt
15 from matplotlib import font_manager
16 
17 us_file_path = r"/Users/vito/PycharmProjects/study_121/venv//youtube_video_data/US_video_data_numbers.csv"
18 uk_file_path = r"/Users/vito/PycharmProjects/study_121/venv/youtube_video_data/GB_video_data_numbers.csv"
19 
20 t_us = np.loadtxt(us_file_path,delimiter=",",dtype="int")
21 
22 # 读取数据
23 t_us_comments = t_us[:,-1]
24 print(t_us_comments.max(),t_us_comments.min())
25 
26 # 选出比5000小的数据
27 t_us_comments_n = t_us_comments[t_us_comments<=5000]
28 print(t_us_comments_n.max(),t_us_comments_n.min())
29 
30 
31 d = 250
32 bin_nums = (t_us_comments_n.max() - t_us_comments_n.min())//d
33 
34 # 绘图
35 plt.figure(figsize=(20,8),dpi=80)
36 plt.hist(t_us_comments_n,bin_nums)
37 plt.show()
38 '''
39 
40 import numpy as np
41 from matplotlib import pyplot as plt,font_manager
42 # from matplotlib import font_manager
43 us_file_path = r"/Users/vito/PycharmProjects/study_121/venv//youtube_video_data/US_video_data_numbers.csv"
44 uk_file_path = r"/Users/vito/PycharmProjects/study_121/venv/youtube_video_data/GB_video_data_numbers.csv"
45 
46 # 读取数据 筛选数据
47 t_uk = np.loadtxt(uk_file_path,delimiter=",",dtype="int")
48 t_uk = t_uk[t_uk[:,1]<=500000]
49 
50 t_uk_comment = t_uk[:,-1]
51 t_uk_like = t_uk[:,1]
52 
53 plt.figure(figsize=(20,8),dpi=80)
54 plt.scatter(t_uk_like,t_uk_comment)
55 
56 plt.show()