numpy画图

# coding=utf-8

# np.loadtxt(fname,dtype=np.float,delimiter=None,skiprows=0,usecols=None,unpack=False)
# frame 文件/字符串或产生器,可以是.gz或者bz2压缩文件
# 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
# delimiter 分隔字符串,默认是任何空格,改为 逗号
# skiprows 跳过前x行, 一般跳过第一行表头
# usecols 读取指定的列,索引,元组类型
# 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


'''
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import font_manager

us_file_path = r"/Users/vito/PycharmProjects/study_121/venv//youtube_video_data/US_video_data_numbers.csv"
uk_file_path = r"/Users/vito/PycharmProjects/study_121/venv/youtube_video_data/GB_video_data_numbers.csv"

t_us = np.loadtxt(us_file_path,delimiter=",",dtype="int")

# 读取数据
t_us_comments = t_us[:,-1]
print(t_us_comments.max(),t_us_comments.min())

# 选出比5000小的数据
t_us_comments_n = t_us_comments[t_us_comments<=5000]
print(t_us_comments_n.max(),t_us_comments_n.min())


d = 250
bin_nums = (t_us_comments_n.max() - t_us_comments_n.min())//d

# 绘图
plt.figure(figsize=(20,8),dpi=80)
plt.hist(t_us_comments_n,bin_nums)
plt.show()
'''

import numpy as np
from matplotlib import pyplot as plt,font_manager
# from matplotlib import font_manager
us_file_path = r"/Users/vito/PycharmProjects/study_121/venv//youtube_video_data/US_video_data_numbers.csv"
uk_file_path = r"/Users/vito/PycharmProjects/study_121/venv/youtube_video_data/GB_video_data_numbers.csv"

# 读取数据 筛选数据
t_uk = np.loadtxt(uk_file_path,delimiter=",",dtype="int")
t_uk = t_uk[t_uk[:,1]<=500000]

t_uk_comment = t_uk[:,-1]
t_uk_like = t_uk[:,1]

plt.figure(figsize=(20,8),dpi=80)
plt.scatter(t_uk_like,t_uk_comment)

plt.show()