Python3 数据可视化之matplotlib、Pygal、requests

matplotlib的学习和使用

matplotlib的安装

pip3 install matplotlib

简单的折线图

import  matplotlib.pyplot as plt
#绘制简单的图表
input_values = [1,2,3,4,5]
squares = [1,4,9,16,25]
plt.plot(input_values,squares,linewidth=5)

#设置图表的标题 并给坐标轴加上标签
plt.title("Square Number",fontsize=24)
plt.xlabel("Value",fontsize=24)
plt.ylabel("Square of Value",fontsize=14)

#设置刻度标记的大小
plt.tick_params(axis='both',labelsize=14)
#显示图表
plt.show()
#保存在当前的目录下，文件名为squares_plot.png
#plt.savefig('squares_plot.png', bbox_inches='tight')

绘制简单的散点图

import matplotlib.pyplot as plt

x_values = [1, 2, 3, 4, 5]
y_values = [1, 4, 9, 16, 25]

plt.scatter(x_values, y_values, s=100)

#设置图表的标题 并给坐标轴加上标签
plt.title("Square Number",fontsize=24)
plt.xlabel("Value",fontsize=24)
plt.ylabel("Square of Value",fontsize=14)

#设置刻度标记的大小
plt.tick_params(axis='both',labelsize=14)

plt.show()

import  matplotlib.pyplot as plt

#绘制散点图并设置其样式

x_value = list(range(1,1001))
y_value = [x**2 for x in x_value]

#点的颜色 c=(0,0,1,0.5) edgecolors = 'red'  点的边缘颜色
plt.scatter(x_value,y_value,c=y_value,cmap=plt.cm.Blues,edgecolors='none',s=40)
# plt.scatter(2,4,s=200)

#设置图表的标题 并给坐标轴加上标签
plt.title("Square Number",fontsize=24)
plt.xlabel("Value",fontsize=24)
plt.ylabel("Square of Value",fontsize=14)

#设置刻度标记的大小
plt.tick_params(axis='both',labelsize=14)

#设置每个坐标系的取值范围
# plt.axis([0,110,0,110000])

#显示
plt.show()
#显示并保存
#plt.savefig('pyplot_scatter.png',bbox_inches='tight')

绘制随机漫步图

random_walk.py

from  random import choice

class RandomWalk():
"""一个生成随机漫步数据的类"""

def __init__(self,num_points=5000):
"""一个生成随机漫步的数据的类"""
self.num_points = num_points;
#所有的随机漫步都始于（0，0）
self.x_value = [0]
self.y_value = [0]

def fill_walk(self):
"""计算随机漫步包含的点"""

#不断漫步，直到列表达到指定的长度
while len(self.x_value) < self.num_points:
#决定前进的方向以及沿这个方向前进的距离
x_direction= choice([1,-1])
x_distance = choice([0,1,2,3,4])
x_step = x_direction*x_distance

y_direction = choice([1,-1])
y_distance = choice([0, 1, 2, 3, 4])
y_step = y_direction * y_distance

#拒绝原地踏步
if x_step == 0 and y_step == 0:
continue

#计算下一个点的x和y值
next_x = self.x_value[-1] + x_step
next_y = self.y_value[-1] + y_step

self.x_value.append(next_x)
self.y_value.append(next_y)

rw_visual.py

import  matplotlib.pyplot as plt
#引用同级目录下的文件
from Random_Walk.random_walk import RandomWalk

#创建一个RandomWalk的实例 并将其包含的点都绘制出来
rw = RandomWalk()
rw.fill_walk()
print("test")
point_numbers = list(range(rw.num_points))
plt.scatter(rw.x_value,rw.y_value,c=point_numbers, cmap=plt.cm.Blues,edgecolor='none',s=15)
# 突出起点和终点
plt.scatter(0, 0, c='green',edgecolors='none',s=100)
plt.scatter(rw.x_value[-1], rw.y_value[-1],c='red',edgecolors='none',s=100)

# 设置绘图窗口的尺寸
# plt.figure(figsize=(10, 6))
plt.figure(dpi=128, figsize=(10, 6))

# 隐藏坐标轴
# plt.axes().get_xaxis().set_visible(False)
# plt.axes().get_yaxis().set_visible(False)

plt.show()

Pygal的学习和使用

安装Pygal

pip3 install pygal

绘制简单的直方图

创建骰子类 die.py

from  random import  randint

class Die():

"""表示一个骰子的类"""
def __init__(self,num_sides=6):
"""骰子默认为6面"""
self.num_sides = num_sides

def roll(self):
"""返回一个位于1和骰子面数之间的随机值"""
return  randint(1,self.num_sides)

掷骰子die_visual.py

from  Pygal_learn.die import  Die
import  pygal
#创建一个D6
die = Die()

#掷几次骰子 并将结果存储在一个列表中
results = []
for roll_num in range(1000):
result = die.roll()
results.append(result)

frequencies = []
#分析结果
for value in range(1,die.num_sides+1):
frequency = results.count(value)
frequencies.append(frequency)

#对结果进行可视化
hist = pygal.Bar()
hist.title = "Result of rolling one d6 1000 times"
hist.x_labels = ['1','2','3','4','5','6']
hist.x_title = "Result"
hist.y_title = "Frequency of result"

hist.add("D6",frequencies)

hist.render_to_file("die_visual.svg")

使用Web API

安装requests

pip3 install requests

绘制图表

通过抓取GitHub上受欢迎程度最高的Python项目，绘制出图表

import  requests
import  pygal
from pygal.style  import  LightColorizedStyle as LCS,LightenStyle as LS

#执行API调用并存储响应
url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'
r = requests.get(url)
print("Staus code:",r.status_code)

response_dict = r.json()
print("Total repositories:", response_dict['total_count'])
#探索有关仓库的信息
repo_dicts = response_dict['items']
print('Repositories returned:',len(repo_dicts))

#研究第一个仓库
# repo_dict = repo_dicts[0]
# for key in sorted(repo_dict.keys()):
#     print(key)

#研究仓库有关的信息

# Name: macOS-Security-and-Privacy-Guide
# Owner: drduh
# Stars: 12348
# Repository: https://github.com/drduh/macOS-Security-and-Privacy-Guide # Description: A practical guide to securing macOS.

names,plot_dicts = [],[]
for repo_dict in repo_dicts:
names.append(repo_dict["name"])
# stars.append(repo_dict["stargazers_count"])
plot_dict = {
'value': repo_dict['stargazers_count'],
'label': str(repo_dict['description']),
'xlink': repo_dict['html_url']
}
plot_dicts.append(plot_dict)

#可视化数据

my_config = pygal.Config()
my_config.x_label_rotation = 45
my_config.show_legend = False
my_config.title_font_size = 24
my_config.label_font_size = 14
my_config.major_label_font_size = 18
my_config.truncate_label = 15
my_config.show_y_guides = False
my_config.width = 1000

my_style = LS('#333366',base_style=LCS)
chart = pygal.Bar(my_config,style=my_style)
chart.title = "Most-Stared Python Project on Github"
chart.x_labels = names
print(plot_dicts)
chart.add('',plot_dicts)
chart.render_to_file('python_repos.svg')

监视API的速率限制

大多数API都存在速率限制，即你在特定时间内可执行的请求数存在限制。要获悉你是否接近了GitHub的限制，请在浏览器中输入https://api.github.com/rate_limit ，你将看到类似于下 面的响应:

参考内容:《Python编程：从入门到实践》