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')

4 从json文件中提取数据，并进行可视化

4.1 数据来源：population_data.json。

4.2 一个简单的代码段：

import json #导入json模版
filename = 'population_data.png'
with open(filename) as f:
pop_data = json.load(f) #加载json文件数据

通过小的代码段了解最基本的原理，具体详情还要去查看手册。

4.3制作简单的世界地图（代码如下）

import pygal #导入pygal
wm = pygal.maps.world.World() #正确导入世界地图模块
wm.title = 'populations of Countries in North America'
wm.add('North America',{'ca':34126000,'us':309349000,'mx':113423000})
wm.render_to_file('na_populations.svg') #生成svg文件

结果：

4.4 制作世界地图

代码段：

import json
import pygal
from pygal.style import LightColorizedStyle as LCS, RotateStyle as RS
from country_codes import get_country_code
# Load the data into a list.
filename = 'population_data.json'
with open(filename) as f:
pop_data = json.load(f)
# Build a dictionary of population data.
cc_populations = {}
for pop_dict in pop_data:
if pop_dict['Year'] == '2010':
country_name = pop_dict['Country Name']
population = int(float(pop_dict['Value']))
code = get_country_code(country_name)
if code:
cc_populations[code] = population
# Group the countries into 3 population levels.
cc_pops_1, cc_pops_2, cc_pops_3 = {}, {}, {}
for cc, pop in cc_populations.items():
if pop < 10000000: #分组
cc_pops_1[cc] = pop
elif pop < 1000000000:
cc_pops_2[cc] = pop
else:
cc_pops_3[cc] = pop
# See how many countries are in each level.
print(len(cc_pops_1), len(cc_pops_2), len(cc_pops_3))
wm_style = RS('#336699', base_style=LCS)
wm = pygal.maps.world.World(style=wm_style) #已修改，原代码有错误！
wm.title = 'World Population in 2010, by Country'
wm.add('0-10m', cc_pops_1)
wm.add('10m-1bn', cc_pops_2)
wm.add('>1bn', cc_pops_3)
wm.render_to_file('world_population.svg')

辅助代码段country_code.py如下：

from pygal.maps.world import COUNTRIES
from pygal_maps_world import i18n #原代码也有错误，现已订正
def get_country_code(country_name):
"""Return the Pygal 2-digit country code for the given country."""
for code, name in COUNTRIES.items():
if name == country_name:
return code
# If the country wasn't found, return None.
return None

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:

巴特西