Python编程从入门到实践.——项目二数据可视化

项目2　数据可视化

1　生成数据

1.1　安装matplotlib

1.1.5　matplotlib画廊

https://matplotlib.org/

1.2　绘制简单的折线图

mpl_squares.py

import matplotlib.pyplot as plt

squares = [1, 4,9, 16, 25]
plt.plot(squares)
plt.show()

1.2.1　修改标签文字和线条粗细

import matplotlib.pyplot as plt

squares = [1, 4,9, 16, 25]
plt.plot(squares, linewidth=5)

# 设置图表标题，并给坐标轴加上标签
plt.title("Square Numbers", fontsize=24)
plt.xlabel("Value", fontsize=14)
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()

1.2.2　校正图形

当你向plot() 提供一系列数字时，它假设第一个数据点对应的 x 坐标值为0

import matplotlib.pyplot as plt

input_values = [1, 2, 3, 4, 5]
squares = [1, 4,9, 16, 25]
plt.plot(input_values, squares, linewidth=5)

# 设置图表标题，并给坐标轴加上标签
plt.title("Square Numbers", fontsize=24)
plt.xlabel("Value", fontsize=14)
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()

1.2.3　使用scatter() 绘制散点图并设置其样式

绘制单个点 scatter()

scatter_squares.py

import matplotlib.pyplot as plt
plt.scatter(2, 4)
plt.show()

import matplotlib.pyplot as plt
plt.scatter(2, 4, s=200)
# 实参s设置了绘制图形时使用的点的尺寸

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

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

plt.show()

1.2.4　使用scatter() 绘制一系列点

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)

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

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

plt.show()

1.2.5　自动计算数据

import matplotlib.pyplot as plt

x_values = list(range(1, 1001))
y_values = [x**2 for x in x_values]

plt.scatter(x_values, y_values, s=40)

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

# 设置每个坐标轴的取值范围
plt.axis([0, 1100, 0, 1100000])

plt.show()

1.2.6　删除数据点的轮廓

点的颜色默认为蓝色点和黑色轮廓

plt.scatter(x_values, y_values,edgecolor="none", s=40)

1.2.7　自定义颜色

plt.scatter(x_values, y_values,c="red", edgecolor="none", s=40)

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plt.scatter(x_values, y_values,c=(0,0,0.6), edgecolor="none", s=40)

要指定自定义颜色，可传递参数c ，并将其设置为一个元组，其中包含三个0~1之间的小数值，它们分别表示红色、绿色和蓝色分量。

plt.scatter(x_values, y_values,c="#ac1f18", edgecolor="none", s=40)

1.2.8　使用颜色映射

颜色映射 （colormap）是一系列颜色，它们从起始颜色渐变到结束颜色。

import matplotlib.pyplot as plt

x_values = list(range(1, 1001))
y_values = [x**2 for x in x_values]

plt.scatter(x_values, y_values,c=y_values,cmap=plt.cm.Blues,
            edgecolor="none", s=40)

# 设置图表标题，并给坐标轴加上标签
--snip--

注意 　要了解pyplot 中所有的颜色映射，请访问http://matplotlib.org/ ，单击Examples，向下滚动到Color Examples，再单击colormaps_reference。

1.2.9　自动保存图表

plt.savefig('squares_plot.png', bbox_inches='tight')

第二个实参指定将图表多余的空白区域裁剪掉

1.3　随机漫步

1.3.1　创建RandomWalk() 类

from random import choice

class RandomWalk():
    """一个生成随机漫步数据的类"""
    
    def __init__(self, num_points=5000):
        """初始化随机漫步的属性"""
        self.num_points = num_points
        
        # 所有随机漫步都始于（0，0）
        self.x.values = [0]
        self.y.values = [0]

1.3.2　选择方向

from random import choice

class RandomWalk():
    """一个生成随机漫步数据的类"""
    
    def __init__(self, num_points=5000):
        """初始化随机漫步的属性"""
        self.num_points = num_points
        
        # 所有随机漫步都始于（0，0）
        self.x_values = [0]
        self.y_values = [0]
    
    def fill_walk(self):
        """计算随机漫步包含的所有点"""
        
        # 不断漫步，知道列表达到指点长度
        while len(self.x_values) < self.num_points:
            
            # 决定前进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_values[-1] + x_step
            next_y = self.y_values[-1] + y_step
            
            self.x_values.apped(next_x)
           self.y_values.append(next_y)

1.3.3　绘制随机漫步图

rw_visual.py

import matplotlib.pyplot as plt

from 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()
plt.scatter(rw.x_values, rw.y_values, s=15)
plt.show()

1.3.4　模拟多次随机漫步

import matplotlib.pyplot as plt

from random_walk import RandomWalk

# 只要程序处于活动状态，就不断地模拟随机漫步
while True:
    # 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()
    plt.scatter(rw.x_values, rw.y_values, s=15)
    plt.show()
    keep_running = input("Make anither walk?(y/n):")
    if keep_running == "n":
        break

1.3.5　设置随机漫步图的样式

1.3.6　给点着色

import matplotlib.pyplot as plt

from random_walk import RandomWalk

# 只要程序处于活动状态，就不断地模拟随机漫步
while True:
    # 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()
    point_number = list(range(rw.num_points))
    plt.scatter(rw.x_values, rw.y_values,c=point_number,
                cmap=plt.cm.Blues,edgecolors="none", s=1)
    plt.show()
    --snip--

1.3.7　重新绘制起点和终点

import matplotlib.pyplot as plt

from random_walk import RandomWalk

    --snip--
    
    # 突出起点和终点
    plt.scatter(0, 0, c="green", edgecolors="none", s=50)
    plt.scatter(rw.x_values[-1], rw.y_values[-1], c='red',
                edgecolors='none',s=50)
    plt.show()
    --snip--

1.3.8　隐藏坐标轴

       --snip--
    plt.scatter(rw.x_values[-1], rw.y_values[-1], c='red',
                edgecolors='none',s=50)
    
    # 隐藏坐标轴
    plt.axes().get_xaxis().set_visible(False)
    plt.axes().get_yaxis().set_visible(False)
    
    plt.show()
       --snip--

1.3.9　增加点数

 --snip--
while True:
    # 创建一个RandomWalk的实例，并将其包含的点都绘制出来
    rw = RandomWalk(50000)
    rw.fill_walk()
     --snip--

1.3.10　调整尺寸以适合屏幕

 rw = RandomWalk()
rw.fill_walk()
    # 设置绘图窗口的尺寸
plt.figure(dpi=200, figsize=(10, 6))

 plt.figure(dpi=128, figsize=(10, 6))

1.4　使用Pygal模拟掷骰子

可视化包Pygal来生成可缩放的矢量图形文件。

1.4.1　安装Pygal

1.4.2　Pygal画廊

要了解使用Pygal可创建什么样的图表，请查看图表类型画廊：访问http://www.pygal.org/ ，单击Documentation，再单击Chart types。每个示例都包含源代码，让你知道这些图表是如何生成的。

pygal说明书：http://www.pygal.org/en/stable/index.html

1.4.3　创建Die 类

die.py

# -*- coding: utf-8 -*-
"""
Created on Wed Mar 30 17:28:15 2022

@author: zixiao
"""
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个面"""
        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)

1.4.4　掷骰子

from die import Die

# 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(100):
    result = die.roll()
    results.append(result)
    
print(results)

[2, 2, 2, 1, 4, 3, 5, 3, 5, 3, 2, 3, 4, 6, 2, 2, 5, 1, 2, 4, 5, 3, 3, 2, 6, 6, 5, 3, 4, 5, 5, 6, 6, 5, 5, 1, 1, 4, 2, 3, 2, 2, 4, 3, 3, 5, 4, 2, 6, 1, 2, 3, 3, 1, 3, 2, 4, 2, 4, 5, 3, 5, 2, 3, 1, 1, 3, 2, 6, 1, 2, 3, 2, 2, 2, 6, 4, 1, 1, 1, 6, 3, 6, 4, 6, 3, 3, 2, 1, 3, 3, 4, 5, 1, 6, 4, 1, 5, 4, 5]

1.4.5　分析结果

from die import Die

# 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(100):
    result = die.roll()
    results.append(result)
    
# 分析结果
frequencies = []
for value in range(1, die.num_sides+1):
    frequency = results.count(value)
    frequencies.append(frequency)

print(frequencies)

[16, 18, 15, 19, 19, 13]

1.4.6　绘制直方图

from 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)
    
# 分析结果
frequencies = []
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 = "Results 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')

1.4.7　同时掷两个骰子

from die import Die
import pygal

# 创建一个D6
die1 = Die()
die2 = 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 = die1.roll() + die2.roll()
    results.append(result)
    
# 分析结果
frequencies = []
max_num_sides = die1.num_sides + die1.num_sides
for value in range(2, max_num_sides+1):
    frequency = results.count(value)
    frequencies.append(frequency)

# 对结果进行可视化
hist = pygal.Bar()

hist.title = "Results of rolling two D6 dice 1000 times."
hist.x_labels = ['2', '3', '4', '5','6','7','8',
                 '9','10','11','12']
hist.x_title = "Result"
hist.y_title = "Frequency of Result"

hist.add("D6 + D6", frequencies)
hist.render_to_file('die_visual.svg')

1.4.8　同时掷两个面数不同的骰子

from die import Die
import pygal

# 创建一个D6
die1 = Die()
die2 = Die(10)

# 掷几次骰子，并将结果存储在一个列表中
results = []
for roll_num in range(50000):
    result = die1.roll() + die2.roll()
    results.append(result)
    
# 分析结果
frequencies = []
max_num_sides = die1.num_sides + die2.num_sides
for value in range(2, max_num_sides+1):
    frequency = results.count(value)
    frequencies.append(frequency)

# 对结果进行可视化
hist = pygal.Bar()

hist.title = "Results of rolling D6 D10 dice 50 000 times."
hist.x_labels = ['2', '3', '4', '5','6','7','8',
                 '9','10','11','12','13','14','15','16']
hist.x_title = "Result"
hist.y_title = "Frequency of Result"

hist.add("D6 + D10", frequencies)
hist.render_to_file('die_visual.svg')

2　下载数据

2.1　CSV文件格式

2.1.1　分析CSV文件头

highs_lows.py

import csv

filename = 'data/sitka_weather_07-2014.csv'
with open(filename) as f:
    reader = csv.reader(f)
    header_row = next(reader)
    print(header_row)

模块csv 包含函数next() ，调用它并将阅读器对象传递给它时，它将返回文件中的下一行。

[‘AKDT’, ‘Max TemperatureF’, ‘Mean TemperatureF’, ‘Min TemperatureF’, ‘Max Dew PointF’, ‘MeanDew PointF’, ‘Min DewpointF’, ‘Max Humidity’, ’ Mean Humidity’, ’ Min Humidity’, ’ Max Sea Level PressureIn’, ’ Mean Sea Level PressureIn’, ’ Min Sea Level PressureIn’, ’ Max VisibilityMiles’, ’ Mean VisibilityMiles’, ’ Min VisibilityMiles’, ’ Max Wind SpeedMPH’, ’ Mean Wind SpeedMPH’, ’ Max Gust SpeedMPH’, ‘PrecipitationIn’, ’ CloudCover’, ’ Events’, ’ WindDirDegrees’]

2.1.2　打印文件头及其位置

highs_lows.py

import csv

filename = 'data/sitka_weather_07-2014.csv'
with open(filename) as f:
    reader = csv.reader(f)
    header_row = next(reader)
    
    for index, column_header in enumerate(header_row):
        print(index, column_header)

0 AKDT
1 Max TemperatureF
2 Mean TemperatureF
–snip–
21 Events
22 WindDirDegrees

2.1.3　提取并读取数据

首先读取每天的最高气温：

highs_lows.py

import csv

# 从文件中获取最高气温
filename = 'data/sitka_weather_07-2014.csv'
with open(filename) as f:
    reader = csv.reader(f)
    header_row = next(reader)
    
    hights = []
    for row in reader:
        hights.append(row[1])
        
    print(hights)
        

遍历文件中的各行

下面使用int() 将这些字符串转换为数字，让matplotlib能够读取它们：

highs_lows.py

--snip--
    
    hights = []
    for row in reader:
        hight = int(row[1])
        hights.append(hight)
        
    print(hights)
        

[64, 71, 64, 59, 69, 62, 61, 55, 57, 61, 57, 59, 57, 61, 64, 61, 59, 63, 60, 57, 69, 63, 62, 59, 57, 57, 61, 59, 61, 61, 66]

2.1.4　绘制气温图表

highs_lows.py

import csv
from matplotlib import pyplot as plt

--snip--
# 根据书籍绘制图形
fig = plt.figure(dpi=128, figsize = (10, 6))
plt.plot(highs, c="red")

# 设置图形的格式
plt.title("Daily high temperatures, July 2014", fontsize=24)
plt.xlabel("", fontsize=16)
plt.ylabel("Temperature(F)",fontsize=16)
plt.tick_params(axis="both", which='major', labelsize=16)

plt.show()
        

2.1.5　模块datetime

模块datetime中设置日期和时间格式的实参

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2.1.6　在图表中添加日期

highs_lows.py

import csv
from datetime import datetime
from matplotlib import pyplot as plt
--snip--
    dates, highs = [], []
    for row in reader:
        current_date = datetime.strptime(row[0], "%Y-%m-%d")
        dates.append(current_date)
        
        high = int(row[1])
        highs.append(high)
--snip--
# 设置图形的格式
plt.title("Daily high temperatures, July 2014", fontsize=24)
plt.xlabel("", fontsize=16)
fig.autofmt_xdate()
plt.ylabel("Temperature(F)",fontsize=16)
plt.tick_params(axis="both", which='major', labelsize=16)

plt.show()

我们调用了fig.autofmt_xdate() 来绘制斜的日期标签，以免它们彼此重叠

2.1.7 涵盖更长的时间

import csv
from datetime import datetime
from matplotlib import pyplot as plt

# 从文件中获取最高气温
filename = 'data/sitka_weather_2014.csv'
with open(filename) as f:
    reader = csv.reader(f)
    header_row = next(reader)
    
    dates, highs = [], []
    for row in reader:
        current_date = datetime.strptime(row[0], "%Y-%m-%d")
        dates.append(current_date)
        
        high = int(row[1])
        highs.append(high)
        
# 根据书籍绘制图形
fig = plt.figure(dpi=128, figsize = (10, 6))
plt.plot(dates, highs, c="red")

# 设置图形的格式
plt.title("Daily high temperatures - 2014", fontsize=24)
plt.xlabel("", fontsize=16)
fig.autofmt_xdate()
plt.ylabel("Temperature(F)",fontsize=16)
plt.tick_params(axis="both", which='major', labelsize=16)

plt.show()
        

2.1.8　再绘制一个数据系列

--snip--
    dates, highs, lows = [], [], []
    for row in reader:
        current_date = datetime.strptime(row[0], "%Y-%m-%d")
        dates.append(current_date)
        
        high = int(row[1])
        highs.append(high)
        
        low = int(row[3])
        lows.append(low)
        
# 根据书籍绘制图形
fig = plt.figure(dpi=128, figsize = (10, 6))
plt.plot(dates, highs, c="red")
plt.plot(dates,lows, c="blue")

# 设置图形的格式
plt.title("Daily high and low temperatures - 2014", fontsize=24)
--snip--
        

2.1.9　给图表区域着色

--snip--# 根据书籍绘制图形
fig = plt.figure(dpi=128, figsize = (10, 6))
plt.plot(dates, highs, c="red")
plt.plot(dates,lows, c="blue")
--snip--

2.1.10　错误检查

--snip--
    dates, highs, lows = [], [], []
    for row in reader:
        try:
            current_date = datetime.strptime(row[0], "%Y-%m-%d")
            high = int(row[1])
            low = int(row[3])
        except ValueError:
            print(current_date, "missing data")
        else:
            dates.append(current_date)
            highs.append(high)
            lows.append(low)
        
# 根据书籍绘制图形
--snip--
# 设置图形的格式
plt.title("Daily high and low temperatures - 2014\nDeath value, CA", fontsize=24)
plt.xlabel("", fontsize=16)
--snip--

2014-02-16 00:00:00 missing data

2.2 制作世界人口地图：JSON格式

2.2.2　提取相关的数据

import json

# 将数据加载到一个列表中
filename = 'data/population_data.json'
with open(filename) as f:
    pop_data = json.load(f)

# 打印每个国家2010年的人口数量
for pop_dict in pop_data:
    if pop_dict["Year"] == "2010":
        country_name = pop_dict["Country Name"]
        population = pop_dict['Value']
        print(country_name + ":" + population)
    

Arab World:357868000
Caribbean small states:6880000
East Asia & Pacific (all income levels):2201536674

–snip–

Zambia:12927000
Zimbabwe:12571000

2.2.3　将字符串转换为数字值

# 打印每个国家2010年的人口数量
for pop_dict in pop_data:
    if pop_dict["Year"] == "2010":
        country_name = pop_dict["Country Name"]
        population = int(pop_dict['Value'])
        print(country_name + ":" + str(population))

Python不能直接将包含小数点的字符串’1127437398.85751’ 转换为整数

# 打印每个国家2010年的人口数量
for pop_dict in pop_data:
    if pop_dict["Year"] == "2010":
        country_name = pop_dict["Country Name"]
        population = int(float(pop_dict['Value']))
        print(country_name + ":" + str(population))

Arab World:357868000
Caribbean small states:6880000
East Asia & Pacific (all income levels):2201536674

–snip–

Zambia:12927000
Zimbabwe:12571000

2.2.4　获取两个字母的国别码

from pygal_maps_world.i18n import COUNTRIES

for country_code in sorted(COUNTRIES.keys()):
    print(country_code, COUNTRIES[country_code])

ad Andorra
ae United Arab Emirates
af Afghanistan
–snip–
zm Zambia
zw Zimbabwe

country_codes.py

from pygal_maps_world.i18n import COUNTRIES

def get_country_code(country_name):
    """根据指定的公家，返回pygal使用的两个字母的国别码"""
    for code, name in COUNTRIES.items():
        if name == country_name:
            return code
    # 如果没有找到指定的国家，就返回None
    return None

print(get_country_code('Andorra'))
print(get_country_code('United Arab Emirates'))
print(get_country_code('Afghanistan'))

ad
ae
af

world_population.py

import json
from country_codes import get_country_code

--snip--
# 打印每个国家2010年的人口数量
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:
            print(code + ":" + str(population))
        else:
            print('ERROR - ' + country_name)

ERROR - Arab World
ERROR - Caribbean small states
ERROR - East Asia & Pacific (all income levels)
–snip–
af:34385000
al:3205000
dz:35468000
ERROR - American Samoa
ad:84864
–snip–
zm:12927000
zw:12571000

2.2.5　制作世界地图

import pygal_maps_world.maps
wm = pygal_maps_world.maps.World()
wm.title = 'North, Central, and South America'

wm.add('North America', ['ca','mx', 'us'])
wm.add('Central America', ['bz', 'cr', 'gt', 'hn', 'ni', 'pa', 'sv'])
wm.add('South America', ['ar','bo','br','cl','co','ec',
                           'gf', 'gy', 'pe','py','sr','uy','ve'])
wm.render_to_file("americas.svg")

如果图片打开错误，可试试使用谷歌浏览器打开

2.2.6　在世界地图上呈现数字数据

na_populations.py

import pygal_maps_world.maps

wm = pygal_maps_world.maps.World()
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')

2.2.7　绘制完整的世界人口地图

world_population.py

import json
import pygal_maps_world.maps
from country_codes import get_country_code

# 将数据加载到一个列表中
filename = 'data/population_data.json'
with open(filename) as f:
    pop_data = json.load(f)

# 打印每个国家2010年的人口数量
cc_populations = {} ## 构建一个空的字典
for pop_dict in pop_data:
    if pop_dict['Year'] == '2010':
        country = pop_dict['Country Name']
        population = int(float(pop_dict['Value']))
        code = get_country_code(country)
        if code:
            cc_populations[code] = population

wm = pygal_maps_world.maps.World()
wm.title = "World Population in 2010, by Country"
wm.add('2010', cc_populations)

wm.render_to_file('world_population.svg')

2.2.8　根据人口数量将国家分组

根据人口数量分成三组

--snip--
# 将数据加载到一个列表中
--snip--
# 打印每个国家2010年的人口数量
--snip--
# 根据人口数量将所有的国家分成三组
cc_pops_1, cc_pops_2, cc_pops_3 = {},{},{}
for cc, pop in cc_populations.items():
    if pop < 10000000:
        cc_pops_1[cc] = pop
    elif pop < 1000000000:
        cc_pops_2[cc] = pop
    else:
        cc_pops_3[cc] = pop
        
# 看看每组分别包含多少国家
print(len(cc_pops_1), len(cc_pops_2), len(cc_pops_3))

wm = pygal_maps_world.maps.World()
wm.title = "World Population in 2010, by Country"
wm.add('2010', cc_pops_1)
wm.add('2010', cc_pops_2)
wm.add('2010', cc_pops_3)

wm.render_to_file('world_population.svg')

85 69 2

2.2.9　使用Pygal设置世界地图的样式

world_population.py

--snip--
from pygal.style import RotateStyle
--snip--
wm_style = RotateStyle('#336699')
wm = pygal_maps_world.maps.World(style=wm_style)
--snip--

2.2.10　加亮颜色主题

--snip--
from pygal.style import RotateStyle as RS, LightColorizedStyle as LCS
--snip--
wm_style = RS('#336699',base_style=LCS)
wm = pygal_maps_world.maps.World(style=wm_style)
wm.title = "World Population in 2010, by Country"
wm.add('2010', cc_pops_1)
wm.add('2010', cc_pops_2)
wm.add('2010', cc_pops_3)

wm.render_to_file('world_population.svg')

3　使用API

3.1　使用Web API

3.1.1　Git和GitHub

3.1.2　使用API调用请求数据

https://api.github.com/search/repositories?q=language:python&sort=stars

3.1.3　安装requests

$ pip install --user requests

3.1.4　处理API响应

import requests

# 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("Status code:", r.status_code)

# 将API响应存储在一个变量中
response_dict = r.json()

# 处理结果
print(response_dict.keys())

Status code: 200
dict_keys([‘total_count’, ‘incomplete_results’, ‘items’])

状态码为200

3.1.5　处理响应字典

import requests

# 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("Status code:", r.status_code)

# 将API响应存储在一个变量中
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]
print("\nKeys:", len(repo_dict))
for key in sorted(repo_dict.keys()):
    print(key)

Status code: 200
Total repositories: 8599845
Repositories returned: 30

Keys: 78
allow_forking
archive_url
–snip–
watchers
watchers_count

python_repos.py

--snip--
# 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]
print('Name:', repo_dict['name'])
print('Owner:', repo_dict['owner']['login'])
print('Stars:', repo_dict['stargazers_count'])
print('Repository:',repo_dict['html_url'] )
print("Created:", repo_dict["created_at"])
print('Updated:',repo_dict['updated_at'])
print('Description:', repo_dict['description'])

Status code: 200
Total repositories: 8966328
Repositories returned: 30
Name: public-apis
Owner: public-apis
Stars: 187542
Repository: https://github.com/public-apis/public-apis
Created: 2016-03-20T23:49:42Z
Updated: 2022-04-04T06:01:17Z
Description: A collective list of free APIs

3.1.6　概述最受欢迎的仓库

import requests

# 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("Status code:", r.status_code)

# 将API响应存储在一个变量中
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))

print('\nSelected information about each repository:')
for repo_dict in repo_dicts:
    print('\nName:', repo_dict['name'])
    print('Owner:', repo_dict['owner']['login'])
    print('Stars:', repo_dict['stargazers_count'])
    print('Repository:',repo_dict['html_url'])
    print('Description:', repo_dict['description'])

Status code: 200
Total repositories: 8799051
Repositories returned: 30

Selected information about each repository:

Name: public-apis
Owner: public-apis
Stars: 187544
Repository: https://github.com/public-apis/public-apis
Description: A collective list of free APIs

Name: system-design-primer
Owner: donnemartin
Stars: 170436
Repository: https://github.com/donnemartin/system-design-primer
Description: Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

3.1.7　监视API的速率限制

3.2　使用Pygal可视化仓库

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("Status code:", r.status_code)

# 将API响应存储在一个变量中
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']

names, stars = [], []
for repo_dict in repo_dicts:
    names.append(repo_dict['name'])
    stars.append(repo_dict['stargazers_count'])
    
# 可视化
my_style = LS('#333366', base_style=LCS)
chart = pygal.Bar(style=my_style,x_label_rotation=45,
                  show_legend=False)
chart.title = 'Most-Starred Python Projects on GitHub'
chart.x_labels = names

chart.add(" ",stars)
chart.render_to_file('python_repos.svg')

x_label_rotation=45 让标签绕x轴旋转45度；chart.add(" ",stars)标签设为空字符

my_style = LS(‘#333366’, base_style=LCS) 可以更改颜色

例如将颜色改为**海棠红**my_style = LS(‘#f03752’, base_style=LCS)

3.2.1　改进Pygal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("Status code:", r.status_code)

# 将API响应存储在一个变量中
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']

names, stars = [], []
for repo_dict in repo_dicts:
    names.append(repo_dict['name'])
    stars.append(repo_dict['stargazers_count'])
    
# 可视化
my_style = LS('#333366', base_style=LCS) 

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 # 设置主标签大小
my_config.truncate_label = 15 # 将较长的项目名缩短为15个字符
my_config.show_y_guides = False # 以隐藏图表中的水平线
my_config.width = 1000 # 自定义宽度

chart = pygal.Bar(my_config, style=my_style)
chart.title = 'Most-Starred Python Projects on GitHub'
chart.x_labels = names

chart.add(" ",stars)
chart.render_to_file('python_repos.svg')

关于无法区分主副标签的问题参考https://blog.csdn.net/weixin_43091089/article/details/99792892

my_style = LS(‘#333366’, major_label_font_size= 18, base_style=LCS)

或者：my_style.major_label_font_size= 18

3.2.2　添加自定义工具提示

在Pygal中，将鼠标指向条形将显示它表示的信息，这通常称为工具提示 。

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

my_style = LS('#333366', base_style=LCS)
chart = pygal.Bar(style=my_style, x_label_rotation = 45,
                  show_legend = False)

chart.title = 'Python Projects'
chart.x_labels = ["httpie", 'django', 'flask']

plot_dicts = [
    {'value':16101, 'label':'Description of httpie.'},
    {'value':15028, 'label':'Description of django.'},
    {'value':14798, 'label':'Description of flask.'},
    ]

chart.add('', plot_dicts)
chart.render_to_file('bar_descriptions.svg')

3.2.3　根据数据绘图

--snip--
# 探索有关仓库的信息
repo_dicts = response_dict['items']

names, plot_dicts = [], []
for repo_dict in repo_dicts:
    names.append(repo_dict['name'])
    plot_dict = {'value': repo_dict['stargazers_count'],
                 'label': repo_dict['description'],
                 }
    plot_dicts.append(plot_dict)
    
# 可视化
--snip--
chart.add(" ",plot_dicts)
chart.render_to_file('python_repos.svg')

3.2.4　在图表中添加可单击的链接

--snip--
# 探索有关仓库的信息
repo_dicts = response_dict['items']

names, plot_dicts = [], []
for repo_dict in repo_dicts:
    names.append(repo_dict['name'])
    plot_dict = {'value': repo_dict['stargazers_count'],
                 'label': repo_dict['description'],
                 'xlink': repo_dict['html_url'],
                 }
    plot_dicts.append(plot_dict)
    
# 可视化
--snip--

3.3　Hacker News API

Hacker News

https://hacker-news.firebaseio.com/v0/item/9884165.json

import requests
from operator import itemgetter

# 执行API调用并存储响应
url = 'https://hacker-news.firebaseio.com/v0/topstories.json'
r = requests.get(url)
print("Status code:", r.status_code)

# 处理有关没骗文章的信息
submission_ids = r.json()
submission_dicts = []
for submission_id in submission_ids[:30]:
    # 对于每篇文章，都执行一个API调用
    url = ('https://hacker-news.firebaseio.com/v0/item/' +
            str(submission_id) + '.json')
    submission_r = requests.get(url)
    print(submission_r.status_code)
    response_dict = submission_r.json()
    
    submission_dict = {
        'title':response_dict['title'],
        'link': 'http://news.ycombinator.com/item?id=' + 
        str(submission_id),
        'comments':response_dict.get('descendants', 0)
        }
    submission_dicts.append(submission_dict)

submission_dicts = sorted(submission_dicts,
                          key=itemgetter('comments'),
                          reverse=True)

for submission_dict in submission_dicts:
    print("\nTitle:",submission_dict['title'])
    print("Discussion link:", submission_dict['link'])
    print('Comments:', submission_dict['comments'])