Python编程实现超炫动态排序图

# 导入库函数
import random
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.animation as animation
from IPython.display import HTML
import matplotlib
#防止动漫内存太大，报错
matplotlib.rcParams['animation.embed_limit'] = 2**128

#pandas读取数据，且去列名分别为name,group,year和value的值；
url = 'https://gist.githubusercontent.com/johnburnmurdoch/4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'
df = pd.read_csv(url, usecols=['name', 'group', 'year', 'value'])
df.head()

#导入random函数，randomcolor用于生成颜色代码
# randomcolor生成颜色代码原理，
# 【1-9/A-F】15个数字随机组合成6位字符串前面再加上一个“#”号键
import random
def randomcolor():
    colorlist = ['1','2','3','4','5','6','7','8','9','A','B','C','D','E','F']
    color =''
    for i in range(6):
        color += random.choice(colorlist)
    return '#'+ color

#对地区列表进行去重，分类；
area_list1 = set(df['name'])
# color_list用于存放随机生成颜色代码个数
# 因为后面区域个数 要与颜色个数保持一致，这里用了len函数；
color_list =[]
for i in range(len(area_list1)):
    str_1 = randomcolor()
    color_list.append(str_1)
    str_1 = randomcolor()    
#area_list转化为列表
area_list_1 = [i for i in area_list1]
print(color_list)
print(area_list_1)

#colors表示 所在城市：颜色 一一对应字典形式；
colors =dict(zip(area_list_1,color_list))
print(colors)
#group_lk为 城市：所在区域 --对应字典形式；
group_lk = df.set_index('name')['group'].to_dict()
print(group_lk)

# 用plt加理图表，figsize表示图标长宽，ax表示标签
fig, ax = plt.subplots(figsize=(15, 8))
#dras_barchart生成current_year这一年各城市人口基本情况；
def draw_barchart(current_year):   
    #dff对year==current_year的行，以value从升序方式排序，取后十名也就是最大值；
    dff = df[df['year'].eq(current_year)].sort_values(by='value',ascending = True).tail(12)
    # 所有坐标、标签清除
    ax.clear()
    #显示颜色、城市名字
    ax.barh(dff['name'],dff['value'],color = [colors[x] for x in dff['name']])
    dx = dff['value'].max()/200    
    #ax.text(x,y,name,font,va,ha)
    # x,y表示位置；
    # name表示显示文本；
    # va,ba分别表示水平位置，垂直放置位置；
    for i ,(value,name) in enumerate(zip(dff['value'], dff['name'])):
        ax.text(value-dx,i,name,size=14,weight=600,ha ='right',va = 'bottom')
        ax.text(value-dx,i-.25,group_lk[name],size = 10,color ='#444444',ha ='right',va = 'baseline')
        ax.text(value+dx,i ,f'{value:,.0f}',size = 14,ha = 'left',va ='center')    
    #ax.transAxes表示轴坐标系，(1,0.4)表示放置位置
    ax.text(1,0.4,current_year,transform = ax.transAxes,color ='#777777',size = 46,ha ='right',weight=800) 
    ax.text(0,1.06,'Population (throusands)',transform = ax.transAxes,size=12,color='#777777')    
    #set_major_formatter表示刻度尺格式；
    ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.0f}'))
    ax.xaxis.set_ticks_position('top')
    ax.tick_params(axis='x',colors='#777777',labelsize=12)
    ax.set_yticks([])
    #margins表示自动缩放余额；
    ax.margins(0,0.01)
    # 设置后面的网格
    ax.grid(which='major',axis='x',linestyle='-')
    #刻度线和网格线是在图标上方还是下方，True为下方
    ax.set_axisbelow(True)
    ax.text(0,1.15,'The most population cities in the word from 1500 to 2018',
           transform=ax.transAxes,size=24,weight=600,ha='left',va='top')
    ax.text(1,0,'by@zeroing1',transform = ax.transAxes,color ='#777777',ha = 'right',
           bbox = dict(facecolor='white',alpha = 0.8,edgecolor='white'))
    #取消图表周围的方框显示
    plt.box(False)

#绘制2018年各城市人口情况
draw_barchart(2018)

#将原来的静态图拼接成动画
fig, ax = plt.subplots(figsize=(15, 8))
animator = animation.FuncAnimation(fig, draw_barchart, frames=range(1500, 2019))
#保存到jshtml
HTML(animator.to_jshtml())

#生成video，并保存至指定文件夹中
animator.to_html5_video()
animator.save('E:/ceshi/country_populations1.mp4')