Python Data Visualization Practise

Python Data Visualization Practise

import json
import requests  # 3rd party
import jsonpath
from pyecharts.charts import Map
from pyecharts import options as opts
from src.covid19_data import nameMap

url = 'https://api.inews.qq.com/newsqa/v1/automation/foreign/country/ranklist'
resp = requests.post(url).text
data = json.loads(resp)
# get contry names and confirmed cases $:outer {}, ..: fuzzy match + key
name = jsonpath.jsonpath(data, "$..name")  # ['美国', '巴西', '印度', '俄罗斯', ... ]
confirm = jsonpath.jsonpath(data, "$..confirm")
pairs = list(zip(name, confirm))  # [('美国', 5321520), ('巴西', 3109630), ('印度', 2329638), ...]

# 2. data visualization map plotting - size,title,color,data
map_ = Map(opts.InitOpts(width='1200px', height='600px')) \
    .add(series_name="Global Covid-19 Confirmed Cases",
         data_pair=pairs,  # data entry - country : confirmed
         maptype="world",  # world map
         name_map=nameMap,  # country name mapping
         is_map_symbol_show=False)  # 不显示标记点

# set series options
map_.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
map_.set_global_opts(title_opts=opts.TitleOpts(title="Global Covid-19 Pandemic (Chinese)"),
                     visualmap_opts=opts.VisualMapOpts(max_=6000000, is_piecewise=True))

map_.render('../result/covid19.html')
nameMap = {
    'Singapore Rep.': '新加坡',
    'Dominican Rep.': '多米尼加',
    'Palestine': '巴勒斯坦',
    'Bahamas': '巴哈马',
    'Timor-Leste': '东帝汶',
    'Afghanistan': '阿富汗',
    'Guinea-Bissau': '几内亚比绍',
    "Côte d'Ivoire": '科特迪瓦',
    'Siachen Glacier': '锡亚琴冰川',
    "Br. Indian Ocean Ter.": '英属印度洋领土',
    'Angola': '安哥拉',
    'Albania': '阿尔巴尼亚',
    'United Arab Emirates': '阿联酋',
    'Argentina': '阿根廷',
    'Armenia': '亚美尼亚',
    'French Southern and Antarctic Lands': '法属南半球和南极领地',
    'Australia': '澳大利亚',
    'Austria': '奥地利',
    'Azerbaijan': '阿塞拜疆',
    'Burundi': '布隆迪',
    'Belgium': '比利时',
    'Benin': '贝宁',
    'Burkina Faso': '布基纳法索',
    'Bangladesh': '孟加拉国',
    'Bulgaria': '保加利亚',
    'The Bahamas': '巴哈马',
    'Bosnia and Herz.': '波斯尼亚和黑塞哥维那',
    'Belarus': '白俄罗斯',
    'Belize': '伯利兹',
    'Bermuda': '百慕大',
    'Bolivia': '玻利维亚',
    'Brazil': '巴西',
    'Brunei': '文莱',
    'Bhutan': '不丹',
    'Botswana': '博茨瓦纳',
    'Central African Rep.': '中非共和国',
    'Canada': '加拿大',
    'Switzerland': '瑞士',
    'Chile': '智利',
    'China': '中国',
    'Ivory Coast': '象牙海岸',
    'Cameroon': '喀麦隆',
    'Dem. Rep. Congo': '刚果(金)',
    'Congo': '刚果(布)',
    'Colombia': '哥伦比亚',
    'Costa Rica': '哥斯达黎加',
    'Cuba': '古巴',
    'N. Cyprus': '北塞浦路斯',
    'Cyprus': '塞浦路斯',
    'Czech Rep.': '捷克',
    'Germany': '德国',
    'Djibouti': '吉布提',
    'Denmark': '丹麦',
    'Algeria': '阿尔及利亚',
    'Ecuador': '厄瓜多尔',
    'Egypt': '埃及',
    'Eritrea': '厄立特里亚',
    'Spain': '西班牙',
    'Estonia': '爱沙尼亚',
    'Ethiopia': '埃塞俄比亚',
    'Finland': '芬兰',
    'Fiji': '斐',
    'Falkland Islands': '福克兰群岛',
    'France': '法国',
    'Gabon': '加蓬',
    'United Kingdom': '英国',
    'Georgia': '格鲁吉亚',
    'Ghana': '加纳',
    'Guinea': '几内亚',
    'Gambia': '冈比亚',
    'Guinea Bissau': '几内亚比绍',
    'Eq. Guinea': '赤道几内亚',
    'Greece': '希腊',
    'Greenland': '格陵兰',
    'Guatemala': '危地马拉',
    'French Guiana': '法属圭亚那',
    'Guyana': '圭亚那',
    'Honduras': '洪都拉斯',
    'Croatia': '克罗地亚',
    'Haiti': '海地',
    'Hungary': '匈牙利',
    'Indonesia': '印度尼西亚',
    'India': '印度',
    'Ireland': '爱尔兰',
    'Iran': '伊朗',
    'Iraq': '伊拉克',
    'Iceland': '冰岛',
    'Israel': '以色列',
    'Italy': '意大利',
    'Jamaica': '牙买加',
    'Jordan': '约旦',
    'Japan': '日本',
    'Kazakhstan': '哈萨克斯坦',
    'Kenya': '肯尼亚',
    'Kyrgyzstan': '吉尔吉斯斯坦',
    'Cambodia': '柬埔寨',
    'Korea': '韩国',
    'Kosovo': '科索沃',
    'Kuwait': '科威特',
    'Lao PDR': '老挝',
    'Lebanon': '黎巴嫩',
    'Liberia': '利比里亚',
    'Libya': '利比亚',
    'Sri Lanka': '斯里兰卡',
    'Lesotho': '莱索托',
    'Lithuania': '立陶宛',
    'Luxembourg': '卢森堡',
    'Latvia': '拉脱维亚',
    'Morocco': '摩洛哥',
    'Moldova': '摩尔多瓦',
    'Madagascar': '马达加斯加',
    'Mexico': '墨西哥',
    'Macedonia': '马其顿',
    'Mali': '马里',
    'Myanmar': '缅甸',
    'Montenegro': '黑山',
    'Mongolia': '蒙古',
    'Mozambique': '莫桑比克',
    'Mauritania': '毛里塔尼亚',
    'Malawi': '马拉维',
    'Malaysia': '马来西亚',
    'Namibia': '纳米比亚',
    'New Caledonia': '新喀里多尼亚',
    'Niger': '尼日尔',
    'Nigeria': '尼日利亚',
    'Nicaragua': '尼加拉瓜',
    'Netherlands': '荷兰',
    'Norway': '挪威',
    'Nepal': '尼泊尔',
    'New Zealand': '新西兰',
    'Oman': '阿曼',
    'Pakistan': '巴基斯坦',
    'Panama': '巴拿马',
    'Peru': '秘鲁',
    'Philippines': '菲律宾',
    'Papua New Guinea': '巴布亚新几内亚',
    'Poland': '波兰',
    'Puerto Rico': '波多黎各',
    'Dem. Rep. Korea': '朝鲜',
    'Portugal': '葡萄牙',
    'Paraguay': '巴拉圭',
    'Qatar': '卡塔尔',
    'Romania': '罗马尼亚',
    'Russia': '俄罗斯',
    'Rwanda': '卢旺达',
    'W. Sahara': '西撒哈拉',
    'Saudi Arabia': '沙特阿拉伯',
    'Sudan': '苏丹',
    'S. Sudan': '南苏丹',
    'Senegal': '塞内加尔',
    'Solomon Is.': '所罗门群岛',
    'Sierra Leone': '塞拉利昂',
    'El Salvador': '萨尔瓦多',
    'Somaliland': '索马里兰',
    'Somalia': '索马里',
    'Serbia': '塞尔维亚',
    'Suriname': '苏里南',
    'Slovakia': '斯洛伐克',
    'Slovenia': '斯洛文尼亚',
    'Sweden': '瑞典',
    'Swaziland': '斯威士兰',
    'Syria': '叙利亚',
    'Chad': '乍得',
    'Togo': '多哥',
    'Thailand': '泰国',
    'Tajikistan': '塔吉克斯坦',
    'Turkmenistan': '土库曼斯坦',
    'East Timor': '东帝汶',
    'Trinidad and Tobago': '特里尼达和多巴哥',
    'Tunisia': '突尼斯',
    'Turkey': '土耳其',
    'Tanzania': '坦桑尼亚',
    'Uganda': '乌干达',
    'Ukraine': '乌克兰',
    'Uruguay': '乌拉圭',
    'United States': '美国',
    'Uzbekistan': '乌兹别克斯坦',
    'Venezuela': '委内瑞拉',
    'Vietnam': '越南',
    'Vanuatu': '瓦努阿图',
    'West Bank': '西岸',
    'Yemen': '也门',
    'South Africa': '南非',
    'Zambia': '赞比亚',
    'Zimbabwe': '津巴布韦'
}
import pandas
from pyecharts.charts import Pie
from pyecharts import options as opts

# data
# source: https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html
provinces = ['NL', 'PE', 'NS', 'NB', 'QC', 'ON', 'MB', 'SK', 'AB', 'BC', 'YT', 'NT', 'NU']
num = [268, 41, 1071, 178, 60813, 40289, 578, 1484, 11893, 4196, 15, 5, 0]
color_series = ['#f00', '#f50', '#ea0', '#aa0', '#a50', '#5f0', '#0f0', '#0f5', '#5f7', '#0ff', '#0af', '#07f', '#00f']

# data frame
df = pandas.DataFrame({'provinces': provinces, 'num': num})

# descending order
df.sort_values(by='num', ascending=False, inplace=True)

# data list
v = df['provinces'].values.tolist()
d = df['num'].values.tolist()

# get pie instance
pie = Pie(init_opts=opts.InitOpts(width='1350px', height='750px'))

# set color
pie.set_colors(color_series)

# pie chart settings
pie.add("", [list(z) for z in zip(v, d)],
         radius=["50%", "85%"],
         center=["35%", "55%"],
         rosetype="area"
         )

# set global options
pie.set_global_opts(
    title_opts=opts.TitleOpts(title='2020-08-12 Covid-19 Canada'),
                     legend_opts=opts.LegendOpts(is_show=True),
                     toolbox_opts=opts.ToolboxOpts()
                     )

# set series options
pie.set_series_opts(
    label_opts=opts.LabelOpts(is_show=True, position="inside", font_size=12,
                              formatter="{b}\n{c}", font_style="normal",
                              font_weight="normal", font_family="Microsoft YaHei"
                              ),
)
# render html
pie.render('./result/nightingalerosediagram.html')
# 先导入Jupyter notebook渲染插件
from pyecharts.globals import CurrentConfig, NotebookType

CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_NOTEBOOK
# 注意区分大小写,如果使用Jupyter Lab 则改为如下代码
# CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB

# 导入pyecharts,并显示版本
import pyecharts

print(pyecharts.__version__)

# 绘图测试
from pyecharts.charts import Bar
from pyecharts import options as opts

# from pyecharts.render import make_snapshot
bar = (
    Bar()
    .add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"])
    .add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105])
    .add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49])
    .set_global_opts(title_opts=opts.TitleOpts(title="某商场销售情况"))
)
bar.render_notebook()  # 在Notebook中进行渲染图像
bar.render("result/barchart.html")  # 在本地生成静态网页
# make_snapshot(snapshot, bar.render(), "result/bar.png")#在本地生成图表截图
# from  pyecharts import Line
from pyecharts.charts import Line

x = ['2018-{:0>2d}'.format(s) for s in range(1,13)]
y1 = [5,10,26,30,35,30,20,26,40,46,40,50]
y2 = [8,20,24,36,40,36,40,45,50,53,48,58]

# line = Line(title="月销售总额", width=600, height=420)
line = Line()
line.page_title = "商家折线图"
line.set_colors(['blue', 'green'])

line.add_xaxis(x)
line.add_yaxis("商家A", y1)

line.add_xaxis(x)
line.add_yaxis("商家B", y2)

line.render('../result/linechart.html')
line
from pyecharts.charts import Line3D

import math
_data = []
for t in range(0, 25000):
    _t = t / 1000
    x = (1 + 0.25 * math.cos(75 * _t)) * math.cos(_t)
    y = (1 + 0.25 * math.cos(75 * _t)) * math.sin(_t)
    z = _t + 2.0 * math.sin(75 * _t)
    _data.append([x, y, z])
range_color = ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
               '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
line3D = Line3D()
line3D.page_title = '3D 折线图'
line3D.add("", _data)

line3D.render('../result/3dline.html')

Author: Albert

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