1. Introduction and installation of Pyecharts
1 Introduction
Echarts is a data visualization open sourced by Baidu. It has been recognized by many developers for its good interactivity and exquisite chart design. Python is an expressive language and is very suitable for data processing. When data analysis meets data visualization, pyecharts was born.
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Simple API design, silky smooth use, supports chain calls
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Contains 30+ common charts, everything you need
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Supports mainstream Notebook environments, Jupyter Notebook and JupyterLab
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Can be easily integrated into mainstream web frameworks such as Flask, Sanic, and Django
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Highly flexible configuration items can easily match beautiful charts
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Detailed documentation and examples help developers get started with projects faster
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Up to 400+ map files, and supports native Baidu maps, providing strong support for geographic data visualization
There is incompatibility between pyecharts version v0.5.x and v1. v1 is a brand new version and the syntax is very different.
2. Installation
Install pyecharts
pip install pyecharts -i http://pypi.douban.com/simple --trusted-host pypi.douban.com
import pyecharts
print(pyecharts.__version__) # 查看pyecharts版本
Install relevant map expansion packs
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple echarts-countries-pypkg # 全球国家地图
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple echarts-china-provinces-pypkg # 中国省级地图
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple echarts-china-cities-pypkg # 中国市级地图
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple echarts-china-counties-pypkg # 中国县区级地图
2. Draw geographical charts
1. World Map—Data Visualization
Using the data in Starbucks.csv, first calculate the number of stores corresponding to each country (Country), and then use a world map to represent the global distribution of Starbucks stores.
import pandas as pd
from pyecharts.charts import Map
from pyecharts import options as opts
from pyecharts.globals import ThemeType, CurrentConfig
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
# 用pandas读取csv文件里的数据
df = pd.read_csv("Starbucks.csv")['Country']
data = df.value_counts()
datas = [(i, int(j)) for i, j in zip(data.index, data.values)]
# 实例化一个Map对象
map_ = Map(init_opts=opts.InitOpts(theme=ThemeType.PURPLE_PASSION))
# 世界地图
map_.add("门店数量", data_pair=datas, maptype="world")
map_.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) # 不显示label
map_.set_global_opts(
title_opts=opts.TitleOpts(title="星巴克门店数量在全球分布", pos_left='40%', pos_top='10'), # 调整title位置
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(max_=13608, min_=1, is_piecewise=True,
pieces=[{
"max": 9, "min": 1, "label": "1-9", "color": "#00FFFF"}, # 分段 添加图例注释和颜色
{
"max": 99, "min": 10, "label": "10-99", "color": "#A52A2A"},
{
"max": 499, "min": 100, "label": "100-499", "color": "#0000FF "},
{
"max": 999, "min": 500, "label": "500-999", "color": "#FF00FF"},
{
"max": 2000, "min": 1000, "label": "1000-2000", "color": "#228B22"},
{
"max": 3000, "min": 2000, "label": "2000-3000", "color": "#FF0000"},
{
"max": 20000, "min": 10000, "label": ">=10000", "color": "#FFD700"}
])
)
# 渲染在网页上 有交互性
map_.render('星巴克门店在全球的分布.html')
The running effect is as follows:
2. Country map—data visualization
Ripple scatter plot
Using the data in china.csv, first calculate the number of stores corresponding to each city (City), and then use the Geo module in the pyecharts package to draw a ripple scatter map of the distribution of Starbucks stores in China.
import pandas as pd
from pyecharts.globals import ThemeType, CurrentConfig, GeoType
from pyecharts import options as opts
from pyecharts.charts import Geo
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
# pandas读取csv文件数据
df = pd.read_csv("china.csv")['City']
data = df.value_counts()
datas = [(i, int(j)) for i, j in zip(data.index, data.values)]
print(datas)
geo = Geo(init_opts=opts.InitOpts(width='1000px', height='600px', theme=ThemeType.DARK))
geo.add_schema(maptype='china', label_opts=opts.LabelOpts(is_show=True)) # 显示label 省名
geo.add('门店数量', data_pair=datas, type_=GeoType.EFFECT_SCATTER, symbol_size=8)
geo.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
geo.set_global_opts(title_opts=opts.TitleOpts(title='星巴克门店在中国的分布'),
visualmap_opts=opts.VisualMapOpts(max_=550, is_piecewise=True,
pieces=[{
"max": 50, "min": 0, "label": "0-50", "color": "#708090"}, # 分段 添加图例注释 和颜色
{
"max": 100, "min": 51, "label": "51-100", "color": "#00FFFF"},
{
"max": 200, "min": 101, "label": "101-200", "color": "#00008B"},
{
"max": 300, "min": 201, "label": "201-300", "color": "#8B008B"},
{
"max": 600, "min": 500, "label": "500-600", "color": "#FF0000"},
])
)
geo.render("星巴克门店在中国的分布.html")
The running effect is as follows:
Dynamic trajectory map
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType, SymbolType, CurrentConfig
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
# 链式调用
c = (
Geo()
.add_schema(
maptype="china",
itemstyle_opts=opts.ItemStyleOpts(color="#323c48", border_color="#111"),
label_opts=opts.LabelOpts(is_show=True)
)
.add(
"",
[("广州", 55), ("北京", 66), ("杭州", 77), ("重庆", 88), ('成都', 100), ('海口', 80)],
type_=ChartType.EFFECT_SCATTER,
color="white",
)
.add(
"",
[("广州", "上海"), ("广州", "北京"), ("广州", "杭州"), ("广州", "重庆"),
('成都', '海口'), ('海口', '北京'), ('海口', '重庆'), ('重庆', '上海')
],
type_=ChartType.LINES,
effect_opts=opts.EffectOpts(
symbol=SymbolType.ARROW, symbol_size=6, color="blue"
),
linestyle_opts=opts.LineStyleOpts(curve=0.2),
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title="动态轨迹图"))
.render("geo_lines_background.html")
)
The running effect is as follows:
3. Province and city maps—data visualization
heat map
code show as below:
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.faker import Faker
from pyecharts.globals import GeoType, CurrentConfig
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
c = (
Geo()
.add_schema(maptype="广东", label_opts=opts.LabelOpts(is_show=True))
.add(
"热力图",
[list(z) for z in zip(Faker.guangdong_city, Faker.values())],
type_=GeoType.HEATMAP,
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(), title_opts=opts.TitleOpts(title="Geo-广东地图")
)
.render("geo_guangdong.html")
)
The running effect is as follows:
Add addresses, latitude and longitude data in batches on the map, and visualize geographic data
code show as below:
import pandas as pd # 导入数据分析模块
from pyecharts.charts import Geo # 导入地理信息处理模块
from pyecharts import options as opts # 配置
from pyecharts.globals import GeoType, CurrentConfig, ThemeType
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
df = pd.read_excel("hotel.xlsx")
# 获取 地点 经纬度信息
geo_sight_coord = {
df.iloc[i]['酒店地址']: [df.iloc[i]['经度'], df.iloc[i]['纬度']] for i in range(len(df))}
data = [(df['酒店地址'][j], f"{int(df['最低价'][j])}元(最低价)") for j in range(len(df))]
# print(data)
# print(geo_sight_coord)
g = Geo(init_opts=opts.InitOpts(theme=ThemeType.PURPLE_PASSION, width="1000px", height="600px"))
g.add_schema(maptype="北京")
for k, v in list(geo_sight_coord.items()):
# 添加地址、经纬度数据
g.add_coordinate(k, v[0], v[1])
# 涟漪散点图
g.add("", data_pair=data, type_=GeoType.EFFECT_SCATTER, symbol_size=6)
g.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
g.set_global_opts(title_opts=opts.TitleOpts(title="北京-酒店地址分布"))
g.render("酒店地址分布.html")
The running effect is as follows:
3. Column chart
code show as below:
from pyecharts.charts import Bar
from pyecharts.faker import Faker
from pyecharts.globals import ThemeType, CurrentConfig
from pyecharts import options as opts
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
# 链式调用
c = (
Bar(
init_opts=opts.InitOpts( # 初始配置项
theme=ThemeType.MACARONS,
animation_opts=opts.AnimationOpts(
animation_delay=1000, animation_easing="cubicOut" # 初始动画延迟和缓动效果
))
)
.add_xaxis(xaxis_data=Faker.choose()) # x轴
.add_yaxis(series_name="商家A", yaxis_data=Faker.values()) # y轴
.add_yaxis(series_name="商家B", yaxis_data=Faker.values()) # y轴
.set_global_opts(
title_opts=opts.TitleOpts(title='标题', subtitle='副标题', # 标题配置和调整位置
title_textstyle_opts=opts.TextStyleOpts(
font_family='SimHei', font_size=25, font_weight='bold', color='red',
), pos_left="90%", pos_top="10",
),
xaxis_opts=opts.AxisOpts(name='x轴名称', axislabel_opts=opts.LabelOpts(rotate=45)), # 设置x名称和Label rotate解决标签名字过长使用
yaxis_opts=opts.AxisOpts(name='y轴名称'),
)
.render("bar_001.html")
)
The running effect is as follows:
code show as below:
import pandas as pd
import collections
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.globals import ThemeType, CurrentConfig
import random
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
df = pd.read_excel("hotel.xlsx")
area = list(df['酒店地址'])
area_list = []
for i in area:
_index = i.find("区")
# 字符串切片得到行政区名
i = i[:_index + 1]
area_list.append(i)
area_count = collections.Counter(area_list)
area_dic = dict(area_count)
# 两个列表对应 行政区 对应的酒店数量
area = [x for x in list(area_dic.keys())][0:10]
nums = [y for y in list(area_dic.values())][:10]
# 定制风格
bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.MACARONS))
colors = ['red', '#0000CD', '#000000', '#008000', '#FF1493', '#FFD700', '#FF4500', '#00FA9A', '#191970', '#9932CC']
random.shuffle(colors)
# 配置y轴数据 Baritem
y = []
for i in range(10):
y.append(
opts.BarItem(
value=nums[i],
itemstyle_opts=opts.ItemStyleOpts(color=colors[i]) # 设置每根柱子的颜色
)
)
bar.add_xaxis(xaxis_data=area)
bar.add_yaxis("酒店数量", yaxis_data=y)
bar.set_global_opts(xaxis_opts=opts.AxisOpts(
name='行政区',
axislabel_opts=opts.LabelOpts(rotate=45)
),
yaxis_opts=opts.AxisOpts(
name='酒店数量', min_=0, max_=330, # y轴刻度的最小值 最大值
),
title_opts=opts.TitleOpts(
title="行政区-酒店数量",
title_textstyle_opts=opts.TextStyleOpts(
font_family="KaiTi", font_size=25, color="black"
)
))
# 标记最大值 最小值 平均值 标记平均线
bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="average", name="平均值")]),
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="average", name="平均值")]))
bar.render("行政区酒店数量最多的Top10.html")
The running effect is as follows:
code show as below:
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
from pyecharts.globals import ThemeType, CurrentConfig
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
c = (
Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK))
.add_xaxis(xaxis_data=Faker.days_attrs)
.add_yaxis("商家A", yaxis_data=Faker.days_values)
.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom（slider+inside）"),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
)
.render("bar_datazoom_both.html")
)
The running effect is as follows:
4. Pie chart
code show as below:
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
from pyecharts.globals import CurrentConfig
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
c = (
Pie()
.add(
"",
[list(z) for z in zip(Faker.choose(), Faker.values())],
# 饼图的中心（圆心）坐标，数组的第一项是横坐标，第二项是纵坐标
# 默认设置成百分比，设置成百分比时第一项是相对于容器宽度，第二项是相对于容器高度
center=["35%", "50%"],
)
.set_colors(["blue", "green", "yellow", "red", "pink", "orange", "purple"]) # 设置颜色
.set_global_opts(
title_opts=opts.TitleOpts(title="Pie-设置颜色-调整图例位置"),
legend_opts=opts.LegendOpts(type_="scroll", pos_left="70%", orient="vertical"), # 调整图例位置
)
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
.render("pie_set_color.html")
)
The running effect is as follows:
code show as below:
import pyecharts.options as opts
from pyecharts.charts import Pie
from pyecharts.globals import CurrentConfig
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
x_data = ["深度学习", "数据分析", "Web开发", "爬虫", "图像处理"]
y_data = [688, 888, 560, 388, 480]
data_pair = [list(z) for z in zip(x_data, y_data)]
data_pair.sort(key=lambda x: x[1])
c = (
# 宽 高 背景颜色
Pie(init_opts=opts.InitOpts(width="1200px", height="800px", bg_color="#2c343c"))
.add(
series_name="学习方向", # 系列名称
data_pair=data_pair, # 系列数据项，格式为 [(key1, value1), (key2, value2)]
rosetype="radius", # radius：扇区圆心角展现数据的百分比，半径展现数据的大小
radius="55%", # 饼图的半径
center=["50%", "50%"], # 饼图的中心（圆心）坐标，数组的第一项是横坐标，第二项是纵坐标
label_opts=opts.LabelOpts(is_show=False, position="center"), # 标签配置项
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="Customized Pie",
pos_left="center",
pos_top="20",
title_textstyle_opts=opts.TextStyleOpts(color="#fff"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
.set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)" # 'item': 数据项图形触发，主要在散点图，饼图等无类目轴的图表中使用
),
label_opts=opts.LabelOpts(color="rgba(255, 255, 255, 0.3)"),
)
.render("customized_pie.html")
)
The running effect is as follows:
5. Ring diagram
code show as below:
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
from pyecharts.globals import CurrentConfig
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
c = (
Pie()
.add(
"",
[list(z) for z in zip(Faker.choose(), Faker.values())],
# 饼图的半径，数组的第一项是内半径，第二项是外半径
# 默认设置成百分比，相对于容器高宽中较小的一项的一半
radius=["40%", "60%"],
)
.set_colors(["blue", "green", " #800000", "red", "#000000", "orange", "purple"])
.set_global_opts(
title_opts=opts.TitleOpts(title="Pie-Radius"),
legend_opts=opts.LegendOpts(orient="vertical", pos_top="15%", pos_left="2%"),
)
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
.render("pie_radius.html")
)
The running effect is as follows:
code show as below:
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
from pyecharts.globals import CurrentConfig
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
c = (
Pie()
.add(
"",
[list(z) for z in zip(Faker.choose(), Faker.values())],
radius=["40%", "60%"],
label_opts=opts.LabelOpts(
position="outside",
formatter="{a|{a}}{abg|}\n{hr|}\n {b|{b}: }{c} {per|{d}%} ",
background_color="#eee",
border_color="#aaa",
border_width=1,
border_radius=4,
rich={
"a": {
"color": "#999", "lineHeight": 22, "align": "center"},
"abg": {
"backgroundColor": "#e3e3e3",
"width": "100%",
"align": "right",
"height": 22,
"borderRadius": [4, 4, 0, 0],
},
"hr": {
"borderColor": "#aaa",
"width": "100%",
"borderWidth": 0.5,
"height": 0,
},
"b": {
"fontSize": 16, "lineHeight": 33},
"per": {
"color": "#eee",
"backgroundColor": "#334455",
"padding": [2, 4],
"borderRadius": 2,
},
},
),
)
.set_global_opts(title_opts=opts.TitleOpts(title="Pie-富文本示例"))
.render("pie_rich_label.html")
)
The running effect is as follows:
6. Rose Diagram
code show as below:
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
from pyecharts.globals import CurrentConfig
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
labels = ['可乐', '雪碧', '橙汁', '奶茶', '冰啤酒', '柠檬水']
values = [6, 12, 28, 52, 72, 96]
v = Faker.choose()
c = (
Pie()
.add(
"",
[list(z) for z in zip(v, Faker.values())],
radius=["40%", "75%"],
center=["22%", "50%"],
rosetype="radius",
label_opts=opts.LabelOpts(is_show=False),
)
.add(
"",
[list(z) for z in zip(labels, values)],
radius=["40%", "75%"],
center=["70%", "50%"],
rosetype="area",
)
.set_global_opts(title_opts=opts.TitleOpts(title="Pie-玫瑰图示例"),
legend_opts=opts.LegendOpts(is_show=False)
)
.render("pie_rosetype.html")
)
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.globals import CurrentConfig
import pandas as pd
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
provinces = ['北京','上海','黑龙江','吉林','辽宁','内蒙古','新疆','西藏','青海','四川','云南','陕西','重庆',
'贵州','广西','海南','澳门','湖南','江西','福建','安徽','浙江','江苏','宁夏','山西','河北','天津']
num = [1,1,1,17,9,22,23,42,35,7,20,21,16,24,16,21,37,12,13,14,13,7,22,8,16,13,13]
color_series = ['#FAE927','#E9E416','#C9DA36','#9ECB3C','#6DBC49',
'#37B44E','#3DBA78','#14ADCF','#209AC9','#1E91CA',
'#2C6BA0','#2B55A1','#2D3D8E','#44388E','#6A368B'
'#7D3990','#A63F98','#C31C88','#D52178','#D5225B',
'#D02C2A','#D44C2D','#F57A34','#FA8F2F','#D99D21',
'#CF7B25','#CF7B25','#CF7B25']
# 创建DataFrame
df = pd.DataFrame({
'provinces': provinces, 'num': num})
# 降序排序
df.sort_values(by='num', ascending=False, inplace=True)
# 提取数据
v = df['provinces'].values.tolist()
d = df['num'].values.tolist()
# 绘制饼图
pie1 = Pie(init_opts=opts.InitOpts(width='1250px', height='750px'))
# 设置颜色
pie1.set_colors(color_series)
pie1.add("", [list(z) for z in zip(v, d)],
radius=["30%", "100%"],
center=["50%", "50%"],
rosetype="area"
)
# 设置全局配置项
pie1.set_global_opts(title_opts=opts.TitleOpts(title='多省区市\n确诊病例连续多日',subtitle='零新增',
title_textstyle_opts=opts.TextStyleOpts(font_size=25,color= '#0085c3'),
subtitle_textstyle_opts= opts.TextStyleOpts(font_size=50,color= '#003399'),
pos_right= 'center',pos_left= 'center',pos_top='42%',pos_bottom='center'
),
legend_opts=opts.LegendOpts(is_show=False),
toolbox_opts=opts.ToolboxOpts())
# 设置系列配置项
pie1.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position="inside", font_size=12,
formatter="{b}:{c}天", font_style="italic",
font_weight="bold", font_family="SimHei"
),
)
# 渲染在html页面上
pie1.render('南丁格尔玫瑰图示例.html')
The running effect is as follows:
7. Word cloud chart
Word cloud is to filter out a large amount of text information by forming a keyword cloud layer or keyword rendering to visually highlight keywords that appear more frequently in online texts.
import jieba
import collections
import re
from pyecharts.charts import WordCloud
from pyecharts.globals import SymbolType
from pyecharts import options as opts
from pyecharts.globals import ThemeType, CurrentConfig
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
with open('barrages.txt') as f:
data = f.read()
# 文本预处理 去除一些无用的字符 只提取出中文出来
new_data = re.findall('[\u4e00-\u9fa5]+', data, re.S) # 只要字符串中的中文
new_data = " ".join(new_data)
# 文本分词--精确模式
seg_list_exact = jieba.cut(new_data, cut_all=False)
result_list = []
with open('stop_words.txt', encoding='utf-8') as f:
con = f.readlines()
stop_words = set()
for i in con:
i = i.replace("\n", "") # 去掉读取每一行数据的\n
stop_words.add(i)
for word in seg_list_exact:
# 设置停用词并去除单个词
if word not in stop_words and len(word) > 1:
result_list.append(word)
print(result_list)
# 筛选后统计
word_counts = collections.Counter(result_list)
# 获取前100最高频的词
word_counts_top100 = word_counts.most_common(100)
# 打印出来看看统计的词频
print(word_counts_top100)
# 链式调用
c = (
WordCloud(
init_opts=opts.InitOpts(width='1350px', height='750px', theme=ThemeType.MACARONS)
)
.add(
series_name="词频", # 系列名称
data_pair=word_counts_top100, # 系列数据项 [(word1, count1), (word2, count2)]
word_size_range=[15, 108], # 单词字体大小范围
textstyle_opts=opts.TextStyleOpts( # 词云图文字的配置
font_family='KaiTi',
),
shape=SymbolType.DIAMOND, # 词云图轮廓，有 'circle', 'cardioid', 'diamond', 'triangle-forward', 'triangle', 'pentagon', 'star' 可选
pos_left='100', # 距离左侧的距离
pos_top='50', # 距离顶部的距离
)
.set_global_opts(
title_opts=opts.TitleOpts( # 标题配置项
title='弹幕词云图',
title_textstyle_opts=opts.TextStyleOpts(
font_family='SimHei',
font_size=25,
color='black'
),
pos_left='500',
pos_top='10',
),
tooltip_opts=opts.TooltipOpts( # 提示框配置项
is_show=True,
background_color='red',
border_color='yellow',
),
toolbox_opts=opts.ToolboxOpts( # 工具箱配置项
is_show=True,
orient='vertical',
)
)
.render('弹幕词云图.html')
)
The running effect is as follows:
8. Dashboard
from pyecharts.charts import Gauge
from pyecharts.globals import CurrentConfig
from pyecharts import options as opts
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
c = (
Gauge()
.add(
series_name='业务指标', # 系列名称，用于 tooltip 的显示，legend 的图例筛选。
data_pair=[['完成率', 88.8]], # 系列数据项，格式为 [(key1, value1), (key2, value2)]
radius='70%', # 仪表盘半径，可以是相对于容器高宽中较小的一项的一半的百分比，也可以是绝对的数值。
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts( # 坐标轴轴线配置项
color=[(0.3, "#67e0e3"), (0.7, "#37a2da"), (1, "#fd666d")],
width=30,
)
),
title_label_opts=opts.LabelOpts( # 轮盘内标题文本项标签配置项
font_size=25, color='blue', font_family='KaiTi'
)
)
.set_global_opts(
title_opts=opts.TitleOpts( # 标题配置项
title='仪表盘',
title_textstyle_opts=opts.TextStyleOpts(
font_size=25, font_family='SimHei',
color='black', font_weight='bold',
),
pos_left="410", pos_top="8",
),
legend_opts=opts.LegendOpts( # 图例配置项
is_show=False
),
tooltip_opts=opts.TooltipOpts( # 提示框配置项
is_show=True,
formatter="{a} <br/>{b} : {c}%",
)
)
.render('gauge.html')
)
The running effect is as follows:
9. Water polo diagram
from pyecharts import options as opts
from pyecharts.charts import Grid, Liquid
from pyecharts.commons.utils import JsCode
from pyecharts.globals import CurrentConfig, ThemeType
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
lq_1 = (
Liquid()
.add(
series_name='电量', # 系列名称，用于 tooltip 的显示，legend 的图例筛选。
data=[0.25], # 系列数据，格式为 [value1, value2, ....]
center=['60%', '50%'],
# 水球外形，有' circle', 'rect', 'roundRect', 'triangle', 'diamond', 'pin', 'arrow' 可选。
# 默认 'circle' 也可以为自定义的 SVG 路径
shape='circle',
color=['yellow'], # 波浪颜色 Optional[Sequence[str]] = None,
is_animation=True, # 是否显示波浪动画
is_outline_show=False, # 是否显示边框
)
.set_global_opts(title_opts=opts.TitleOpts(title='多个Liquid显示'))
)
lq_2 = (
Liquid()
.add(
series_name='数据精度',
data=[0.8866],
center=['25%', '50%'],
label_opts=opts.LabelOpts(
font_size=50,
formatter=JsCode(
"""function (param) {
return (Math.floor(param.value * 10000) / 100) + '%';
}"""
),
position='inside'
)
)
)
grid = Grid(init_opts=opts.InitOpts(theme=ThemeType.DARK)).add(lq_1, grid_opts=opts.GridOpts()).add(lq_2, grid_opts=opts.GridOpts())
grid.render("multiple_liquid.html")
The running effect is as follows:
data collection
Data source: http://www.tianqihoubao.com/aqi/chengdu-201901.html
Crawling Chengdu air quality data for the whole year of 2019
import pandas as pd
dates = pd.date_range('20190101', '20191201', freq='MS').strftime('%Y%m') # 构造出日期序列 便于之后构造url
for i in range(len(dates)):
df = pd.read_html(f'http://www.tianqihoubao.com/aqi/chengdu-{dates[i]}.html', encoding='gbk', header=0)[0]
if i == 0:
df.to_csv('2019年成都空气质量数据.csv', mode='a+', index=False) # 追加写入
i += 1
else:
df.to_csv('2019年成都空气质量数据.csv', mode='a+', index=False, header=False)
View crawled data
10. Line chart
Line Chart Data arranged in columns or rows of a worksheet can be plotted into a line chart. Line charts can display continuous data over time (according to common scale settings), making them ideal for showing trends in data at equal time intervals.
Draw the trend chart of Chengdu AQI index in 2019
import pandas as pd
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.globals import CurrentConfig
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
df = pd.read_csv('2019年成都空气质量数据.csv')
date = [x for x in range(len(df['日期']))]
value = [int(i) for i in df['AQI指数']]
# 绘制折线图
line = Line()
line.add_xaxis(xaxis_data=date)
line.add_yaxis(
"AQI指数", # 系列数据项
value, # y轴数据
areastyle_opts=opts.AreaStyleOpts(opacity=0.5, color='#00FFFF'), # 设置图形透明度 填充颜色
label_opts=opts.LabelOpts(is_show=False), # 标签配置项
markpoint_opts=opts.MarkPointOpts( # 标记点配置项
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="average", name="平均值")
]
),
markline_opts=opts.MarkLineOpts( # 标记线配置项
data=[opts.MarkLineItem(type_="average", name="平均值")])
)
line.set_global_opts(
title_opts=opts.TitleOpts(title='2019成都AQI指数走势图(按日统计)')
)
line.render('2019成都AQI指数走势图(按日统计).html')
The running effect is as follows:
import pandas as pd
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.globals import CurrentConfig, ThemeType
import math
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
df = pd.read_csv('2019年成都空气质量数据.csv')[['日期', 'AQI指数']]
data = df['日期'].str.split('-', expand=True)[1]
df['月份'] = data
# 按月份分组 聚合 统计每月AQI指数平均值
counts = df.groupby('月份').agg({
'AQI指数': 'mean'})
date = [f'{x}月' for x in range(1, 13)]
value = [math.ceil(i) for i in counts['AQI指数']]
line = Line(init_opts=opts.InitOpts(theme=ThemeType.DARK))
line.set_colors(['red'])
line.add_xaxis(xaxis_data=date)
line.add_yaxis(
"AQI指数均值", # 系列数据项 用于图例筛选
value, # y轴数据
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts( # 标记点配置项
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="average", name="平均值")
]
),
markline_opts=opts.MarkLineOpts( # 标记线配置项
data=[opts.MarkLineItem(type_="average", name="平均值")])
)
line.set_global_opts( # 全局配置项
title_opts=opts.TitleOpts(
title='2019成都AQI全年走势图(按月统计)',
pos_left='32%', pos_top='3%',
title_textstyle_opts=opts.TextStyleOpts(
font_family='SimHei', font_size=20, color='#F0FFF0'
)
),
xaxis_opts=opts.AxisOpts(name='月份'), # x轴标签
yaxis_opts=opts.AxisOpts(name='AQI指数均值') # y轴标签
)
line.render('2019成都AQI指数走势图(按月统计).html')
The running effect is as follows:
11. Box plot
Box-plot, also known as box-and-whisker plot, box plot or box plot, is a statistical chart used to display the dispersion of a set of data. It is named after its shape like a box. It is also often used in various fields and is commonly used in quality management. It is mainly used to reflect the characteristics of the original data distribution, and can also compare the distribution characteristics of multiple groups of data. The method of drawing a boxplot is: first find the upper edge, lower edge, median and two quartiles of a set of data; then, connect the two quartiles to draw the box; then draw the upper edge The lower edge is connected to the box, and the median is in the middle of the box.
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Boxplot
from pyecharts.globals import CurrentConfig, ThemeType
CurrentConfig.ONLINE_HOST = 'D:/python/pyecharts-assets-master/assets/'
df = pd.read_csv('2019年成都空气质量数据.csv')[['日期', 'AQI指数']]
df.sort_values(by='AQI指数', inplace=True) # 按AQI指数大小排序 升序
data = df['日期'].str.split('-', expand=True)[1]
df['月份'] = data
item1, item2, item3, item4 = [], [], [], []
# 分为4个季度
for i, j in zip(df['月份'], df['AQI指数']):
if i in ['01', '02', '03']:
item1.append(j)
elif i in ['04', '05', '06']:
item2.append(j)
elif i in ['07', '08', '09']:
item3.append(j)
elif i in ['10', '11', '12']:
item4.append(j)
x_data = [f'第{i}季度' for i in range(1, 5)]
y_data = [item1, item2, item3, item4]
boxplot = Boxplot(init_opts=opts.InitOpts(theme=ThemeType.MACARONS))
boxplot.set_colors(['red'])
boxplot.add_xaxis(xaxis_data=x_data)
boxplot.add_yaxis(series_name='', y_axis=boxplot.prepare_data(y_data))
boxplot.set_global_opts(
title_opts=opts.TitleOpts(
title='2019年成都季度AQI指数箱型图',
pos_left='300', pos_top='5',
title_textstyle_opts=opts.TextStyleOpts(
font_family='KaiTi', font_size=20, color='black'
)
),
xaxis_opts=opts.AxisOpts(name='季度'),
yaxis_opts=opts.AxisOpts(name='AQI指数')
)
boxplot.render('2019年成都季度AQI指数箱型图.html')
The running effect is as follows:
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