案例1
import pyecharts
pyecharts.__version__
import pandas as pd
from pyecharts.charts import Map
from pyecharts.charts import Bar
from pyecharts.charts import Line
from pyecharts.charts import Grid
from pyecharts.charts import Pie
from pyecharts.charts import Scatter
from pyecharts import options as opts
import os
df = pd.read_csv('./data/二手房数据.csv', encoding = 'gb18030')
df.head(10)
# 3.2 查看表格数据描述
df.describe()
# 3.3 查看表格是否有数据缺失
print( df.isnull().sum())
# 可以看到电梯数据缺失 8255行。
# 3.4 查看电梯列共有几种值
print( df['电梯'].unique())
# 3.5 缺失值填充 用“未知”填充缺失数据：
df['电梯'].fillna('未知', inplace=True)
print( df.isnull().sum())
print( df['朝向'].unique() )
# 朝向数据包含了“西南”和“南西”两个方向，将其合并为一个方向“西南”：
df['朝向'] = df['朝向'].str.replace('南西','西南')
# df = df[df['朝向'] != '97']
df.drop(df[(df['朝向'] == '97') | (df['朝向'] == '有电梯')].index, inplace=True)
print( df['朝向'].unique() )
# 3.7 统计各城区二手房数量¶
g = df.groupby('市区')
df_region = g.count()['小区']
region = df_region.index.tolist()
count = df_region.values.tolist()
print( df_region)
new = [x + '区' for x in region]
m = (
Map()
.add('', [list(z) for z in zip(new, count)], '北京')
.set_global_opts(
title_opts=opts.TitleOpts(title='北京市二手房各区分布'),
visualmap_opts=opts.VisualMapOpts(max_=3000),
)
.render()
)
# 生成HTML文件（可选）
os.system("render.html")
查看详情
市区 0
小区 0
户型 0
朝向 0
楼层 0
装修情况 0
电梯 8255
面积(㎡) 0
价格(万元) 0
年份 0
Id 0
Unnamed: 11 23673
dtype: int64
['有电梯' '无电梯' nan '81' '3室1厅' '2室1厅' '光熙家园一期']
市区 0
小区 0
户型 0
朝向 0
楼层 0
装修情况 0
电梯 0
面积(㎡) 0
价格(万元) 0
年份 0
Id 0
Unnamed: 11 23673
dtype: int64
['西' '南北' '东西' '南西' '西南' '东南' '南' '东北' '东' '西北' '北']
['西' '南北' '东西' '西南' '东南' '南' '东北' '东' '西北' '北']
市区
东城 1533
丰台 2952
大兴 2115
密云 12
平谷 41
延庆 469
怀柔 15
房山 1441
昌平 2811
朝阳 2972
海淀 2983
石景山 882
西城 2129
通州 1602
门头沟 496
顺义 1221
Name: 小区, dtype: int64
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Awesome-pyecharts</title>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/echarts.min.js"></script>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/maps/beijing.js"></script>
</head>
<body >
<div id="53008fb50ae5488eaee52616dfa966c2" class="chart-container" style="width:900px; height:500px; "></div>
<script>
var chart_53008fb50ae5488eaee52616dfa966c2 = echarts.init(
document.getElementById('53008fb50ae5488eaee52616dfa966c2'), 'white', {renderer: 'canvas'});
var option_53008fb50ae5488eaee52616dfa966c2 = {
"animation": true,
"animationThreshold": 2000,
"animationDuration": 1000,
"animationEasing": "cubicOut",
"animationDelay": 0,
"animationDurationUpdate": 300,
"animationEasingUpdate": "cubicOut",
"animationDelayUpdate": 0,
"aria": {
"enabled": false
},
"color": [
"#5470c6",
"#91cc75",
"#fac858",
"#ee6666",
"#73c0de",
"#3ba272",
"#fc8452",
"#9a60b4",
"#ea7ccc"
],
"series": [
{
"type": "map",
"label": {
"show": true,
"margin": 8
},
"map": "\u5317\u4eac",
"data": [
{
"name": "\u4e1c\u57ce\u533a",
"value": 1533
},
{
"name": "\u4e30\u53f0\u533a",
"value": 2952
},
{
"name": "\u5927\u5174\u533a",
"value": 2115
},
{
"name": "\u5bc6\u4e91\u533a",
"value": 12
},
{
"name": "\u5e73\u8c37\u533a",
"value": 41
},
{
"name": "\u5ef6\u5e86\u533a",
"value": 469
},
{
"name": "\u6000\u67d4\u533a",
"value": 15
},
{
"name": "\u623f\u5c71\u533a",
"value": 1441
},
{
"name": "\u660c\u5e73\u533a",
"value": 2811
},
{
"name": "\u671d\u9633\u533a",
"value": 2972
},
{
"name": "\u6d77\u6dc0\u533a",
"value": 2983
},
{
"name": "\u77f3\u666f\u5c71\u533a",
"value": 882
},
{
"name": "\u897f\u57ce\u533a",
"value": 2129
},
{
"name": "\u901a\u5dde\u533a",
"value": 1602
},
{
"name": "\u95e8\u5934\u6c9f\u533a",
"value": 496
},
{
"name": "\u987a\u4e49\u533a",
"value": 1221
}
],
"roam": true,
"aspectScale": 0.75,
"nameProperty": "name",
"selectedMode": false,
"zoom": 1,
"zlevel": 0,
"z": 2,
"seriesLayoutBy": "column",
"datasetIndex": 0,
"mapValueCalculation": "sum",
"showLegendSymbol": true,
"emphasis": {}
}
],
"legend": [
{
"data": [
""
],
"selected": {},
"show": true,
"padding": 5,
"itemGap": 10,
"itemWidth": 25,
"itemHeight": 14,
"backgroundColor": "transparent",
"borderColor": "#ccc",
"borderWidth": 1,
"borderRadius": 0,
"pageButtonItemGap": 5,
"pageButtonPosition": "end",
"pageFormatter": "{current}/{total}",
"pageIconColor": "#2f4554",
"pageIconInactiveColor": "#aaa",
"pageIconSize": 15,
"animationDurationUpdate": 800,
"selector": false,
"selectorPosition": "auto",
"selectorItemGap": 7,
"selectorButtonGap": 10
}
],
"tooltip": {
"show": true,
"trigger": "item",
"triggerOn": "mousemove|click",
"axisPointer": {
"type": "line"
},
"showContent": true,
"alwaysShowContent": false,
"showDelay": 0,
"hideDelay": 100,
"enterable": false,
"confine": false,
"appendToBody": false,
"transitionDuration": 0.4,
"textStyle": {
"fontSize": 14
},
"borderWidth": 0,
"padding": 5,
"order": "seriesAsc"
},
"title": [
{
"show": true,
"text": "\u5317\u4eac\u5e02\u4e8c\u624b\u623f\u5404\u533a\u5206\u5e03",
"target": "blank",
"subtarget": "blank",
"padding": 5,
"itemGap": 10,
"textAlign": "auto",
"textVerticalAlign": "auto",
"triggerEvent": false
}
],
"visualMap": {
"show": true,
"type": "continuous",
"min": 0,
"max": 3000,
"inRange": {
"color": [
"#50a3ba",
"#eac763",
"#d94e5d"
]
},
"calculable": true,
"inverse": false,
"splitNumber": 5,
"hoverLink": true,
"orient": "vertical",
"padding": 5,
"showLabel": true,
"itemWidth": 20,
"itemHeight": 140,
"borderWidth": 0
}
};
chart_53008fb50ae5488eaee52616dfa966c2.setOption(option_53008fb50ae5488eaee52616dfa966c2);
</script>
</body>
</html>
- 查看
案例2
# 4.1 北京各城区二手房数量地图分布
print( df.info())
df['价格(万元)'] = pd.to_numeric(df['价格(万元)'], errors='coerce').astype(float)
top_price = df.sort_values(by="价格(万元)",ascending=False)[:15]
print("最高价格================")
print(top_price )
area0 = top_price['小区'].values.tolist()
count = top_price['价格(万元)'].values.tolist()
bar = (
Bar()
.add_xaxis(area0)
.add_yaxis('数量', count,category_gap = '50%')
.set_global_opts(
yaxis_opts=opts.AxisOpts(name='价格(万元)'),
xaxis_opts=opts.AxisOpts(name='数量'),
)
.render()
)
os.system("render.html")
查看详情
<class 'pandas.core.frame.DataFrame'>
Int64Index: 23674 entries, 0 to 23673
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 市区 23674 non-null object
1 小区 23674 non-null object
2 户型 23674 non-null object
3 朝向 23674 non-null object
4 楼层 23674 non-null object
5 装修情况 23674 non-null object
6 电梯 23674 non-null object
7 面积(㎡) 23674 non-null object
8 价格(万元) 23674 non-null object
9 年份 23674 non-null object
10 Id 23674 non-null object
11 Unnamed: 11 1 non-null object
dtypes: object(12)
memory usage: 2.3+ MB
None
最高价格================
市区 小区 户型 朝向 楼层 ... 面积(㎡) 价格(万元) 年份 Id Unnamed: 11
20388 西城 朱雀门 4室2厅 东南 5 ... 376 6000.0 2008 1.01E+11 NaN
22225 东城 贡院六号 5室2厅 南北 23 ... 459 5500.0 2002 1.01E+11 NaN
3218 顺义 丽宫 5室2厅 南北 3 ... 685 5000.0 2007 1.01E+11 NaN
22904 东城 NAGA上院 6室2厅 东南 12 ... 608 5000.0 2008 1.01E+11 NaN
22979 东城 当代MOMA 5室2厅 东南 7 ... 384 4988.0 2006 1.01E+11 NaN
20200 西城 耕天下 5室3厅 南北 7 ... 330 4650.0 2003 1.01E+11 NaN
6190 昌平 碧水庄园 5室3厅 南北 2 ... 571 4600.0 2005 1.01E+11 NaN
17284 朝阳 首府官邸 叠拼别墅 南北 5 ... 523.4 4500.0 2007 1.01E+11 NaN
2390 顺义 丽嘉花园 4室2厅 东南 2 ... 548 4500.0 2007 1.01E+11 NaN
15326 海淀 紫御府 4室2厅 南北 12 ... 374 4368.0 2008 1.01E+11 NaN
23237 东城 长安太和 4室1厅 东 24 ... 314 4350.0 2012 1.01E+11 NaN
21009 西城 西派国际公寓 5室2厅 东南 17 ... 355 4270.0 2009 1.01E+11 NaN
20238 西城 金融世家 4室2厅 西北 15 ... 300 4250.0 2008 1.01E+11 NaN
14530 海淀 西山壹号院 4室3厅 东南 6 ... 561 4150.0 2011 1.01E+11 NaN
21757 西城 丽豪园 4室2厅 西南 6 ... 289 4055.0 1999 1.01E+11 NaN
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Awesome-pyecharts</title>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/echarts.min.js"></script>
</head>
<body >
<div id="a718b695e8954ead87108c07feacda57" class="chart-container" style="width:900px; height:500px; "></div>
<script>
var chart_a718b695e8954ead87108c07feacda57 = echarts.init(
document.getElementById('a718b695e8954ead87108c07feacda57'), 'white', {renderer: 'canvas'});
var option_a718b695e8954ead87108c07feacda57 = {
"animation": true,
"animationThreshold": 2000,
"animationDuration": 1000,
"animationEasing": "cubicOut",
"animationDelay": 0,
"animationDurationUpdate": 300,
"animationEasingUpdate": "cubicOut",
"animationDelayUpdate": 0,
"aria": {
"enabled": false
},
"color": [
"#5470c6",
"#91cc75",
"#fac858",
"#ee6666",
"#73c0de",
"#3ba272",
"#fc8452",
"#9a60b4",
"#ea7ccc"
],
"series": [
{
"type": "bar",
"name": "\u6570\u91cf",
"legendHoverLink": true,
"data": [
6000.0,
5500.0,
5000.0,
5000.0,
4988.0,
4650.0,
4600.0,
4500.0,
4500.0,
4368.0,
4350.0,
4270.0,
4250.0,
4150.0,
4055.0
],
"realtimeSort": false,
"showBackground": false,
"stackStrategy": "samesign",
"cursor": "pointer",
"barMinHeight": 0,
"barCategoryGap": "50%",
"barGap": "30%",
"large": false,
"largeThreshold": 400,
"seriesLayoutBy": "column",
"datasetIndex": 0,
"clip": true,
"zlevel": 0,
"z": 2,
"label": {
"show": true,
"margin": 8
}
}
],
"legend": [
{
"data": [
"\u6570\u91cf"
],
"selected": {},
"show": true,
"padding": 5,
"itemGap": 10,
"itemWidth": 25,
"itemHeight": 14,
"backgroundColor": "transparent",
"borderColor": "#ccc",
"borderWidth": 1,
"borderRadius": 0,
"pageButtonItemGap": 5,
"pageButtonPosition": "end",
"pageFormatter": "{current}/{total}",
"pageIconColor": "#2f4554",
"pageIconInactiveColor": "#aaa",
"pageIconSize": 15,
"animationDurationUpdate": 800,
"selector": false,
"selectorPosition": "auto",
"selectorItemGap": 7,
"selectorButtonGap": 10
}
],
"tooltip": {
"show": true,
"trigger": "item",
"triggerOn": "mousemove|click",
"axisPointer": {
"type": "line"
},
"showContent": true,
"alwaysShowContent": false,
"showDelay": 0,
"hideDelay": 100,
"enterable": false,
"confine": false,
"appendToBody": false,
"transitionDuration": 0.4,
"textStyle": {
"fontSize": 14
},
"borderWidth": 0,
"padding": 5,
"order": "seriesAsc"
},
"xAxis": [
{
"name": "\u6570\u91cf",
"show": true,
"scale": false,
"nameLocation": "end",
"nameGap": 15,
"gridIndex": 0,
"inverse": false,
"offset": 0,
"splitNumber": 5,
"minInterval": 0,
"splitLine": {
"show": true,
"lineStyle": {
"show": true,
"width": 1,
"opacity": 1,
"curveness": 0,
"type": "solid"
}
},
"data": [
"\u6731\u96c0\u95e8",
"\u8d21\u9662\u516d\u53f7",
"\u4e3d\u5bab",
"NAGA\u4e0a\u9662",
"\u5f53\u4ee3MOMA",
"\u8015\u5929\u4e0b",
"\u78a7\u6c34\u5e84\u56ed",
"\u9996\u5e9c\u5b98\u90b8",
"\u4e3d\u5609\u82b1\u56ed",
"\u7d2b\u5fa1\u5e9c",
"\u957f\u5b89\u592a\u548c",
"\u897f\u6d3e\u56fd\u9645\u516c\u5bd3",
"\u91d1\u878d\u4e16\u5bb6",
"\u897f\u5c71\u58f9\u53f7\u9662",
"\u4e3d\u8c6a\u56ed"
]
}
],
"yAxis": [
{
"name": "\u4ef7\u683c(\u4e07\u5143)",
"show": true,
"scale": false,
"nameLocation": "end",
"nameGap": 15,
"gridIndex": 0,
"inverse": false,
"offset": 0,
"splitNumber": 5,
"minInterval": 0,
"splitLine": {
"show": true,
"lineStyle": {
"show": true,
"width": 1,
"opacity": 1,
"curveness": 0,
"type": "solid"
}
}
}
],
"title": [
{
"show": true,
"target": "blank",
"subtarget": "blank",
"padding": 5,
"itemGap": 10,
"textAlign": "auto",
"textVerticalAlign": "auto",
"triggerEvent": false
}
]
};
chart_a718b695e8954ead87108c07feacda57.setOption(option_a718b695e8954ead87108c07feacda57);
</script>
</body>
</html>
- 查看
案例3
# 4.5 房屋朝向饼图 根据房屋朝向分组
g = df.groupby('朝向')
df_direction = g.count()['小区']
directions = df_direction.index.tolist()
count = df_direction.values.tolist()
c1 = (
Pie(init_opts=opts.InitOpts(
width='800px', height='600px',
)
)
.add(
'',
[list(z) for z in zip(directions, count)],
radius=['20%', '60%'],
center=['50%', '50%'],
# rosetype="radius",
label_opts=opts.LabelOpts(is_show=True),
)
.set_global_opts(title_opts=opts.TitleOpts(title='房屋朝向占比',pos_left='33%',pos_top="5%"),
legend_opts=opts.LegendOpts(type_="scroll", pos_left="90%",pos_top="25%",orient="vertical")
)
.set_series_opts(label_opts=opts.LabelOpts(formatter='{b}:{c} ({d}%)'),position="outside")
.render()
)
# 生成HTML文件（可选）
os.system("render.html")
查看详情
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Awesome-pyecharts</title>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/echarts.min.js"></script>
</head>
<body >
<div id="317fd6b6e1f24d729ee69750b7f14a33" class="chart-container" style="width:800px; height:600px; "></div>
<script>
var chart_317fd6b6e1f24d729ee69750b7f14a33 = echarts.init(
document.getElementById('317fd6b6e1f24d729ee69750b7f14a33'), 'white', {renderer: 'canvas'});
var option_317fd6b6e1f24d729ee69750b7f14a33 = {
"animation": true,
"animationThreshold": 2000,
"animationDuration": 1000,
"animationEasing": "cubicOut",
"animationDelay": 0,
"animationDurationUpdate": 300,
"animationEasingUpdate": "cubicOut",
"animationDelayUpdate": 0,
"aria": {
"enabled": false
},
"color": [
"#5470c6",
"#91cc75",
"#fac858",
"#ee6666",
"#73c0de",
"#3ba272",
"#fc8452",
"#9a60b4",
"#ea7ccc"
],
"series": [
{
"type": "pie",
"colorBy": "data",
"legendHoverLink": true,
"selectedMode": false,
"selectedOffset": 10,
"clockwise": true,
"startAngle": 90,
"minAngle": 0,
"minShowLabelAngle": 0,
"avoidLabelOverlap": true,
"stillShowZeroSum": true,
"percentPrecision": 2,
"showEmptyCircle": true,
"emptyCircleStyle": {
"color": "lightgray",
"borderColor": "#000",
"borderWidth": 0,
"borderType": "solid",
"borderDashOffset": 0,
"borderCap": "butt",
"borderJoin": "bevel",
"borderMiterLimit": 10,
"opacity": 1
},
"data": [
{
"name": "\u4e1c",
"value": 934
},
{
"name": "\u4e1c\u5317",
"value": 681
},
{
"name": "\u4e1c\u5357",
"value": 2096
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{
"name": "\u4e1c\u897f",
"value": 1559
},
{
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"value": 612
},
{
"name": "\u5357",
"value": 2847
},
{
"name": "\u5357\u5317",
"value": 11431
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{
"name": "\u897f",
"value": 888
},
{
"name": "\u897f\u5317",
"value": 781
},
{
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"value": 1845
}
],
"radius": [
"20%",
"60%"
],
"center": [
"50%",
"50%"
],
"label": {
"show": true,
"margin": 8,
"formatter": "{b}:{c} ({d}%)"
},
"labelLine": {
"show": true,
"showAbove": false,
"length": 15,
"length2": 15,
"smooth": false,
"minTurnAngle": 90,
"maxSurfaceAngle": 90
},
"rippleEffect": {
"show": true,
"brushType": "stroke",
"scale": 2.5,
"period": 4
},
"position": "outside"
}
],
"legend": [
{
"data": [
"\u4e1c",
"\u4e1c\u5317",
"\u4e1c\u5357",
"\u4e1c\u897f",
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"\u5357",
"\u5357\u5317",
"\u897f",
"\u897f\u5317",
"\u897f\u5357"
],
"selected": {},
"type": "scroll",
"show": true,
"left": "90%",
"top": "25%",
"orient": "vertical",
"padding": 5,
"itemGap": 10,
"itemWidth": 25,
"itemHeight": 14,
"backgroundColor": "transparent",
"borderColor": "#ccc",
"borderWidth": 1,
"borderRadius": 0,
"pageButtonItemGap": 5,
"pageButtonPosition": "end",
"pageFormatter": "{current}/{total}",
"pageIconColor": "#2f4554",
"pageIconInactiveColor": "#aaa",
"pageIconSize": 15,
"animationDurationUpdate": 800,
"selector": false,
"selectorPosition": "auto",
"selectorItemGap": 7,
"selectorButtonGap": 10
}
],
"tooltip": {
"show": true,
"trigger": "item",
"triggerOn": "mousemove|click",
"axisPointer": {
"type": "line"
},
"showContent": true,
"alwaysShowContent": false,
"showDelay": 0,
"hideDelay": 100,
"enterable": false,
"confine": false,
"appendToBody": false,
"transitionDuration": 0.4,
"textStyle": {
"fontSize": 14
},
"borderWidth": 0,
"padding": 5,
"order": "seriesAsc"
},
"title": [
{
"show": true,
"text": "\u623f\u5c4b\u671d\u5411\u5360\u6bd4",
"target": "blank",
"subtarget": "blank",
"left": "33%",
"top": "5%",
"padding": 5,
"itemGap": 10,
"textAlign": "auto",
"textVerticalAlign": "auto",
"triggerEvent": false
}
]
};
chart_317fd6b6e1f24d729ee69750b7f14a33.setOption(option_317fd6b6e1f24d729ee69750b7f14a33);
</script>
</body>
</html>
- 查看
案例4
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.faker import Faker
import os
# 基础数据
# 省和直辖市
province_distribution = {'河南': 45.23, '北京': 37.56, '河北': 21, '辽宁': 12, '江西': 6, '上海': 20, '安徽': 10, '江苏': 16, '湖南': 9,
'浙江': 13, '海南': 2, '广东': 22, '湖北': 8, '黑龙江': 11, '澳门': 1, '陕西': 11, '四川': 7, '内蒙古': 3, '重庆': 3,
'云南': 6, '贵州': 2, '吉林': 3, '山西': 12, '山东': 11, '福建': 4, '青海': 1, '舵主科技，质量保证': 1, '天津': 1,
'其他': 1}
provice = list(province_distribution.keys())
values = list(province_distribution.values())
print( values)
map_chart = (
Map()
.add("", [list(z) for z in zip(provice, values)], "china")
.set_global_opts(title_opts=opts.TitleOpts(title="中国地图"))
.render()
)
# 生成HTML文件（可选）
os.system("render.html")
查看详情
[45.23, 37.56, 21, 12, 6, 20, 10, 16, 9, 13, 2, 22, 8, 11, 1, 11, 7, 3, 3, 6, 2, 3, 12, 11, 4, 1, 1, 1, 1]
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Awesome-pyecharts</title>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/echarts.min.js"></script>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/maps/china.js"></script>
</head>
<body >
<div id="dae383f7bae94833a2efc95bf5cb41fd" class="chart-container" style="width:900px; height:500px; "></div>
<script>
var chart_dae383f7bae94833a2efc95bf5cb41fd = echarts.init(
document.getElementById('dae383f7bae94833a2efc95bf5cb41fd'), 'white', {renderer: 'canvas'});
var option_dae383f7bae94833a2efc95bf5cb41fd = {
"animation": true,
"animationThreshold": 2000,
"animationDuration": 1000,
"animationEasing": "cubicOut",
"animationDelay": 0,
"animationDurationUpdate": 300,
"animationEasingUpdate": "cubicOut",
"animationDelayUpdate": 0,
"aria": {
"enabled": false
},
"color": [
"#5470c6",
"#91cc75",
"#fac858",
"#ee6666",
"#73c0de",
"#3ba272",
"#fc8452",
"#9a60b4",
"#ea7ccc"
],
"series": [
{
"type": "map",
"label": {
"show": true,
"margin": 8
},
"map": "china",
"data": [
{
"name": "\u6cb3\u5357",
"value": 45.23
},
{
"name": "\u5317\u4eac",
"value": 37.56
},
{
"name": "\u6cb3\u5317",
"value": 21
},
{
"name": "\u8fbd\u5b81",
"value": 12
},
{
"name": "\u6c5f\u897f",
"value": 6
},
{
"name": "\u4e0a\u6d77",
"value": 20
},
{
"name": "\u5b89\u5fbd",
"value": 10
},
{
"name": "\u6c5f\u82cf",
"value": 16
},
{
"name": "\u6e56\u5357",
"value": 9
},
{
"name": "\u6d59\u6c5f",
"value": 13
},
{
"name": "\u6d77\u5357",
"value": 2
},
{
"name": "\u5e7f\u4e1c",
"value": 22
},
{
"name": "\u6e56\u5317",
"value": 8
},
{
"name": "\u9ed1\u9f99\u6c5f",
"value": 11
},
{
"name": "\u6fb3\u95e8",
"value": 1
},
{
"name": "\u9655\u897f",
"value": 11
},
{
"name": "\u56db\u5ddd",
"value": 7
},
{
"name": "\u5185\u8499\u53e4",
"value": 3
},
{
"name": "\u91cd\u5e86",
"value": 3
},
{
"name": "\u4e91\u5357",
"value": 6
},
{
"name": "\u8d35\u5dde",
"value": 2
},
{
"name": "\u5409\u6797",
"value": 3
},
{
"name": "\u5c71\u897f",
"value": 12
},
{
"name": "\u5c71\u4e1c",
"value": 11
},
{
"name": "\u798f\u5efa",
"value": 4
},
{
"name": "\u9752\u6d77",
"value": 1
},
{
"name": "\u8235\u4e3b\u79d1\u6280\uff0c\u8d28\u91cf\u4fdd\u8bc1",
"value": 1
},
{
"name": "\u5929\u6d25",
"value": 1
},
{
"name": "\u5176\u4ed6",
"value": 1
}
],
"roam": true,
"aspectScale": 0.75,
"nameProperty": "name",
"selectedMode": false,
"zoom": 1,
"zlevel": 0,
"z": 2,
"seriesLayoutBy": "column",
"datasetIndex": 0,
"mapValueCalculation": "sum",
"showLegendSymbol": true,
"emphasis": {}
}
],
"legend": [
{
"data": [
""
],
"selected": {},
"show": true,
"padding": 5,
"itemGap": 10,
"itemWidth": 25,
"itemHeight": 14,
"backgroundColor": "transparent",
"borderColor": "#ccc",
"borderWidth": 1,
"borderRadius": 0,
"pageButtonItemGap": 5,
"pageButtonPosition": "end",
"pageFormatter": "{current}/{total}",
"pageIconColor": "#2f4554",
"pageIconInactiveColor": "#aaa",
"pageIconSize": 15,
"animationDurationUpdate": 800,
"selector": false,
"selectorPosition": "auto",
"selectorItemGap": 7,
"selectorButtonGap": 10
}
],
"tooltip": {
"show": true,
"trigger": "item",
"triggerOn": "mousemove|click",
"axisPointer": {
"type": "line"
},
"showContent": true,
"alwaysShowContent": false,
"showDelay": 0,
"hideDelay": 100,
"enterable": false,
"confine": false,
"appendToBody": false,
"transitionDuration": 0.4,
"textStyle": {
"fontSize": 14
},
"borderWidth": 0,
"padding": 5,
"order": "seriesAsc"
},
"title": [
{
"show": true,
"text": "\u4e2d\u56fd\u5730\u56fe",
"target": "blank",
"subtarget": "blank",
"padding": 5,
"itemGap": 10,
"textAlign": "auto",
"textVerticalAlign": "auto",
"triggerEvent": false
}
]
};
chart_dae383f7bae94833a2efc95bf5cb41fd.setOption(option_dae383f7bae94833a2efc95bf5cb41fd);
</script>
</body>
</html>
- 查看
案例5
from pyecharts.charts import Geo
from pyecharts import options as opts
import random
# 方式1
province = ['广东', '湖北', '湖南', '四川', '重庆', '黑龙江', '浙江', '山西']
data_province = [(i, random.randint(100, 200)) for i in province]
# 方式2，直接写成如下
# data_province = [('重庆', 383), ('浙江', 927), ('甘肃', 91)]
init_opts=opts.InitOpts(width="400px", height="600px") # 设置初始化选项
# 创建一个 Geo 对象，并设置初始化选项（包括图表大小）
china_province = (
Geo()
.add_schema(maptype="china") # 指定地图类型为'china'
.add("", data_province, type_="effectScatter") # 使用 effectScatter 类型显示数据点
.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) # 这里设置是否显示数据点标签，默认为不显示
.set_global_opts(
title_opts=opts.TitleOpts(title='中国省级地图'),
visualmap_opts=opts.VisualMapOpts(
min_=1,
max_=1000,
is_piecewise=True
),
)
.render(path='中国省级地图.html')
)
点击查看详情
案例6
from pyecharts.charts import Line, Bar, Funnel
from pyecharts.faker import Faker
import pyecharts.options as opts
from pyecharts.commons.utils import JsCode
import os
line1 = (
Line()
.add_xaxis(['2015', '2016', '2017', '2018', '2019'])
.add_yaxis('进入党政机关及事业单位的比例%', [30.23, 15.06, 17.6, 16.56, 18.51])
.render()
)
# 生成HTML文件（可选）
os.system("render.html")
查看详情
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Awesome-pyecharts</title>
<script type="text/javascript" src="https://assets.pyecharts.org/assets/v5/echarts.min.js"></script>
</head>
<body >
<div id="bcb5363b545d4b02a26c4c15af2d448d" class="chart-container" style="width:900px; height:500px; "></div>
<script>
var chart_bcb5363b545d4b02a26c4c15af2d448d = echarts.init(
document.getElementById('bcb5363b545d4b02a26c4c15af2d448d'), 'white', {renderer: 'canvas'});
var option_bcb5363b545d4b02a26c4c15af2d448d = {
"animation": true,
"animationThreshold": 2000,
"animationDuration": 1000,
"animationEasing": "cubicOut",
"animationDelay": 0,
"animationDurationUpdate": 300,
"animationEasingUpdate": "cubicOut",
"animationDelayUpdate": 0,
"aria": {
"enabled": false
},
"color": [
"#5470c6",
"#91cc75",
"#fac858",
"#ee6666",
"#73c0de",
"#3ba272",
"#fc8452",
"#9a60b4",
"#ea7ccc"
],
"series": [
{
"type": "line",
"name": "\u8fdb\u5165\u515a\u653f\u673a\u5173\u53ca\u4e8b\u4e1a\u5355\u4f4d\u7684\u6bd4\u4f8b%",
"connectNulls": false,
"xAxisIndex": 0,
"symbolSize": 4,
"showSymbol": true,
"smooth": false,
"clip": true,
"step": false,
"data": [
[
"2015",
30.23
],
[
"2016",
15.06
],
[
"2017",
17.6
],
[
"2018",
16.56
],
[
"2019",
18.51
]
],
"hoverAnimation": true,
"label": {
"show": true,
"margin": 8
},
"logBase": 10,
"seriesLayoutBy": "column",
"lineStyle": {
"show": true,
"width": 1,
"opacity": 1,
"curveness": 0,
"type": "solid"
},
"areaStyle": {
"opacity": 0
},
"zlevel": 0,
"z": 0
}
],
"legend": [
{
"data": [
"\u8fdb\u5165\u515a\u653f\u673a\u5173\u53ca\u4e8b\u4e1a\u5355\u4f4d\u7684\u6bd4\u4f8b%"
],
"selected": {}
}
],
"tooltip": {
"show": true,
"trigger": "item",
"triggerOn": "mousemove|click",
"axisPointer": {
"type": "line"
},
"showContent": true,
"alwaysShowContent": false,
"showDelay": 0,
"hideDelay": 100,
"enterable": false,
"confine": false,
"appendToBody": false,
"transitionDuration": 0.4,
"textStyle": {
"fontSize": 14
},
"borderWidth": 0,
"padding": 5,
"order": "seriesAsc"
},
"xAxis": [
{
"show": true,
"scale": false,
"nameLocation": "end",
"nameGap": 15,
"gridIndex": 0,
"inverse": false,
"offset": 0,
"splitNumber": 5,
"minInterval": 0,
"splitLine": {
"show": true,
"lineStyle": {
"show": true,
"width": 1,
"opacity": 1,
"curveness": 0,
"type": "solid"
}
},
"data": [
"2015",
"2016",
"2017",
"2018",
"2019"
]
}
],
"yAxis": [
{
"show": true,
"scale": false,
"nameLocation": "end",
"nameGap": 15,
"gridIndex": 0,
"inverse": false,
"offset": 0,
"splitNumber": 5,
"minInterval": 0,
"splitLine": {
"show": true,
"lineStyle": {
"show": true,
"width": 1,
"opacity": 1,
"curveness": 0,
"type": "solid"
}
}
}
]
};
chart_bcb5363b545d4b02a26c4c15af2d448d.setOption(option_bcb5363b545d4b02a26c4c15af2d448d);
</script>
</body>
</html>
- 查看
