python操作echarts

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

控制台打印

 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

render.html

 <!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

控制台打印

 <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

render.html

 <!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

render.html

 <!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
                },
                {
                    "name": "\u4e1c\u897f",
                    "value": 1559
                },
                {
                    "name": "\u5317",
                    "value": 612
                },
                {
                    "name": "\u5357",
                    "value": 2847
                },
                {
                    "name": "\u5357\u5317",
                    "value": 11431
                },
                {
                    "name": "\u897f",
                    "value": 888
                },
                {
                    "name": "\u897f\u5317",
                    "value": 781
                },
                {
                    "name": "\u897f\u5357",
                    "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",
                "\u5317",
                "\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

控制台打印

 [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]

render.html

 <!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

render.html

 <!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

posted @ 2024-01-11 15:33 DogLeftover 阅读(33) 评论(0) 编辑收藏举报

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python操作echarts

案例1

案例2

案例3

案例4

案例5

案例6

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合集 (4)

随笔分类 (1350)

阅读排行榜

推荐排行榜

	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")

	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>
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	# 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")

	<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

	# 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")

	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")

	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')
	)

	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")