如何使用颜色对气泡图进行分类答案

【问题标题】：how to categorize Bubble Map using colors如何使用颜色对气泡图进行分类
【发布时间】：2021-06-06 12:56:26
【问题描述】：

我正在尝试使用 plotly.graph_objects(go.Scattergeo) 创建气泡图，使用列值 0 到 50,50 到 70,71 到 80,81 到 100 的颜色（黄色、蓝色、绿色、红色）进行分类

颜色 = [“黄色”、“绿色”、“蓝色”、“红色”] 限制 = [(0,50),(51,70),(71,80),(81,100)]

for i in range(len(limits)):
lim = limits[i]
df_sub = df[lim[0]:lim[1]]
fig.add_trace(go.Scattergeo(
    lon = df['Longitude'],
    lat = df['Latitude'],
    text = df['text']  ,
    mode = 'markers',
    #marker_color = df['value'],
    marker = dict(
        #size = limits[i]*20000,
        size = 10,
        color = colors[i],
        line_color='rgb(40,40,40)',
        line_width=0.5,
        sizemode = 'area'
    ),
    [name = '{0} - {1}'.format(lim\[0\],lim\[1\])
    ))][1]

【问题讨论】：

请参考official reference for examples。

标签： python pandas plotly

【解决方案1】：

您没有提供任何样本数据。我已包含苏格兰的 COVID 数据
你真的是在描述pandas cut 功能
已经用您定义的范围和颜色证明了这一点

df = pd.DataFrame({"areaCode": ["S12000034", "S12000006", "S12000047", "S12000017", "S12000018", "S12000020", "S12000013", "S12000023", "S12000026", "S12000027", "S12000030", "S12000041", "S12000035", "S12000014", "S12000039", "S12000033", "S12000045", "S12000021", "S12000029", "S12000040", "S12000036", "S12000005", "S12000042", "S12000008", "S12000010", "S12000011", "S12000049", "S12000019", "S12000050", "S12000048", "S12000038", "S12000028"], 
 "areaName": ["Aberdeenshire", "Dumfries and Galloway", "Fife", "Highland", "Inverclyde", "Moray", "Na h-Eileanan Siar", "Orkney Islands", "Scottish Borders", "Shetland Islands", "Stirling", "Angus", "Argyll and Bute", "Falkirk", "West Dunbartonshire", "Aberdeen City", "East Dunbartonshire", "North Ayrshire", "South Lanarkshire", "West Lothian", "City of Edinburgh", "Clackmannanshire", "Dundee City", "East Ayrshire", "East Lothian", "East Renfrewshire", "Glasgow City", "Midlothian", "North Lanarkshire", "Perth and Kinross", "Renfrewshire", "South Ayrshire"], 
 "long": [-2.79208, -4.02863, -2.98251, -4.66103, -4.75387, -3.20202, -6.65722, -2.90025, -2.85869, -1.37344, -4.32595, -2.8921, -5.22114, -3.83619, -4.52074, -2.20398, -4.22417, -4.7246, -3.83272, -3.60909, -3.27826, -3.75316, -2.97095, -4.29057, -2.72435, -4.3606, -4.21479, -3.11738, -3.9514, -3.88484, -4.56834, -4.72899], 
 "lat": [57.234692, 55.09621, 56.231121, 57.586571, 55.900299, 57.476799, 58.199379, 58.94334, 55.525951, 60.504951, 56.249531, 56.725182, 56.28944, 56.000751, 56.0014, 57.166969, 55.95829, 55.72789, 55.60453, 55.8992, 55.911201, 56.147839, 56.4776, 55.496738, 55.94207, 55.74868, 55.876492, 55.82111, 55.868141, 56.575279, 55.848621, 55.23008], 
 "newCasesByPublishDate": [8, 6, 20, 5, 4, 0, 1, 0, 5, 0, 5, 10, 7, 12, 5, 22, 15, 16, 39, 24, 140, 10, 41, 24, 28, 16, 117, 22, 54, 32, 44, 38], 
 "pop_total": [261210.0, 148860.0, 373550.0, 235830.0, 77800.0, 95820.0, 26720.0, 22270.0, 115510.0, 22920.0, 94210.0, 116200.0, 85870.0, 160890.0, 88930.0, 228670.0, 108640.0, 134740.0, 320530.0, 183100.0, 524930.0, 51540.0, 149320.0, 122010.0, 107090.0, 95530.0, 633120.0, 92460.0, 341370.0, 151950.0, 179100.0, 112610.0]})


fig = go.Figure(
    go.Scattergeo(
        lat=df["lat"],
        lon=df["long"],
        text=df["areaName"],
        marker={
            "color": pd.cut(
                df["newCasesByPublishDate"],
                bins=[0, 50, 70, 80, 100],
                labels=["yellow", "green", "blue", "red"],
            )
        },
    )
)
fig.update_layout(geo={"fitbounds": "locations"})

【讨论】：