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大家好#xff0c;今天我们以全国各地区衣食住行消费数据为例#xff0c;来分析2022年中国统计年鉴数据#xff0c;统计全国各地人民的消费地图#xff0c;看看#xff1a;
哪个省份的人最能花钱 哪个省份的人最舍得花钱 哪个省份的人最抠门 全国各地区人民在吃、穿…前言
大家好今天我们以全国各地区衣食住行消费数据为例来分析2022年中国统计年鉴数据统计全国各地人民的消费地图看看
哪个省份的人最能花钱 哪个省份的人最舍得花钱 哪个省份的人最抠门 全国各地区人民在吃、穿、住、行方面的消费习惯 … 希望对小伙伴们有所帮助如有疑问或者需要改进的地方可以在评论区留言。
本文涉及到的库 Pandas — 数据处理 Pyecharts — 数据可视化
可视化部分 柱状图 — Bar 地图 — Map 组合图 — Grid
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import pandas as pd
from pyecharts.charts import Bar
from pyecharts.charts import Map
from pyecharts.charts import Grid
from pyecharts import options as opts
from pyecharts.globals import SymbolType
from pyecharts.commons.utils import JsCode2.Pandas数据处理
2.1 读取数据
df pd.read_csv(/home/mw/input/202302048885/居民人均消费支出.txt,sep )
df地区 人均可支配收入 消费支出 食品烟酒 衣着 居住 生活用品及服务 交通通信 教育文化娱乐 医疗保健 其他用品及服务 Unnamed: 11
0 全国 32188.8 21209.9 6397.3 1238.4 5215.3 1259.5 2761.8 2032.2 1843.1 462.2 NaN
1 北京 69433.5 38903.3 8373.9 1803.5 15710.5 2145.8 3789.5 2766.0 3513.3 800.7 NaN
2 天津 43854.1 28461.4 8516.0 1711.8 7035.3 1669.4 3778.7 2253.7 2646.0 850.5 NaN
3 河北 27135.9 18037.0 4992.5 1249.7 4394.5 1171.2 2356.9 1799.1 1692.0 381.2 NaN
4 山西 25213.7 15732.7 4362.4 1235.8 3460.4 863.9 1980.9 1608.4 1854.0 366.9 NaN
5 内蒙古 31497.3 19794.5 5686.1 1568.3 4148.6 1119.2 3099.2 1835.9 1891.5 445.8 NaN
6 辽宁 32738.3 20672.1 6110.1 1378.2 4473.8 1091.8 2660.0 1950.8 2303.2 704.1 NaN
7 吉林 25751.0 17317.7 5021.6 1293.9 3448.2 906.7 2386.0 1742.0 2031.2 488.1 NaN
8 黑龙江 24902.0 17056.4 5287.2 1300.6 3450.7 895.4 2122.2 1602.9 2023.2 374.4 NaN
9 上海 72232.4 42536.3 11224.7 1694.0 15247.3 2091.2 4557.5 3662.9 3033.4 1025.3 NaN
10 江苏 43390.4 26225.1 7258.4 1450.5 7505.9 1523.0 3588.8 2298.2 2018.6 581.8 NaN
11 浙江 52397.4 31294.7 8922.1 1703.2 9009.1 1789.3 4301.2 2889.4 1955.9 724.4 NaN
12 安徽 28103.2 18877.3 6280.4 1210.4 4375.9 1108.4 2172.1 1855.3 1548.0 326.8 NaN
13 福建 37202.4 25125.8 8385.1 1182.4 7304.8 1274.8 2972.0 1895.9 1583.2 527.5 NaN
14 江西 28016.5 17955.3 5780.6 987.2 4454.9 966.5 2146.4 1879.0 1437.3 303.3 NaN
15 山东 32885.7 20940.1 5757.3 1438.0 4437.0 1571.0 3004.1 2373.7 1914.0 444.8 NaN
16 河南 24810.1 16142.6 4417.9 1221.8 3807.6 1077.6 1917.2 1685.4 1621.9 393.2 NaN
17 湖北 27880.6 19245.9 5897.7 1173.0 4659.6 1088.9 2559.5 1755.9 1764.9 346.4 NaN
18 湖南 29379.9 20997.6 6251.7 1236.9 4436.2 1289.0 2745.5 2587.3 2034.7 416.3 NaN
19 广东 41028.6 28491.9 9629.3 1044.5 7733.0 1560.6 3808.7 2442.9 1677.9 595.1 NaN
20 广西 24562.3 16356.8 5591.5 595.0 3579.0 929.1 2107.9 1766.2 1540.7 247.3 NaN
21 海南 27904.1 18971.6 7514.0 660.6 4168.0 890.0 2118.9 1880.5 1407.3 332.3 NaN
22 重庆 30823.9 21678.1 7284.6 1459.1 4062.1 1517.4 2630.9 2120.9 2101.5 501.6 NaN
23 四川 26522.1 19783.4 7026.4 1190.4 3855.7 1234.8 2465.1 1650.5 1908.0 452.4 NaN
24 贵州 21795.4 14873.8 4606.9 944.6 2998.2 901.1 2218.0 1636.7 1269.6 298.7 NaN
25 云南 23294.9 16792.4 5092.1 868.3 3469.8 958.5 2709.4 1835.8 1547.4 311.0 NaN
26 西藏 21744.1 13224.8 4786.6 1137.2 2970.5 838.6 1987.5 550.9 589.9 363.6 NaN
27 陕西 26226.0 17417.6 4819.5 1156.6 3857.6 1179.3 2194.0 1756.6 2078.4 375.6 NaN
28 甘肃 20335.1 16174.9 4768.8 1140.6 3557.3 1045.5 2020.4 1728.6 1544.7 369.1 NaN
29 青海 24037.4 18284.2 5224.5 1301.4 3618.5 1073.4 3121.0 1521.3 1975.7 448.5 NaN
30 宁夏 25734.9 17505.8 4816.3 1263.9 3348.8 1037.2 2922.0 1760.6 1906.3 450.7 NaN
31 新疆 23844.7 16512.1 5225.9 1138.9 3304.7 1031.0 2318.9 1488.4 1611.7 392.7 NaN2.2 数据清理
df1 df.iloc[1:,:-1]
df1.head()2.3 计算各项占比
df1[消费支出占比] df1[消费支出]/df1[人均可支配收入]
df1[食品烟酒消费占比] df1[食品烟酒]/df1[消费支出]
df1[衣着消费占比] df1[衣着]/df1[消费支出]
df1[居住消费占比] df1[居住]/df1[消费支出]
df1[生活用品及服务] df1[生活用品及服务]/df1[消费支出]
df1[交通通信消费占比] df1[交通通信]/df1[消费支出]
df1[教育文化娱乐消费占比] df1[教育文化娱乐]/df1[消费支出]
df1[医疗保健消费占比] df1[医疗保健]/df1[消费支出]
df1[其他用品及服务消费占比] df1[其他用品及服务]/df1[消费支出]
df1[人均净收入] df1[人均可支配收入]-df1[消费支出]df13. Pyecharts数据可视化
3.1 全国各地区人均收入、消费支出排行榜
color_function function (params) {if (params.value 0.66) return #8E0036;else return #327B94;}df_income df1.sort_values(by[人均可支配收入],ascendingFalse).round(2)
x_data1 df_income[地区].values.tolist()[::-1]
y_data1 df_income[消费支出].values.tolist()[::-1]
y_data2 df_income[人均净收入].values.tolist()[::-1]
y_data3 df_income[消费支出占比].values.tolist()[::-1]
y_data4 df_income[人均可支配收入].values.tolist()[::-1]b1 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(消费支出, y_data1,category_gap35%, stackstack1,label_optsopts.LabelOpts(positioninside),itemstyle_opts{normal: {shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,color:#203fb6,}},).add_yaxis(人均净收入, y_data2, category_gap35%, stackstack1,label_optsopts.LabelOpts(positioninside, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {barBorderRadius: [0, 30, 30, 0],shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,color:#e7298a}},).set_global_opts(xaxis_optsopts.AxisOpts(positiontop),yaxis_optsopts.AxisOpts(axislabel_optsopts.LabelOpts(font_size13,formatter{value})),graphic_opts[opts.GraphicGroup(graphic_itemopts.GraphicItem(right39%,bottom58%,z10,),children[opts.GraphicText(graphic_itemopts.GraphicItem(leftcenter,bottomcenter, z100),graphic_textstyle_optsopts.GraphicTextStyleOpts(text全国人均可支配收入32188.8全国人均消费支出21209.9人均消费支出/人均收入0.66,fontbold 18px Microsoft YaHei,graphic_basicstyle_optsopts.GraphicBasicStyleOpts(fillrgba(255, 171, 65,0.6)),),),],)],title_optsopts.TitleOpts(title1-全国各地区人均收入、消费支出排行榜,subtitle-- 制图公众号Python当打之年 --,pos_top2%,pos_left2%,title_textstyle_optsopts.TextStyleOpts(color#fff200,font_size20)),legend_optsopts.LegendOpts(pos_right8%, pos_top9%, orientvertical)).reversal_axis()
)b2 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(消费支出/人均收入, y_data3,category_gap35%,label_optsopts.LabelOpts(positioninsideLeft, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {barBorderRadius: [30, 30, 30, 30],shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,opacity: 1,color:JsCode(color_function)}},).set_global_opts(xaxis_optsopts.AxisOpts(is_showFalse),yaxis_optsopts.AxisOpts(is_showFalse),legend_optsopts.LegendOpts(pos_right3.8%, pos_top12.2%, orientvertical)).reversal_axis()
)
grid Grid(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735))
grid.add(b1, grid_optsopts.GridOpts(pos_left15%,pos_top9%,pos_right40%))
grid.add(b2, grid_optsopts.GridOpts(pos_left65%,pos_top9%,pos_right20%))
grid.render_notebook() 全国人均可支配收入32188.8全国人均消费支出21209.9人均消费支出/人均可支配收入0.66 北京、上海、浙江、天津、江苏五个地区的人均可支配收入位居前5但消费支出占比均低于全国平均水平0.66挣得多花的少 从消费支出占比方面来看最抠门的几个地区北京0.56、上海0.59、浙江0.6、江苏0.6 从消费支出占比方面来看最舍得花钱的地区甘肃0.8、青海0.76、四川0.75、云南0.72、湖南0.71
3.2 全国各地区人均可支配收入地图
# 省份字典
provs [上海, 云南, 内蒙古, 北京, 台湾, 吉林, 四川, 天津, 宁夏, 安徽, 山东, 山西, 广东, 广西,新疆, 江苏, 江西, 河北, 河南, 浙江, 海南, 湖北, 湖南, 澳门, 甘肃, 福建, 西藏, 贵州, 辽宁,重庆, 陕西, 青海, 香港, 黑龙江]
provs_fin [上海市, 云南省, 内蒙古自治区, 北京市, 台湾省, 吉林省, 四川省, 天津市, 宁夏回族自治区, 安徽省, 山东省, 山西省, 广东省, 广西壮族自治区,新疆维吾尔自治区, 江苏省, 江西省, 河北省, 河南省, 浙江省, 海南省, 湖北省, 湖南省, 澳门香港特别行政区, 甘肃省, 福建省, 西藏自治区, 贵州省, 辽宁省,重庆市, 陕西省, 青海省, 香港特别行政区, 黑龙江省]
prov_dic dict(zip(provs,provs_fin))df_income df1.sort_values(by[人均可支配收入],ascendingFalse).round(2)
df_income[地区] df_income[地区].replace(prov_dic)
x_data1 df_income[地区].values.tolist()[::-1]
y_data1 df_income[消费支出].values.tolist()[::-1]
y_data2 df_income[人均净收入].values.tolist()[::-1]
y_data3 df_income[消费支出占比].values.tolist()[::-1]m1 (Map(init_optsopts.InitOpts(themedark,width1000px, height600px,bg_color#0d0735)).add(,[list(z) for z in zip(x_data1, y_data1)],maptypechina,is_map_symbol_showFalse,label_optsopts.LabelOpts(is_showFalse,colorred),itemstyle_opts{normal: {shadowColor: rgba(0, 0, 0, .5), # 阴影颜色shadowBlur: 5, # 阴影大小shadowOffsetY: 0, # Y轴方向阴影偏移shadowOffsetX: 0, # x轴方向阴影偏移borderColor: #fff}}).set_global_opts(visualmap_optsopts.VisualMapOpts(is_showTrue,min_ 10000,max_ 40000,series_index0,pos_top70%,pos_left10%,range_color[#9ecae1,#6baed6,#4292c6,#2171b5,#08519c,#08306b,#d4b9da,#c994c7,#df65b0,#e7298a,#ce1256,#980043,#67001f]),tooltip_optsopts.TooltipOpts(formatter{b}:{c}),title_optsopts.TitleOpts(title2-全国各地区人均可支配收入地图,subtitle制图公众号Python当打之年,pos_top2%,pos_left2%,title_textstyle_optsopts.TextStyleOpts(color#fff200,font_size20)))
)
m1.render_notebook()3.3 全国各地区消费支出占比地图
m2 (Map(init_optsopts.InitOpts(themedark,width1000px, height600px,bg_color#0d0735)).add(,[list(z) for z in zip(x_data1, y_data3)],maptypechina,is_map_symbol_showFalse,label_optsopts.LabelOpts(is_showFalse,colorred),itemstyle_opts{normal: {shadowColor: rgba(0, 0, 0, .5), # 阴影颜色shadowBlur: 5, # 阴影大小shadowOffsetY: 0, # Y轴方向阴影偏移shadowOffsetX: 0, # x轴方向阴影偏移borderColor: #fff}}).set_global_opts(visualmap_optsopts.VisualMapOpts(is_showTrue,min_ 0.49,max_ 0.8,series_index0,pos_top70%,pos_left10%,range_color[#9ecae1,#6baed6,#4292c6,#2171b5,#08519c,#08306b,#d4b9da,#c994c7,#df65b0,#e7298a,#ce1256,#980043,#67001f]),tooltip_optsopts.TooltipOpts(formatter{b}:{c}),title_optsopts.TitleOpts(title3-全国各地区消费支出占比地图,subtitle-- 制图公众号Python当打之年 --,pos_top2%,pos_left2%,title_textstyle_optsopts.TextStyleOpts(color#fff200,font_size20)))
)
m2.render_notebook()3.4 ‘衣’-全国衣着消费排行榜
df_house df1.sort_values(by[衣着消费占比],ascendingFalse).round(2)
x_data1 df_house[地区].values.tolist()[::-1]
y_data1 df_house[衣着消费占比].values.tolist()[::-1]
y_data2 df_house[衣着].values.tolist()[::-1]b1 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(, y_data2,category_gap35%).set_series_opts(label_optsopts.LabelOpts(positioninsideRight, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {barBorderRadius: [0, 30, 30, 0],shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,opacity: 0.8,color:#E91E63}},).set_global_opts(xaxis_optsopts.AxisOpts(is_showFalse),yaxis_optsopts.AxisOpts(is_showFalse),).reversal_axis()
)b2 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(, [2000]*len(y_data2),category_gap35%).set_series_opts(label_optsopts.LabelOpts(is_showFalse,positionright, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,opacity: 0.2,color:#fff}},).set_global_opts(xaxis_optsopts.AxisOpts(positiontop),yaxis_optsopts.AxisOpts(axislabel_optsopts.LabelOpts(font_size13,formatter{value})),title_optsopts.TitleOpts(title4-全国衣着消费大省排行榜,subtitle-- 制图公众号Python当打之年 --,pos_top2%,pos_left2%,title_textstyle_optsopts.TextStyleOpts(color#fff200,font_size20)),legend_optsopts.LegendOpts(pos_right5%, pos_top5%, orientvertical)).reversal_axis()
)b3 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(, y_data1, category_gap35%).set_series_opts(label_optsopts.LabelOpts(positioninsideLeft, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {barBorderRadius: [30, 30, 30, 30],shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,opacity: 0.8,}},).set_global_opts(xaxis_optsopts.AxisOpts(is_showFalse),yaxis_optsopts.AxisOpts(is_showFalse),visualmap_optsopts.VisualMapOpts(dimension0,pos_right2%,pos_bottom4%,is_showFalse, min_0.03,max_0.09,range_color[#203fb6, #008afb, #ffec4a, #ff6611, #862e9c]),).reversal_axis()
)grid Grid(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735))
grid.add(b3, grid_optsopts.GridOpts(pos_left70%,pos_top8%,pos_right15%))
grid.add(b2, grid_optsopts.GridOpts(pos_left15%,pos_top8%,pos_right40%))
grid.add(b1, grid_optsopts.GridOpts(pos_left15%,pos_top8%,pos_right40%))grid.render_notebook() 最舍得在衣服上花钱的地区是西藏0.09最抠门的是海南0.03相差足足三倍 就衣着消费占比来看北方地区消费占比要明显高于南方地区
3.5 ‘食’-全国吃货大省排行榜
df_eat df1.sort_values(by[食品烟酒],ascendingFalse).round(2)
x_data1 df_eat[地区].values.tolist()[::-1]
y_data1 df_eat[食品烟酒消费占比].values.tolist()[::-1]
y_data2 df_eat[食品烟酒].values.tolist()[::-1]b1 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(, y_data2,category_gap35%).set_series_opts(label_optsopts.LabelOpts(positioninsideRight, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {barBorderRadius: [0, 30, 30, 0],shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,opacity: 0.8,color:#E91E63}},).set_global_opts(xaxis_optsopts.AxisOpts(is_showFalse),yaxis_optsopts.AxisOpts(is_showFalse),).reversal_axis()
)b2 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(, [12000]*len(y_data2),category_gap35%).set_series_opts(label_optsopts.LabelOpts(is_showFalse,positionright, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,opacity: 0.2,color:#fff}},).set_global_opts(xaxis_optsopts.AxisOpts(positiontop),yaxis_optsopts.AxisOpts(axislabel_optsopts.LabelOpts(font_size13,formatter{value})),title_optsopts.TitleOpts(title5-全国吃货大省排行榜,subtitle-- 制图公众号Python当打之年 --,pos_top2%,pos_left2%,title_textstyle_optsopts.TextStyleOpts(color#fff200,font_size20)),legend_optsopts.LegendOpts(pos_right5%, pos_top5%, orientvertical)).reversal_axis()
)b3 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(, y_data1, category_gap35%).set_series_opts(label_optsopts.LabelOpts(positioninsideLeft, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {barBorderRadius: [30, 30, 30, 30],shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,opacity: 0.8,}},).set_global_opts(xaxis_optsopts.AxisOpts(is_showFalse),yaxis_optsopts.AxisOpts(is_showFalse),visualmap_optsopts.VisualMapOpts(dimension0,pos_right2%,pos_bottom4%,is_showFalse, min_0.2,max_0.4,range_color[#203fb6, #008afb, #ffec4a, #ff6611, #f62336]),).reversal_axis()
)
grid Grid(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735))
grid.add(b3, grid_optsopts.GridOpts(pos_left70%,pos_top8%,pos_right15%))
grid.add(b2, grid_optsopts.GridOpts(pos_left15%,pos_top8%,pos_right40%))
grid.add(b1, grid_optsopts.GridOpts(pos_left15%,pos_top8%,pos_right40%))grid.render_notebook() 全国居民人均食品烟酒消费支出达 6397 元占全年人均消费支出的近三分之一 食品烟酒支出前十的省市中上海再次荣登榜首北方只有北京和天津上榜但是从占比方面来看北京、上海是垫底的两个地区 山西、河南在食品烟酒上的支出排名最后两位
3.6 ‘住’-全国住房消费排行榜
df_house df1.sort_values(by[居住消费占比],ascendingFalse).round(2)
x_data1 df_house[地区].values.tolist()[::-1]
y_data1 df_house[居住消费占比].values.tolist()[::-1]
y_data2 df_house[居住].values.tolist()[::-1]b1 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(, y_data2,category_gap35%).set_series_opts(label_optsopts.LabelOpts(positioninsideRight, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {barBorderRadius: [0, 30, 30, 0],shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,opacity: 0.8,color:#E91E63}},).set_global_opts(xaxis_optsopts.AxisOpts(is_showFalse),yaxis_optsopts.AxisOpts(is_showFalse),).reversal_axis()
)b2 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(, [18000]*len(y_data2),category_gap35%).set_series_opts(label_optsopts.LabelOpts(is_showFalse,positionright, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,opacity: 0.2,color:#fff}},).set_global_opts(xaxis_optsopts.AxisOpts(positiontop),yaxis_optsopts.AxisOpts(axislabel_optsopts.LabelOpts(font_size13,formatter{value})),title_optsopts.TitleOpts(title6-全国住房消费大省排行榜,subtitle-- 制图公众号Python当打之年 --,pos_top2%,pos_left2%,title_textstyle_optsopts.TextStyleOpts(color#fff200,font_size20)),legend_optsopts.LegendOpts(pos_right5%, pos_top5%, orientvertical)).reversal_axis()
)b3 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(, y_data1, category_gap35%).set_series_opts(label_optsopts.LabelOpts(positioninsideLeft, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {barBorderRadius: [30, 30, 30, 30],shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,opacity: 0.8,}},).set_global_opts(xaxis_optsopts.AxisOpts(is_showFalse),yaxis_optsopts.AxisOpts(is_showFalse),visualmap_optsopts.VisualMapOpts(dimension0,pos_right2%,pos_bottom4%,is_showFalse, min_0.2,max_0.4,range_color[#203fb6, #008afb, #ffec4a, #ff6611, #006064]),).reversal_axis()
)
# b1.render_notebook()
grid Grid(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735))
grid.add(b3, grid_optsopts.GridOpts(pos_left70%,pos_top8%,pos_right15%))
grid.add(b2, grid_optsopts.GridOpts(pos_left15%,pos_top8%,pos_right40%))
grid.add(b1, grid_optsopts.GridOpts(pos_left15%,pos_top8%,pos_right40%))grid.render_notebook() 北京0.4、上海0.36两地人民在居住上的消费排名前两位果然房价还是得看北上广接近40%的消费都在住房上面 重庆、宁夏、四川以0.19的占比排在最后三位这方面看住房压力还是比较小的
3.7 ‘行’-全国交通消费排行榜
df_house df1.sort_values(by[交通通信],ascendingFalse).round(2)
x_data1 df_house[地区].values.tolist()[::-1]
y_data1 df_house[交通通信消费占比].values.tolist()[::-1]
y_data2 df_house[交通通信].values.tolist()[::-1]b1 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(, y_data2,category_gap35%).set_series_opts(label_optsopts.LabelOpts(positioninsideRight, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {barBorderRadius: [0, 30, 30, 0],shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,opacity: 0.8,color:#E91E63}},).set_global_opts(xaxis_optsopts.AxisOpts(is_showFalse),yaxis_optsopts.AxisOpts(is_showFalse),).reversal_axis()
)b2 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(, [5000]*len(y_data2),category_gap35%).set_series_opts(label_optsopts.LabelOpts(is_showFalse,positionright, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,opacity: 0.2,color:#fff}},).set_global_opts(xaxis_optsopts.AxisOpts(positiontop),yaxis_optsopts.AxisOpts(axislabel_optsopts.LabelOpts(font_size13,formatter{value})),title_optsopts.TitleOpts(title7-全国交通消费大省排行榜,subtitle-- 制图公众号Python当打之年 --,pos_top2%,pos_left2%,title_textstyle_optsopts.TextStyleOpts(color#fff200,font_size20)),legend_optsopts.LegendOpts(pos_right5%, pos_top5%, orientvertical)).reversal_axis()
)b3 (Bar(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735)).add_xaxis(x_data1).add_yaxis(, y_data1, category_gap35%).set_series_opts(label_optsopts.LabelOpts(positioninsideLeft, font_size12, font_weightbold, formatter{c}),itemstyle_opts{normal: {barBorderRadius: [30, 30, 30, 30],shadowBlur: 10,shadowColor: rgba(0,191,255,0.5),shadowOffsetY: 1,opacity: 0.8,}},).set_global_opts(xaxis_optsopts.AxisOpts(is_showFalse),yaxis_optsopts.AxisOpts(is_showFalse),visualmap_optsopts.VisualMapOpts(dimension0,pos_right2%,pos_bottom4%,is_showFalse, min_0.1,max_0.17,range_color[#203fb6, #008afb, #ffec4a, #ff6611, #33691e]),).reversal_axis()
)grid Grid(init_optsopts.InitOpts(themedark,width1000px, height1500px,bg_color#0d0735))
grid.add(b3, grid_optsopts.GridOpts(pos_left70%,pos_top8%,pos_right15%))
grid.add(b2, grid_optsopts.GridOpts(pos_left15%,pos_top8%,pos_right40%))
grid.add(b1, grid_optsopts.GridOpts(pos_left15%,pos_top8%,pos_right40%))grid.render_notebook() 上海、浙江、广东、北京、天津等地居民在交通通信上的实际花费排名前五位青海、宁夏两地以0.17的交通通信消费占比排名前二位北京、上海在这一项上的占比分别为0.1、0.11
