这款 Python 工具进行数据分析及数据可视化真的很棒啊

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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1. 导入模块

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 JsCode

2.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	NaN

2.2 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']df1

3. 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'],ascending=False).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_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("消费支出", y_data1,category_gap='35%', stack="stack1",label_opts=opts.LabelOpts(position="inside"),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_gap='35%', stack="stack1",label_opts=opts.LabelOpts(position="inside", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(position='top'),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=13,formatter="{value}")),graphic_opts=[opts.GraphicGroup(graphic_item=opts.GraphicItem(right='39%',bottom='58%',z=10,),children=[opts.GraphicText(graphic_item=opts.GraphicItem(left="center",bottom='center', z=100),graphic_textstyle_opts=opts.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''',font="bold 18px Microsoft YaHei",graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(fill='rgba(255, 171, 65,0.6)'),),),],)],title_opts=opts.TitleOpts(title='1-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_top='2%',pos_left="2%",title_textstyle_opts=opts.TextStyleOpts(color='#fff200',font_size=20)),legend_opts=opts.LegendOpts(pos_right="8%",  pos_top="9%",  orient="vertical")).reversal_axis()
)b2 = (Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("消费支出/人均收入", y_data3,category_gap='35%',label_opts=opts.LabelOpts(position="insideLeft", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(is_show=False),yaxis_opts=opts.AxisOpts(is_show=False),legend_opts=opts.LegendOpts(pos_right="3.8%",  pos_top="12.2%",  orient="vertical")).reversal_axis()
)
grid = Grid(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735'))
grid.add(b1, grid_opts=opts.GridOpts(pos_left='15%',pos_top='9%',pos_right='40%'))
grid.add(b2, grid_opts=opts.GridOpts(pos_left='65%',pos_top='9%',pos_right='20%'))
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'],ascending=False).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_opts=opts.InitOpts(theme='dark',width='1000px', height='600px',bg_color='#0d0735')).add('',[list(z) for z in zip(x_data1, y_data1)],maptype='china',is_map_symbol_show=False,label_opts=opts.LabelOpts(is_show=False,color='red'),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_opts=opts.VisualMapOpts(is_show=True,min_ = 10000,max_ = 40000,series_index=0,pos_top='70%',pos_left='10%',range_color=['#9ecae1','#6baed6','#4292c6','#2171b5','#08519c','#08306b','#d4b9da','#c994c7','#df65b0','#e7298a','#ce1256','#980043','#67001f']),tooltip_opts=opts.TooltipOpts(formatter='{b}:{c}'),title_opts=opts.TitleOpts(title='2-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_top='2%',pos_left="2%",title_textstyle_opts=opts.TextStyleOpts(color='#fff200',font_size=20)))
)
m1.render_notebook()

3.3 全国各地区消费支出占比地图

m2 = (Map(init_opts=opts.InitOpts(theme='dark',width='1000px', height='600px',bg_color='#0d0735')).add('',[list(z) for z in zip(x_data1, y_data3)],maptype='china',is_map_symbol_show=False,label_opts=opts.LabelOpts(is_show=False,color='red'),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_opts=opts.VisualMapOpts(is_show=True,min_ = 0.49,max_ = 0.8,series_index=0,pos_top='70%',pos_left='10%',range_color=['#9ecae1','#6baed6','#4292c6','#2171b5','#08519c','#08306b','#d4b9da','#c994c7','#df65b0','#e7298a','#ce1256','#980043','#67001f']),tooltip_opts=opts.TooltipOpts(formatter='{b}:{c}'),title_opts=opts.TitleOpts(title='3-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_top='2%',pos_left="2%",title_textstyle_opts=opts.TextStyleOpts(color='#fff200',font_size=20)))
)
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'],ascending=False).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_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("", y_data2,category_gap='35%').set_series_opts(label_opts=opts.LabelOpts(position="insideRight", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(is_show=False),yaxis_opts=opts.AxisOpts(is_show=False),).reversal_axis()
)b2 = (Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("", [2000]*len(y_data2),category_gap='35%').set_series_opts(label_opts=opts.LabelOpts(is_show=False,position="right", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(position='top'),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=13,formatter="{value}")),title_opts=opts.TitleOpts(title='4-全国衣着消费大省排行榜',subtitle='-- 制图@公众号：Python当打之年 --',pos_top='2%',pos_left="2%",title_textstyle_opts=opts.TextStyleOpts(color='#fff200',font_size=20)),legend_opts=opts.LegendOpts(pos_right="5%",  pos_top="5%",  orient="vertical")).reversal_axis()
)b3 = (Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("", y_data1, category_gap='35%').set_series_opts(label_opts=opts.LabelOpts(position="insideLeft", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(is_show=False),yaxis_opts=opts.AxisOpts(is_show=False),visualmap_opts=opts.VisualMapOpts(dimension=0,pos_right='2%',pos_bottom='4%',is_show=False, min_=0.03,max_=0.09,range_color=['#203fb6', '#008afb', '#ffec4a', '#ff6611', '#862e9c']),).reversal_axis()
)grid = Grid(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735'))
grid.add(b3, grid_opts=opts.GridOpts(pos_left='70%',pos_top='8%',pos_right='15%'))
grid.add(b2, grid_opts=opts.GridOpts(pos_left='15%',pos_top='8%',pos_right='40%'))
grid.add(b1, grid_opts=opts.GridOpts(pos_left='15%',pos_top='8%',pos_right='40%'))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'],ascending=False).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_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("", y_data2,category_gap='35%').set_series_opts(label_opts=opts.LabelOpts(position="insideRight", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(is_show=False),yaxis_opts=opts.AxisOpts(is_show=False),).reversal_axis()
)b2 = (Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("", [12000]*len(y_data2),category_gap='35%').set_series_opts(label_opts=opts.LabelOpts(is_show=False,position="right", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(position='top'),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=13,formatter="{value}")),title_opts=opts.TitleOpts(title='5-全国吃货大省排行榜',subtitle='-- 制图@公众号：Python当打之年 --',pos_top='2%',pos_left="2%",title_textstyle_opts=opts.TextStyleOpts(color='#fff200',font_size=20)),legend_opts=opts.LegendOpts(pos_right="5%",  pos_top="5%",  orient="vertical")).reversal_axis()
)b3 = (Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("", y_data1, category_gap='35%').set_series_opts(label_opts=opts.LabelOpts(position="insideLeft", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(is_show=False),yaxis_opts=opts.AxisOpts(is_show=False),visualmap_opts=opts.VisualMapOpts(dimension=0,pos_right='2%',pos_bottom='4%',is_show=False, min_=0.2,max_=0.4,range_color=['#203fb6', '#008afb', '#ffec4a', '#ff6611', '#f62336']),).reversal_axis()
)
grid = Grid(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735'))
grid.add(b3, grid_opts=opts.GridOpts(pos_left='70%',pos_top='8%',pos_right='15%'))
grid.add(b2, grid_opts=opts.GridOpts(pos_left='15%',pos_top='8%',pos_right='40%'))
grid.add(b1, grid_opts=opts.GridOpts(pos_left='15%',pos_top='8%',pos_right='40%'))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'],ascending=False).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_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("", y_data2,category_gap='35%').set_series_opts(label_opts=opts.LabelOpts(position="insideRight", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(is_show=False),yaxis_opts=opts.AxisOpts(is_show=False),).reversal_axis()
)b2 = (Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("", [18000]*len(y_data2),category_gap='35%').set_series_opts(label_opts=opts.LabelOpts(is_show=False,position="right", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(position='top'),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=13,formatter="{value}")),title_opts=opts.TitleOpts(title='6-全国住房消费大省排行榜',subtitle='-- 制图@公众号：Python当打之年 --',pos_top='2%',pos_left="2%",title_textstyle_opts=opts.TextStyleOpts(color='#fff200',font_size=20)),legend_opts=opts.LegendOpts(pos_right="5%",  pos_top="5%",  orient="vertical")).reversal_axis()
)b3 = (Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("", y_data1, category_gap='35%').set_series_opts(label_opts=opts.LabelOpts(position="insideLeft", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(is_show=False),yaxis_opts=opts.AxisOpts(is_show=False),visualmap_opts=opts.VisualMapOpts(dimension=0,pos_right='2%',pos_bottom='4%',is_show=False, min_=0.2,max_=0.4,range_color=['#203fb6', '#008afb', '#ffec4a', '#ff6611', '#006064']),).reversal_axis()
)
# b1.render_notebook()
grid = Grid(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735'))
grid.add(b3, grid_opts=opts.GridOpts(pos_left='70%',pos_top='8%',pos_right='15%'))
grid.add(b2, grid_opts=opts.GridOpts(pos_left='15%',pos_top='8%',pos_right='40%'))
grid.add(b1, grid_opts=opts.GridOpts(pos_left='15%',pos_top='8%',pos_right='40%'))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'],ascending=False).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_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("", y_data2,category_gap='35%').set_series_opts(label_opts=opts.LabelOpts(position="insideRight", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(is_show=False),yaxis_opts=opts.AxisOpts(is_show=False),).reversal_axis()
)b2 = (Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("", [5000]*len(y_data2),category_gap='35%').set_series_opts(label_opts=opts.LabelOpts(is_show=False,position="right", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(position='top'),yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(font_size=13,formatter="{value}")),title_opts=opts.TitleOpts(title='7-全国交通消费大省排行榜',subtitle='-- 制图@公众号：Python当打之年 --',pos_top='2%',pos_left="2%",title_textstyle_opts=opts.TextStyleOpts(color='#fff200',font_size=20)),legend_opts=opts.LegendOpts(pos_right="5%",  pos_top="5%",  orient="vertical")).reversal_axis()
)b3 = (Bar(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735')).add_xaxis(x_data1).add_yaxis("", y_data1, category_gap='35%').set_series_opts(label_opts=opts.LabelOpts(position="insideLeft", font_size=12, font_weight='bold', 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_opts=opts.AxisOpts(is_show=False),yaxis_opts=opts.AxisOpts(is_show=False),visualmap_opts=opts.VisualMapOpts(dimension=0,pos_right='2%',pos_bottom='4%',is_show=False, min_=0.1,max_=0.17,range_color=['#203fb6', '#008afb', '#ffec4a', '#ff6611', '#33691e']),).reversal_axis()
)grid = Grid(init_opts=opts.InitOpts(theme='dark',width='1000px', height='1500px',bg_color='#0d0735'))
grid.add(b3, grid_opts=opts.GridOpts(pos_left='70%',pos_top='8%',pos_right='15%'))
grid.add(b2, grid_opts=opts.GridOpts(pos_left='15%',pos_top='8%',pos_right='40%'))
grid.add(b1, grid_opts=opts.GridOpts(pos_left='15%',pos_top='8%',pos_right='40%'))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