China's GDP In Province

China's GDP in Province with Chinese and English

@kaggle.concyclics_chinas_gdp_in_province

Table List

2 tables(54.41 KB)

Chinas Gdp In Province Zh

@kaggle.concyclics_chinas_gdp_in_province.chinas_gdp_in_province_zh

27.33 KB
29 rows
32 columns

unnamed_0 Unnamed: 0	n 北京市	n_02e762 天津市	n_8495ef 河北省	n_26d2a7 山西省	n_fa6203 内蒙古自治区	n_596fda 辽宁省	n_ce10bb 吉林省	n_5324d0 黑龙江省	n_1471cd 上海市	n_548b96 江苏省	n_e76655 浙江省	n_e8ea56 安徽省	n_2236ef 福建省	n_ec0692 江西省	n_17dd8d 山东省	n_52b71b 河南省	n_27c9f2 湖北省	n_fffd35 湖南省	n_f85eb4 广东省	n_0a8140 广西壮族自治区	n_66c7a8 海南省	n_b94cfd 重庆市	n_e053ca 四川省	n_76463e 贵州省	n_d02866 云南省	n_d36431 西藏自治区	n_78fa10 陕西省	n_e1b13d 甘肃省	n_30fd79 青海省	n_f76bd0 宁夏回族自治区	n_8b2760 新疆维吾尔自治区
2020	36102.6	14083.7	36206.9	17651.9	17359.8	25115	12311.3	13698.5	38700.6	102719	64613.3	38680.6	43903.9	25691.5	73129	54997.1	43443.5	41781.5	110760.9	22156.7	5532.4	25002.8	48598.8	17826.6	24521.9	1902.7	26181.9	9016.7	3005.9	3920.6	13797.6
2019	35445.1	14055.5	34978.6	16961.6	17212.5	24855.3	11726.8	13544.4	37987.6	98656.8	62462	36845.5	42326.6	24667.3	70540.5	53717.8	45429	39894.1	107986.9	21237.1	5330.8	23605.8	46363.8	16769.3	23223.8	1697.8	25793.2	8718.3	2941.1	3748.5	13597.1
2018	33106	13362.9	32494.6	15958.1	16140.8	23510.5	11253.8	12846.5	36011.8	93207.6	58002.8	34010.9	38687.8	22716.5	66648.9	49935.9	42022	36329.7	99945.2	19627.8	4910.7	21588.8	42902.1	15353.2	20880.6	1548.4	23941.9	8104.1	2748	3510.2	12809.4
2017	29883	12450.6	30640.8	14484.3	14898.1	21693	10922	12313	32925	85869.8	52403.1	29676.2	33842.4	20210.8	63012.1	44824.9	37235	33828.1	91648.7	17790.7	4497.5	20066.3	37905.1	13605.4	18486	1349	21473.5	7336.7	2465.1	3200.3	11159.9
2016	27041.2	11477.2	28474.1	11946.4	13789.3	20392.5	10427	11895	29887	77350.9	47254	26307.7	29609.4	18388.6	58762.5	40249.3	33353	30853.5	82163.2	16116.6	4090.2	18023	33138.5	11792.4	16369	1173	19045.8	6907.9	2258.2	2781.4	9630.8
2015	24779.1	10879.5	26398.4	11836.4	12949	20210.3	10018	11690	26887	71255.9	43507.7	23831.2	26819.5	16780.9	55288.8	37084.1	30344	28538.6	74732.4	14797.8	3734.2	16040.5	30342	10541	14960	1043	17898.8	6556.6	2011	2579.4	9306.9
2014	22926	10640.6	25208.9	12094.7	12158.2	20025.7	9966.5	12170.8	25269.8	64830.5	40023.5	22519.7	24942.1	15667.8	50774.8	34574.8	28242.1	25881.3	68173	13587.8	3449	14623.8	28891.3	9173.1	14041.7	939.7	17402.5	6518.4	1847.7	2473.9	9264.5
2013	21134.6	9945.4	24259.6	11987.2	11392.4	19208.8	9427.9	11849.1	23204.1	59349.4	37334.6	20584	22503.8	14300.2	47344.3	31632.5	25378	23545.2	62503.4	12448.4	3115.9	13027.6	26518	7973.1	12825.5	828.2	15905.4	6014.5	1713.3	2327.7	8392.6
2012	19024.7	9043	23077.5	11683.1	10470.1	17848.6	8678	11015.8	21305.6	53701.9	34382.4	18341.7	20190.7	12807.7	42957.3	28961.9	22590.9	21207.2	57007.7	11303.6	2789.4	11595.4	23922.4	6742.2	11097.4	710.2	14142.4	5393.1	1528.5	2131	7411.8
2011	17188.8	8112.5	21384.7	10894.4	9458.1	16354.9	7734.6	9935	20009.7	48839.2	31854.8	16284.9	17917.7	11584.5	39064.9	26318.7	19942.5	18915	53072.8	10299.9	2463.8	10161.2	21050.9	5615.6	9523.1	611.5	12175.1	4816.9	1370.4	1931.8	6532


CREATE TABLE chinas_gdp_in_province_zh (
  "unnamed_0" BIGINT,
  "n" DOUBLE,
  "n_02e762" DOUBLE,
  "n_8495ef" DOUBLE,
  "n_26d2a7" DOUBLE,
  "n_fa6203" DOUBLE,
  "n_596fda" DOUBLE,
  "n_ce10bb" DOUBLE,
  "n_5324d0" DOUBLE,
  "n_1471cd" DOUBLE,
  "n_548b96" DOUBLE,
  "n_e76655" DOUBLE,
  "n_e8ea56" DOUBLE,
  "n_2236ef" DOUBLE,
  "n_ec0692" DOUBLE,
  "n_17dd8d" DOUBLE,
  "n_52b71b" DOUBLE,
  "n_27c9f2" DOUBLE,
  "n_fffd35" DOUBLE,
  "n_f85eb4" DOUBLE,
  "n_0a8140" DOUBLE,
  "n_66c7a8" DOUBLE,
  "n_b94cfd" DOUBLE,
  "n_e053ca" DOUBLE,
  "n_76463e" DOUBLE,
  "n_d02866" DOUBLE,
  "n_d36431" DOUBLE,
  "n_78fa10" DOUBLE,
  "n_e1b13d" DOUBLE,
  "n_30fd79" DOUBLE,
  "n_f76bd0" DOUBLE,
  "n_8b2760" DOUBLE
);