import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
font = FontProperties(fname="../../simhei.ttf")
GDP = pd.read_csv("../datasets/Chinas GDP in Province Zh.csv",index_col=0)
GDP
北京市 | 天津市 | 河北省 | 山西省 | 内蒙古自治区 | 辽宁省 | 吉林省 | 黑龙江省 | 上海市 | 江苏省 | ... | 重庆市 | 四川省 | 贵州省 | 云南省 | 西藏自治区 | 陕西省 | 甘肃省 | 青海省 | 宁夏回族自治区 | 新疆维吾尔自治区 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2020 | 36102.6 | 14083.7 | 36206.9 | 17651.9 | 17359.8 | 25115.0 | 12311.3 | 13698.5 | 38700.6 | 102719.0 | ... | 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 | ... | 23605.8 | 46363.8 | 16769.3 | 23223.8 | 1697.8 | 25793.2 | 8718.3 | 2941.1 | 3748.5 | 13597.1 |
2018 | 33106.0 | 13362.9 | 32494.6 | 15958.1 | 16140.8 | 23510.5 | 11253.8 | 12846.5 | 36011.8 | 93207.6 | ... | 21588.8 | 42902.1 | 15353.2 | 20880.6 | 1548.4 | 23941.9 | 8104.1 | 2748.0 | 3510.2 | 12809.4 |
2017 | 29883.0 | 12450.6 | 30640.8 | 14484.3 | 14898.1 | 21693.0 | 10922.0 | 12313.0 | 32925.0 | 85869.8 | ... | 20066.3 | 37905.1 | 13605.4 | 18486.0 | 1349.0 | 21473.5 | 7336.7 | 2465.1 | 3200.3 | 11159.9 |
2016 | 27041.2 | 11477.2 | 28474.1 | 11946.4 | 13789.3 | 20392.5 | 10427.0 | 11895.0 | 29887.0 | 77350.9 | ... | 18023.0 | 33138.5 | 11792.4 | 16369.0 | 1173.0 | 19045.8 | 6907.9 | 2258.2 | 2781.4 | 9630.8 |
2015 | 24779.1 | 10879.5 | 26398.4 | 11836.4 | 12949.0 | 20210.3 | 10018.0 | 11690.0 | 26887.0 | 71255.9 | ... | 16040.5 | 30342.0 | 10541.0 | 14960.0 | 1043.0 | 17898.8 | 6556.6 | 2011.0 | 2579.4 | 9306.9 |
2014 | 22926.0 | 10640.6 | 25208.9 | 12094.7 | 12158.2 | 20025.7 | 9966.5 | 12170.8 | 25269.8 | 64830.5 | ... | 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 | ... | 13027.6 | 26518.0 | 7973.1 | 12825.5 | 828.2 | 15905.4 | 6014.5 | 1713.3 | 2327.7 | 8392.6 |
2012 | 19024.7 | 9043.0 | 23077.5 | 11683.1 | 10470.1 | 17848.6 | 8678.0 | 11015.8 | 21305.6 | 53701.9 | ... | 11595.4 | 23922.4 | 6742.2 | 11097.4 | 710.2 | 14142.4 | 5393.1 | 1528.5 | 2131.0 | 7411.8 |
2011 | 17188.8 | 8112.5 | 21384.7 | 10894.4 | 9458.1 | 16354.9 | 7734.6 | 9935.0 | 20009.7 | 48839.2 | ... | 10161.2 | 21050.9 | 5615.6 | 9523.1 | 611.5 | 12175.1 | 4816.9 | 1370.4 | 1931.8 | 6532.0 |
2010 | 14964.0 | 6830.8 | 18003.6 | 8903.9 | 8199.9 | 13896.3 | 6410.5 | 8308.3 | 17915.4 | 41383.9 | ... | 8065.3 | 17224.8 | 4519.0 | 7735.3 | 512.9 | 9845.2 | 3943.7 | 1144.2 | 1571.7 | 5360.2 |
2009 | 12900.9 | 5709.6 | 15306.9 | 7147.6 | 7104.2 | 12815.7 | 5434.8 | 7218.9 | 15742.4 | 34471.7 | ... | 6651.2 | 14190.6 | 3856.7 | 6574.4 | 445.7 | 7997.8 | 3268.3 | 939.7 | 1266.7 | 4237.0 |
2008 | 11813.1 | 5182.4 | 14200.1 | 7223.0 | 6242.4 | 12137.7 | 4834.7 | 7134.2 | 14536.9 | 30945.5 | ... | 5899.5 | 12756.2 | 3504.5 | 6016.6 | 398.2 | 7177.8 | 3071.7 | 896.9 | 1139.2 | 4142.5 |
2007 | 10425.5 | 4158.4 | 12152.9 | 5935.6 | 5166.9 | 10292.2 | 4080.3 | 6126.3 | 12878.7 | 25988.4 | ... | 4770.7 | 10562.1 | 2847.5 | 5077.4 | 344.1 | 5681.8 | 2675.1 | 720.1 | 877.6 | 3500.0 |
2006 | 8387.0 | 3538.2 | 10043.0 | 4713.6 | 4161.8 | 8390.3 | 3226.5 | 5329.8 | 10598.9 | 21240.8 | ... | 3900.3 | 8494.7 | 2264.1 | 4090.7 | 285.9 | 4595.6 | 2203.0 | 585.2 | 683.3 | 2957.3 |
2005 | 7149.8 | 3158.6 | 8773.4 | 4079.4 | 3523.7 | 7260.8 | 2776.5 | 4756.4 | 9197.1 | 18121.3 | ... | 3448.4 | 7195.9 | 1939.9 | 3497.7 | 243.1 | 3817.2 | 1864.6 | 499.4 | 579.9 | 2520.5 |
2004 | 6252.5 | 2621.1 | 7588.6 | 3496.0 | 2942.4 | 6469.8 | 2455.2 | 4134.7 | 8101.6 | 14823.1 | ... | 3059.5 | 6304.0 | 1649.4 | 3136.4 | 217.9 | 3141.6 | 1653.6 | 443.7 | 519.9 | 2170.4 |
2003 | 5267.2 | 2257.8 | 6333.6 | 2854.3 | 2388.4 | 5906.3 | 2141.0 | 3609.7 | 6804.0 | 12442.9 | ... | 2615.6 | 5346.2 | 1429.0 | 2633.4 | 186.0 | 2587.7 | 1399.9 | 385.0 | 442.6 | 1889.2 |
2002 | 4525.7 | 1926.9 | 5518.9 | 2324.8 | 1940.9 | 5458.2 | 2043.1 | 3242.7 | 5795.0 | 10606.9 | ... | 2279.8 | 4725.0 | 1243.4 | 2358.7 | 162.0 | 2253.4 | 1232.0 | 340.7 | 377.2 | 1612.6 |
2001 | 3861.5 | 1756.9 | 5062.9 | 2029.5 | 1713.8 | 5033.1 | 1900.9 | 3043.4 | 5257.7 | 9456.8 | ... | 2014.6 | 4293.5 | 1133.3 | 2159.0 | 139.2 | 2010.6 | 1125.4 | 300.1 | 337.4 | 1491.6 |
2000 | 3277.8 | 1591.7 | 4628.2 | 1845.7 | 1539.1 | 4669.1 | 1751.4 | 2855.5 | 4812.2 | 8553.7 | ... | 1822.1 | 3928.2 | 1029.9 | 2030.1 | 117.8 | 1804.0 | 1052.9 | 263.7 | 295.0 | 1363.6 |
1999 | 2759.8 | 1435.6 | 4158.9 | 1667.1 | 1379.3 | 4171.7 | 1673.0 | 2536.9 | 4222.3 | 7697.8 | ... | 1687.8 | 3649.1 | 937.5 | 1899.8 | 106.0 | 1592.6 | 956.3 | 239.4 | 264.6 | 1163.2 |
1998 | 2439.1 | 1344.7 | 3924.5 | 1611.1 | 1262.5 | 3881.7 | 1577.1 | 2470.2 | 3831.0 | 7200.0 | ... | 1622.4 | 3474.1 | 858.4 | 1831.3 | 91.5 | 1458.4 | 887.7 | 220.9 | 245.5 | 1107.0 |
1997 | 2118.1 | 1264.6 | 3652.1 | 1476.0 | 1153.5 | 3582.5 | 1464.3 | 2397.6 | 3465.3 | 6680.3 | ... | 1525.3 | 3241.5 | 805.8 | 1676.2 | 77.2 | 1363.6 | 793.6 | 202.8 | 224.6 | 1039.8 |
1996 | 1819.4 | 1121.9 | 3198.0 | 1292.1 | 1023.1 | 3157.7 | 1346.8 | 2137.6 | 2980.8 | 6004.2 | ... | 1326.4 | 2871.7 | 723.2 | 1517.7 | 65.0 | 1215.8 | 722.5 | 184.2 | 202.9 | 900.9 |
1995 | 1516.2 | 932.0 | 2701.2 | 1076.0 | 857.1 | 2793.4 | 1137.2 | 1790.2 | 2518.1 | 5155.3 | ... | 1130.6 | 2443.2 | 636.2 | 1222.2 | 56.1 | 1036.9 | 557.8 | 167.8 | 175.2 | 814.9 |
1994 | 1149.8 | 732.9 | 2114.5 | 826.7 | 695.1 | 2461.8 | 937.7 | 1448.1 | 1990.9 | 4057.4 | ... | 838.1 | 2001.4 | 524.5 | 983.8 | 46.0 | 839.0 | 453.6 | 138.4 | 136.3 | 662.3 |
1993 | 888.9 | 538.9 | 1620.8 | 680.4 | 537.8 | 2010.8 | 718.6 | 1075.3 | 1519.2 | 2998.2 | ... | 611.1 | 1486.1 | 417.7 | 783.3 | 37.4 | 678.2 | 372.2 | 109.7 | 104.5 | 495.3 |
1992 | 710.2 | 411.0 | 1278.5 | 551.1 | 421.7 | 1473.0 | 558.1 | 857.4 | 1114.3 | 2136.0 | ... | 462.5 | 1177.3 | 339.9 | 618.7 | 33.3 | 531.6 | 317.8 | 87.5 | 83.1 | 402.3 |
29 rows × 31 columns
GDP.T
2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | ... | 2001 | 2000 | 1999 | 1998 | 1997 | 1996 | 1995 | 1994 | 1993 | 1992 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
北京市 | 36102.6 | 35445.1 | 33106.0 | 29883.0 | 27041.2 | 24779.1 | 22926.0 | 21134.6 | 19024.7 | 17188.8 | ... | 3861.5 | 3277.8 | 2759.8 | 2439.1 | 2118.1 | 1819.4 | 1516.2 | 1149.8 | 888.9 | 710.2 |
天津市 | 14083.7 | 14055.5 | 13362.9 | 12450.6 | 11477.2 | 10879.5 | 10640.6 | 9945.4 | 9043.0 | 8112.5 | ... | 1756.9 | 1591.7 | 1435.6 | 1344.7 | 1264.6 | 1121.9 | 932.0 | 732.9 | 538.9 | 411.0 |
河北省 | 36206.9 | 34978.6 | 32494.6 | 30640.8 | 28474.1 | 26398.4 | 25208.9 | 24259.6 | 23077.5 | 21384.7 | ... | 5062.9 | 4628.2 | 4158.9 | 3924.5 | 3652.1 | 3198.0 | 2701.2 | 2114.5 | 1620.8 | 1278.5 |
山西省 | 17651.9 | 16961.6 | 15958.1 | 14484.3 | 11946.4 | 11836.4 | 12094.7 | 11987.2 | 11683.1 | 10894.4 | ... | 2029.5 | 1845.7 | 1667.1 | 1611.1 | 1476.0 | 1292.1 | 1076.0 | 826.7 | 680.4 | 551.1 |
内蒙古自治区 | 17359.8 | 17212.5 | 16140.8 | 14898.1 | 13789.3 | 12949.0 | 12158.2 | 11392.4 | 10470.1 | 9458.1 | ... | 1713.8 | 1539.1 | 1379.3 | 1262.5 | 1153.5 | 1023.1 | 857.1 | 695.1 | 537.8 | 421.7 |
辽宁省 | 25115.0 | 24855.3 | 23510.5 | 21693.0 | 20392.5 | 20210.3 | 20025.7 | 19208.8 | 17848.6 | 16354.9 | ... | 5033.1 | 4669.1 | 4171.7 | 3881.7 | 3582.5 | 3157.7 | 2793.4 | 2461.8 | 2010.8 | 1473.0 |
吉林省 | 12311.3 | 11726.8 | 11253.8 | 10922.0 | 10427.0 | 10018.0 | 9966.5 | 9427.9 | 8678.0 | 7734.6 | ... | 1900.9 | 1751.4 | 1673.0 | 1577.1 | 1464.3 | 1346.8 | 1137.2 | 937.7 | 718.6 | 558.1 |
黑龙江省 | 13698.5 | 13544.4 | 12846.5 | 12313.0 | 11895.0 | 11690.0 | 12170.8 | 11849.1 | 11015.8 | 9935.0 | ... | 3043.4 | 2855.5 | 2536.9 | 2470.2 | 2397.6 | 2137.6 | 1790.2 | 1448.1 | 1075.3 | 857.4 |
上海市 | 38700.6 | 37987.6 | 36011.8 | 32925.0 | 29887.0 | 26887.0 | 25269.8 | 23204.1 | 21305.6 | 20009.7 | ... | 5257.7 | 4812.2 | 4222.3 | 3831.0 | 3465.3 | 2980.8 | 2518.1 | 1990.9 | 1519.2 | 1114.3 |
江苏省 | 102719.0 | 98656.8 | 93207.6 | 85869.8 | 77350.9 | 71255.9 | 64830.5 | 59349.4 | 53701.9 | 48839.2 | ... | 9456.8 | 8553.7 | 7697.8 | 7200.0 | 6680.3 | 6004.2 | 5155.3 | 4057.4 | 2998.2 | 2136.0 |
浙江省 | 64613.3 | 62462.0 | 58002.8 | 52403.1 | 47254.0 | 43507.7 | 40023.5 | 37334.6 | 34382.4 | 31854.8 | ... | 6927.7 | 6164.8 | 5461.3 | 5065.5 | 4695.9 | 4195.8 | 3563.9 | 2689.3 | 1925.9 | 1375.7 |
安徽省 | 38680.6 | 36845.5 | 34010.9 | 29676.2 | 26307.7 | 23831.2 | 22519.7 | 20584.0 | 18341.7 | 16284.9 | ... | 3502.8 | 3125.3 | 2907.8 | 2711.7 | 2485.4 | 2199.7 | 1891.2 | 1378.9 | 1073.5 | 827.0 |
福建省 | 43903.9 | 42326.6 | 38687.8 | 33842.4 | 29609.4 | 26819.5 | 24942.1 | 22503.8 | 20190.7 | 17917.7 | ... | 4072.9 | 3764.5 | 3414.2 | 3159.9 | 2870.9 | 2484.3 | 2094.9 | 1644.4 | 1114.2 | 784.7 |
江西省 | 25691.5 | 24667.3 | 22716.5 | 20210.8 | 18388.6 | 16780.9 | 15667.8 | 14300.2 | 12807.7 | 11584.5 | ... | 2175.7 | 2003.1 | 1853.7 | 1719.9 | 1605.8 | 1409.7 | 1169.7 | 948.2 | 723.0 | 572.6 |
山东省 | 73129.0 | 70540.5 | 66648.9 | 63012.1 | 58762.5 | 55288.8 | 50774.8 | 47344.3 | 42957.3 | 39064.9 | ... | 9076.2 | 8278.1 | 7493.8 | 7021.4 | 6537.1 | 5883.8 | 4953.4 | 3844.5 | 2770.4 | 2196.5 |
河南省 | 54997.1 | 53717.8 | 49935.9 | 44824.9 | 40249.3 | 37084.1 | 34574.8 | 31632.5 | 28961.9 | 26318.7 | ... | 5533.0 | 5053.0 | 4517.9 | 4308.2 | 4041.1 | 3634.7 | 2988.4 | 2216.8 | 1660.2 | 1279.8 |
湖北省 | 43443.5 | 45429.0 | 42022.0 | 37235.0 | 33353.0 | 30344.0 | 28242.1 | 25378.0 | 22590.9 | 19942.5 | ... | 3880.5 | 3545.4 | 3229.3 | 3114.0 | 2856.5 | 2499.8 | 2109.4 | 1700.9 | 1325.8 | 1088.4 |
湖南省 | 41781.5 | 39894.1 | 36329.7 | 33828.1 | 30853.5 | 28538.6 | 25881.3 | 23545.2 | 21207.2 | 18915.0 | ... | 3831.9 | 3551.5 | 3214.5 | 3025.5 | 2849.3 | 2540.1 | 2132.1 | 1650.0 | 1244.7 | 987.0 |
广东省 | 110760.9 | 107986.9 | 99945.2 | 91648.7 | 82163.2 | 74732.4 | 68173.0 | 62503.4 | 57007.7 | 53072.8 | ... | 12126.6 | 10810.2 | 9289.6 | 8555.3 | 7793.0 | 6848.2 | 5940.3 | 4619.0 | 3469.3 | 2447.5 |
广西壮族自治区 | 22156.7 | 21237.1 | 19627.8 | 17790.7 | 16116.6 | 14797.8 | 13587.8 | 12448.4 | 11303.6 | 10299.9 | ... | 2279.3 | 2080.0 | 1971.4 | 1911.3 | 1817.3 | 1697.9 | 1497.6 | 1198.3 | 871.7 | 646.6 |
海南省 | 5532.4 | 5330.8 | 4910.7 | 4497.5 | 4090.2 | 3734.2 | 3449.0 | 3115.9 | 2789.4 | 2463.8 | ... | 579.2 | 526.8 | 476.7 | 442.1 | 411.2 | 389.7 | 363.3 | 332.0 | 260.4 | 184.9 |
重庆市 | 25002.8 | 23605.8 | 21588.8 | 20066.3 | 18023.0 | 16040.5 | 14623.8 | 13027.6 | 11595.4 | 10161.2 | ... | 2014.6 | 1822.1 | 1687.8 | 1622.4 | 1525.3 | 1326.4 | 1130.6 | 838.1 | 611.1 | 462.5 |
四川省 | 48598.8 | 46363.8 | 42902.1 | 37905.1 | 33138.5 | 30342.0 | 28891.3 | 26518.0 | 23922.4 | 21050.9 | ... | 4293.5 | 3928.2 | 3649.1 | 3474.1 | 3241.5 | 2871.7 | 2443.2 | 2001.4 | 1486.1 | 1177.3 |
贵州省 | 17826.6 | 16769.3 | 15353.2 | 13605.4 | 11792.4 | 10541.0 | 9173.1 | 7973.1 | 6742.2 | 5615.6 | ... | 1133.3 | 1029.9 | 937.5 | 858.4 | 805.8 | 723.2 | 636.2 | 524.5 | 417.7 | 339.9 |
云南省 | 24521.9 | 23223.8 | 20880.6 | 18486.0 | 16369.0 | 14960.0 | 14041.7 | 12825.5 | 11097.4 | 9523.1 | ... | 2159.0 | 2030.1 | 1899.8 | 1831.3 | 1676.2 | 1517.7 | 1222.2 | 983.8 | 783.3 | 618.7 |
西藏自治区 | 1902.7 | 1697.8 | 1548.4 | 1349.0 | 1173.0 | 1043.0 | 939.7 | 828.2 | 710.2 | 611.5 | ... | 139.2 | 117.8 | 106.0 | 91.5 | 77.2 | 65.0 | 56.1 | 46.0 | 37.4 | 33.3 |
陕西省 | 26181.9 | 25793.2 | 23941.9 | 21473.5 | 19045.8 | 17898.8 | 17402.5 | 15905.4 | 14142.4 | 12175.1 | ... | 2010.6 | 1804.0 | 1592.6 | 1458.4 | 1363.6 | 1215.8 | 1036.9 | 839.0 | 678.2 | 531.6 |
甘肃省 | 9016.7 | 8718.3 | 8104.1 | 7336.7 | 6907.9 | 6556.6 | 6518.4 | 6014.5 | 5393.1 | 4816.9 | ... | 1125.4 | 1052.9 | 956.3 | 887.7 | 793.6 | 722.5 | 557.8 | 453.6 | 372.2 | 317.8 |
青海省 | 3005.9 | 2941.1 | 2748.0 | 2465.1 | 2258.2 | 2011.0 | 1847.7 | 1713.3 | 1528.5 | 1370.4 | ... | 300.1 | 263.7 | 239.4 | 220.9 | 202.8 | 184.2 | 167.8 | 138.4 | 109.7 | 87.5 |
宁夏回族自治区 | 3920.6 | 3748.5 | 3510.2 | 3200.3 | 2781.4 | 2579.4 | 2473.9 | 2327.7 | 2131.0 | 1931.8 | ... | 337.4 | 295.0 | 264.6 | 245.5 | 224.6 | 202.9 | 175.2 | 136.3 | 104.5 | 83.1 |
新疆维吾尔自治区 | 13797.6 | 13597.1 | 12809.4 | 11159.9 | 9630.8 | 9306.9 | 9264.5 | 8392.6 | 7411.8 | 6532.0 | ... | 1491.6 | 1363.6 | 1163.2 | 1107.0 | 1039.8 | 900.9 | 814.9 | 662.3 | 495.3 | 402.3 |
31 rows × 29 columns
GDP.T
2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | ... | 2001 | 2000 | 1999 | 1998 | 1997 | 1996 | 1995 | 1994 | 1993 | 1992 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
北京市 | 36102.6 | 35445.1 | 33106.0 | 29883.0 | 27041.2 | 24779.1 | 22926.0 | 21134.6 | 19024.7 | 17188.8 | ... | 3861.5 | 3277.8 | 2759.8 | 2439.1 | 2118.1 | 1819.4 | 1516.2 | 1149.8 | 888.9 | 710.2 |
天津市 | 14083.7 | 14055.5 | 13362.9 | 12450.6 | 11477.2 | 10879.5 | 10640.6 | 9945.4 | 9043.0 | 8112.5 | ... | 1756.9 | 1591.7 | 1435.6 | 1344.7 | 1264.6 | 1121.9 | 932.0 | 732.9 | 538.9 | 411.0 |
河北省 | 36206.9 | 34978.6 | 32494.6 | 30640.8 | 28474.1 | 26398.4 | 25208.9 | 24259.6 | 23077.5 | 21384.7 | ... | 5062.9 | 4628.2 | 4158.9 | 3924.5 | 3652.1 | 3198.0 | 2701.2 | 2114.5 | 1620.8 | 1278.5 |
山西省 | 17651.9 | 16961.6 | 15958.1 | 14484.3 | 11946.4 | 11836.4 | 12094.7 | 11987.2 | 11683.1 | 10894.4 | ... | 2029.5 | 1845.7 | 1667.1 | 1611.1 | 1476.0 | 1292.1 | 1076.0 | 826.7 | 680.4 | 551.1 |
内蒙古自治区 | 17359.8 | 17212.5 | 16140.8 | 14898.1 | 13789.3 | 12949.0 | 12158.2 | 11392.4 | 10470.1 | 9458.1 | ... | 1713.8 | 1539.1 | 1379.3 | 1262.5 | 1153.5 | 1023.1 | 857.1 | 695.1 | 537.8 | 421.7 |
辽宁省 | 25115.0 | 24855.3 | 23510.5 | 21693.0 | 20392.5 | 20210.3 | 20025.7 | 19208.8 | 17848.6 | 16354.9 | ... | 5033.1 | 4669.1 | 4171.7 | 3881.7 | 3582.5 | 3157.7 | 2793.4 | 2461.8 | 2010.8 | 1473.0 |
吉林省 | 12311.3 | 11726.8 | 11253.8 | 10922.0 | 10427.0 | 10018.0 | 9966.5 | 9427.9 | 8678.0 | 7734.6 | ... | 1900.9 | 1751.4 | 1673.0 | 1577.1 | 1464.3 | 1346.8 | 1137.2 | 937.7 | 718.6 | 558.1 |
黑龙江省 | 13698.5 | 13544.4 | 12846.5 | 12313.0 | 11895.0 | 11690.0 | 12170.8 | 11849.1 | 11015.8 | 9935.0 | ... | 3043.4 | 2855.5 | 2536.9 | 2470.2 | 2397.6 | 2137.6 | 1790.2 | 1448.1 | 1075.3 | 857.4 |
上海市 | 38700.6 | 37987.6 | 36011.8 | 32925.0 | 29887.0 | 26887.0 | 25269.8 | 23204.1 | 21305.6 | 20009.7 | ... | 5257.7 | 4812.2 | 4222.3 | 3831.0 | 3465.3 | 2980.8 | 2518.1 | 1990.9 | 1519.2 | 1114.3 |
江苏省 | 102719.0 | 98656.8 | 93207.6 | 85869.8 | 77350.9 | 71255.9 | 64830.5 | 59349.4 | 53701.9 | 48839.2 | ... | 9456.8 | 8553.7 | 7697.8 | 7200.0 | 6680.3 | 6004.2 | 5155.3 | 4057.4 | 2998.2 | 2136.0 |
浙江省 | 64613.3 | 62462.0 | 58002.8 | 52403.1 | 47254.0 | 43507.7 | 40023.5 | 37334.6 | 34382.4 | 31854.8 | ... | 6927.7 | 6164.8 | 5461.3 | 5065.5 | 4695.9 | 4195.8 | 3563.9 | 2689.3 | 1925.9 | 1375.7 |
安徽省 | 38680.6 | 36845.5 | 34010.9 | 29676.2 | 26307.7 | 23831.2 | 22519.7 | 20584.0 | 18341.7 | 16284.9 | ... | 3502.8 | 3125.3 | 2907.8 | 2711.7 | 2485.4 | 2199.7 | 1891.2 | 1378.9 | 1073.5 | 827.0 |
福建省 | 43903.9 | 42326.6 | 38687.8 | 33842.4 | 29609.4 | 26819.5 | 24942.1 | 22503.8 | 20190.7 | 17917.7 | ... | 4072.9 | 3764.5 | 3414.2 | 3159.9 | 2870.9 | 2484.3 | 2094.9 | 1644.4 | 1114.2 | 784.7 |
江西省 | 25691.5 | 24667.3 | 22716.5 | 20210.8 | 18388.6 | 16780.9 | 15667.8 | 14300.2 | 12807.7 | 11584.5 | ... | 2175.7 | 2003.1 | 1853.7 | 1719.9 | 1605.8 | 1409.7 | 1169.7 | 948.2 | 723.0 | 572.6 |
山东省 | 73129.0 | 70540.5 | 66648.9 | 63012.1 | 58762.5 | 55288.8 | 50774.8 | 47344.3 | 42957.3 | 39064.9 | ... | 9076.2 | 8278.1 | 7493.8 | 7021.4 | 6537.1 | 5883.8 | 4953.4 | 3844.5 | 2770.4 | 2196.5 |
河南省 | 54997.1 | 53717.8 | 49935.9 | 44824.9 | 40249.3 | 37084.1 | 34574.8 | 31632.5 | 28961.9 | 26318.7 | ... | 5533.0 | 5053.0 | 4517.9 | 4308.2 | 4041.1 | 3634.7 | 2988.4 | 2216.8 | 1660.2 | 1279.8 |
湖北省 | 43443.5 | 45429.0 | 42022.0 | 37235.0 | 33353.0 | 30344.0 | 28242.1 | 25378.0 | 22590.9 | 19942.5 | ... | 3880.5 | 3545.4 | 3229.3 | 3114.0 | 2856.5 | 2499.8 | 2109.4 | 1700.9 | 1325.8 | 1088.4 |
湖南省 | 41781.5 | 39894.1 | 36329.7 | 33828.1 | 30853.5 | 28538.6 | 25881.3 | 23545.2 | 21207.2 | 18915.0 | ... | 3831.9 | 3551.5 | 3214.5 | 3025.5 | 2849.3 | 2540.1 | 2132.1 | 1650.0 | 1244.7 | 987.0 |
广东省 | 110760.9 | 107986.9 | 99945.2 | 91648.7 | 82163.2 | 74732.4 | 68173.0 | 62503.4 | 57007.7 | 53072.8 | ... | 12126.6 | 10810.2 | 9289.6 | 8555.3 | 7793.0 | 6848.2 | 5940.3 | 4619.0 | 3469.3 | 2447.5 |
广西壮族自治区 | 22156.7 | 21237.1 | 19627.8 | 17790.7 | 16116.6 | 14797.8 | 13587.8 | 12448.4 | 11303.6 | 10299.9 | ... | 2279.3 | 2080.0 | 1971.4 | 1911.3 | 1817.3 | 1697.9 | 1497.6 | 1198.3 | 871.7 | 646.6 |
海南省 | 5532.4 | 5330.8 | 4910.7 | 4497.5 | 4090.2 | 3734.2 | 3449.0 | 3115.9 | 2789.4 | 2463.8 | ... | 579.2 | 526.8 | 476.7 | 442.1 | 411.2 | 389.7 | 363.3 | 332.0 | 260.4 | 184.9 |
重庆市 | 25002.8 | 23605.8 | 21588.8 | 20066.3 | 18023.0 | 16040.5 | 14623.8 | 13027.6 | 11595.4 | 10161.2 | ... | 2014.6 | 1822.1 | 1687.8 | 1622.4 | 1525.3 | 1326.4 | 1130.6 | 838.1 | 611.1 | 462.5 |
四川省 | 48598.8 | 46363.8 | 42902.1 | 37905.1 | 33138.5 | 30342.0 | 28891.3 | 26518.0 | 23922.4 | 21050.9 | ... | 4293.5 | 3928.2 | 3649.1 | 3474.1 | 3241.5 | 2871.7 | 2443.2 | 2001.4 | 1486.1 | 1177.3 |
贵州省 | 17826.6 | 16769.3 | 15353.2 | 13605.4 | 11792.4 | 10541.0 | 9173.1 | 7973.1 | 6742.2 | 5615.6 | ... | 1133.3 | 1029.9 | 937.5 | 858.4 | 805.8 | 723.2 | 636.2 | 524.5 | 417.7 | 339.9 |
云南省 | 24521.9 | 23223.8 | 20880.6 | 18486.0 | 16369.0 | 14960.0 | 14041.7 | 12825.5 | 11097.4 | 9523.1 | ... | 2159.0 | 2030.1 | 1899.8 | 1831.3 | 1676.2 | 1517.7 | 1222.2 | 983.8 | 783.3 | 618.7 |
西藏自治区 | 1902.7 | 1697.8 | 1548.4 | 1349.0 | 1173.0 | 1043.0 | 939.7 | 828.2 | 710.2 | 611.5 | ... | 139.2 | 117.8 | 106.0 | 91.5 | 77.2 | 65.0 | 56.1 | 46.0 | 37.4 | 33.3 |
陕西省 | 26181.9 | 25793.2 | 23941.9 | 21473.5 | 19045.8 | 17898.8 | 17402.5 | 15905.4 | 14142.4 | 12175.1 | ... | 2010.6 | 1804.0 | 1592.6 | 1458.4 | 1363.6 | 1215.8 | 1036.9 | 839.0 | 678.2 | 531.6 |
甘肃省 | 9016.7 | 8718.3 | 8104.1 | 7336.7 | 6907.9 | 6556.6 | 6518.4 | 6014.5 | 5393.1 | 4816.9 | ... | 1125.4 | 1052.9 | 956.3 | 887.7 | 793.6 | 722.5 | 557.8 | 453.6 | 372.2 | 317.8 |
青海省 | 3005.9 | 2941.1 | 2748.0 | 2465.1 | 2258.2 | 2011.0 | 1847.7 | 1713.3 | 1528.5 | 1370.4 | ... | 300.1 | 263.7 | 239.4 | 220.9 | 202.8 | 184.2 | 167.8 | 138.4 | 109.7 | 87.5 |
宁夏回族自治区 | 3920.6 | 3748.5 | 3510.2 | 3200.3 | 2781.4 | 2579.4 | 2473.9 | 2327.7 | 2131.0 | 1931.8 | ... | 337.4 | 295.0 | 264.6 | 245.5 | 224.6 | 202.9 | 175.2 | 136.3 | 104.5 | 83.1 |
新疆维吾尔自治区 | 13797.6 | 13597.1 | 12809.4 | 11159.9 | 9630.8 | 9306.9 | 9264.5 | 8392.6 | 7411.8 | 6532.0 | ... | 1491.6 | 1363.6 | 1163.2 | 1107.0 | 1039.8 | 900.9 | 814.9 | 662.3 | 495.3 | 402.3 |
31 rows × 29 columns
plt.figure(figsize=(28,10))
plt.bar(x=GDP.columns.values,height=GDP.iloc[0],width=0.5)
plt.xticks(GDP.columns.values,font=font,rotation=40,fontsize=14)
plt.yticks(fontsize=16)
plt.xlabel("省份",font=font,fontsize=20)
plt.ylabel("GDP",font=font,fontsize=20)
plt.title("2020年GDP情况",font=font,fontsize=20)
plt.show()
help(GDP.iloc)
Help on _iLocIndexer in module pandas.core.indexing object: class _iLocIndexer(_LocationIndexer) | Purely integer-location based indexing for selection by position. | | ``.iloc[]`` is primarily integer position based (from ``0`` to | ``length-1`` of the axis), but may also be used with a boolean | array. | | Allowed inputs are: | | - An integer, e.g. ``5``. | - A list or array of integers, e.g. ``[4, 3, 0]``. | - A slice object with ints, e.g. ``1:7``. | - A boolean array. | - A ``callable`` function with one argument (the calling Series or | DataFrame) and that returns valid output for indexing (one of the above). | This is useful in method chains, when you don't have a reference to the | calling object, but would like to base your selection on some value. | | ``.iloc`` will raise ``IndexError`` if a requested indexer is | out-of-bounds, except *slice* indexers which allow out-of-bounds | indexing (this conforms with python/numpy *slice* semantics). | | See more at :ref:`Selection by Position <indexing.integer>`. | | See Also | -------- | DataFrame.iat : Fast integer location scalar accessor. | DataFrame.loc : Purely label-location based indexer for selection by label. | Series.iloc : Purely integer-location based indexing for | selection by position. | | Examples | -------- | >>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4}, | ... {'a': 100, 'b': 200, 'c': 300, 'd': 400}, | ... {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000 }] | >>> df = pd.DataFrame(mydict) | >>> df | a b c d | 0 1 2 3 4 | 1 100 200 300 400 | 2 1000 2000 3000 4000 | | **Indexing just the rows** | | With a scalar integer. | | >>> type(df.iloc[0]) | <class 'pandas.core.series.Series'> | >>> df.iloc[0] | a 1 | b 2 | c 3 | d 4 | Name: 0, dtype: int64 | | With a list of integers. | | >>> df.iloc[[0]] | a b c d | 0 1 2 3 4 | >>> type(df.iloc[[0]]) | <class 'pandas.core.frame.DataFrame'> | | >>> df.iloc[[0, 1]] | a b c d | 0 1 2 3 4 | 1 100 200 300 400 | | With a `slice` object. | | >>> df.iloc[:3] | a b c d | 0 1 2 3 4 | 1 100 200 300 400 | 2 1000 2000 3000 4000 | | With a boolean mask the same length as the index. | | >>> df.iloc[[True, False, True]] | a b c d | 0 1 2 3 4 | 2 1000 2000 3000 4000 | | With a callable, useful in method chains. The `x` passed | to the ``lambda`` is the DataFrame being sliced. This selects | the rows whose index label even. | | >>> df.iloc[lambda x: x.index % 2 == 0] | a b c d | 0 1 2 3 4 | 2 1000 2000 3000 4000 | | **Indexing both axes** | | You can mix the indexer types for the index and columns. Use ``:`` to | select the entire axis. | | With scalar integers. | | >>> df.iloc[0, 1] | 2 | | With lists of integers. | | >>> df.iloc[[0, 2], [1, 3]] | b d | 0 2 4 | 2 2000 4000 | | With `slice` objects. | | >>> df.iloc[1:3, 0:3] | a b c | 1 100 200 300 | 2 1000 2000 3000 | | With a boolean array whose length matches the columns. | | >>> df.iloc[:, [True, False, True, False]] | a c | 0 1 3 | 1 100 300 | 2 1000 3000 | | With a callable function that expects the Series or DataFrame. | | >>> df.iloc[:, lambda df: [0, 2]] | a c | 0 1 3 | 1 100 300 | 2 1000 3000 | | Method resolution order: | _iLocIndexer | _LocationIndexer | pandas._libs.indexing._NDFrameIndexerBase | builtins.object | | Methods inherited from _LocationIndexer: | | __call__(self, axis=None) | Call self as a function. | | __getitem__(self, key) | | __setitem__(self, key, value) | | ---------------------------------------------------------------------- | Data descriptors inherited from _LocationIndexer: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) | | ---------------------------------------------------------------------- | Data and other attributes inherited from _LocationIndexer: | | __annotations__ = {'_valid_types': <class 'str'>} | | axis = None | | ---------------------------------------------------------------------- | Methods inherited from pandas._libs.indexing._NDFrameIndexerBase: | | __init__(self, /, *args, **kwargs) | Initialize self. See help(type(self)) for accurate signature. | | __new__(*args, **kwargs) from builtins.type | Create and return a new object. See help(type) for accurate signature. | | __reduce__ = __reduce_cython__(...) | | __setstate__ = __setstate_cython__(...) | | ---------------------------------------------------------------------- | Data descriptors inherited from pandas._libs.indexing._NDFrameIndexerBase: | | name | | ndim | | obj
taitanic = pd.read_csv("../datasets/taitanic_train.csv")
taitanic.dropna(subset=["Age"],inplace=True)
plt.hist(x=taitanic["Age"],edgecolor="red",bins=20,density=True)
plt.xlabel("年龄",font=font)
plt.title("年龄分布图",font=font)
plt.ylabel("频率",font=font)
plt.show()
def normalFun(x,mu,sigma):
return np.exp(-((x-mu)**2) / (2*sigma*2)) / (sigma * np.sqrt(2*np.pi))
mean_x = taitanic["Age"].mean()
std_x = taitanic["Age"].std()
x = np.arange(taitanic["Age"].min(), taitanic["Age"].max()+10,1)
x
array([ 0.42, 1.42, 2.42, 3.42, 4.42, 5.42, 6.42, 7.42, 8.42, 9.42, 10.42, 11.42, 12.42, 13.42, 14.42, 15.42, 16.42, 17.42, 18.42, 19.42, 20.42, 21.42, 22.42, 23.42, 24.42, 25.42, 26.42, 27.42, 28.42, 29.42, 30.42, 31.42, 32.42, 33.42, 34.42, 35.42, 36.42, 37.42, 38.42, 39.42, 40.42, 41.42, 42.42, 43.42, 44.42, 45.42, 46.42, 47.42, 48.42, 49.42, 50.42, 51.42, 52.42, 53.42, 54.42, 55.42, 56.42, 57.42, 58.42, 59.42, 60.42, 61.42, 62.42, 63.42, 64.42, 65.42, 66.42, 67.42, 68.42, 69.42, 70.42, 71.42, 72.42, 73.42, 74.42, 75.42, 76.42, 77.42, 78.42, 79.42, 80.42, 81.42, 82.42, 83.42, 84.42, 85.42, 86.42, 87.42, 88.42, 89.42])
y = normalFun(x,mean_x,std_x)
y
array([1.07494290e-08, 2.89458205e-08, 7.53074567e-08, 1.89296133e-07, 4.59723969e-07, 1.07870874e-06, 2.44547312e-06, 5.35640286e-06, 1.13353574e-05, 2.31765532e-05, 4.57840411e-05, 8.73838233e-05, 1.61138608e-04, 2.87091158e-04, 4.94187403e-04, 8.21892789e-04, 1.32065784e-03, 2.05029854e-03, 3.07535699e-03, 4.45682534e-03, 6.24032687e-03, 8.44190979e-03, 1.10338156e-02, 1.39335692e-02, 1.70000689e-02, 2.00396729e-02, 2.28234988e-02, 2.51145534e-02, 2.67005583e-02, 2.74262777e-02, 2.72185535e-02, 2.60984586e-02, 2.41777743e-02, 2.16406064e-02, 1.87143255e-02, 1.56361769e-02, 1.26223034e-02, 9.84460516e-03, 7.41838952e-03, 5.40098050e-03, 3.79915690e-03, 2.58198370e-03, 1.69539699e-03, 1.07557565e-03, 6.59268252e-04, 3.90422652e-04, 2.23387824e-04, 1.23491092e-04, 6.59573824e-05, 3.40363387e-05, 1.69696899e-05, 8.17441384e-06, 3.80444211e-06, 1.71071232e-06, 7.43215285e-07, 3.11963602e-07, 1.26515829e-07, 4.95721151e-08, 1.87664306e-08, 6.86400436e-09, 2.42563287e-09, 8.28179034e-10, 2.73196443e-10, 8.70717940e-11, 2.68121469e-11, 7.97695850e-12, 2.29295072e-12, 6.36801023e-13, 1.70869453e-13, 4.42972486e-14, 1.10953426e-14, 2.68507444e-15, 6.27803289e-16, 1.41821635e-16, 3.09537332e-17, 6.52732483e-18, 1.32986961e-18, 2.61778842e-19, 4.97865083e-20, 9.14830087e-21, 1.62413025e-21, 2.78581912e-22, 4.61675279e-23, 7.39217042e-24, 1.14356003e-24, 1.70921858e-25, 2.46824286e-26, 3.44373576e-27, 4.64219516e-28, 6.04600655e-29])
plt.hist(x=taitanic["Age"],color="c",edgecolor="r",bins=20,density=True)
plt.plot(x,y,color="r",linewidth=3,label="正态分布图")
plt.xlabel("年龄",font=font)
plt.title("年龄分布图",font=font)
plt.ylabel("频率",font=font)
plt.show()
plt.hist(x=taitanic["Age"],color="c",edgecolor="r",bins=20,density=True,label="直方分布图")
taitanic["Age"].plot(kind="kde",color="r",linewidth=3,xlim=[0,90],label="核密度图")
plt.plot(x,y,color="blue",linewidth=3,label="正态分布图")
plt.xlabel("年龄",font=font)
plt.title("年龄分布图",font=font)
plt.ylabel("频率",font=font)
plt.legend(loc="best",prop=font) #设置字体
plt.show()
sec_house = pd.read_csv("../datasets/house.csv")
plt.figure(figsize=(10,16))
plt.boxplot(x=sec_house["均价"],showmeans=True,showfliers=True)
plt.title("二手房箱线图",font=font)
plt.show()
iris = pd.read_csv("../datasets/iris.data.txt")
iris
sepal_length | sepal_width | petal_length | petal_width | species | |
---|---|---|---|---|---|
0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |
2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |
3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |
4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |
... | ... | ... | ... | ... | ... |
145 | 6.7 | 3.0 | 5.2 | 2.3 | virginica |
146 | 6.3 | 2.5 | 5.0 | 1.9 | virginica |
147 | 6.5 | 3.0 | 5.2 | 2.0 | virginica |
148 | 6.2 | 3.4 | 5.4 | 2.3 | virginica |
149 | 5.9 | 3.0 | 5.1 | 1.8 | virginica |
150 rows × 5 columns
plt.scatter(x=iris.petal_width,y=iris.petal_length)
plt.xlabel("花瓣宽度",font=font)
plt.ylabel("花瓣长度",font=font)
plt.title("鸢尾花花瓣长宽之间的关系",font=font)
plt.show()
color_iris = ['b','r','g']
species=["setosa","virginica","versicolor"]
marker_iris = ["o","s","x"]
iris.petal_width
0 0.2 1 0.2 2 0.2 3 0.2 4 0.2 ... 145 2.3 146 1.9 147 2.0 148 2.3 149 1.8 Name: petal_width, Length: 150, dtype: float64
for i in range(3):
plt.scatter(x = iris.petal_width[iris["species"]==species[i]],
y = iris.petal_length[iris["species"]==species[i]],
marker=marker_iris[i],
color=color_iris[i])
plt.ylabel("花瓣长度",font=font)
plt.title("鸢尾花花瓣长宽之间的关系",font=font)
plt.show()
iris["species"].value_counts()
versicolor 50 setosa 50 virginica 50 Name: species, dtype: int64
GDP_new= GDP.reset_index()
GDP_new.sort_values(by="index",inplace=True)
plt.figure(figsize=(8,7))
plt.plot(
GDP_new["index"],GDP_new["北京市"],'bs--',
GDP_new["index"],GDP_new["天津市"],'rs--',
GDP_new["index"],GDP_new["上海市"],'gs--',
GDP_new["index"],GDP_new["重庆市"],'cs--',
)
plt.legend(labels=['北京市','天津市','上海市','重庆市'],loc="best",prop=font)
plt.xlabel("年份",font=font)
plt.ylabel("GDP",font=font)
plt.title("不同直辖市GDP的变化",font=font)
plt.show()
sec_house
价格 | 小区 | 房屋户型 | 建筑面积 | 户型结构 | 建筑类型 | 房屋朝向 | 建筑结构 | 装修情况 | 梯户比例 | ... | 环 | 室 | 厅 | 厨 | 卫 | 所处楼层 | 总层数 | 有无抵押 | 抵押情况 | 均价 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1410.0 | 江临天下 | 4室2厅1厨2卫 | 165.73 | 平层 | 板楼 | 南 西南 | 钢混结构 | 精装 | 六梯六户 | ... | 内环内 | 4 | 2 | 1 | 2 | 低楼层 | 35 | 无 | NaN | 85078.14 |
1 | 680.0 | 樱花路309弄 | 2室2厅1厨1卫 | 78.65 | 复式 | 板楼 | 南 北 | 钢混结构 | 精装 | 一梯两户 | ... | 内环内 | 2 | 2 | 1 | 1 | 高楼层 | 6 | 有 | 150万元 银行抵押 业主自还 | 86459.00 |
2 | 2088.0 | 盛大金磐 | 3室2厅1厨3卫 | 194.65 | 平层 | 板楼 | 南 | 钢混结构 | 精装 | 三梯三户 | ... | 内环内 | 3 | 2 | 1 | 3 | 中楼层 | 43 | 无 | NaN | 107269.46 |
3 | 266.0 | 康桥月苑 | 2室2厅1厨1卫 | 72.27 | 平层 | 板楼 | 南 | 钢混结构 | 精装 | 一梯两户 | ... | 外环外 | 2 | 2 | 1 | 1 | 高楼层 | 6 | 无 | NaN | 36806.42 |
4 | 2288.0 | 城市经典 | 4室2厅1厨3卫 | 324.29 | 暂无数据 | 暂无数据 | 北 | 钢混结构 | 精装 | 暂无数据 | ... | 内环至中环 | 4 | 2 | 1 | 3 | 中楼层 | 4 | 无 | NaN | 70554.13 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
37478 | 260.0 | 绿地金卫新家园 | 4室2厅1厨2卫 | 154.74 | 复式 | 板楼 | 南 | 钢混结构 | 精装 | 一梯两户 | ... | 外环外 | 4 | 2 | 1 | 2 | 高楼层 | 6 | 有 | 30万元 公积金 | 16802.38 |
37479 | 135.0 | 城西花苑 | 2室2厅1厨1卫 | 94.00 | 平层 | 板楼 | 南 | 钢混结构 | 毛坯 | 一梯两户 | ... | 外环外 | 2 | 2 | 1 | 1 | 中楼层 | 6 | 无 | NaN | 14361.70 |
37480 | 135.0 | 东礁四村 | 3室1厅1厨1卫 | 73.00 | 平层 | 板楼 | 南 | 钢混结构 | 简装 | 一梯三户 | ... | 外环外 | 3 | 1 | 1 | 1 | 高楼层 | 6 | 有 | 20万元 业主自还 | 18493.15 |
37481 | 320.0 | 万盛金邸 | 4室1厅1厨2卫 | 128.56 | 平层 | 板楼 | 南 | 钢混结构 | 精装 | 一梯两户 | ... | 外环外 | 4 | 1 | 1 | 2 | 低楼层 | 14 | 有 | 8万元 业主自还 | 24891.10 |
37482 | 208.0 | 育秀六区 | 3室1厅1厨1卫 | 88.00 | 平层 | 板楼 | 南 | 砖混结构 | 精装 | 一梯两户 | ... | NaN | 3 | 1 | 1 | 1 | 中楼层 | 5 | 无 | NaN | 23636.36 |
37483 rows × 29 columns
sec_house.columns
Index(['价格', '小区', '房屋户型', '建筑面积', '户型结构', '建筑类型', '房屋朝向', '建筑结构', '装修情况', '梯户比例', '配备电梯', '挂牌时间', '交易权属', '上次交易', '房屋用途', '房屋年限', '产权所有', '区', '镇', '环', '室', '厅', '厨', '卫', '所处楼层', '总层数', '有无抵押', '抵押情况', '均价'], dtype='object')
sec_house_1 = sec_house.groupby("区")[["价格","均价"]].mean()
sec_house_1["均价"] =sec_house_1["均价"]/10000
sec_house_1
价格 | 均价 | |
---|---|---|
区 | ||
嘉定区 | 298.100956 | 3.346117 |
奉贤区 | 244.201167 | 2.349471 |
宝山区 | 392.535022 | 4.403582 |
徐汇区 | 661.666783 | 7.670740 |
普陀区 | 522.432921 | 6.090767 |
杨浦区 | 478.377571 | 6.364288 |
松江区 | 517.619973 | 3.798396 |
浦东新区 | 573.529333 | 5.670892 |
虹口区 | 570.579395 | 6.647432 |
金山区 | 206.206807 | 2.029167 |
长宁区 | 733.222088 | 7.141097 |
闵行区 | 521.933185 | 5.112870 |
青浦区 | 685.365884 | 3.667959 |
静安区 | 698.138963 | 7.651275 |
黄浦区 | 1255.955154 | 9.825114 |
fig=plt.figure(figsize=(9,6))
ax1 = fig.add_subplot(111)
ax1.plot(sec_house_1.index,sec_house_1["价格"],"bs-",label="价格")
ax1.set_ylabel("不同地区的总价",font=font)
# plt.legend(loc="upper left",prop=font)
plt.xticks(sec_house_1.index,font=font,rotation=40)
ax2 = ax1.twinx()
ax2.plot(sec_house_1.index,sec_house_1["均价"],"ro-",label="均价")
ax2.set_ylabel("不同地区的均价",font=font)
fig.legend(labels=('价格','均价'),prop=font)
plt.title("不用地区总价与均价的折线图",font=font)
plt.show()