import pandas as pd
import numpy as np
import seaborn as sns
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.sort_index(inplace=True)
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
29 rows × 31 columns
GDP.index.values
array([1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020], dtype=int64)
GDP.loc[1996]
北京市 1819.4 天津市 1121.9 河北省 3198.0 山西省 1292.1 内蒙古自治区 1023.1 辽宁省 3157.7 吉林省 1346.8 黑龙江省 2137.6 上海市 2980.8 江苏省 6004.2 浙江省 4195.8 安徽省 2199.7 福建省 2484.3 江西省 1409.7 山东省 5883.8 河南省 3634.7 湖北省 2499.8 湖南省 2540.1 广东省 6848.2 广西壮族自治区 1697.9 海南省 389.7 重庆市 1326.4 四川省 2871.7 贵州省 723.2 云南省 1517.7 西藏自治区 65.0 陕西省 1215.8 甘肃省 722.5 青海省 184.2 宁夏回族自治区 202.9 新疆维吾尔自治区 900.9 Name: 1996, dtype: float64
plt.figure(figsize=(16,8))
plt.bar(x=GDP.columns.values,height=GDP.loc[1992])
plt.xticks(GDP.columns.values,font=font,rotation=60,fontsize=10)
plt.ylabel("GDP",font=font)
plt.show()
# 第一种调用方式
plt.style.use("seaborn")
plt.figure(figsize=(16,8))
plt.bar(x=GDP.columns.values,height=GDP.loc[1992])
plt.xticks(GDP.columns.values,font=font,rotation=60,fontsize=10)
plt.ylabel("GDP",font=font,fontsize=16)
plt.show()
#第二种调用方式
sns.set(style="darkgrid",context='notebook',font_scale=1.2)
plt.figure(figsize=(16,8))
plt.bar(x=GDP.columns.values,height=GDP.loc[1992])
plt.xticks(GDP.columns.values,font=font,rotation=60,fontsize=10)
plt.ylabel("GDP",font=font,fontsize=16)
plt.show()
GDP_new = GDP.T.reset_index()
GDP_new
index | 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | 2000 | ... | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 北京市 | 710.2 | 888.9 | 1149.8 | 1516.2 | 1819.4 | 2118.1 | 2439.1 | 2759.8 | 3277.8 | ... | 17188.8 | 19024.7 | 21134.6 | 22926.0 | 24779.1 | 27041.2 | 29883.0 | 33106.0 | 35445.1 | 36102.6 |
1 | 天津市 | 411.0 | 538.9 | 732.9 | 932.0 | 1121.9 | 1264.6 | 1344.7 | 1435.6 | 1591.7 | ... | 8112.5 | 9043.0 | 9945.4 | 10640.6 | 10879.5 | 11477.2 | 12450.6 | 13362.9 | 14055.5 | 14083.7 |
2 | 河北省 | 1278.5 | 1620.8 | 2114.5 | 2701.2 | 3198.0 | 3652.1 | 3924.5 | 4158.9 | 4628.2 | ... | 21384.7 | 23077.5 | 24259.6 | 25208.9 | 26398.4 | 28474.1 | 30640.8 | 32494.6 | 34978.6 | 36206.9 |
3 | 山西省 | 551.1 | 680.4 | 826.7 | 1076.0 | 1292.1 | 1476.0 | 1611.1 | 1667.1 | 1845.7 | ... | 10894.4 | 11683.1 | 11987.2 | 12094.7 | 11836.4 | 11946.4 | 14484.3 | 15958.1 | 16961.6 | 17651.9 |
4 | 内蒙古自治区 | 421.7 | 537.8 | 695.1 | 857.1 | 1023.1 | 1153.5 | 1262.5 | 1379.3 | 1539.1 | ... | 9458.1 | 10470.1 | 11392.4 | 12158.2 | 12949.0 | 13789.3 | 14898.1 | 16140.8 | 17212.5 | 17359.8 |
5 | 辽宁省 | 1473.0 | 2010.8 | 2461.8 | 2793.4 | 3157.7 | 3582.5 | 3881.7 | 4171.7 | 4669.1 | ... | 16354.9 | 17848.6 | 19208.8 | 20025.7 | 20210.3 | 20392.5 | 21693.0 | 23510.5 | 24855.3 | 25115.0 |
6 | 吉林省 | 558.1 | 718.6 | 937.7 | 1137.2 | 1346.8 | 1464.3 | 1577.1 | 1673.0 | 1751.4 | ... | 7734.6 | 8678.0 | 9427.9 | 9966.5 | 10018.0 | 10427.0 | 10922.0 | 11253.8 | 11726.8 | 12311.3 |
7 | 黑龙江省 | 857.4 | 1075.3 | 1448.1 | 1790.2 | 2137.6 | 2397.6 | 2470.2 | 2536.9 | 2855.5 | ... | 9935.0 | 11015.8 | 11849.1 | 12170.8 | 11690.0 | 11895.0 | 12313.0 | 12846.5 | 13544.4 | 13698.5 |
8 | 上海市 | 1114.3 | 1519.2 | 1990.9 | 2518.1 | 2980.8 | 3465.3 | 3831.0 | 4222.3 | 4812.2 | ... | 20009.7 | 21305.6 | 23204.1 | 25269.8 | 26887.0 | 29887.0 | 32925.0 | 36011.8 | 37987.6 | 38700.6 |
9 | 江苏省 | 2136.0 | 2998.2 | 4057.4 | 5155.3 | 6004.2 | 6680.3 | 7200.0 | 7697.8 | 8553.7 | ... | 48839.2 | 53701.9 | 59349.4 | 64830.5 | 71255.9 | 77350.9 | 85869.8 | 93207.6 | 98656.8 | 102719.0 |
10 | 浙江省 | 1375.7 | 1925.9 | 2689.3 | 3563.9 | 4195.8 | 4695.9 | 5065.5 | 5461.3 | 6164.8 | ... | 31854.8 | 34382.4 | 37334.6 | 40023.5 | 43507.7 | 47254.0 | 52403.1 | 58002.8 | 62462.0 | 64613.3 |
11 | 安徽省 | 827.0 | 1073.5 | 1378.9 | 1891.2 | 2199.7 | 2485.4 | 2711.7 | 2907.8 | 3125.3 | ... | 16284.9 | 18341.7 | 20584.0 | 22519.7 | 23831.2 | 26307.7 | 29676.2 | 34010.9 | 36845.5 | 38680.6 |
12 | 福建省 | 784.7 | 1114.2 | 1644.4 | 2094.9 | 2484.3 | 2870.9 | 3159.9 | 3414.2 | 3764.5 | ... | 17917.7 | 20190.7 | 22503.8 | 24942.1 | 26819.5 | 29609.4 | 33842.4 | 38687.8 | 42326.6 | 43903.9 |
13 | 江西省 | 572.6 | 723.0 | 948.2 | 1169.7 | 1409.7 | 1605.8 | 1719.9 | 1853.7 | 2003.1 | ... | 11584.5 | 12807.7 | 14300.2 | 15667.8 | 16780.9 | 18388.6 | 20210.8 | 22716.5 | 24667.3 | 25691.5 |
14 | 山东省 | 2196.5 | 2770.4 | 3844.5 | 4953.4 | 5883.8 | 6537.1 | 7021.4 | 7493.8 | 8278.1 | ... | 39064.9 | 42957.3 | 47344.3 | 50774.8 | 55288.8 | 58762.5 | 63012.1 | 66648.9 | 70540.5 | 73129.0 |
15 | 河南省 | 1279.8 | 1660.2 | 2216.8 | 2988.4 | 3634.7 | 4041.1 | 4308.2 | 4517.9 | 5053.0 | ... | 26318.7 | 28961.9 | 31632.5 | 34574.8 | 37084.1 | 40249.3 | 44824.9 | 49935.9 | 53717.8 | 54997.1 |
16 | 湖北省 | 1088.4 | 1325.8 | 1700.9 | 2109.4 | 2499.8 | 2856.5 | 3114.0 | 3229.3 | 3545.4 | ... | 19942.5 | 22590.9 | 25378.0 | 28242.1 | 30344.0 | 33353.0 | 37235.0 | 42022.0 | 45429.0 | 43443.5 |
17 | 湖南省 | 987.0 | 1244.7 | 1650.0 | 2132.1 | 2540.1 | 2849.3 | 3025.5 | 3214.5 | 3551.5 | ... | 18915.0 | 21207.2 | 23545.2 | 25881.3 | 28538.6 | 30853.5 | 33828.1 | 36329.7 | 39894.1 | 41781.5 |
18 | 广东省 | 2447.5 | 3469.3 | 4619.0 | 5940.3 | 6848.2 | 7793.0 | 8555.3 | 9289.6 | 10810.2 | ... | 53072.8 | 57007.7 | 62503.4 | 68173.0 | 74732.4 | 82163.2 | 91648.7 | 99945.2 | 107986.9 | 110760.9 |
19 | 广西壮族自治区 | 646.6 | 871.7 | 1198.3 | 1497.6 | 1697.9 | 1817.3 | 1911.3 | 1971.4 | 2080.0 | ... | 10299.9 | 11303.6 | 12448.4 | 13587.8 | 14797.8 | 16116.6 | 17790.7 | 19627.8 | 21237.1 | 22156.7 |
20 | 海南省 | 184.9 | 260.4 | 332.0 | 363.3 | 389.7 | 411.2 | 442.1 | 476.7 | 526.8 | ... | 2463.8 | 2789.4 | 3115.9 | 3449.0 | 3734.2 | 4090.2 | 4497.5 | 4910.7 | 5330.8 | 5532.4 |
21 | 重庆市 | 462.5 | 611.1 | 838.1 | 1130.6 | 1326.4 | 1525.3 | 1622.4 | 1687.8 | 1822.1 | ... | 10161.2 | 11595.4 | 13027.6 | 14623.8 | 16040.5 | 18023.0 | 20066.3 | 21588.8 | 23605.8 | 25002.8 |
22 | 四川省 | 1177.3 | 1486.1 | 2001.4 | 2443.2 | 2871.7 | 3241.5 | 3474.1 | 3649.1 | 3928.2 | ... | 21050.9 | 23922.4 | 26518.0 | 28891.3 | 30342.0 | 33138.5 | 37905.1 | 42902.1 | 46363.8 | 48598.8 |
23 | 贵州省 | 339.9 | 417.7 | 524.5 | 636.2 | 723.2 | 805.8 | 858.4 | 937.5 | 1029.9 | ... | 5615.6 | 6742.2 | 7973.1 | 9173.1 | 10541.0 | 11792.4 | 13605.4 | 15353.2 | 16769.3 | 17826.6 |
24 | 云南省 | 618.7 | 783.3 | 983.8 | 1222.2 | 1517.7 | 1676.2 | 1831.3 | 1899.8 | 2030.1 | ... | 9523.1 | 11097.4 | 12825.5 | 14041.7 | 14960.0 | 16369.0 | 18486.0 | 20880.6 | 23223.8 | 24521.9 |
25 | 西藏自治区 | 33.3 | 37.4 | 46.0 | 56.1 | 65.0 | 77.2 | 91.5 | 106.0 | 117.8 | ... | 611.5 | 710.2 | 828.2 | 939.7 | 1043.0 | 1173.0 | 1349.0 | 1548.4 | 1697.8 | 1902.7 |
26 | 陕西省 | 531.6 | 678.2 | 839.0 | 1036.9 | 1215.8 | 1363.6 | 1458.4 | 1592.6 | 1804.0 | ... | 12175.1 | 14142.4 | 15905.4 | 17402.5 | 17898.8 | 19045.8 | 21473.5 | 23941.9 | 25793.2 | 26181.9 |
27 | 甘肃省 | 317.8 | 372.2 | 453.6 | 557.8 | 722.5 | 793.6 | 887.7 | 956.3 | 1052.9 | ... | 4816.9 | 5393.1 | 6014.5 | 6518.4 | 6556.6 | 6907.9 | 7336.7 | 8104.1 | 8718.3 | 9016.7 |
28 | 青海省 | 87.5 | 109.7 | 138.4 | 167.8 | 184.2 | 202.8 | 220.9 | 239.4 | 263.7 | ... | 1370.4 | 1528.5 | 1713.3 | 1847.7 | 2011.0 | 2258.2 | 2465.1 | 2748.0 | 2941.1 | 3005.9 |
29 | 宁夏回族自治区 | 83.1 | 104.5 | 136.3 | 175.2 | 202.9 | 224.6 | 245.5 | 264.6 | 295.0 | ... | 1931.8 | 2131.0 | 2327.7 | 2473.9 | 2579.4 | 2781.4 | 3200.3 | 3510.2 | 3748.5 | 3920.6 |
30 | 新疆维吾尔自治区 | 402.3 | 495.3 | 662.3 | 814.9 | 900.9 | 1039.8 | 1107.0 | 1163.2 | 1363.6 | ... | 6532.0 | 7411.8 | 8392.6 | 9264.5 | 9306.9 | 9630.8 | 11159.9 | 12809.4 | 13597.1 | 13797.6 |
31 rows × 30 columns
# 第三种调用方式
plt.figure(figsize=(16,8))
sns.barplot(data=GDP_new,x="index",y=1992)
sns.set(font=font.get_name(),font_scale=0.8)
plt.title("不同地区的GDP")
plt.xticks(rotation=40)
plt.show()
# 第三种调用方式
plt.figure(figsize=(16,12))
sns.barplot(data=GDP_new.sort_values(1992,ascending=False),x=1992,y="index",orient="h")
sns.set(font=font.get_name(),font_scale=0.8)
plt.title("不同地区的GDP")
plt.xticks(rotation=40)
plt.show()