数据分析&数据挖掘2000和2017年各产业生产总值占比饼图

Posted 2020-11-26 tree0108

 1 import matplotlib.pyplot as plt
 2 import numpy as np
 3 
 4 def draw_data(n, a, b, d, values, fig, plt, t, l, cl):
 5     fig.add_subplot(2, 2, n)
 6     x = values[d, a:b+1]
 7     # explode = (0.01)
 8     plt.pie(x, labels=l, colors=cl, autopct="%.2f%%", shadow=False, labeldistance=1.05)
 9     plt.axis("equal")
10     plt.legend(l)
11     plt.title(t)
12     plt.savefig("./2000和2017年各产业生产总值占比饼图.png")
13     return plt
14 
15 def show_data():
16     fig = plt.figure(figsize=(20, 12), dpi=120)
17     plt.rcParams[‘font.sans-serif‘] = ‘SimHei‘
18     plt.rcParams[‘axes.unicode_minus‘] = False
19     # plt.subplots_adjust(hspace=0.3)
20     return fig, plt
21 
22 def save_data():
23     plt.savefig("./2000-2017年各个产业、行业的柱状图.png")
24     plt.show()
25     return None
26 
27 def build_data():
28     res = np.load("./国民经济核算季度数据.npz", allow_pickle=True)
29     columns = res["columns"]
30     values = res["values"]
31     # print(columns)
32     # print(values)
33     return columns, values
34 
35 def main():
36     columns, values = build_data()
37     fig, plt = show_data()
38     title1 = "2000年第一季度国民总值产业构成分布饼图"
39     title2 = "2017年第一季度国民总值产业构成分布饼图"
40     labels1 = [tmp[:4] for tmp in columns[3:6]]
41     labels2 = [tmp[:2] for tmp in columns[6:]]
42     colors1 = ["#53868B", "#7AC5CD", "#8EE5EE"]
43     colors2 = ["#CD9B9B", "#EEB4B4", "#8B658B"]
44     colors3 = ["#548B54", "#7CCD7C", "#90EE90", "#9AFF9A", "#2E8B57", "#43CD80", "#4EEE94", "#54FF9F", "#698B69"]
45     colors4 = ["#CD950C", "#EEAD0E", "#FFB90F", "#8B6914", "#CD9B1D", "#EEB422", "#FFC125", "#8B7500", "#CDAD00"]
46     draw_data(1, 3, 5, 0, values, fig, plt, title1, labels1, colors1)
47     draw_data(2, 3, 5, 68, values, fig, plt, title2, labels1, colors2)
48     draw_data(3, 6, 14, 0, values, fig, plt, title1, labels2, colors3)
49     draw_data(4, 6, 14, 68, values, fig, plt, title2, labels2, colors4)
50     save_data()
51 
52 
53 if __name__ == ‘__main__‘:
54     main()