堆叠柱状图叠加折线图

app/utils/draw.py

@@ -9,6 +9,7 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
+from matplotlib import ticker
 from matplotlib.ticker import FuncFormatter
 from matplotlib.font_manager import FontProperties
 
@@ -16,58 +17,59 @@ from matplotlib.font_manager import FontProperties
 def to_percent(temp, position):
     return '%1.0f' % temp + '%'
 
-def draw_stacked_column_chart():
+def draw_stacked_column_chart(product_list, cumulative):
     """堆叠柱状图"""
 
-    plt.title('Scores by group and gender')
-
-    # ind = np.arange(12)  # [ 0  1  2  3  4  5  6  7  8  9 10 11 12]
-    # plt.xticks(ind, ('M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9', 'M10', 'M11', 'M12'))
-    #
-    # plt.yticks(np.arange(0, 81, 20))
-    #
-    # Bottom = (52, 49, 48, 47, 44, 43, 41, 41, 40, 38, 36, 31, 29)
-    # Center = (38, 40, 45, 42, 48, 51, 53, 54, 57, 59, 57, 64, 62)
-    # Top = (10, 11, 7, 11, 8, 6, 6, 5, 3, 3, 7, 5, 9)
-    #
-    # d = []
-    # for i in range(0, len(Bottom)):
-    #     sum = Bottom[i] + Center[i]
-    #     d.append(sum)
-    #
-    # width = 0.8  # 设置条形图一个长条的宽度
-    # p1 = plt.bar(ind, Bottom, width, color='blue')
-    # p2 = plt.bar(ind, Center, width, bottom=Bottom, color='green')
-    # p3 = plt.bar(ind, Top, width, bottom=d, color='red')
-    #
-    # plt.legend((p1[0], p2[0], p3[0]), ('Bottom', 'Center', 'Top'), loc=3)
-    #
-    # plt.show()
-    N = 5
-    product1 = np.array([10, 20, 30, 40, 50])
-    product2 = np.array([20, 20, 20, 20, 20])
-    product3 = np.array([20, 20, 20, 20, 20])
-    line = np.array([10, 15, 5, 20, 36])
-    ind = np.arange(N)  # the x locations for the groups
+    # plt.title('Scores by group and gender')
+    # plt.ylabel('Scores')
+    # 初始化
+    fig = plt.figure(figsize=(12, 8))
+    ax1 = fig.add_subplot()
+    ax2 = ax1.twiny()
+    max_x_count = max([x['data'].size for x in product_list])
+    loc = np.arange(max_x_count)  # the x locations for the groups
     width = 0.35  # the width of the bars: can also be len(x) sequence
+    color_list = ['#222A77', '#6C71AA', '#E1BC95', '#102A77', '#6CB1AA', '#CCBC95']
 
-    p1 = plt.bar(ind, product1, width, color='#222A77', alpha=0.8)
-    p2 = plt.bar(ind, product2, width, bottom=product1, color='#6C71AA', alpha=0.8)
-    p3 = plt.bar(ind, product3, width, bottom=product2+product1, color='#E1BC95', alpha=0.8)
-
-    # plt.ylabel('Scores')
-    plt.title('Scores by group and gender')
-    plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))
-    # plt.yticks(np.arange(0, 81, 10))
+    # 堆叠柱状图
+    prod_legend = []
+    for i in range(len(product_list)):
+        ax = None
+        bottom = np.zeros(max_x_count)
+        if i == 0:
+            ax = ax1.bar(loc, product_list[i]['data'], width, color=color_list[i], alpha=0.8)
+        else:
+            for j in range(i):
+                bottom = bottom + product_list[j]['data']
+            ax = ax1.bar(loc, product_list[i]['data'], width, bottom=bottom, color=color_list[i], alpha=0.8)
+        prod_legend.append(ax[0])
 
-    ax1 = plt.legend((p1[0], p2[0], p3[0]), ('product1', 'product2', 'product3'))
+    # 坐标轴
+    ax1.set_xticks(loc)
+    ax1.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))
+    plt.gca().yaxis.set_major_formatter(FuncFormatter(to_percent))
+    ax1.legend(prod_legend, [prod['name'] for prod in product_list], bbox_to_anchor=(0.7, -0.1), ncol=4)
 
     # 画折线图
-    ax2 = ax1.twinx()
-    line = ax2.plot(ind, line, color='#C6A774', marker='', ms=1000, label=u"总体收益")
+    # 最大收益
+    temp_rate = np.zeros(max_x_count)
+    for i in range(len(product_list)):
+        temp_rate += product_list[i]['data']
+    max_rate = np.max(np.hstack((temp_rate, cumulative['data'])))
+    ax2.set_xticks([])
+    ax2.set_ylim(0, max_rate + 10)
+    ax2.plot(loc, cumulative['data'], color='#C6A774', marker='', linewidth=3, label=cumulative['name'])
+    ax2.legend(loc='upper left')
 
-    plt.gca().yaxis.set_major_formatter(FuncFormatter(to_percent))
     plt.show()
 
 if __name__ == '__main__':
-    draw_stacked_column_chart()
+    product1 = {'name': 'product1', 'data': np.array([10, 20, 30, 40, 50])}
+    product2 = {'name': 'product2', 'data': np.array([20, 20, 20, 20, 20])}
+    product3 = {'name': 'product3', 'data': np.array([20, 20, 20, 20, 20])}
+    product4 = {'name': 'product4', 'data': np.array([20, 20, 20, 20, 20])}
+    product5 = {'name': 'product5', 'data': np.array([20, 20, 20, 20, 20])}
+    product6 = {'name': 'product6', 'data': np.array([20, 20, 20, 20, 20])}
+    product_list = [product1, product2, product3, product4, product5, product6]
+    cumulative = {'name': 'totalincome', 'data': np.array([10, 50, 120, 100, 36])}
+    draw_stacked_column_chart(product_list, cumulative)