參考官方文檔:https://docs.scipy.org/doc/scipy/reference/index.html
此次使用聚類分析是因為文章需要,然后參考官方文檔簡單制作滿足分析要求的樹狀圖。
因為到時候在A4紙上需要把三個圖排版一下,所以在輸出的時候就把圖片設置的比較小。

1 import pandas as pd 2 import matplotlib.pyplot as plt 3 from scipy.cluster import hierarchy 4 5 plt.rcParams['font.sans-serif'] = ['SimHei'] #解決中文顯示 6 plt.rcParams['axes.unicode_minus'] = False #解決符號無法顯示 7 #設置顏色 8 #hierarchy.set_link_color_palette(['m', 'c', 'y', 'k']) 9 10 df = pd.read_excel('品種日產量聚類.xlsx',sheet_name='zaoju') 11 fig, axes = plt.subplots(1,figsize=(4,4)) 12 Z = hierarchy.linkage(df,'centroid',metric='euclidean') 13 names = ['E1','E2','E3','E4','E5','E6','E7','E8','E9','E10','E11','E12','E13','E14','E15','E16','E17','E18','E19','E20','E21','E22','E23','E24','E25','E26','E27','E28'] 14 15 dn=hierarchy.dendrogram(Z,orientation='right',labels=names) 16 print(dn['color_list']) 17 18 plt.show() 19 #plt.savefig('早稻聚類01.jpg',dpi=300)

1 import pandas as pd 2 import matplotlib.pyplot as plt 3 from scipy.cluster import hierarchy 4 5 fig, axes = plt.subplots(1,figsize=(4,4)) 6 7 # 晚粳聚 8 df3 = pd.read_excel('品種日產量聚類.xlsx',sheet_name='晚粳聚') 9 Z3 = hierarchy.linkage(df3,'centroid',metric='euclidean') 10 names3 = ['L24','L25','L26','L27','L28','L29', 11 'L30','L31','L32','L33','L34','L35', 12 'L36','L37','L38','L39','L40','L41', 13 'L42','L43','L44','L45','L46','L47','L48','L49'] 14 dn3=hierarchy.dendrogram(Z3,orientation='right',labels=names3) 15 #dn3=hierarchy.dendrogram(Z3,orientation='right') 16 #plt.show() 17 plt.savefig('晚粳聚01.jpg',dpi=300)

1 import pandas as pd 2 import matplotlib.pyplot as plt 3 from scipy.cluster import hierarchy 4 5 fig, axes = plt.subplots(1,figsize=(4,4)) 6 7 # 晚秈聚 8 df2 = pd.read_excel('品種日產量聚類.xlsx',sheet_name='晚秈聚') 9 Z2 = hierarchy.linkage(df2,'centroid',metric='euclidean') 10 names2 = ['L1','L2','L3','L4','L5','L6','L7', 11 'L8','L9','L10','L11','L12','L13','L14', 12 'L15','L16','L17','L18','L19','L20','L21', 13 'L22','23'] 14 dn2=hierarchy.dendrogram(Z2,orientation='right',labels=names2) 15 16 #plt.show() 17 plt.savefig('晚秈01.jpg',dpi=300)