Python高級數據處理與可視化（一）---- 聚類分析

1. 聚類分析

　　聚類分析(cluster analysis)：以相似性為基礎把相似的對象通過靜態分類的方法分成不同的組別或更多的子集。特性：基於相似性，有多個聚類中心。

　　K-Means：「K-均值」算法表示以空間中K個點為中心進行聚類，對最靠近他們的對象歸類。

　　　　

In [47]: from numpy import vstack

In [48]: from scipy.cluster.vq import kmeans,vq

In [49]: list1 = [88.0,74.0,96.0,85.0]

In [50]: list2 = [92.0,99.0,95.0,94.0]

In [51]: list3 = [91.0,87.0,99.0,95.0]

In [52]: list4 = [78.0,99.0,97.0,81.0]

In [53]: list5 = [88.0,78.0,98.0,84.0]

In [54]: list6 = [100.0,95.0,100.0,92.0]

In [55]: data = vstack((list1,list2,list3,list4,list5,list6))  # Stack arrays in sequence vertically (row wise).Take a sequence of arrays and stack them vertically to make a single array. 

In [61]: centroids,_ = kmeans(data,2)  # Performs k-means on a set of observation vectors forming k clusters.

In [62]: result,_ = vq(data,centroids)  # Assign codes from a code book to observations.

In [63]: result
Out[63]: array([1, 0, 0, 0, 1, 0])

　　numpy.vstack: https://docs.scipy.org/doc/numpy/reference/generated/numpy.vstack.html

　　scipy.cluster.vq.kmeans: https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.vq.kmeans.html#scipy.cluster.vq.kmeans

　　scipy.cluster.vq.vq: https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.vq.vq.html

In [88]: from numpy import vstack

In [89]: from scipy.cluster.vq import kmeans,vq

In [90]: from matplotlib.finance import quotes_historical_yahoo_ochl

In [91]: from datetime import datetime,date

In [92]: start = datetime(2016,1,1)

In [93]: end = datetime(2016,6,30)

In [94]: listDji = ['AXP','BA','CAT','CSCO','CVX','DD','DIS','GE','GS','HD','IBM','INTC','JNJ','JPM','KO','MCD','MMM','MRK','MSFT','NKE','PFE','PG','T','TRV','UNH','UTX','V','VZ','WMT','XOM']

In [105]: quote = [[0 for col in range(180)] for row in range(30)]

In [106]: listTemp = [[0 for col in range(180)] for row in range(30)]

In [107]: for i in range(30):
     ...:     quotes[i] = quotes_historical_yahoo_ochl(listDji[i],start,end)

In [108]: days = len(quotes[0])

In [116]: for i in range(30):
     ...:     for j in range(days-1):
     ...:         if (quotes[i][j][2] and quotes[i][j+1][2] and (quotes[i][j+1][2]>=quotes[i][j][2])):
     ...:             listTemp[i][j] = 1.0
     ...:         else:
     ...:             listTemp[i][j] = -1.0      

In [117]: data = vstack(listTemp)

In [118]: centroids,_ = kmeans(data,4)

In [119]: result,_ = vq(data,centroids)

In [120]: result
Out[120]: 
array([2, 0, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 1, 1, 2, 1, 0, 0,
       0, 1, 2, 2, 0, 0, 0])

　　查看聚類出的四類的結果：dfDji[(dfDji.result==1)]

2. Matplotlib繪圖基礎

3. Matplotlib圖像屬性控制

4. Pandas作圖

5. 數據存取

6. Python的理工類應用

7. Python的人文社科類應用