Python advanced data processing and visualization (i)

1. Cluster analysis

Clustering (cluster analysis): Based on similarity, similar objects are divided into groups or more subsets by static classification methods. Characteristics: Based on similarity, there are multiple clusters of centers.

The k-means:"k-mean algorithm represents clustering at the center of K points in space, classifying objects closest to them.

　　　　

In [47]: fromNumPyImportVstackin [48]: fromScipy.cluster.vqImportKmeans,vqin [Further]: List1 = [88.0,74.0,96.0,85.0]in []: List2 = [92.0,99.0,95.0,94.0]in [Wuyi]: list3 = [91.0,87.0,99.0,95.0]in ["List4" = [78.0,99.0,97.0,81.0]in [+]: list5 = [88.0,78.0,98.0,84.0]in [SI]: list6 = [100.0,95.0,100.0,92.0]in [: Data = Vstack ((LIST1,LIST2,LIST3,LIST4,LIST5,LIST6))#Stack Arrays in sequence vertically (row wise). Take a sequence of arrays and the stack them vertically to make a single array. In ["Centroids,_" = Kmeans (data,2)#performs K-means on a set of observation vectors forming K clusters.In [+]: result,_ = VQ (data,centroids)#Assign codes from a code book to observations.In [63]: resultout[+]: Array ([1, 0, 0, 0, 1, 0])

cluster analysis based on results

　　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

2. Matplotlib Drawing Basics

3. Matplotlib Image Attribute Control

4. Pandas drawing

5. Data access

6. Python's Engineering applications

7. Python's humanities and social sciences applications

Python advanced data processing and visualization (i)