Python Data Mining Course three. Kmeans Clustering code implementation, operations and optimization __python

This article directly gives the last time about the Kmeans cluster basketball far mobilization data analysis case, at the same time introduced this homework students completed the legend, and finally introduced the Matplotlib package drawing optimization knowledge.
Previous recommendation:
The Python data Mining course. Introduction to installing Python and crawler
"Python Data Mining Course" two. Kmeans clustering data analysis and Anaconda introduction
Hopefully this article will help you, especially students who have just come in contact with data mining and large data, and are ready to try the case-oriented approach. If there are deficiencies or errors in the article, please Haihan ~

I. Case Realization

Here is no longer to repeat, see the second article, directly on the code, this is my students completed the work.
Data set:
Download Address: Keel-dataset-basketball Data set
Basketball player data, assists per minute and scores per minute. The data set is used to determine what position a basketball player belongs to (Control, Division, center, etc.). The complete dataset consists of 5 features, the number of assists per minute, the athlete's height, the athlete's appearance time, the athlete's age, and the score per minute.[Python]  View Plain  copy   Assists_per_minute  height  time_played  age   points_per_minute     0                0.0888     201         36.02   28              0.5885     1                0.1399     198         39.32   30              0.8291     2                0.0747     198        38.80    26             0.4974     3                0.0983      191        40.71   30              0.5772     4                0.1276      196        38.40   28              0.5703     5                0.1671     201         34.10   31              0.5835     6                0.1906     193        36.20    30             0.5276      7                0.1061     191        36.75    27             0.5523     8               0.2446      185        38.43   29              0.4007     9                0.1670     203         33.54   24              0.4770     10               0.2485     188         35.01   27              0.4313     11               0.1227     198         36.67   29              0.4909     12               0.1240     185        33.88   24              0.5668     13               0.1461      191        35.59   30              0.5113     14               0.2315     191         38.01