使用panads处理数据

 

	import pandas as pd
	import numpy as np

	#使用pandas读入并简单处理csv数据
	column_names=['Sample code number', 'Clump Thickness', 'Uniformity of \
	Cell Size', 'Uniformity of Cell shape', 'Marginal Adhesion', 'Single \
	Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nuvleoli',
	'Mitoses', 'Class']

	data=pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', \
	names=column_names)

	data=data.replace(to_replace='?', value=np.nan)
	data=data.dropna(how='any')
	data.shape

	#准备训练、测试数据
	from sklearn.cross_validation import train_test_split
	x_train, x_test, y_train, y_test=train_test_split(data[colum_names[1:\
	10]], data[column_names[10]], test_size=0.25, random_state=33)
	y_train.value_counts()