pandas 数据归一化以及行删除例程的方法

如下所示：

#coding:utf8import pandas as pdimport numpy as npfrom pandas import Series,DataFrame # 如果有id列，则需先删除id列再进行对应操作，最后再补上# 统计的时候不需要用到id列,删除的时候需要考虑# delete rowdef row_del(df, num_percent, label_len = 0):#print list(df.count(axis=1))col_num = len(list(list(df.values)[1])) - label_len # -1为考虑带标签if col_num<0:print 'Error'#print int(col_num*num_percent)return df.dropna(axis=0, how='any', thresh=int(col_num*num_percent)) # 如果有字符串类型，则报错# data normalization -1 to 1# label_col: 不需考虑的类标，可以为字符串或字符串列表# 数值类型统一到float64def data_normalization(df, label_col = []):lab_len = len(label_col)print label_colif lab_len>0:df_temp = df.drop(label_col, axis = 1)df_lab = df[label_col]print df_labelse:df_temp = dfmax_val = list(df_temp.max(axis=0))min_val = list(df_temp.min(axis=0))mean_val = list((df_temp.max(axis=0) + df_temp.min(axis=0)) / 2)nan_values = df_temp.isnull().valuesrow_num = len(list(df_temp.values))col_num = len(list(df_temp.values)[1])for rn in range(row_num):#data_values_r = list(data_values[rn])nan_values_r = list(nan_values[rn])for cn in range(col_num):if nan_values_r[cn] == False:df_temp.values[rn][cn] = 2 * (df_temp.values[rn][cn] - mean_val[cn])/(max_val[cn] - min_val[cn])else:print 'Wrong'for index,lab in enumerate(label_col):df_temp.insert(index, lab, df_lab[lab])return df_temp  # 创建一个带有缺失值的数据框：df = pd.DataFrame(np.random.randn(5,3), index=list('abcde'), columns=['one','two','three'])df.ix[1,:-1]=np.nandf.ix[1:-1,2]=np.nandf.ix[0,0]=int(1)df.ix[2,2]='abc' # 查看一下数据内容：print '\ndf1'print df print row_del(df, 0.8) print '-------------------------' df = data_normalization(df, ['two', 'three'])print df print df.dtypes print (type(df.ix[2,2]))

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