如下所示：

#coding:utf8import pandas as pdimport numpy as npfrom pandas import Series,DataFrame # 如果有id列，則需先刪除id列再進行對應操作，最后再補上# 統計的時候不需要用到id列,刪除的時候需要考慮# 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為考慮帶標簽	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)) # 如果有字符串類型，則報錯# data normalization -1 to 1# label_col: 不需考慮的類標，可以為字符串或字符串列表# 數值類型統一到float64def data_normalization(df, label_col = []):	lab_len = len(label_col)	print label_col	if lab_len>0:		df_temp = df.drop(label_col, axis = 1)		df_lab = df[label_col]		print df_lab	else:		df_temp = df	max_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().values	row_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  # 創建一個帶有缺失值的數據框：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' # 查看一下數據內容：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]))

以上這篇pandas 數據歸一化以及行刪除例程的方法就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持武林站長站。