pandas 数据归一化以及行删除例程的方法_Python

pandas 数据归一化以及行删除例程的方法

2021-04-18 00:09夜月xl Python

今天小编就为大家分享一篇pandas 数据归一化以及行删除例程的方法，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

如下所示：

									#coding:utf8

									import pandas as pd

									import numpy as np

									from 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 row

									def 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float64

									def 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

									# 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.nan

									df.ix[1:-1,2]=np.nan

									df.ix[0,0]=int(1)

									df.ix[2,2]='abc'

									# 查看一下数据内容：

									print ' df1'

									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]))