pandas 数据归一化以及行删除例程的方法

时间:2021-11-04 01:53:04

如下所示:

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#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]))

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原文链接:https://blog.csdn.net/u013045749/article/details/47019493