Python 數據清洗--處理Nan

參考：http://blog.sina.com.cn/s/blog_13050351e0102xfis.html
https://www.sogou.com/link?url=DOb0bgH2eKh1ibpaMGjuy-bS_O7xQYLPIOogrOFmc02ueKW9M67CaVLpMY1k7wxTCB1NmnNSzM-t5pUc3zy0dg..
https://www.sogou.com/link?url=DOb0bgH2eKh1ibpaMGjuy6YnbQPc3cuKWH5w_8iuvJBomuBEhdSpHkUUZED5fr2OXwl-dB-nkEs_c1NbUyGLxQ..
https://jingyan.baidu.com/article/ca00d56c1b3647e99eebcfbd.html

import numpy as np
import pandas as pd
from pandas import Series,DataFrame
from numpy import nan as NaN

import tensorflow as tf
import matplotlib.pyplot as plt

import scipy.io as sio
import os

from sklearn import preprocessing

讀取mat數據
load_path="08_1.mat"
load_data = sio.loadmat(load_path)
a = load_data['D']
print(a)

data = DataFrame(a)
print(data)

data.fillna(0)
print(data.fillna(0))

b=data.fillna(0).values
print(b)

數據歸一化
a2 = preprocessing.scale(b)
print('數據歸一化：')
print(a2)

數據清洗方法2 刪除NAN所在的列

load_path2="08_1.mat"
load_data2 = sio.loadmat(load_path2)
a2 = load_data2['D']

print(a2)

data2 = DataFrame(a2)
data2.dropna(axis=0, how='any')

print(data2.dropna(axis=0, how='any'))`

處理前：

處理后：