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初学Python,快来点我吧
1. 概述
首先对数据缺失的原因、类型以及处理方法做一个简单地总结,如下图所示:
2. 直接删除法
当缺失值的个数只占整体很小一部分的时候,可直接删除缺失值(行)。但是如果缺失值占比比较大,这种直接删除缺失值的处理方法就会丢失重要信息。
直接删除法处理缺失值时,需要检测样本总体中缺失值的个数。Python中统计缺失值的方法如下(下面结合具体数据集,直接上代码):
import numpy as np
import pandas as pd
data = pd.read_csv('1.csv') # 需要具体数据(公开的海藻数据集)请留言,并附上邮箱!
data.head()
null_all = data.isnull().sum() # 检测缺失值个数(方法1)
null_all
data.info() # 检测缺失值个数(方法2)
# new_data = data.dropna() # 1--删除存在缺失值的行
# new_data = data.dropna(subset=['C1','Chla']) # 2--删除指定列存在缺失值的行
new_data = data.dropna(thresh=15) # 3--删除行属性值不足k个的行(即删除缺失元素比较多的行-->n-15)
new_data.info()
3. 前填充/后填充
import numpy as np
import pandas as pd
data = pd.read_csv('1.csv')
data[50:60] # 展示缺失值情况
data = data.fillna(method='ffill') # ffill---前填充;bfill--后填充
data[50:60]
4. 均值、众数、中位数填充
通常可以根据样本之间的相似性(中心趋势)填补缺失值,通常使用能代表变量中心趋势的值进行填补,代表变量中心趋势的指标包括 平均值(mean)、中位数(median)、众数(mode) 等,那么我们采用哪些指标来填补缺失值呢?
(4.1)方法一(.fillna())
import numpy as np
import pandas as pd
data = pd.read_csv('1.csv')
data['C1'] = data['C1'].fillna(data['C1'].mean()) # 均值填充:.mean()--->.median()--->.mode()
data[50:60]
注:当使用众数进行填充时,需特别注意众数不存在或者多于一个的情况!
(4.2)方法二(SimpleImputer)
SimpleImputer 提供了缺失数值处理的基本策略,比如使用缺失数值所在行或列的均值、中位数、众数来替代缺失值。
import numpy as np
import pandas as pd
data = pd.read_csv('1.csv')
# from sklearn.preprocessing import Imputer # scikit-learn (较早版本)
from sklearn.impute import SimpleImputer # scikit-learn 0.22.2(最新版)
imputer = SimpleImputer(strategy='mean')
imputer = imputer.fit(data.iloc[:,3:].values)
imputer_data = pd.DataFrame(imputer.transform(data.iloc[:,3:].values),columns=data.columns[3:])
imputer_data[53:64]
5. 插值法
interpolate() 插值法,计算的是缺失值前一个值和后一个值的平均数。
import numpy as np
import pandas as pd
data = pd.read_csv('1.csv')
data['C1'] = data_5['C1'].interpolate()
data[53:63]
6. KNN填充(均值)
为了实现KNN填充,我们先通过其他方法处理缺失值比较少的数据(因为该方法必须借助于其他非缺失数据寻找最邻近的数据,然后进行加权平均求值填充的),得到如下特征数据:
(6.1)from fancyimpute import KNN
首先需要安装第三方包----fancyimpute,此包的安装比较费劲(尤其在windows下,这里的坑有点深啊)!
【1】包的下载(7包+1软件)
链接:https://pan.baidu.com/s/1CUfiaEyE-k4G560L2JsOYQ
提取码:nriv
提示:此包需要与Python版本保持一致(Python3.6),其它版本点击我下载!
【2】包的安装(for windows)
pip install D:\fancyimpute\包1
pip install D:\fancyimpute\包2
pip install D:\fancyimpute\包3
pip install D:\fancyimpute\包4
pip install D:\fancyimpute\包5
pip install D:\fancyimpute\包6
pip install D:\fancyimpute\fancyimpute-0.5.4.tar.gz
【3】可能会出现如下报错
ERROR: tensorflow 2.1.0 has requirement scipy==1.4.1; python_version >= “3”, but you’ll have scipy 1.1.0 which is incompatible.
ERROR: tensorflow 2.1.0 has requirement six>=1.12.0, but you’ll have six 1.11.0 which is incompatible.
ERROR: Cannot uninstall ‘wrapt’. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
【4】不要怕,继续往下安装
pip install --upgrade scipy==1.4.1
pip install --upgrade six==1.12.0
pip install wrapt --ignore-installed
【5】哇塞,咋还有可能出错
ImportError: Could not find the DLL(s) ‘msvcp140_1.dll’
【6】没办法,继续安装
继续安装网盘下载中的vc_redist.x64.exe 就好了
哎,终于可以继续写代码了!!!
data = pd.read_csv('1.csv')
# 插值填充缺失值少的特征
data['mxPH'] = data['mxPH'].interpolate()
data['MNO2'] = data['MNO2'].interpolate()
data['NO3'] = data['NO3'].interpolate()
data['NH4'] = data['NH4'].interpolate()
data['Opo4'] = data['Opo4'].interpolate()
data['PO4'] = data['PO4'].interpolate()
data.info()
new_data = data.iloc[:,3:11]
new_data[53:64]
from fancyimpute import KNN # 事先安装:fancyimpute
fill_knn = KNN(k=3).fit_transform(new_data)
new_data = pd.DataFrame(fill_knn,columns=data.columns[3:11])
new_data[53:64]
(6.2)from sklearn.neighbors import KNeighborsRegressor
data = pd.read_csv('1.csv')
# 插值填充缺失值少的特征
data['mxPH'] = data['mxPH'].interpolate()
data['MNO2'] = data['MNO2'].interpolate()
data['NO3'] = data['mxPH'].interpolate()
data['NH4'] = data['MNO2'].interpolate()
data['Opo4'] = data['mxPH'].interpolate()
data['PO4'] = data['MNO2'].interpolate()
C1_data = data[['mxPH','MNO2', 'NO3', 'NH4', 'Opo4', 'PO4', 'C1']]
C1_data[53:64]
known_C1 = C1_data[C1_data.C1.notnull()]
unknown_C1 = C1_data[C1_data.C1.isnull()]、
import numpy as np
y = known_C1.iloc[:, 6]
y_train = np.array(y)
X = known_C1.iloc[:, :6]
X_train = np.array(X)
X_test = np.array(unknown_C1.iloc[:, :6])
y_test = np.array(unknown_C1.iloc[:, 6])
from sklearn.neighbors import KNeighborsRegressor
clf = KNeighborsRegressor(n_neighbors = 6, weights = "distance").fit(X_train,y_train)
y_test = clf.predict(X_test)
y_test
7. 随机森林填充
上面啰嗦了很多了,直接上代码吧!
data = pd.read_csv('1.csv')
data.mxPH = data.mxPH.fillna(data.mxPH.mean())
data.MNO2 = data.MNO2.fillna(data.MNO2.mean())
C1_data = data[['mxPH','MNO2', 'C1']]
C1_data[53:64]
known_C1 = C1_data[C1_data.C1.notnull()]
unknown_C1 = C1_data[C1_data.C1.isnull()]
import numpy as np
y = known_C1.iloc[:, 2]
y = np.array(y)
X = known_C1.iloc[:, :2]
X = np.array(X)
from sklearn.ensemble import RandomForestRegressor
rfr = RandomForestRegressor(random_state=0, n_estimators=200, n_jobs=-1)
rfr.fit(X, y)
data.loc[(data.C1.isnull()), 'C1'] = rfr.predict(unknown_C1.iloc[:, :2])
data[53:64]
8. 小结
暂时先写到这吧,有点累了,休息!!(后续再继续补充新方法)
类似的下一篇准备介绍一下数据特征工程的一些方法,让我们一起期待吧!!
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