数据清洗MapReduce综合应用案例招聘数据清洗

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mob64ca13fe1aa6 2024-07-11 20:04:14

文章标签 数据分析大数据数据缺失值 文章分类 架构后端开发

文章目录

一、简要分析
二、缺失值处理

主要思路分析：

三、异常值处理

主要思路分析

四、深度清洗

主要思路分析

数据清洗工作是数据分析工作中不可缺少的步骤，这是因为数据清洗能够处理掉肮脏数据，如果不清洗数据的话，那么数据分析的结果准确率会变得极低。

一般来说，数据清理是将数据库精简以除去重复记录，并使剩余部分转换成标准可接收格式的过程。数据清理标准模型是将数据输入到数据清理处理器，通过一系列步骤“ 清理”数据，然后以期望的格式输出清理过的数据。数据清理从数据的准确性、完整性、一致性、惟一性、适时性、有效性几个方面来处理数据的丢失值、越界值、不一致代码、重复数据等问题。

一、简要分析

在任务一中，我们对于赛题、数据总体情况、缺失值、特征分布等信息做了简要的分析。在本次任务中就是基于任务一的分析做数据清理工作。

在一些场景中，任务一和任务二合并起来会被称作EDA(Exploratory Data Analysis-探索性数据分析)。当然真正的EDA包含的内容远不止这两份参考示例所展示了，大家可以自行学习尝试。

二、缺失值处理

由于调查、编码和录入误差，数据中可能存在一些无效值和缺失值，需要给予适当的处理。常用的处理方法有：估算，整例删除，变量删除和成对删除。

#coding:utf-8
#导入warnings包，利用过滤器来实现忽略警告语句。
import warnings
warnings.filterwarnings('ignore')

# GBDT
from sklearn.ensemble import GradientBoostingRegressor
# XGBoost
import xgboost as xgb
# LightGBM
import lightgbm as lgb

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import KFold
from sklearn.metrics import r2_score
from sklearn.preprocessing import LabelEncoder
import pickle
import multiprocessing
from sklearn.preprocessing import StandardScaler
ss = StandardScaler() 
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC,LinearRegression,LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import IsolationForest

#载入数据
data_train = pd.read_csv('F:\\实验\\比赛\\房价预测\\数据集\\train_data.csv')
data_train['Type'] = 'Train'
data_test = pd.read_csv('F:\\实验\\比赛\\房价预测\\数据集\\test_a.csv')
data_test['Type'] = 'Test'
data_all = pd.concat([data_train, data_test], ignore_index=True)

主要思路分析：

虽然这步骤是缺失值处理，但还会涉及到一些最最基础的数据处理。

1.缺失值处理：

缺失值的处理手段大体可以分为：删除、填充、映射到高维(当做类别处理)。
根据任务一，直接找到的缺失值情况是pu和pv；
但是，根据特征nunique分布的分析，可以发现rentType存在"–"的情况，这也算是一种缺失值。
此外，诸如rentType的"未知方式"；houseToward的"暂无数据"等，本质上也算是一种缺失值，但是对于这些缺失方式，我们可以把它当做是特殊的一类处理，而不需要去主动修改或填充值。
将rentType的"–"转换成"未知方式"类别；
pv/pu的缺失值用均值填充；
buildYear存在"暂无信息"，将其用众数填充。

2.转换object类型数据：

这里直接采用LabelEncoder的方式编码，详细的编码方式请自行查阅相关资料学习。

3.时间字段的处理：

buildYear由于存在"暂无信息",所以需要主动将其转换int类型；
tradeTime，将其分割成月和日。

4.删除无关字段：

ID是唯一码，建模无用，所以直接删除；
city只有一个SH值，也直接删除；
tradeTime已经分割成月和日，删除原来字段

def preprocessingData(data):
    # 填充缺失值
    data['rentType'][data['rentType'] == '--'] = '未知方式'
    
    # 转换object类型数据
    columns = ['rentType','communityName','houseType', 'houseFloor', 'houseToward', 'houseDecoration',  'region', 'plate']
    
    for feature in columns:
        data[feature] = LabelEncoder().fit_transform(data[feature])

    # 将buildYear列转换为整型数据
    buildYearmean = pd.DataFrame(data[data['buildYear'] != '暂无信息']['buildYear'].mode())
    data.loc[data[data['buildYear'] == '暂无信息'].index, 'buildYear'] = buildYearmean.iloc[0, 0]
    data['buildYear'] = data['buildYear'].astype('int')

    # 处理pv和uv的空值
    data['pv'].fillna(data['pv'].mean(), inplace=True)
    data['uv'].fillna(data['uv'].mean(), inplace=True)
    data['pv'] = data['pv'].astype('int')
    data['uv'] = data['uv'].astype('int')

    # 分割交易时间
    def month(x):
        month = int(x.split('/')[1])
        return month
    def day(x):
        day = int(x.split('/')[2])
        return day
    data['month'] = data['tradeTime'].apply(lambda x: month(x))
    data['day'] = data['tradeTime'].apply(lambda x: day(x))
    
    # 去掉部分特征
    data.drop('city', axis=1, inplace=True)
    data.drop('tradeTime', axis=1, inplace=True)
    data.drop('ID', axis=1, inplace=True)
    return data

data_train = preprocessingData(data_train)

三、异常值处理

主要思路分析

这里主要针对area和tradeMoney两个维度处理。
针对tradeMoney，这里采用的是IsolationForest模型自动处理；
针对areahetotalFloor是主观+数据可视化的方式得到的结果。

参考资料：iForest （Isolation Forest）孤立森林异常检测入门篇

# clean data
def IF_drop(train):
    IForest = IsolationForest(contamination=0.01)
    IForest.fit(train["tradeMoney"].values.reshape(-1,1))
    y_pred = IForest.predict(train["tradeMoney"].values.reshape(-1,1))
    drop_index = train.loc[y_pred==-1].index
    print(drop_index)
    train.drop(drop_index,inplace=True)
    return train

data_train = IF_drop(data_train)

结果：

数据清洗MapReduce综合应用案例招聘数据清洗_数据分析

def dropData(train):
    # 丢弃部分异常值
    train = train[train.area <= 200]
    train = train[(train.tradeMoney <=16000) & (train.tradeMoney >=700)]
    train.drop(train[(train['totalFloor'] == 0)].index, inplace=True)
    return train  
#数据集异常值处理
data_train = dropData(data_train)

# 处理异常值后再次查看面积和租金分布图
plt.figure(figsize=(15,5))
sns.boxplot(data_train.area)
plt.show()
plt.figure(figsize=(15,5))
sns.boxplot(data_train.tradeMoney),
plt.show()

结果：

数据清洗MapReduce综合应用案例招聘数据清洗_数据_02

四、深度清洗

主要思路分析

针对每一个region的数据，对area和tradeMoney两个维度进行深度清洗。采用主观+数据可视化的方式。

def cleanData(data):
    data.drop(data[(data['region']=='RG00001') & (data['tradeMoney']<1000)&(data['area']>50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00001') & (data['tradeMoney']>25000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00001') & (data['area']>250)&(data['tradeMoney']<20000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00001') & (data['area']>400)&(data['tradeMoney']>50000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00001') & (data['area']>100)&(data['tradeMoney']<2000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00002') & (data['area']<100)&(data['tradeMoney']>60000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['area']<300)&(data['tradeMoney']>30000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']<500)&(data['area']<50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']<1500)&(data['area']>100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']<2000)&(data['area']>300)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']>5000)&(data['area']<20)].index,inplace=True)
    data.drop(data[(data['region']=='RG00003') & (data['area']>600)&(data['tradeMoney']>40000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00004') & (data['tradeMoney']<1000)&(data['area']>80)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['tradeMoney']<200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']<2000)&(data['area']>180)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']>50000)&(data['area']<200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['area']>200)&(data['tradeMoney']<2000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00007') & (data['area']>100)&(data['tradeMoney']<2500)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['area']>200)&(data['tradeMoney']>25000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['area']>400)&(data['tradeMoney']<15000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['tradeMoney']<3000)&(data['area']>200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['tradeMoney']>7000)&(data['area']<75)].index,inplace=True)
    data.drop(data[(data['region']=='RG00010') & (data['tradeMoney']>12500)&(data['area']<100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00004') & (data['area']>400)&(data['tradeMoney']>20000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00008') & (data['tradeMoney']<2000)&(data['area']>80)].index,inplace=True)
    data.drop(data[(data['region']=='RG00009') & (data['tradeMoney']>40000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00009') & (data['area']>300)].index,inplace=True)
    data.drop(data[(data['region']=='RG00009') & (data['area']>100)&(data['tradeMoney']<2000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00011') & (data['tradeMoney']<10000)&(data['area']>390)].index,inplace=True)
    data.drop(data[(data['region']=='RG00012') & (data['area']>120)&(data['tradeMoney']<5000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00013') & (data['area']<100)&(data['tradeMoney']>40000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00013') & (data['area']>400)&(data['tradeMoney']>50000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00013') & (data['area']>80)&(data['tradeMoney']<2000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['area']>300)&(data['tradeMoney']>40000)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<1300)&(data['area']>80)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<8000)&(data['area']>200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<1000)&(data['area']>20)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']>25000)&(data['area']>200)].index,inplace=True)
    data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<20000)&(data['area']>250)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']>30000)&(data['area']<100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']<50000)&(data['area']>600)].index,inplace=True)
    data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']>50000)&(data['area']>350)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['tradeMoney']>4000)&(data['area']<100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['tradeMoney']<600)&(data['area']>100)].index,inplace=True)
    data.drop(data[(data['region']=='RG00006') & (data['area']>165)].index,inplace=True)
    data.drop(data[(data['region']=='RG00012') & (data['tradeMoney']<800)&(data['area']<30)].index,inplace=True)
    data.drop(data[(data['region']=='RG00007') & (data['tradeMoney']<1100)&(data['area']>50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00004') & (data['tradeMoney']>8000)&(data['area']<80)].index,inplace=True)
    data.loc[(data['region']=='RG00002')&(data['area']>50)&(data['rentType']=='合租'),'rentType']='整租'
    data.loc[(data['region']=='RG00014')&(data['rentType']=='合租')&(data['area']>60),'rentType']='整租'
    data.drop(data[(data['region']=='RG00008')&(data['tradeMoney']>15000)&(data['area']<110)].index,inplace=True)
    data.drop(data[(data['region']=='RG00008')&(data['tradeMoney']>20000)&(data['area']>110)].index,inplace=True)
    data.drop(data[(data['region']=='RG00008')&(data['tradeMoney']<1500)&(data['area']<50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00008')&(data['rentType']=='合租')&(data['area']>50)].index,inplace=True)
    data.drop(data[(data['region']=='RG00015') ].index,inplace=True)
    data.reset_index(drop=True, inplace=True)
    return data

data_train = cleanData(data_train)

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