python如何处理很大的数据集

原函数

df=pd.read_csv('UserBehavior.csv',header=None)

df = pd.concat((chunk for chunk in pd.read_csv('UserBehavior.csv.zip',header=None, chunksize = 10000)))

        上面这段代码分段加载csv文件，不会造成内存不够用的情况，但是这个方法超级慢 

原函数

traindf=traindata.pivot(index='userid', columns='proid', values='num')

data = data1.drop(df[df['userid'] > 10000].index)

print(data.shape)

print(data.head(5))

traindata, testdata = train_test_split(data,test_size=0.2, random_state=1)
traindata1 = traindata.groupby(['userid','proid'], as_index= False).count()
testdata1 = testdata.groupby(['userid','proid'], as_index= False).count()

print(traindata1.head(5))
print(testdata1.head(5))

traindf = traindata1.pivot(index = 'userid', columns= 'proid', values= 'num')

这里所作的处理仅仅是通过减小数据量达到pivot函数正常运行的目的。所用函数为drop，对userid大于10000的直接去除

import pandas as pd
import numpy as np
import math
from sklearn.model_selection import train_test_split

def prediction(df,userdf,Nn=15):#Nn邻居个数
    corr=df.T.corr()#建立用户相似度的协方差矩阵
    rats=userdf.copy()
    for usrid in userdf.index:
        dfnull=df.loc[usrid][df.loc[usrid].isnull()]
        usrv=df.loc[usrid].mean()#评价平均值
        for i in range(len(dfnull)):
            nft=(df[dfnull.index[i]]).notnull()
            #获取邻居列表
            if(Nn<=len(nft)):
                nlist=df[dfnull.index[i]][nft][:Nn]
            else:
                nlist=df[dfnull.index[i]][nft][:len(nft)]
            nlist=nlist[corr.loc[usrid,nlist.index].notnull()]
            nratsum=0
            corsum=0
            if(0!=nlist.size):
                nv=df.loc[nlist.index,:].T.mean()#邻居评价平均值
                for index in nlist.index:
                    ncor=corr.loc[usrid,index]
                    nratsum+=ncor*(df[dfnull.index[i]][index]-nv[index])
                    corsum+=abs(ncor)
                if(corsum!=0):
                    rats.at[usrid,dfnull.index[i]]= usrv + nratsum/corsum
                else:
                    rats.at[usrid,dfnull.index[i]]= usrv
            else:
                rats.at[usrid,dfnull.index[i]]= 0

    return rats

#推荐

def recomm(df,userdf,Nn=15,TopN=1):
    ratings=prediction(df,userdf,Nn)#获取预测评分
    recomm=[]#存放推荐结果
    for usrid in userdf.index:
        #获取按NA值获取未评分项
        ratft=userdf.loc[usrid].isnull()
        ratnull=ratings.loc[usrid][ratft]
        #对预测评分进行排序
        if(len(ratnull)>=TopN):
            sortlist=(ratnull.sort_values(ascending=False)).index[:TopN]
        else:
            sortlist=ratnull.sort_values(ascending=False).index[:len(ratnull)]
        recomm.append(sortlist)
    return ratings,recomm


# 读取数据
# df = pd.read_csv('UserBehavior.csv.zip',header=None)

df = pd.concat((chunk for chunk in pd.read_csv('UserBehavior.csv.zip',header=None, chunksize = 10000)))


#给数据加上列名称
df.columns = ['userid', 'num', 'proid', 'D','E']

print(df.head(5))
data1 = df.drop(['D','E'],axis=1)
data = data1.drop(df[df['userid'] > 10000].index)

print(data.shape)

print(data.head(5))

traindata, testdata = train_test_split(data,test_size=0.2, random_state=1)
traindata1 = traindata.groupby(['userid','proid'], as_index= False).count()
testdata1 = testdata.groupby(['userid','proid'], as_index= False).count()

print(traindata1.head(5))
print(testdata1.head(5))

traindf = traindata1.pivot(index = 'userid', columns= 'proid', values= 'num')
print(traindf.head(5))

testdf = testdata1.pivot(index= 'userid', columns= 'proid', values= 'num')
print(testdf.head(5))

traindf.rename(index={i:'usr%d'%(i) for i in traindf.index} , inplace=True)
traindf.rename(columns={i:'pro%d'%(i) for i in traindf.columns} , inplace=True)
testdf.rename(index={i:'usr%d'%(i) for i in testdf.index} , inplace=True)
testdf.rename(columns={i:'pro%d'%(i) for i in testdf.columns} , inplace=True)
userdf=traindf.loc[testdf.index]


#获取预测评分和推荐列表

trainnums,trainrecomm=recomm(traindf,userdf)
print(trainnums)
print(trainrecomm)

代码段见资源student.csv

开发平台

anacondaspiderpycharm对于大数据的运算，前两个的运行速度要远高于第三个。当数据集过大，内存不能分配这么多空间的时候

相关链接

read_line = 10000 # 设置每次读取数据的行数
df.get_chunk(read_line)

reader = pd.read_csv('some_data.csv', iterator=True)
reader.get_chunk(100)

reader = pd.read_csv(’some_data.csv’, chunksize=100)

此时应该输入

df = pd.concat((chunk for chunk in pd.read_csv(csvname,chunksize=5000)))