用pandas处理MovieLens 1m 数据集

1、环境

Python2.7.13，安装了numpy，pandas等

import math

import random

from numpy import *

import numpy as np

import sys

import os

from pandas import Series,DataFrame

import pandas as pd

from sklearn.model_selection import train_test_split

2、读取数据集

rnames = ['user_id', 'movie_id', 'rating', 'timestamp']
ratings = pd.read_table('ml-1m/ratings.dat', sep='::', header=None, names=rnames,engine = 'python')
data = ratings.filter(regex='user_id|movie_id|rating')

read_table函数如果没添加最后一个参数的话，会出现如下的警告：

ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.

将得到的ratings过滤掉时间戳这一列得到data

3、查看数据

h10 = ratings.head(10)
print h10
# or print data.head(10)查看前10个记录，显示：

查看一些统计值：

pd.options.display.float_format = '{:,.4f}'.format
print data.describe()
显示：

4、将数据集划分为训练集和测试集
y = [i for i in range(1,1000210)]
train_X,test_X, train_y, test_y = train_test_split(data,y,test_size = 0.2,random_state=0)20%作为测试集，80%作为训练集
5、求每部电影的平均评分

groupMovie = data.groupby('movie_id')
movieMean = groupMovie['rating'].agg('mean')
print movieMean[1193]

movieMean存放了所有电影的平均评分，通过movieMean[i]就能得到第i部电影的平均评分值。

这部分借鉴了博文http://blog.csdn.net/elecjack/article/details/50760736