Question

我已经在Internet上搜索了很长时间，但无济于事。

import pandas as pd
import numpy as np
import math

unames = ['user_id', 'gender', 'age', 'occupation', 'zip'] 
users = pd.read_table('ml-1m/users.dat', sep='::', 
header=None,names=unames,engine='python')
rnames = ['user_id', 'movie_id', 'rating', 'timestamp']
ratings = pd.read_table('ml-1m/ratings.dat', sep='::', 
header=None,names=rnames,engine='python')
mnames = ['movie_id', 'title', 'genres']
movies = pd.read_table('ml-1m/movies.dat', sep='::', 
header=None,names=mnames,engine='python')
data=pd.merge(pd.merge(ratings,users),movies)

然后我得到一个这样的表： a DataFrame of the data

然后，我像这样处理数据

data1=pd.pivot_table(data[(data.user_id==1)],index=['title'],columns='user_id',values='rating')

输出： a DataFrame of the data1

然后我尝试将data1转换为字典

Dict=data1.to_dict()

字典是这样的：

{1: {'Airplane! (1980)': 4,
 'Aladdin (1992)': 4,
 'Antz (1998)': 4,
 'Apollo 13 (1995)': 5,
 'Awakenings (1990)': 5,
 'Back to the Future (1985)': 5,
 'Bambi (1942)': 4,
 'Beauty and the Beast (1991)': 5,
 'Ben-Hur (1959)': 5,
 'Big (1988)': 4,
 "Bug's Life, A (1998)": 5,
 'Christmas Story, A (1983)': 5,
 'Cinderella (1950)': 5,
 'Close Shave, A (1995)': 3,
 'Dead Poets Society (1989)': 4,
 'Driving Miss Daisy (1989)': 4,
 'Dumbo (1941)': 5,}}

但是，我想将data1转换成这样的字典：

{'Airplane! (1980)': 4,
 'Aladdin (1992)': 4,
 'Antz (1998)': 4,
 'Apollo 13 (1995)': 5,
 'Awakenings (1990)': 5,
 'Back to the Future (1985)': 5,
 'Bambi (1942)': 4,
 'Beauty and the Beast (1991)': 5,
 'Ben-Hur (1959)': 5,
 'Big (1988)': 4,
 "Bug's Life, A (1998)": 5,
 'Christmas Story, A (1983)': 5,
 'Cinderella (1950)': 5,
 'Close Shave, A (1995)': 3,
 'Dead Poets Society (1989)': 4,
 'Driving Miss Daisy (1989)': 4,
 'Dumbo (1941)': 5,}

区别在于第一个版本具有一个1作为密钥，那么我该如何像第二个那样进行转换？

Answer 1

按1的列Series的名称进行选择，并转换为dict，也最好更改变量名称Dict，如注释中提到的@jpp：

d = data1[1].to_dict()

将DataFrame转换为dict

1 个答案: