[英]Convert Numpy matrix to pandas dataframe
Given a rating matrix in .dat: 给定.dat中的评分矩阵:
rating = np.load(os.path.join(data_dir, "rating.dat"))
matrix([[ 5, 4, 0, 0],
[ 0, 0, 5, 0],
[ 0, 0, 0, 1],
[ 0, 0, 0, 1]])
And a df such as: 和df之类的:
df=pd.read_csv('data_path')
df
user item
0 foo qw
1 foo rt
2 coo ty
3 doo yu
4 moo yu
The rating matrix row corresponds user
and column item
and values are ratings. 评分矩阵行对应于
user
和列item
,值是评分。 I want to add this matrix to my df
as an additional column, in order to have a result like this: 我想将此矩阵添加到我的
df
作为附加列,以得到如下结果:
user item rating
0 foo qw 5
1 foo rt 4
2 coo ty 5
3 doo yu 1
4 moo yu 1
Thank you in advance! 先感谢您!
Given a rating matrix: 给定一个评分矩阵:
ratings = np.asarray([
[ 5, 4, 0, 0],
[ 0, 0, 5, 0],
[ 0, 0, 0, 1],
[ 0, 0, 0, 1]
])
ratings.flatten()[ratings.flatten().nonzero()]
Out[1]: array([5, 4, 5, 1, 1])
The trick is to flatten the matrix and remove the non-zero elements. 诀窍是使矩阵变平并删除非零元素。 Then simply
df['ratings'] = ratings
and you will have your column filled in the proper order. 然后只需
df['ratings'] = ratings
,您就可以按正确的顺序填充列。 Note that if some user makes several reviews, also has several rows in your df
. 请注意,如果某位用户进行了多条评论,则
df
也会有几行。
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