CODEWITHSUNDEEP
pythonscipysparse-matrix
theantomc
theantomc

Reputation: 649

Scipy sparse from json file

i try to create with scipy.sparse a matrix from json file.

I have json file in this way 

{"reviewerID": "A10000012B7CGYKOMPQ4L", "asin": "000100039X", "reviewerName": "Adam", "helpful": [0, 0], "reviewText": "Spiritually and mentally inspiring! A book that allows you to question your morals and will help you discover who you really are!", "overall": 5.0, "summary": "Wonderful!", "unixReviewTime": 1355616000, "reviewTime": "12 16, 2012"}

this is my Json format...more elements like this(based on Amazon Review file)

and want performe a scipy sparse for have this matrix 

    count            
object       a   b   c   d
id                   
him       NaN   1 NaN   1
me          1 NaN NaN   1
you         1 NaN   1 NaN

i m trying to do this

i

mport numpy as np
import pandas as pd
from scipy.sparse import csr_matrix

df= pd.read_json('C:\\Users\\anto-\\Desktop\\university\\Big Data computing\\Ex. Resource\\test2.json',lines=True)


a= df['reviewerID']
b= df['asin']
data= df.groupby(["reviewerID"]).size()



row = df.reviewerID.astype('category', categories=a).cat.codes
col = df.asin.astype('category', categories=b).cat.codes
sparse_matrix = csr_matrix((data, (row, col)), shape=(len(a), len(b)))

reading from this old example

Efficiently create sparse pivot tables in pandas?

I have some error for deprecates element in my code, but i dont underestand how to costruct this matrix.

this is the error log: 

 FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead
  from ipykernel import kernelapp as app

I m bit confused. Anyone can give me some suggestion or similar example?

Upvotes: 0

Views: 1148

Answers (1)

hpaulj
hpaulj

Reputation: 231385

To produce a sparse matrix that looks like 

    count            
object       a   b   c   d
id                   
him       NaN   1 NaN   1
me          1 NaN NaN   1
you         1 NaN   1 NaN

You need to generate 3 arrays like:

In [215]: from scipy import sparse
In [216]: data = np.array([1,1,1,1,1,1])
In [217]: row = np.array([1,2,0,2,0,1])
In [218]: col = np.array([0,0,1,2,3,3])
In [219]: M = sparse.csr_matrix((data, (row, col)), shape=(3,4))
In [220]: M
Out[220]: 
<3x4 sparse matrix of type '<class 'numpy.int64'>'
    with 6 stored elements in Compressed Sparse Row format>
In [221]: M.A
Out[221]: 
array([[0, 1, 0, 1],
       [1, 0, 0, 1],
       [1, 0, 1, 0]], dtype=int64)

Categories like 'him','me','you' have to be mapped onto unique indices like 0,1,2. Likewise for 'a','b','c','d'.

Upvotes: 0

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