scipyで文字列を扱いたい

前提・実現したいこと

amazonのレコメンドを作りたい
scipyの型が違うと言われている。

発生している問題・エラーメッセージ

Traceback (most recent call last):
  File "example03.py", line 24, in <module>
    reviews = sparse.csc_matrix((values, ij.T)).astype(float)
  File "/usr/local/lib/python3.8/site-packages/scipy/sparse/compressed.py", line 54, in __init__
    other = self.__class__(coo_matrix(arg1, shape=shape))
  File "/usr/local/lib/python3.8/site-packages/scipy/sparse/coo.py", line 148, in __init__
    M = operator.index(np.max(row)) + 1
TypeError: 'str' object cannot be interpreted as an integer

該当のソースコード

# This code is supporting material for the book                                 
# Building Machine Learning Systems with Python                                 
# by Willi Richert and Luis Pedro Coelho                                        
# published by PACKT Publishing                                                 
#                                                                               
# It is made available under the MIT License                                    

import numpy as np
from scipy import sparse
from sklearn.linear_model import LassoCV, RidgeCV, ElasticNetCV
from sklearn.model_selection import KFold
from sklearn.model_selection import StratifiedKFold
import pandas as pd

#data = np.array([[int(tok) for tok in line.split('\t')[:3]]                    
 #               for line in open('ml-100k/u.data')])                           
data=pd.read_csv("Magazine_Subscriptions.csv")
data = data.drop('1005177600',axis=1)
data = data.values
ij = data[:, :2]
#ij -= 1  # original data is in 1-based system                                  
values = data[:, 2]
type(values)
reviews = sparse.csc_matrix((values, ij.T)).astype(float)

reg = ElasticNetCV(fit_intercept=True, alphas=[
                   0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.])
def movie_norm(xc):
    xc = xc.copy().toarray()
    x1 = np.array([xi[xi > 0].mean() for xi in xc])
    x1 = np.nan_to_num(x1)

    for i in range(xc.shape[0]):
        xc[i] -= (xc[i] > 0) * x1[i]
    return xc, x1


def learn_for(i):
    u = reviews[i]
    us = np.delete(np.arange(reviews.shape[0]), i)
    ps, = np.where(u.toarray().ravel() > 0)
    x = reviews[us][:, ps].T
    y = u.data
    err = 0
    eb = 0
    kf = KFold(n_splits=4)
    for i,(train, test) in enumerate(kf.split(y)):
        xc, x1 = movie_norm(x[train])
        reg.fit(xc, y[train] - x1)
        xc, x1 = movie_norm(x[test])
        p = np.array([reg.predict(xi.reshape(1,-1)) for xi in xc]).ravel()
        e = (p + x1) - y[test]
        err += np.sum(e * e)
        eb += np.sum((y[train].mean() - y[test]) ** 2)
    return np.sqrt(err / float(len(y))), np.sqrt(eb / float(len(y)))

whole_data = []
#for i in range(reviews.shape[0]):                                              
#s=learn_for(i)
print(s[0] < s[1])
print(s)
whole_data.append(s)
print(whole_data)