Python 索引错误 - 轴 0 超出范围

Question

I have dataset like the following in the txt file. (First column is=userid, second column is=locationid) Normally my dataset is big but I created a dummy dataset to better explain my problem.

I'm trying to create a matrix like in the code below. row will be userid column location id. Since this dataset shows the location ids visited by the users, I assign the value 1 in the code to the locations they visited in the matrix.

I am getting an indexerror. IndexError: index 801 is out of bounds for axis 0 with size 50

I tried different user_num and poi_num but still doesn't work

datausers.txt

801 32332
801 14470
801 33847
501 10259
501 34041
501 10201
301 15810
301 34827
301 19264
401 34834
401 35407
401 36115

Code

import numpy as np
from collections import defaultdict
from itertools import islice
import pandas as pd 

train_file = "datausers.txt"
user_num = 20
poi_num = 20

training_matrix = np.zeros((user_num, poi_num))
train_data = list(islice(open(train_file, 'r'), 10))

for eachline in train_data:
    uid, lid= eachline.strip().split()
    uid, lid = int(uid), int(lid)
    training_matrix[uid, lid] = 1.0

Error

Expected Output

4x12 Matrix because we have 4 unique users and 12 unique location

[1 0 1 0 1 0 0 0 0 0 0 0
 0 1 0 1 0 1 0 0 0 0 0 0
...
]

For example for first row 1 0 1 0 1 0 0 0 0 0 0 0

User 801 visited 3 locations and those are 1. (The location of the 1's can be variable I gave it to be an example)

Answer 1

As you have tagged the question with pandas , here is one way of approaching the problem with str.get_dummies method of the pandas Series :

df = pd.read_csv('datausers.txt', sep='\s+', names=['userid', 'locationid'], index_col=0)
out = df['locationid'].astype(str).str.get_dummies().sum(level=0)

Result

For the sample data

>>> out
        10201  10259  14470  15810  19264  32332  33847  34041  34827  34834  35407  36115
userid                                                                                    
801         0      0      1      0      0      1      1      0      0      0      0      0
501         1      1      0      0      0      0      0      1      0      0      0      0
301         0      0      0      1      1      0      0      0      1      0      0      0
401         0      0      0      0      0      0      0      0      0      1      1      1

If you need numpy array instead:

>>> out.to_numpy()

array([[0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0],
       [1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
       [0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1]])