Using NumPy to convert user/item ratings into 2-D array
multidimensional-array, numpy, pandas, python
Solution
This is pivot, if I get your idea right, with pandas it will be as follows.
Load data:
import pandas as pd
df = pd.read_csv(fname, sep='\s+', header=None)
df.columns = ['User','Item','ItemRating']
Pivot it:
>>> df
User Item ItemRating
0 1 23 3
1 2 204 4
2 1 492 2
3 3 23 4
>>> df.pivot(index='User', columns='Item', values='ItemRating')
Item 23 204 492
User
1 3 NaN 2
2 NaN 4 NaN
3 4 NaN NaN
For a numpy example, let's emulate file with `StringIO`:
from StringIO import StringIO
data ="""1 23 3
2 204 4
1 492 2
3 23 4"""
and load it:
>>> arr = np.genfromtxt(StringIO(data), dtype=int)
>>> arr
array([[ 1, 23, 3],
[ 2, 204, 4],
[ 1, 492, 2],
[ 3, 23, 4]])
pivot is based on this answer
rows, row_pos = np.unique(arr[:, 0], return_inverse=True)
cols, col_pos = np.unique(arr[:, 1], return_inverse=True)
rows, row_pos = np.unique(arr[:, 0], return_inverse=True)
cols, col_pos = np.unique(arr[:, 1], return_inverse=True)
pivot_table = np.zeros((len(rows), len(cols)), dtype=arr.dtype)
pivot_table[row_pos, col_pos] = arr[:, 2]
and the result:
>>> pivot_table
array([[ 3, 0, 2],
[ 0, 4, 0],
[ 4, 0, 0]])
Note that results differ, as in second approach non-existing values are set to zero.
Select one that suits you better ;)
Problem
With performing some classificion using some user/item/rating data. My issue is how to I convert these 3 columns into a matrix of user(row), item(columns) and the ratings data populating the matrix. ``` User Item ItemRating 1 23 3 2 204 4 1 492 2 3 23 4 ``` and so on. I tried using DataFrame but was getting NULL errors.